%load_ext autoreload
%autoreload 2
import sys
sys.path.insert(0,"..")
# print(sys.path)
from optimus import Optimus
# op = Optimus(engine="dask",verbose=True)
op = Optimus("dask", n_workers=1, threads_per_worker=8, processes=False, memory_limit="3G")
C:\Users\argenisleon\Anaconda3\lib\site-packages\numpy\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs: C:\Users\argenisleon\Anaconda3\lib\site-packages\numpy\.libs\libopenblas.NOIJJG62EMASZI6NYURL6JBKM4EVBGM7.gfortran-win_amd64.dll C:\Users\argenisleon\Anaconda3\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll stacklevel=1)
# df = op.load.file("data/Meteorite_Landings.csv")
# df = op.load.file("https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv")
df = op.load.file("data/crime.csv").cache()
# df = op.load.file("data/crime.csv")
# df= df.to_optimus_pandas()
# op.load.csv("data/airline-safety_csv.csv", sep=",", error_bad_lines=False, header=True, null_value="null",infer_schema="true").ext.cache()
df.head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | I182070945 | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | I182070943 | 01402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) | |
5 | I182070936 | 03820 | Motor Vehicle Accident Response | M/V ACCIDENT INVOLVING PEDESTRIAN - INJURY | C11 | 398 | 2018-09-03 21:09:00 | 2018 | 9 | Monday | 21 | Part Three | TALBOT AVE | 42.29019621 | -71.07159012 | (42.29019621, -71.07159012) | |
6 | I182070933 | 00724 | Auto Theft | AUTO THEFT | B2 | 330 | 2018-09-03 21:25:00 | 2018 | 9 | Monday | 21 | Part One | NORMANDY ST | 42.30607218 | -71.08273260 | (42.30607218, -71.08273260) | |
7 | I182070932 | 03301 | Verbal Disputes | VERBAL DISPUTE | B2 | 584 | 2018-09-03 20:39:37 | 2018 | 9 | Monday | 20 | Part Three | LAWN ST | 42.32701648 | -71.10555088 | (42.32701648, -71.10555088) | |
8 | I182070931 | 00301 | Robbery | ROBBERY - STREET | C6 | 177 | 2018-09-03 20:48:00 | 2018 | 9 | Monday | 20 | Part One | MASSACHUSETTS AVE | 42.33152148 | -71.07085307 | (42.33152148, -71.07085307) | |
9 | I182070929 | 03301 | Verbal Disputes | VERBAL DISPUTE | C11 | 364 | 2018-09-03 20:38:00 | 2018 | 9 | Monday | 20 | Part Three | LESLIE ST | 42.29514664 | -71.05860832 | (42.29514664, -71.05860832) |
df1 = df.cols.lower("INCIDENT_NUMBER")
df1.meta
{'file_name': 'data/crime.csv', 'name': 'crime.csv', 'transformations': {'actions': [{'lower': ['INCIDENT_NUMBER']}]}}
df[0]
INCIDENT_NUMBER
1 (object)
not nullable
|
---|
I182016471
|
I182062395
|
I162052877
|
I162067216
|
I172079919
|
I162027167
|
I162039177
|
I172093225
|
I162030885
|
I162028326
|
<class 'optimus.engines.dask.dataframe.DaskDataFrame'>
import dask
from dask.dataframe import from_delayed
def oset(col_name, expr, where):
where = (where.data[where.cols.names(0)[0]]).to_delayed()
ddf = df.to_delayed()
def func(_df, _col_name, _mask):
_df.loc[_mask,[_col_name]]=expr(_df)
return _df
for _part,_mask in zip(ddf, where):
delayed_parts = dask.delayed(func)(_part, col_name, _mask)
return from_delayed(delayed_parts)
col_name="INCIDENT_NUMBER"
where= ((df[col_name]=="I182070945") | (df["DISTRICT"]=="I182070945"))
F = df.functions
def expr(series):
return F.lower(series["OFFENSE_CODE_GROUP"])
oset(col_name, expr, where).compute()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | larceny | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | I182070943 | 01402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
319068 | I050310906-00 | 03125 | Warrant Arrests | WARRANT ARREST | D4 | 285 | 2016-06-05 17:25:00 | 2016 | 6 | Sunday | 17 | Part Three | COVENTRY ST | 42.33695098 | -71.08574813 | (42.33695098, -71.08574813) | |
319069 | I030217815-08 | 00111 | Homicide | MURDER, NON-NEGLIGIENT MANSLAUGHTER | E18 | 520 | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part One | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) | |
319070 | I030217815-08 | 03125 | Warrant Arrests | WARRANT ARREST | E18 | 520 | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part Three | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) | |
319071 | I010370257-00 | 03125 | Warrant Arrests | WARRANT ARREST | E13 | 569 | 2016-05-31 19:35:00 | 2016 | 5 | Tuesday | 19 | Part Three | NEW WASHINGTON ST | 42.30233307 | -71.11156487 | (42.30233307, -71.11156487) | |
319072 | 142052550 | 03125 | Warrant Arrests | WARRANT ARREST | D4 | 903 | 2015-06-22 00:12:00 | 2015 | 6 | Monday | 0 | Part Three | WASHINGTON ST | 42.33383935 | -71.08029038 | (42.33383935, -71.08029038) |
319073 rows × 17 columns
df.head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | larceny | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | I182070943 | 01402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) | |
5 | I182070936 | 03820 | Motor Vehicle Accident Response | M/V ACCIDENT INVOLVING PEDESTRIAN - INJURY | C11 | 398 | 2018-09-03 21:09:00 | 2018 | 9 | Monday | 21 | Part Three | TALBOT AVE | 42.29019621 | -71.07159012 | (42.29019621, -71.07159012) | |
6 | I182070933 | 00724 | Auto Theft | AUTO THEFT | B2 | 330 | 2018-09-03 21:25:00 | 2018 | 9 | Monday | 21 | Part One | NORMANDY ST | 42.30607218 | -71.08273260 | (42.30607218, -71.08273260) | |
7 | I182070932 | 03301 | Verbal Disputes | VERBAL DISPUTE | B2 | 584 | 2018-09-03 20:39:37 | 2018 | 9 | Monday | 20 | Part Three | LAWN ST | 42.32701648 | -71.10555088 | (42.32701648, -71.10555088) | |
8 | I182070931 | 00301 | Robbery | ROBBERY - STREET | C6 | 177 | 2018-09-03 20:48:00 | 2018 | 9 | Monday | 20 | Part One | MASSACHUSETTS AVE | 42.33152148 | -71.07085307 | (42.33152148, -71.07085307) | |
9 | I182070929 | 03301 | Verbal Disputes | VERBAL DISPUTE | C11 | 364 | 2018-09-03 20:38:00 | 2018 | 9 | Monday | 20 | Part Three | LESLIE ST | 42.29514664 | -71.05860832 | (42.29514664, -71.05860832) |
df["INCIDENT_NUMBER"].cols.names(0)
['INCIDENT_NUMBER']
((df["INCIDENT_NUMBER"].data["INCIDENT_NUMBER"]=="i182070945") | (df["DISTRICT"].data["DISTRICT"]=="I182070945")).compute()
0 False 1 False 2 False 3 False 4 False ... 319068 False 319069 False 319070 False 319071 False 319072 False Length: 319073, dtype: bool
df.cols.names()
['INCIDENT_NUMBER', 'OFFENSE_CODE', 'OFFENSE_CODE_GROUP', 'OFFENSE_DESCRIPTION', 'DISTRICT', 'REPORTING_AREA', 'SHOOTING', 'OCCURRED_ON_DATE', 'YEAR', 'MONTH', 'DAY_OF_WEEK', 'HOUR', 'UCR_PART', 'STREET', 'Lat', 'Long', 'Location']
df.set_buffer()
%%time
df.profile("INCIDENT_NUMBER")
Wall time: 1.67 s
{'columns': {'INCIDENT_NUMBER': {'profiler_dtype': {'dtype': 'string'}, 'stats': {'match': 319073, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'I162030584', 'count': 13}, {'value': 'I152080623', 'count': 11}, {'value': 'I172013170', 'count': 10}, {'value': 'I182065208', 'count': 10}, {'value': 'I172096394', 'count': 10}, {'value': 'I162071327', 'count': 9}, {'value': 'I162001871', 'count': 9}, {'value': 'I172056883', 'count': 9}, {'value': 'I172054429', 'count': 9}, {'value': 'I162098170', 'count': 9}, {'value': 'I172022524', 'count': 9}, {'value': 'I152076465', 'count': 8}, {'value': 'I172053616', 'count': 8}, {'value': 'I152105431', 'count': 8}, {'value': 'I162082917', 'count': 8}, {'value': 'I162064331', 'count': 8}, {'value': 'I162056703', 'count': 8}, {'value': 'I130041200-00', 'count': 8}, {'value': 'I162087224', 'count': 8}, {'value': 'I162022140', 'count': 8}, {'value': 'I172069723', 'count': 8}, {'value': 'I162090278', 'count': 8}, {'value': 'I162074826', 'count': 8}, {'value': 'I162078338', 'count': 8}, {'value': 'I152101399', 'count': 7}, {'value': 'I152095733', 'count': 7}, {'value': 'I162054378', 'count': 7}, {'value': 'I152067057', 'count': 7}, {'value': 'I152096998', 'count': 7}, {'value': 'I172018004', 'count': 7}, {'value': 'I162083089', 'count': 7}, {'value': 'I162045680', 'count': 7}, {'value': 'I152071480', 'count': 7}], 'count_uniques': 282517}, 'dtype': 'object'}}, 'name': None, 'file_name': None, 'summary': {'cols_count': 17, 'rows_count': 319073, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}
df.meta
{'profile': {'columns': {'INCIDENT_NUMBER': {'profiler_dtype': {'dtype': 'string'}, 'stats': {'match': 319073, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'I162030584', 'count': 13}, {'value': 'I152080623', 'count': 11}, {'value': 'I172013170', 'count': 10}, {'value': 'I182065208', 'count': 10}, {'value': 'I172096394', 'count': 10}, {'value': 'I162071327', 'count': 9}, {'value': 'I162001871', 'count': 9}, {'value': 'I172056883', 'count': 9}, {'value': 'I172054429', 'count': 9}, {'value': 'I162098170', 'count': 9}, {'value': 'I172022524', 'count': 9}, {'value': 'I152076465', 'count': 8}, {'value': 'I172053616', 'count': 8}, {'value': 'I152105431', 'count': 8}, {'value': 'I162082917', 'count': 8}, {'value': 'I162064331', 'count': 8}, {'value': 'I162056703', 'count': 8}, {'value': 'I130041200-00', 'count': 8}, {'value': 'I162087224', 'count': 8}, {'value': 'I162022140', 'count': 8}, {'value': 'I172069723', 'count': 8}, {'value': 'I162090278', 'count': 8}, {'value': 'I162074826', 'count': 8}, {'value': 'I162078338', 'count': 8}, {'value': 'I152101399', 'count': 7}, {'value': 'I152095733', 'count': 7}, {'value': 'I162054378', 'count': 7}, {'value': 'I152067057', 'count': 7}, {'value': 'I152096998', 'count': 7}, {'value': 'I172018004', 'count': 7}, {'value': 'I162083089', 'count': 7}, {'value': 'I162045680', 'count': 7}, {'value': 'I152071480', 'count': 7}], 'count_uniques': 282517}, 'dtype': 'object'}}, 'name': None, 'file_name': None, 'summary': {'cols_count': 17, 'rows_count': 319073, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}, 'transformations': {'actions': []}}
df = df.cols.cast("OFFENSE_CODE", "int").cache()
df.display()
INCIDENT_NUMBER
1 (object)
not nullable
|
OFFENSE_CODE
2 (int64)
not nullable
|
OFFENSE_CODE_GROUP
3 (object)
not nullable
|
OFFENSE_DESCRIPTION
4 (object)
not nullable
|
DISTRICT
5 (object)
not nullable
|
REPORTING_AREA
6 (object)
not nullable
|
SHOOTING
7 (object)
not nullable
|
OCCURRED_ON_DATE
8 (object)
not nullable
|
YEAR
9 (object)
not nullable
|
MONTH
10 (object)
not nullable
|
DAY_OF_WEEK
11 (object)
not nullable
|
HOUR
12 (object)
not nullable
|
UCR_PART
13 (object)
not nullable
|
STREET
14 (object)
not nullable
|
Lat
15 (object)
not nullable
|
Long
16 (object)
not nullable
|
Location
17 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
I172015220
|
3301
|
Verbal⋅Disputes
|
VERBAL⋅DISPUTE
|
C6
|
186
|
|
2017-02-24⋅19:17:00
|
2017
|
2
|
Friday
|
19
|
Part⋅Three
|
ALLSTATE⋅RD
|
|
|
(0.00000000,⋅0.00000000)
|
I172080021
|
3831
|
Motor⋅Vehicle⋅Accident⋅Response
|
M/V⋅-⋅LEAVING⋅SCENE⋅-⋅PROPERTY⋅DAMAGE
|
C11
|
351
|
|
2017-09-23⋅12:00:00
|
2017
|
9
|
Saturday
|
12
|
Part⋅Three
|
ADAMS⋅ST
|
42.30060526
|
-71.05923027
|
(42.30060526,⋅-71.05923027)
|
I172002366
|
3831
|
Motor⋅Vehicle⋅Accident⋅Response
|
M/V⋅-⋅LEAVING⋅SCENE⋅-⋅PROPERTY⋅DAMAGE
|
B2
|
183
|
|
2017-01-06⋅16:40:00
|
2017
|
1
|
Friday
|
16
|
Part⋅Three
|
E⋅COTTAGE⋅ST
|
42.32150685
|
-71.07076276
|
(42.32150685,⋅-71.07076276)
|
I172025121
|
3301
|
Verbal⋅Disputes
|
VERBAL⋅DISPUTE
|
A7
|
30
|
|
2017-03-31⋅22:32:00
|
2017
|
3
|
Friday
|
22
|
Part⋅Three
|
FRANKFORT⋅ST
|
42.37083684
|
-71.03397707
|
(42.37083684,⋅-71.03397707)
|
I172096396
|
3820
|
Motor⋅Vehicle⋅Accident⋅Response
|
M/V⋅ACCIDENT⋅INVOLVING⋅PEDESTRIAN⋅-⋅INJURY
|
A15
|
41
|
|
2017-11-18⋅19:55:00
|
2017
|
11
|
Saturday
|
19
|
Part⋅Three
|
CHELSEA⋅ST
|
42.37530654
|
-71.05628689
|
(42.37530654,⋅-71.05628689)
|
I182008862
|
802
|
Simple⋅Assault
|
ASSAULT⋅SIMPLE⋅-⋅BATTERY
|
C6
|
936
|
|
2018-02-02⋅23:24:00
|
2018
|
2
|
Friday
|
23
|
Part⋅Two
|
B⋅ST
|
42.34061082
|
-71.05414345
|
(42.34061082,⋅-71.05414345)
|
I152052685
|
3810
|
Motor⋅Vehicle⋅Accident⋅Response
|
M/V⋅ACCIDENT⋅-⋅INVOLVING⋅ BICYCLE⋅-⋅INJURY
|
D14
|
772
|
|
2015-06-25⋅19:10:00
|
2015
|
6
|
Thursday
|
19
|
Part⋅Three
|
CHESTNUT⋅HILL⋅AVE
|
42.34420182
|
-71.15346093
|
(42.34420182,⋅-71.15346093)
|
I162041872
|
3112
|
Landlord/Tenant⋅Disputes
|
LANDLORD⋅-⋅TENANT⋅SERVICE
|
C11
|
333
|
|
2016-05-28⋅16:53:00
|
2016
|
5
|
Saturday
|
16
|
Part⋅Three
|
STANLEY⋅ST
|
42.30942585
|
-71.06956549
|
(42.30942585,⋅-71.06956549)
|
I172005594
|
3410
|
Towed
|
TOWED⋅MOTOR⋅VEHICLE
|
A1
|
97
|
|
2017-01-21⋅15:12:00
|
2017
|
1
|
Saturday
|
15
|
Part⋅Three
|
SCHOOL⋅ST
|
42.35800156
|
-71.06044213
|
(42.35800156,⋅-71.06044213)
|
I172004587
|
3807
|
Motor⋅Vehicle⋅Accident⋅Response
|
M/V⋅ACCIDENT⋅-⋅OTHER⋅CITY⋅VEHICLE
|
B2
|
304
|
|
2017-01-18⋅07:59:00
|
2017
|
1
|
Wednesday
|
7
|
Part⋅Three
|
ACADEMY⋅RD
|
42.32060705
|
-71.09809064
|
(42.32060705,⋅-71.09809064)
|
(df["OFFENSE_CODE"] - df["OFFENSE_CODE"]).head()
OFFENSE_CODE | |
---|---|
0 | 0 |
1 | 0 |
2 | 0 |
3 | 0 |
4 | 0 |
5 | 0 |
6 | 0 |
7 | 0 |
8 | 0 |
9 | 0 |
(df["INCIDENT_NUMBER"] == df["INCIDENT_NUMBER"]).compute()
INCIDENT_NUMBER | |
---|---|
0 | True |
1 | True |
2 | True |
3 | True |
4 | True |
... | ... |
319068 | True |
319069 | True |
319070 | True |
319071 | True |
319072 | True |
319073 rows × 1 columns
df.cols.names()
['INCIDENT_NUMBER', 'OFFENSE_CODE', 'OFFENSE_CODE_GROUP', 'OFFENSE_DESCRIPTION', 'DISTRICT', 'REPORTING_AREA', 'SHOOTING', 'OCCURRED_ON_DATE', 'YEAR', 'MONTH', 'DAY_OF_WEEK', 'HOUR', 'UCR_PART', 'STREET', 'Lat', 'Long', 'Location']
df.cols.to_float("*").data.head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | NaN | 619.0 | NaN | NaN | NaN | 808.0 | NaN | NaN | 2018.0 | 9.0 | NaN | 13.0 | NaN | NaN | 42.357791 | -71.139371 | NaN |
1 | NaN | 1402.0 | NaN | NaN | NaN | 347.0 | NaN | NaN | 2018.0 | 8.0 | NaN | 0.0 | NaN | NaN | 42.306821 | -71.060300 | NaN |
2 | NaN | 3410.0 | NaN | NaN | NaN | 151.0 | NaN | NaN | 2018.0 | 9.0 | NaN | 19.0 | NaN | NaN | 42.346589 | -71.072429 | NaN |
3 | NaN | 3114.0 | NaN | NaN | NaN | 272.0 | NaN | NaN | 2018.0 | 9.0 | NaN | 21.0 | NaN | NaN | 42.334182 | -71.078664 | NaN |
4 | NaN | 3114.0 | NaN | NaN | NaN | 421.0 | NaN | NaN | 2018.0 | 9.0 | NaN | 21.0 | NaN | NaN | 42.275365 | -71.090361 | NaN |
%%time
df.profile()
Wall time: 17.8 s
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'count': 7221}, {'value': 'DORCHESTER AVE', 'count': 5149}, {'value': 'TREMONT ST', 'count': 4796}, {'value': 'MASSACHUSETTS AVE', 'count': 4708}, {'value': 'HARRISON AVE', 'count': 4609}, {'value': 'CENTRE ST', 'count': 4383}, {'value': 'COMMONWEALTH AVE', 'count': 4134}, {'value': 'HYDE PARK AVE', 'count': 3470}, {'value': 'COLUMBIA RD', 'count': 3195}, {'value': 'HUNTINGTON AVE', 'count': 2989}, {'value': 'RIVER ST', 'count': 2831}, {'value': 'DUDLEY ST', 'count': 2352}, {'value': 'WARREN ST', 'count': 2336}, {'value': 'COLUMBUS AVE', 'count': 2304}, {'value': 'ADAMS ST', 'count': 2042}, {'value': 'BEACON ST', 'count': 1966}, {'value': 'NEWBURY ST', 'count': 1889}, {'value': 'CAMBRIDGE ST', 'count': 1670}, {'value': 'BOWDOIN ST', 'count': 1656}, {'value': 'AMERICAN LEGION HWY', 'count': 1631}, {'value': 'W BROADWAY', 'count': 1630}, {'value': 'GENEVA AVE', 'count': 1628}, {'value': 'ALBANY ST', 'count': 1429}, {'value': 'ALLSTATE RD', 'count': 1296}, {'value': 'SUMMER ST', 'count': 1250}, {'value': 'HANCOCK ST', 'count': 1246}, {'value': 'NEW SUDBURY ST', 'count': 1213}, {'value': 'BORDER ST', 'count': 1213}, {'value': 'CUMMINS HWY', 'count': 1151}, {'value': 'NORFOLK ST', 'count': 1115}], 'count_uniques': 4658}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'Lat': {'stats': {'match': 0, 'missing': 0, 'mismatch': 319073, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 19999}, {'value': '42.34862382', 'count': 1243}, {'value': '42.36183857', 'count': 1208}, {'value': '42.28482577', 'count': 1121}, {'value': '42.32866284', 'count': 1042}, {'value': '42.25621592', 'count': 898}, {'value': '42.29755533', 'count': 783}, {'value': '42.34128751', 'count': 773}, {'value': '-1.00000000', 'count': 745}, {'value': '42.33152148', 'count': 735}, {'value': '42.35231190', 'count': 688}, {'value': '42.33954199', 'count': 655}, {'value': '42.32696647', 'count': 652}, {'value': '42.35512339', 'count': 584}, {'value': '42.30971857', 'count': 573}, {'value': '42.29848866', 'count': 562}, {'value': '42.33401829', 'count': 561}, {'value': '42.33367922', 'count': 550}, {'value': '42.35095909', 'count': 523}, {'value': '42.31043400', 'count': 523}, {'value': '42.35241815', 'count': 515}, {'value': '42.37081805', 'count': 507}, {'value': '42.33428841', 'count': 504}, {'value': '42.34980175', 'count': 472}, {'value': '42.32696802', 'count': 472}, {'value': '42.33511904', 'count': 455}, {'value': '42.35037870', 'count': 445}, {'value': '42.34653820', 'count': 444}, {'value': '42.36643546', 'count': 440}, {'value': '42.28709355', 'count': 438}, {'value': '42.35602373', 'count': 436}, {'value': '42.34840576', 'count': 432}, {'value': '42.34905600', 'count': 427}], 'count_uniques': 18179}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'Long': {'stats': {'match': 0, 'missing': 0, 'mismatch': 319073, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 19999}, {'value': '-71.08277637', 'count': 1243}, {'value': '-71.05976489', 'count': 1208}, {'value': '-71.09137369', 'count': 1121}, {'value': '-71.08563401', 'count': 1042}, {'value': '-71.12401947', 'count': 898}, {'value': '-71.05970910', 'count': 783}, {'value': '-71.05467933', 'count': 773}, {'value': '-1.00000000', 'count': 745}, {'value': '-71.07085307', 'count': 735}, {'value': '-71.06370510', 'count': 688}, {'value': '-71.06940877', 'count': 655}, {'value': '-71.06198607', 'count': 652}, {'value': '-71.06087980', 'count': 584}, {'value': '-71.10429432', 'count': 573}, {'value': '-71.06313294', 'count': 562}, {'value': '-71.07638124', 'count': 561}, {'value': '-71.09187755', 'count': 550}, {'value': '-71.07412780', 'count': 523}, {'value': '-71.06134010', 'count': 523}, {'value': '-71.06525499', 'count': 515}, {'value': '-71.03929078', 'count': 507}, {'value': '-71.07239518', 'count': 504}, {'value': '-71.07840978', 'count': 472}, {'value': '-71.08051941', 'count': 472}, {'value': '-71.07491710', 'count': 455}, {'value': '-71.07626098', 'count': 445}, {'value': '-71.09880598', 'count': 444}, {'value': '-71.06135413', 'count': 440}, {'value': '-71.14822128', 'count': 438}, {'value': '-71.06177615', 'count': 436}, {'value': '-71.08688339', 'count': 432}, {'value': '-71.15049850', 'count': 427}], 'count_uniques': 18179}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'Location': {'stats': {'match': 319073, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': '(0.00000000, 0.00000000)', 'count': 19999}, {'value': '(42.34862382, -71.08277637)', 'count': 1243}, {'value': '(42.36183857, -71.05976489)', 'count': 1208}, {'value': '(42.28482577, -71.09137369)', 'count': 1121}, {'value': '(42.32866284, -71.08563401)', 'count': 1042}, {'value': '(42.25621592, -71.12401947)', 'count': 898}, {'value': '(42.29755533, -71.05970910)', 'count': 783}, {'value': '(42.34128751, -71.05467933)', 'count': 773}, {'value': '(-1.00000000, -1.00000000)', 'count': 745}, {'value': '(42.33152148, -71.07085307)', 'count': 735}, {'value': '(42.35231190, -71.06370510)', 'count': 688}, {'value': '(42.33954199, -71.06940877)', 'count': 655}, {'value': '(42.32696647, -71.06198607)', 'count': 652}, {'value': '(42.35512339, -71.06087980)', 'count': 584}, {'value': '(42.30971857, -71.10429432)', 'count': 573}, {'value': '(42.29848866, -71.06313294)', 'count': 562}, {'value': '(42.33401829, -71.07638124)', 'count': 561}, {'value': '(42.33367922, -71.09187755)', 'count': 550}, {'value': '(42.31043400, -71.06134010)', 'count': 523}, {'value': '(42.35095909, -71.07412780)', 'count': 523}, {'value': '(42.35241815, -71.06525499)', 'count': 515}, {'value': '(42.37081805, -71.03929078)', 'count': 507}, {'value': '(42.33428841, -71.07239518)', 'count': 504}, {'value': '(42.34980175, -71.07840978)', 'count': 472}, {'value': '(42.32696802, -71.08051941)', 'count': 472}, {'value': '(42.33511904, -71.07491710)', 'count': 455}, {'value': '(42.35037870, -71.07626098)', 'count': 445}, {'value': '(42.34653820, -71.09880598)', 'count': 444}, {'value': '(42.36643546, -71.06135413)', 'count': 440}, {'value': '(42.28709355, -71.14822128)', 'count': 438}, {'value': '(42.35602373, -71.06177615)', 'count': 436}, {'value': '(42.34840576, -71.08688339)', 'count': 432}, {'value': '(42.34905600, -71.15049850)', 'count': 427}], 'count_uniques': 18194}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'name': 'crime.csv', 'file_name': 'data/crime.csv', 'summary': {'cols_count': 17, 'rows_count': 319073, 'dtypes_list': ['object', 'int64'], 'total_count_dtypes': 2, 'missing_count': 0, 'p_missing': 0.0}}
df.cols.lower("INCIDENT_NUMBER").data.head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | i182070945 | 619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | i182070943 | 1402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | i182070941 | 3410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | i182070940 | 3114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | i182070938 | 3114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
# import pandas as pd
# df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
# index=['cobra', 'viper', 'sidewinder'],
# columns=['max_speed', 'shield'])
df.data.head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | I182070945 | 619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | I182070943 | 1402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | I182070941 | 3410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | I182070940 | 3114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | I182070938 | 3114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
df.rows.is_profiler_dtype("INCIDENT_NUMBER", "int").head()
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-26-0f4c517b8dbe> in <module> ----> 1 df.rows.is_profiler_dtype("INCIDENT_NUMBER", "int").head() AttributeError: 'Rows' object has no attribute 'is_profiler_dtype'
df.cols.match("INCIDENT_NUMBER","I").head()
df.cols.names()
['INCIDENT_NUMBER', 'OFFENSE_CODE', 'OFFENSE_CODE_GROUP', 'OFFENSE_DESCRIPTION', 'DISTRICT', 'REPORTING_AREA', 'SHOOTING', 'OCCURRED_ON_DATE', 'YEAR', 'MONTH', 'DAY_OF_WEEK', 'HOUR', 'UCR_PART', 'STREET', 'Lat', 'Long', 'Location']
list(("INCIDENT_NUMBER",))
['INCIDENT_NUMBER']
# df.cols.select(("INCIDENT_NUMBER",))
df.cols.select("INCIDENT_NUMBER")
<optimus.new_optimus.DaskDataFrame at 0x293a70c6cc8>
df.head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | I182070945 | 619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | I182070943 | 1402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | I182070941 | 3410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | I182070940 | 3114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | I182070938 | 3114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) | |
5 | I182070936 | 3820 | Motor Vehicle Accident Response | M/V ACCIDENT INVOLVING PEDESTRIAN - INJURY | C11 | 398 | 2018-09-03 21:09:00 | 2018 | 9 | Monday | 21 | Part Three | TALBOT AVE | 42.29019621 | -71.07159012 | (42.29019621, -71.07159012) | |
6 | I182070933 | 724 | Auto Theft | AUTO THEFT | B2 | 330 | 2018-09-03 21:25:00 | 2018 | 9 | Monday | 21 | Part One | NORMANDY ST | 42.30607218 | -71.08273260 | (42.30607218, -71.08273260) | |
7 | I182070932 | 3301 | Verbal Disputes | VERBAL DISPUTE | B2 | 584 | 2018-09-03 20:39:37 | 2018 | 9 | Monday | 20 | Part Three | LAWN ST | 42.32701648 | -71.10555088 | (42.32701648, -71.10555088) | |
8 | I182070931 | 301 | Robbery | ROBBERY - STREET | C6 | 177 | 2018-09-03 20:48:00 | 2018 | 9 | Monday | 20 | Part One | MASSACHUSETTS AVE | 42.33152148 | -71.07085307 | (42.33152148, -71.07085307) | |
9 | I182070929 | 3301 | Verbal Disputes | VERBAL DISPUTE | C11 | 364 | 2018-09-03 20:38:00 | 2018 | 9 | Monday | 20 | Part Three | LESLIE ST | 42.29514664 | -71.05860832 | (42.29514664, -71.05860832) |
df["INCIDENT_NUMBER"].mask.equal("INCIDENT_NUMBER","I182070945").display()
INCIDENT_NUMBER
1 (bool)
not nullable
|
---|
False
|
False
|
False
|
False
|
False
|
False
|
False
|
False
|
False
|
False
|
<optimus.engines.dask.columns.Cols at 0x293a709db88>
df.mask.match("INCIDENT_NUMBER","int")
<optimus.new_optimus.DaskDataFrame at 0x1997a447708>
import dask
from dask.dataframe import from_delayed
def oset(col_name, expr, where):
where = (where.data[where.cols.names(0)[0]]).to_delayed()
ddf = df.to_delayed()
def func(_df, _col_name, _mask):
# _df.loc[_mask,[_col_name]]=expr(_df)
return _df
for _part,_mask in zip(ddf, where):
delayed_parts = dask.delayed(func)(_part, col_name, _mask)
return from_delayed(delayed_parts)
col_name="INCIDENT_NUMBER"
where= ((df[col_name]=="i182070945") | (df["DISTRICT"]=="I182070945"))
F = df.functions
def expr(series):
return F.lower(series["OFFENSE_CODE_GROUP"])
oset(col_name, expr, where).compute()
# col_name = "INCIDENT_NUMBER"
def func(pdf):
mask = pdf["INCIDENT_NUMBER"]=="I182070945"
pdf.loc[mask,["INCIDENT_NUMBER"]]=pdf["INCIDENT_NUMBER"].str.lower()
return pdf
df.data.map_partitions(func).head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | i182070945 | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | I182070943 | 01402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
df.data.head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | i182070945 | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) | |
1 | I182070943 | 01402 | Vandalism | VANDALISM | C11 | 347 | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) | |
2 | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) | |
3 | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) | |
4 | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
op.client
<module 'pandas' from 'C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\pandas\\__init__.py'>
%%time
df.profile()
Wall time: 6.96 s
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{'lower': 32.902376234375, 'upper': 34.25847128375, 'count': 0}, {'lower': 34.25847128375, 'upper': 35.614566333125, 'count': 0}, {'lower': 35.614566333125, 'upper': 36.9706613825, 'count': 0}, {'lower': 36.9706613825, 'upper': 38.326756431875, 'count': 0}, {'lower': 38.326756431875, 'upper': 39.68285148125, 'count': 0}, {'lower': 39.68285148125, 'upper': 41.038946530625, 'count': 0}, {'lower': 41.038946530625, 'upper': 42.39504158, 'count': 298329}], 'count_uniques': 18179}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'Long': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'hist': [{'lower': -71.17867378, 'upper': -68.985590224375, 'count': 298329}, {'lower': -68.985590224375, 'upper': -66.79250666875, 'count': 0}, {'lower': -66.79250666875, 'upper': -64.599423113125, 'count': 0}, {'lower': -64.599423113125, 'upper': -62.4063395575, 'count': 0}, {'lower': -62.4063395575, 'upper': -60.213256001874996, 'count': 0}, {'lower': -60.213256001874996, 'upper': -58.02017244625, 'count': 0}, {'lower': -58.02017244625, 'upper': -55.827088890625, 'count': 0}, {'lower': -55.827088890625, 'upper': -53.634005335, 'count': 0}, {'lower': -53.634005335, 'upper': -51.440921779374996, 'count': 0}, {'lower': -51.440921779374996, 'upper': -49.247838223749994, 'count': 0}, {'lower': -49.247838223749994, 'upper': -47.054754668125, 'count': 0}, {'lower': -47.054754668125, 'upper': -44.8616711125, 'count': 0}, {'lower': -44.8616711125, 'upper': -42.668587556874996, 'count': 0}, {'lower': -42.668587556874996, 'upper': -40.47550400125, 'count': 0}, {'lower': -40.47550400125, 'upper': -38.282420445625, 'count': 0}, {'lower': -38.282420445625, 'upper': -36.08933689, 'count': 0}, {'lower': -36.08933689, 'upper': -33.896253334375, 'count': 0}, {'lower': -33.896253334375, 'upper': -31.703169778749995, 'count': 0}, {'lower': -31.703169778749995, 'upper': -29.510086223125, 'count': 0}, {'lower': -29.510086223125, 'upper': -27.3170026675, 'count': 0}, {'lower': -27.3170026675, 'upper': -25.123919111874997, 'count': 0}, {'lower': -25.123919111874997, 'upper': -22.930835556250003, 'count': 0}, {'lower': -22.930835556250003, 'upper': -20.737752000625, 'count': 0}, {'lower': -20.737752000625, 'upper': -18.544668445, 'count': 0}, {'lower': -18.544668445, 'upper': -16.351584889374998, 'count': 0}, {'lower': -16.351584889374998, 'upper': -14.158501333749996, 'count': 0}, {'lower': -14.158501333749996, 'upper': -11.965417778125001, 'count': 0}, {'lower': -11.965417778125001, 'upper': -9.7723342225, 'count': 0}, {'lower': -9.7723342225, 'upper': -7.579250666874998, 'count': 0}, {'lower': -7.579250666874998, 'upper': -5.386167111250003, 'count': 0}, {'lower': -5.386167111250003, 'upper': -3.1930835556249946, 'count': 0}, {'lower': -3.1930835556249946, 'upper': -1.0, 'count': 745}], 'count_uniques': 18179}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'Location': {'stats': {'match': 319073, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': '(0.00000000, 0.00000000)', 'count': 19999}, {'value': '(42.34862382, -71.08277637)', 'count': 1243}, {'value': '(42.36183857, -71.05976489)', 'count': 1208}, {'value': '(42.28482577, -71.09137369)', 'count': 1121}, {'value': '(42.32866284, -71.08563401)', 'count': 1042}, {'value': '(42.25621592, -71.12401947)', 'count': 898}, {'value': '(42.29755533, -71.05970910)', 'count': 783}, {'value': '(42.34128751, -71.05467933)', 'count': 773}, {'value': '(-1.00000000, -1.00000000)', 'count': 745}, {'value': '(42.33152148, -71.07085307)', 'count': 735}, {'value': '(42.35231190, -71.06370510)', 'count': 688}, {'value': '(42.33954199, -71.06940877)', 'count': 655}, {'value': '(42.32696647, -71.06198607)', 'count': 652}, {'value': '(42.35512339, -71.06087980)', 'count': 584}, {'value': '(42.30971857, -71.10429432)', 'count': 573}, {'value': '(42.29848866, -71.06313294)', 'count': 562}, {'value': '(42.33401829, -71.07638124)', 'count': 561}, {'value': '(42.33367922, -71.09187755)', 'count': 550}, {'value': '(42.31043400, -71.06134010)', 'count': 523}, {'value': '(42.35095909, -71.07412780)', 'count': 523}, {'value': '(42.35241815, -71.06525499)', 'count': 515}, {'value': '(42.37081805, -71.03929078)', 'count': 507}, {'value': '(42.33428841, -71.07239518)', 'count': 504}, {'value': '(42.32696802, -71.08051941)', 'count': 472}, {'value': '(42.34980175, -71.07840978)', 'count': 472}, {'value': '(42.33511904, -71.07491710)', 'count': 455}, {'value': '(42.35037870, -71.07626098)', 'count': 445}, {'value': '(42.34653820, -71.09880598)', 'count': 444}, {'value': '(42.36643546, -71.06135413)', 'count': 440}, {'value': '(42.28709355, -71.14822128)', 'count': 438}, {'value': '(42.35602373, -71.06177615)', 'count': 436}, {'value': '(42.34840576, -71.08688339)', 'count': 432}, {'value': '(42.34905600, -71.15049850)', 'count': 427}], 'count_uniques': 18194}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'name': None, 'file_name': ('data/crime.csv', 'crime.csv'), 'summary': {'cols_count': 17, 'rows_count': 319073, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}
df
cedula
1 (object)
not nullable
|
edad
2 (object)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
19748383
|
27
|
null
|
23760628
|
24
|
contreras
|
28479393
|
20
|
null
|
21857839
|
26
|
null
|
aaa
|
28
|
null
|
17484892
|
28
|
martinez
|
<class 'optimus.new_optimus.DaskDataFrame'>
df.cols.min("*")
{'cedula': 17484892.0, 'edad': 20.0, 'apellido': nan}
df.profile(flush = False)
{'columns': {'cedula': {'stats': {'match': 5, 'missing': 0, 'mismatch': 1, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': 'aaa', 'count': 1}, {'value': '28479393', 'count': 1}, {'value': '23760628', 'count': 1}, {'value': '21857839', 'count': 1}, {'value': '19748383', 'count': 1}, {'value': '17484892', 'count': 1}], 'count_uniques': 6}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'edad': {'stats': {'match': 6, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'hist': [{'lower': 20.0, 'upper': 20.25, 'count': 1}, {'lower': 20.25, 'upper': 20.5, 'count': 0}, {'lower': 20.5, 'upper': 20.75, 'count': 0}, {'lower': 20.75, 'upper': 21.0, 'count': 0}, {'lower': 21.0, 'upper': 21.25, 'count': 0}, {'lower': 21.25, 'upper': 21.5, 'count': 0}, {'lower': 21.5, 'upper': 21.75, 'count': 0}, {'lower': 21.75, 'upper': 22.0, 'count': 0}, {'lower': 22.0, 'upper': 22.25, 'count': 0}, {'lower': 22.25, 'upper': 22.5, 'count': 0}, {'lower': 22.5, 'upper': 22.75, 'count': 0}, {'lower': 22.75, 'upper': 23.0, 'count': 0}, {'lower': 23.0, 'upper': 23.25, 'count': 0}, {'lower': 23.25, 'upper': 23.5, 'count': 0}, {'lower': 23.5, 'upper': 23.75, 'count': 0}, {'lower': 23.75, 'upper': 24.0, 'count': 0}, {'lower': 24.0, 'upper': 24.25, 'count': 1}, {'lower': 24.25, 'upper': 24.5, 'count': 0}, {'lower': 24.5, 'upper': 24.75, 'count': 0}, {'lower': 24.75, 'upper': 25.0, 'count': 0}, {'lower': 25.0, 'upper': 25.25, 'count': 0}, {'lower': 25.25, 'upper': 25.5, 'count': 0}, {'lower': 25.5, 'upper': 25.75, 'count': 0}, {'lower': 25.75, 'upper': 26.0, 'count': 0}, {'lower': 26.0, 'upper': 26.25, 'count': 1}, {'lower': 26.25, 'upper': 26.5, 'count': 0}, {'lower': 26.5, 'upper': 26.75, 'count': 0}, {'lower': 26.75, 'upper': 27.0, 'count': 0}, {'lower': 27.0, 'upper': 27.25, 'count': 1}, {'lower': 27.25, 'upper': 27.5, 'count': 0}, {'lower': 27.5, 'upper': 27.75, 'count': 0}, {'lower': 27.75, 'upper': 28.0, 'count': 2}], 'count_uniques': 5}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'apellido': {'stats': {'match': 6, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'null', 'count': 4}, {'value': 'martinez', 'count': 1}, {'value': 'contreras', 'count': 1}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'name': 'edad.csv', 'file_name': 'data/edad.csv', 'summary': {'cols_count': 3, 'rows_count': 6, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}
df.cols.to_integer("edad")
cedula
1 (object)
not nullable
|
edad
2 (int64)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
28479393
|
20
|
null
|
aaa
|
28
|
null
|
19748383
|
27
|
null
|
17484892
|
28
|
martinez
|
21857839
|
26
|
null
|
23760628
|
24
|
contreras
|
<class 'optimus.new_optimus.DaskDataFrame'>
df.cols.min("*")
{'cedula': 17484892.0, 'edad': 20.0, 'apellido': nan}
# df["edad"].data = df["edad"].astype("int")
df = df.cols.cast("edad", "int")
df.sample()
cedula
1 (object)
not nullable
|
edad
2 (object)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
19748383
|
27
|
null
|
23760628
|
24
|
contreras
|
28479393
|
20
|
null
|
aaa
|
28
|
null
|
21857839
|
26
|
null
|
17484892
|
28
|
martinez
|
<class 'optimus.new_optimus.DaskDataFrame'>
tornado.application - ERROR - Uncaught exception GET /individual-workers/ws (127.0.0.1) HTTPServerRequest(protocol='http', host='127.0.0.1:8787', method='GET', uri='/individual-workers/ws', version='HTTP/1.1', remote_ip='127.0.0.1') Traceback (most recent call last): File "C:\Users\argenisleon\Anaconda3\lib\site-packages\tornado\websocket.py", line 956, in _accept_connection open_result = handler.open(*handler.open_args, **handler.open_kwargs) File "C:\Users\argenisleon\Anaconda3\lib\site-packages\bokeh\server\views\ws.py", line 135, in open raise ProtocolError("Token is expired.") bokeh.protocol.exceptions.ProtocolError: Token is expired.
_df= df.data
_df.head()
cedula | edad | apellido | |
---|---|---|---|
0 | 28479393 | 20 | null |
1 | 17484892 | 28 | martinez |
2 | 23760628 | 24 | contreras |
3 | 21857839 | 26 | null |
4 | 19748383 | 27 | null |
_df[_df["edad"]>20].head()
cedula | edad | apellido | |
---|---|---|---|
1 | 17484892 | 28 | martinez |
2 | 23760628 | 24 | contreras |
3 | 21857839 | 26 | null |
4 | 19748383 | 27 | null |
5 | aaa | 28 | null |
_df.head(10)
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 6 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
cedula | edad | apellido | |
---|---|---|---|
0 | 28479393 | 20 | null |
1 | 17484892 | 28 | martinez |
2 | 23760628 | 24 | contreras |
3 | 21857839 | 26 | null |
4 | 19748383 | 27 | null |
5 | aaa | 28 | null |
df[col_name].astype("str").str.match(Infer.ProfilerDataTypesFunctions["int"]).head()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-33-95b25c5f0635> in <module> ----> 1 df[col_name].astype("str").str.match(Infer.ProfilerDataTypesFunctions["int"]).head() NameError: name 'col_name' is not defined
[{'cedula': '28479393', 'edad': 20, 'apellido': 'null'}, {'cedula': '17484892', 'edad': 28, 'apellido': 'martinez'}, {'cedula': '23760628', 'edad': 24, 'apellido': 'contreras'}, {'cedula': '21857839', 'edad': 26, 'apellido': 'null'}, {'cedula': '19748383', 'edad': 27, 'apellido': 'null'}, {'cedula': 'aaa', 'edad': 28, 'apellido': 'null'}]
# df.rows.match("cedula","int").ext.display()
df.rows.mismatch("cedula","int").ext.display()
# df.data.head(10)
cedula
1 (object)
not nullable
|
edad
2 (int64)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
aaa
|
28
|
null
|
from optimus.infer import is_dict, Infer
cols_and_inferred_dtype = df.cols.infer_profiler_dtypes("*")
col_dtypes = {}
for col_name,j in cols_and_inferred_dtype.items():
dtype = list(j.values())[0]
col_dtypes[col_name] = Infer.ProfilerDataTypesFunctions[dtype]
mask = _df[col_name].astype("str").str.match(Infer.ProfilerDataTypesFunctions[dtype])
print(df.data[mask].head(10))
# print(col_dtypes)
# df.cols.match("*", ProfilerDataTypesFunctions[dtype]).cols.frequency()
cedula edad apellido 0 28479393 20 null 1 17484892 28 martinez 2 23760628 24 contreras 3 21857839 26 null 4 19748383 27 null cedula edad apellido 0 28479393 20 null 1 17484892 28 martinez 2 23760628 24 contreras 3 21857839 26 null 4 19748383 27 null 5 aaa 28 null cedula edad apellido 0 28479393 20 null 1 17484892 28 martinez 2 23760628 24 contreras 3 21857839 26 null 4 19748383 27 null 5 aaa 28 null
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r))) C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 6 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r))) C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6181: UserWarning: Insufficient elements for `head`. 10 elements requested, only 6 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
# df.rows.greater_than("edad",20)
# df.rows.greater_than_equal("edad",20)
# df.rows.less_than("edad",24)
# df.rows.less_than_equal("edad",24)
# df.rows.between("edad",20, 25)
# df.rows.equal("edad",24)
# df.rows.not_equal("edad",24)
df.rows.mismatch()
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-157-55fb102e2e59> in <module> 10 # df.rows.not_equal("edad",24) 11 ---> 12 df.rows.mismatch() AttributeError: 'Rows' object has no attribute 'mismatch'
df.rows.select(df["edad"]>20)
cedula
1 (object)
not nullable
|
edad
2 (int64)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
17484892
|
28
|
martinez
|
21857839
|
26
|
null
|
19748383
|
27
|
null
|
23760628
|
24
|
contreras
|
<class 'optimus.new_optimus.DaskDataFrame'>
a= df.data
# df.rows.is_in("apellido",["contreras","2"], output_cols="new")
# df.rows.is_in("apellido",["contreras","2"], output_cols="new")
df.rows.greater_than("cedula",2)
# df.greather_than_equal
# df.less_than
# df.less_than_equal
# df.between
df.rows.is_in("apellido",["contreras","2"], output_cols="new")
# a.rows.select(a['new']==True)
# is_greater
# is_betweent
# is_match
# is_null
# endswith
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-41-a8b1671f7173> in <module> 1 # df.rows.is_in("apellido",["contreras","2"], output_cols="new") 2 # df.rows.is_in("apellido",["contreras","2"], output_cols="new") ----> 3 df.rows.greater_than("cedula",2) 4 # df.greather_than_equal 5 # df.less_than AttributeError: 'Rows' object has no attribute 'greater_than'
df
cedula
1 (object)
not nullable
|
edad
2 (object)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
17484892
|
28
|
martinez
|
28479393
|
20
|
null
|
23760628
|
24
|
contreras
|
19748383
|
27
|
null
|
21857839
|
26
|
null
|
<class 'optimus.new_optimus.DaskDataFrame'>
a = df.cols.select("apellido").cols.pattern()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-13-6a230a016828> in <module> ----> 1 a = df.cols.select("apellido").cols.pattern() NameError: name 'df' is not defined
df.data["apellido"]=="lllllllll"
Dask Series Structure: npartitions=1 bool ... Name: apellido, dtype: bool Dask Name: eq, 3 tasks
df.ext.display(10)
cedula
1 (object)
not nullable
|
edad
2 (object)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
23760628
|
24
|
contreras
|
28479393
|
20
|
null
|
21857839
|
26
|
null
|
17484892
|
28
|
martinez
|
19748383
|
27
|
null
|
df.cols.pattern("").rows.select(a=="llllllll")
# a= df.cols.select("apellido").cols.pattern().data["apellido"]
# # a.head()
# (df.data[a=="llllllll"]).head()
# a.cols.match(a["apellido"]=="llllllll").head()
apellido
1 (object)
not nullable
|
---|
llllllll
|
<class 'optimus.new_optimus.DaskDataFrame'>
df.ext.buffer_window("*", 0, 5)["apellido"].cols.pattern("apellido","__output__").rows.find('df["__output__"]=="lllllllll"')
apellido
1 (object)
not nullable
|
__output__
2 (object)
not nullable
|
__match__
3 (bool)
not nullable
|
---|---|---|
martinez
|
llllllll
|
False
|
null
|
llll
|
False
|
contreras
|
lllllllll
|
True
|
null
|
llll
|
False
|
null
|
llll
|
False
|
<class 'optimus.new_optimus.PandasDataFrame'>
df
cedula
1 (object)
not nullable
|
edad
2 (object)
not nullable
|
apellido
3 (bool)
not nullable
|
new
4 (bool)
not nullable
|
---|---|---|---|
17484892
|
28
|
True
|
True
|
21857839
|
26
|
False
|
False
|
23760628
|
24
|
False
|
False
|
28479393
|
20
|
False
|
False
|
19748383
|
27
|
False
|
False
|
<class 'optimus.new_optimus.DaskDataFrame'>
df
cedula
1 (object)
not nullable
|
edad
2 (object)
not nullable
|
apellido
3 (bool)
not nullable
|
---|---|---|
21857839
|
26
|
False
|
28479393
|
20
|
False
|
17484892
|
28
|
True
|
19748383
|
27
|
False
|
23760628
|
24
|
False
|
<class 'optimus.new_optimus.DaskDataFrame'>
apellido, pattern, match
df.ext.buffer_window("*", 0, 5).rows.find('df.cols.select("apellido").cols.pattern().data["apellido"]=="lllllllll"')
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-128-55b8fd86bbdf> in <module> ----> 1 df.ext.buffer_window("*", 0, 5).rows.find('df.cols.select("apellido").cols.pattern().data["apellido"]=="lllllllll"') ~\Documents\Optimus\optimus\engines\base\rows.py in find(self, condition) 47 df = self.parent.data 48 if is_str(condition): ---> 49 condition = eval(condition) 50 51 df["__match__"] = condition ~\Documents\Optimus\optimus\engines\base\rows.py in <module> ~\Anaconda3\lib\site-packages\pandas\core\generic.py in __getattr__(self, name) 5134 if self._info_axis._can_hold_identifiers_and_holds_name(name): 5135 return self[name] -> 5136 return object.__getattribute__(self, name) 5137 5138 def __setattr__(self, name: str, value) -> None: AttributeError: 'DataFrame' object has no attribute 'cols'
df.cols.select("*").rows.limit(10)
cedula
1 (object)
not nullable
|
edad
2 (object)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
17484892
|
28
|
martinez
|
21857839
|
26
|
null
|
28479393
|
20
|
null
|
23760628
|
24
|
contreras
|
19748383
|
27
|
null
|
<class 'optimus.new_optimus.DaskDataFrame'>
df
cedula
1 (int)
not nullable
|
edad
2 (int)
not nullable
|
apellido
3 (string)
not nullable
|
---|---|---|
28479393
|
20
|
null
|
17484892
|
28
|
martinez
|
23760628
|
24
|
contreras
|
21857839
|
26
|
null
|
19748383
|
27
|
null
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.ext.display()
cedula
1 (int)
not nullable
|
edad
2 (int)
not nullable
|
apellido
3 (string)
not nullable
|
---|---|---|
28479393
|
20
|
null
|
17484892
|
28
|
martinez
|
23760628
|
24
|
contreras
|
21857839
|
26
|
null
|
19748383
|
27
|
null
|
df.rows.to_list("apellido")
[['null'], ['martinez'], ['null'], ['null'], ['contreras']]
df = op.load.file("data/edad.csv").ext.cache()
df = df.ext.repartition(8).ext.cache()
assert(df.cols.names()==['cedula', 'edad', 'apellido'])
res = df.ext.buffer_window("*", 0, 5).ext.to_json("*")
res
{'sample': {'columns': [{'title': 'cedula'}, {'title': 'edad'}, {'title': 'apellido'}], 'value': [[28479393, 20, 'null'], [17484892, 28, 'martinez'], [19748383, 27, 'null'], [21857839, 26, 'null'], [23760628, 24, 'contreras']]}}
# df = op.load.file("https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv").ext.cache()
df = op.load.file("https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv").ext.cache()
df = df.ext.repartition(8).ext.cache()
assert(df.cols.names()==['cedula', 'edad', 'apellido'])
res = df.ext.buffer_window("*", 0, 5).ext.to_json("*")
res
# expected_res = res
# diff = DeepDiff(expected_res, res)
# pprint( diff, indent=2 )
# assert(diff == {})
--------------------------------------------------------------------------- Py4JJavaError Traceback (most recent call last) <ipython-input-14-3b38c3527cea> in <module> 1 # df = op.load.file("https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv").ext.cache() ----> 2 df = op.load.file("https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv").ext.cache() 3 df = df.ext.repartition(8).ext.cache() 4 5 assert(df.cols.names()==['cedula', 'edad', 'apellido']) ~\Documents\Optimus\optimus\engines\base\io\load.py in file(self, path, *args, **kwargs) 62 **kwargs, engine="python", na_values='nan') 63 except Exception as err: ---> 64 raise err 65 pass 66 ~\Documents\Optimus\optimus\engines\base\io\load.py in file(self, path, *args, **kwargs) 60 mime_info.update(r) 61 df = self.csv(path, encoding=mime_info["encoding"], dtype=str, **mime_info["properties"], ---> 62 **kwargs, engine="python", na_values='nan') 63 except Exception as err: 64 raise err ~\Documents\Optimus\optimus\engines\spark\io\load.py in csv(path, sep, header, infer_schema, encoding, null_value, n_rows, error_bad_lines, *args, **kwargs) 95 read.options(mode="DROPMALFORMED") 96 ---> 97 df = read.csv(path) 98 99 if n_rows > -1: ~\Anaconda3\lib\site-packages\pyspark\sql\readwriter.py in csv(self, path, schema, sep, encoding, quote, escape, comment, header, inferSchema, ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace, nullValue, nanValue, positiveInf, negativeInf, dateFormat, timestampFormat, maxColumns, maxCharsPerColumn, maxMalformedLogPerPartition, mode, columnNameOfCorruptRecord, multiLine, charToEscapeQuoteEscaping, samplingRatio, enforceSchema, emptyValue) 474 path = [path] 475 if type(path) == list: --> 476 return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path))) 477 elif isinstance(path, RDD): 478 def func(iterator): ~\Anaconda3\lib\site-packages\py4j\java_gateway.py in __call__(self, *args) 1255 answer = self.gateway_client.send_command(command) 1256 return_value = get_return_value( -> 1257 answer, self.gateway_client, self.target_id, self.name) 1258 1259 for temp_arg in temp_args: ~\Anaconda3\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw) 61 def deco(*a, **kw): 62 try: ---> 63 return f(*a, **kw) 64 except py4j.protocol.Py4JJavaError as e: 65 s = e.java_exception.toString() ~\Anaconda3\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name) 326 raise Py4JJavaError( 327 "An error occurred while calling {0}{1}{2}.\n". --> 328 format(target_id, ".", name), value) 329 else: 330 raise Py4JError( Py4JJavaError: An error occurred while calling o211.csv. : java.io.IOException: No FileSystem for scheme: https at org.apache.hadoop.fs.FileSystem.getFileSystemClass(FileSystem.java:2660) at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2667) at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94) at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703) at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685) at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373) at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295) at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:561) at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary$1.apply(DataSource.scala:559) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241) at scala.collection.immutable.List.foreach(List.scala:392) at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241) at scala.collection.immutable.List.flatMap(List.scala:355) at org.apache.spark.sql.execution.datasources.DataSource.org$apache$spark$sql$execution$datasources$DataSource$$checkAndGlobPathIfNecessary(DataSource.scala:559) at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373) at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:242) at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:230) at org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:638) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748)
df.ext.profile("*")
{'columns': {'cedula': {'stats': {'match': 5, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '28479393', 'count': 1}, {'value': '23760628', 'count': 1}, {'value': '21857839', 'count': 1}, {'value': '19748383', 'count': 1}, {'value': '17484892', 'count': 1}], 'count_uniques': 5}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'edad': {'stats': {'match': 5, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '28', 'count': 1}, {'value': '27', 'count': 1}, {'value': '26', 'count': 1}, {'value': '24', 'count': 1}, {'value': '20', 'count': 1}], 'count_uniques': 5}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'apellido': {'stats': {'match': 5, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'null', 'count': 3}, {'value': 'martinez', 'count': 1}, {'value': 'contreras', 'count': 1}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'name': 'edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv', 'file_name': 'https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-32291509-7a3f-4d48-a3a5-a224e088dde7.csv', 'summary': {'cols_count': 3, 'rows_count': 5, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}
df.rows.to_list("*")
[['28479393', '20', 'null'], ['17484892', '28', 'martinez'], ['23760628', '24', 'contreras'], ['21857839', '26', 'null'], ['19748383', '27', 'null']]
df.ext.buffer_window("*", 0, 5).ext.to_json("*")
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-12-25d77e7426a1> in <module> ----> 1 df.ext.buffer_window("*", 0, 5).ext.to_json("*") ~\Documents\Optimus\optimus\engines\base\extension.py in to_json(self, columns, format) 63 columns = parse_columns(odf, columns) 64 result = {"sample": {"columns": [{"title": col_name} for col_name in odf.cols.select(columns).cols.names()], ---> 65 "value": odf.rows.to_list(columns)}} 66 else: 67 result = json.dumps(odf.ext.to_dict(), ensure_ascii=False, default=json_converter) ~\Documents\Optimus\optimus\engines\base\rows.py in to_list(self, input_cols) 86 odf = self.parent 87 input_cols = parse_columns(odf, input_cols) ---> 88 df = odf[input_cols].ext.to_pandas().values.tolist() 89 90 return df AttributeError: 'NoneType' object has no attribute 'values'
df
name
1 (object)
not nullable
|
id
2 (object)
not nullable
|
nametype
3 (object)
not nullable
|
recclass
4 (object)
not nullable
|
mass (g)
5 (object)
not nullable
|
fall
6 (object)
not nullable
|
year
7 (object)
not nullable
|
reclat
8 (object)
not nullable
|
reclong
9 (object)
not nullable
|
GeoLocation
10 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Graves⋅Nunataks⋅06127
|
47380
|
Valid
|
H6
|
254.1
|
Found
|
01/01/2006⋅12:00:00⋅AM
|
|
|
|
Dhofar⋅1564
|
51569
|
Valid
|
H5
|
26
|
Found
|
01/01/2004⋅12:00:00⋅AM
|
18.696170
|
54.370000
|
(18.696170,⋅54.370000)
|
LaPaz⋅Icefield⋅04714
|
46164
|
Valid
|
LL6
|
61.7
|
Found
|
01/01/2004⋅12:00:00⋅AM
|
|
|
|
Sayh⋅al⋅Uhaymir⋅176
|
33999
|
Valid
|
H6
|
19
|
Found
|
01/01/2001⋅12:00:00⋅AM
|
21.048330
|
57.258330
|
(21.048330,⋅57.258330)
|
Elephant⋅Moraine⋅92103
|
9505
|
Valid
|
CM2
|
1.5
|
Found
|
01/01/1992⋅12:00:00⋅AM
|
-76.037010
|
156.129810
|
(-76.037010,⋅156.129810)
|
Landreth⋅Draw
|
12459
|
Valid
|
H5
|
|
Found
|
01/01/1955⋅12:00:00⋅AM
|
37.250000
|
-98.133330
|
(37.250000,⋅-98.133330)
|
Happy⋅(a)
|
11818
|
Valid
|
H3
|
2860
|
Found
|
01/01/1971⋅12:00:00⋅AM
|
35.680000
|
-101.993330
|
(35.680000,⋅-101.993330)
|
Lewis⋅Cliff⋅88079
|
13833
|
Valid
|
H5
|
7
|
Found
|
01/01/1988⋅12:00:00⋅AM
|
-84.244110
|
161.421070
|
(-84.244110,⋅161.421070)
|
Meteorite⋅Hills⋅00718
|
15952
|
Valid
|
H6
|
28.4
|
Found
|
01/01/2000⋅12:00:00⋅AM
|
-79.683330
|
159.750000
|
(-79.683330,⋅159.750000)
|
Northwest⋅Africa⋅2278
|
30937
|
Valid
|
L6
|
62
|
Found
|
01/01/2003⋅12:00:00⋅AM
|
|
|
|
<class 'optimus.new_optimus.DaskDataFrame'>
df["name"].ext.to_pandas().values.tolist()
[['Aachen'], ['Aarhus'], ['Abee'], ['Acapulco'], ['Achiras'], ['Adhi Kot'], ['Adzhi-Bogdo (stone)'], ['Agen'], ['Aguada'], ['Aguila Blanca'], ['Aioun el Atrouss'], ['Aïr'], ['Aire-sur-la-Lys'], ['Akaba'], ['Akbarpur'], ['Akwanga'], ['Akyumak'], ['Al Rais'], ['Al Zarnkh'], ['Alais'], ['Albareto'], ['Alberta'], ['Alby sur Chéran'], ['Aldsworth'], ['Aleppo'], ['Alessandria'], ['Alexandrovsky'], ['Alfianello'], ['Allegan'], ['Allende'], ['Almahata Sitta'], ["Alta'ameem"], ['Ambapur Nagla'], ['Andhara'], ['Andover'], ['Andreevka'], ['Andura'], ['Northwest Africa 5815'], ['Angers'], ['Angra dos Reis (stone)'], ['Ankober'], ['Anlong'], ['Aomori'], ['Appley Bridge'], ['Apt'], ['Arbol Solo'], ['Archie'], ['Arroyo Aguiar'], ['Asco'], ['Ash Creek'], ['Ashdon'], ['Assisi'], ['Atarra'], ['Atemajac'], ['Athens'], ['Atoka'], ['Aubres'], ['Aumale'], ['Aumieres'], ['Ausson'], ['Avanhandava'], ['Avce'], ['Avilez'], ['Awere'], ['Aztec'], ['Bachmut'], ['Bahjoi'], ['Bald Mountain'], ['Baldwyn'], ['Bali'], ['Ban Rong Du'], ['Bandong'], ['Bansur'], ['Banswal'], ['Banten'], ['Barbotan'], ['Barcelona (stone)'], ['Barea'], ['Barnaul'], ['Barntrup'], ['Baroti'], ['Barwell'], ['Bassikounou'], ['Baszkówka'], ['Bath'], ['Bath Furnace'], ['Battle Mountain'], ['Bawku'], ['Baxter'], ['Beardsley'], ['Beaver Creek'], ['Beddgelert'], ['Bells'], ['Belville'], ['Benares (a)'], ['Benguerir'], ["Beni M'hira"], ['Benld'], ['Benoni'], ['Bensour'], ['Benton'], ['Berduc'], ['Béréba'], ['Berlanguillas'], ['Berthoud'], ['Bethlehem'], ['Beuste'], ['Beyrout'], ['Bhagur'], ['Bhawad'], ['Bherai'], ['Bhola'], ['Bholghati'], ['Bialystok'], ['Bielokrynitschie'], ['Bilanga'], ['Binningup'], ["Birni N'konni"], ['Bishopville'], ['Bishunpur'], ['Bjelaja Zerkov'], ['Bjurböle'], ['Black Moshannan Park'], ['Blackwell'], ['Blanket'], ['Blansko'], ['Bloomington'], ['Bo Xian'], ['Bocas'], ['Bogou'], ['Boguslavka'], ['Borgo San Donino'], ['Bori'], ['Boriskino'], ['Borkut'], ['Borodino'], ['Botschetschki'], ['Boumdeid (2003)'], ['Boumdeid (2011)'], ['Bovedy'], ['Bradford Woods'], ['Braunau'], ['Breitscheid'], ['Bremervörde'], ['Brient'], ['Bruderheim'], ['Bukhara'], ['Bulls Run'], ['Bunburra Rockhole'], ['Bununu'], ['Bur-Gheluai'], ['Burnwell'], ['Bursa'], ['Buschhof'], ['Bustee'], ['Butsura'], ['Buzzard Coulee'], ['Cabezo de Mayo'], ['Cabin Creek'], ['Cacak'], ['Cali'], ['Calivo'], ['Campos Sales'], ['Çanakkale'], ['Cañellas'], ['Cangas de Onis'], ['Canon City'], ['Cape Girardeau'], ['Capilla del Monte'], ['Carancas'], ['Caratash'], ['Castalia'], ['Castel Berardenga'], ['Castine'], ['Castrovillari'], ['Caswell County'], ['Ceniceros'], ['Centerville'], ['Cereseto'], ['Chadong'], ['Chail'], ['Chainpur'], ['Chajari'], ['Chandakapur'], ['Chandpur'], ['Changde'], ['Chantonnay'], ['Charlotte'], ['Charsonville'], ['Charwallas'], ['Chassigny'], ['Château-Renard'], ['Chaves'], ['Chela'], ['Chelyabinsk'], ['Chergach '], ['Chernyi Bor'], ['Cherokee Springs'], ['Chervettaz'], ['Chervony Kut'], ['Chetrinahatti'], ['Chiang Khan'], ['Chicora'], ['Chisenga'], ['Chitado'], ['Chitenay'], ['Cilimus'], ['Claxton'], ['Clohars'], ['Colby (Wisconsin)'], ['Cold Bokkeveld'], ['Coleman'], ['Collescipoli'], ['Conquista'], ['Cosina'], ['Cranganore'], ['Crescent'], ['Cronstad'], ['Cross Roads'], ['Crumlin'], ['Cumberland Falls'], ['Cynthiana'], ['Dahmani'], ['Dandapur'], ["Daniel's Kuil"], ['Danville'], ['Darmstadt'], ['Dashoguz'], ['Daule'], ['De Cewsville'], ['Deal'], ['Delhi'], ['Demina'], ['Denver'], ['Dergaon'], ['Desuri'], ['Devgaon'], ['Devri-Khera'], ['Dhajala'], ['Dharwar'], ['Dhurmsala'], ['Didim'], ['Diep River'], ['Distrito Quebracho'], ['Djati-Pengilon'], ['Djermaia'], ['Djoumine'], ['Dokachi'], ['Dolgovoli'], ['Domanitch'], ['Dong Ujimqin Qi'], ['Donga Kohrod'], ['Dongtai'], ['Doroninsk'], ['Dosso'], ['Douar Mghila'], ['Dowa'], ['Drake Creek'], ['Dresden (Ontario)'], ['Dubrovnik'], ['Dunbogan'], ['Dundrum'], ['Dunhua'], ['Durala'], ['Duruma'], ['Duwun'], ['Dwaleni'], ['Dyalpur'], ['Dyarrl Island'], ['Eagle'], ['Ehole'], ['Eichstädt'], ['Ekeby'], ['Ekh Khera'], ['El Idrissia'], ['El Paso de Aguila'], ['El Tigre'], ['Elbert'], ['Elbogen'], ['Elenovka'], ['Ellemeet'], ['Emmaville'], ['Enshi'], ['Ensisheim'], ['Épinal'], ['Erakot'], ['Erevan'], ['Ergheo'], ['Erxleben'], ['Esnandes'], ['Essebi'], ['Estherville'], ['Farmington'], ['Farmville'], ['Favars'], ['Fayetteville'], ['Feid Chair'], ['Felix'], ['Fenghsien-Ku'], ['Ferguson'], ['Fermo'], ['Fisher'], ['Florence'], ['Forest City'], ['Forest Vale'], ['Forksville'], ['Forsbach'], ['Forsyth'], ['Fort Flatters'], ['Frankfort (stone)'], ['Fuhe'], ['Fukutomi'], ['Fünen'], ['Futtehpur'], ['Fuyang'], ['Galapian'], ['Galim (a)'], ['Galim (b)'], ['Galkiv'], ['Gambat'], ['Gao-Guenie'], ['Garhi Yasin'], ['Garland'], ['Gashua'], ['Gasseltepaoua'], ['Geidam'], ['Gifu'], ['Girgenti'], ['Git-Git'], ['Glanerbrug'], ['Glanggang'], ['Glasatovo'], ['Glatton'], ['Gnadenfrei'], ['Gopalpur'], ['Gorlovka'], ['Granes'], ['Grefsheim'], ['Grimsby'], ['Grosnaja'], ['Gross-Divina'], ['Grossliebenthal'], ['Grüneberg'], ['Grzempach'], ['Gualeguaychú'], ['Guangmingshan'], ['Guangnan'], ['Guangrao'], ['Guareña'], ['Guêa'], ['Guibga'], ['Guidder'], ['Gujargaon'], ['Gujba'], ['Gumoschnik'], ['Gurram Konda'], ['Gursum'], ['Gütersloh'], ['Gyokukei'], ['Hachi-oji'], ['Hainaut'], ['Hallingeberg'], ['Hamlet'], ['Haraiya'], ['Haripura'], ['Harleton'], ['Harrison County'], ['Hashima'], ['Hassi-Jekna'], ['Hatford'], ['Haverö'], ['Hedeskoga'], ['Hedjaz'], ['Heredia'], ['Hessle'], ['Higashi-koen'], ['High Possil'], ['Hiroshima'], ['Hoima'], ['Hökmark'], ['Holbrook'], ['Holetta'], ['Homestead'], ['Honolulu'], ['Hotse'], ['Hoxie'], ['Hraschina'], ['Huaxi'], ['Hungen'], ['Hvittis'], ['Ibbenbüren'], ['Ibitira'], ['Ibrisim'], ['Ichkala'], ['Idutywa'], ['Iguaracu'], ['Ijopega'], ['Indarch'], ['Independence'], ['Inner Mongolia'], ['Innisfree'], ['Ipiranga'], ['Ishinga'], ['Isthilart'], ['Itapicuru-Mirim'], ['Itqiy'], ['Ivuna'], ['Jackalsfontein'], ['Jajh deh Kot Lalu'], ['Jalanash'], ['Jalandhar'], ['Jamkheir'], ['Jartai'], ['Jelica'], ['Jemlapur'], ['Jesenice'], ['Jhung'], ['Jiange'], ['Jianshi'], ['Jilin'], ['Jodiya'], ['Jodzie'], ['Johnstown'], ['Jolomba'], ['Jonzac'], ['Juancheng'], ['Judesegeri'], ['Jumapalo'], ['Junan'], ['Juromenha'], ['Juvinas'], ['Kaba'], ['Kabo'], ['Kadonah'], ['Kaee'], ['Kagarlyk'], ['Kaidun'], ['Kainsaz'], ['Kakangari'], ['Kakowa'], ['Kalaba'], ['Kalumbi'], ['Kamalpur'], ['Kamiomi'], ['Kamsagar'], ['Kandahar (Afghanistan)'], ['Kangean'], ['Kangra Valley'], ['Kapoeta'], ['Kaprada'], ['Kaptal-Aryk'], ['Karakol'], ['Karatu'], ['Karewar'], ['Karkh'], ['Karloowala'], ['Karoonda'], ['Kasamatsu'], ['Kasauli'], ['Katagum'], ['Kavarpura'], ['Kayakent'], ['Kediri'], ['Kemer'], ['Kendleton'], ['Kendrapara'], ['Kerilis'], ['Kernouve'], ['Kesen'], ['Khairpur'], ['Khanpur'], ['Kharkov'], ['Kheragur'], ['Khetri'], ['Khmelevka'], ['Khohar'], ['Khor Temiki'], ['Kidairat'], ['Kiel'], ['Kiffa'], ['Kijima (1906)'], ['Kikino'], ['Kilabo'], ['Kilbourn'], ['Killeter'], ['Kingai'], ['Kirbyville'], ['Kisvarsány'], ['Kitchener'], ['Klein-Wenden'], ['Knyahinya'], ['Kobe'], ['Kokubunji'], ['Komagome'], ['Konovo'], ['Košice'], ['Krähenberg'], ['Krasnoi-Ugol'], ['Krasnyi Klyuch'], ['Krutikha'], ['Krymka'], ['Kukschin'], ['Kulak'], ['Kuleschovka'], ['Kulp'], ['Kunashak'], ['Kunya-Urgench'], ['Kushiike'], ['Kusiali'], ['Kutais'], ['Kuttippuram'], ['Kuznetzovo'], ['Kyushu'], ['La Bécasse'], ['La Charca'], ['La Colina'], ['La Criolla'], ['Laborel'], ['Lahrauli'], ["L'Aigle"], ['Lakangaon'], ['Lalitpur'], ['Lancé'], ['Lancon'], ['Långhalsen'], ['Lanxi'], ['Lanzenkirchen'], ['Laochenzhen'], ['Launton'], ['Lavrentievka'], ['Le Pressoir'], ['Le Teilleul'], ['Leedey'], ['Leeuwfontein'], ['Leighlinbridge'], ['Leighton'], ['Leonovka'], ['Les Ormes'], ['Lesves'], ['Lichtenberg'], ['Lillaverke'], ['Limerick'], ['Linum'], ['Lishui'], ['Lissa'], ['Little Piney'], ['Lixna'], ['Lodran'], ['Lohawat'], ['Lorton'], ['Los Martinez'], ['Lost City'], ['Louisville'], ['Łowicz'], ['Lua'], ['Lucé'], ['Lumpkin'], ['Lunan'], ['Lundsgård'], ['Luotolax'], ['Luponnas'], ['Lusaka'], ['Mabwe-Khoywa'], ['Macau'], ['Machinga'], ['Macibini'], ['Madhipura'], ['Madiun'], ['Madrid'], ['Mafra'], ['Magnesia'], ['Magombedze'], ['Mahadevpur'], ['Maigatari-Danduma'], ['Malaga'], ['Malakal'], ['Malampaka'], ['Malotas'], ['Malvern'], ['Mamra Springs'], ['Manbhoom'], ['Manegaon'], ['Mangwendi'], ['Manych'], ['Mardan'], ['Maria Linden'], ['Mariaville'], ['Maribo'], ['Maridi'], ['Marilia'], ['Marion (Iowa)'], ['Marjalahti'], ['Marmande'], ['Maromandia'], ['Maryville'], ['Mascombes'], ['Mason Gully'], ['Mässing'], ['Mauerkirchen'], ['Mauritius'], ['Mayo Belwa'], ['Mazapil'], ['Maziba'], ['Mbale'], ['Medanitos'], ['Meerut'], ['Meester-Cornelis'], ['Menow'], ['Menziswyl'], ['Mern'], ['Meru'], ['Merua'], ['Messina'], ['Meuselbach'], ['Mezel'], ['Mezö-Madaras'], ['Mhow'], ['Mianchi'], ['Middlesbrough'], ['Mifflin'], ['Mighei'], ['Mihonoseki'], ['Mike'], ['Milena'], ['Millbillillie'], ['Miller (Arkansas)'], ['Minamino'], ['Mineo'], ['Min-Fan-Zhun'], ['Minnichhof'], ['Mirzapur'], ['Misshof'], ['Mjelleim'], ['Mocs'], ['Modoc (1905)'], ['Mokoia'], ['Molina'], ['Molteno'], ['Monahans (1998)'], ['Monroe'], ['Monte das Fortes'], ['Monte Milone'], ['Montferré'], ['Montlivault'], ['Monze'], ['Moore County'], ['Mooresfort'], ['Moorleah'], ['Moradabad'], ['Morávka'], ['Mornans'], ['Moss'], ['Moti-ka-nagla'], ['Motta di Conti'], ['Mount Browne'], ['Mount Tazerzait'], ['Mount Vaisi'], ['Mtola'], ['Muddoor'], ['Mulletiwu'], ['Muraid'], ['Murchison'], ['Murray'], ['Muzaffarpur'], ['Myhee Caunta'], ['Nadiabondi'], ['Nagai'], ['Nagaria'], ['Nagy-Borové'], ['Nakhla'], ['Nakhon Pathom'], ['Nammianthal'], ['Nan Yang Pao'], ['Nanjemoy'], ['Nantong'], ['Naoki'], ['Naragh'], ['Narellan'], ['Narni'], ['Nassirah'], ['Natal'], ['Nawapali'], ['Neagari'], ['Nedagolla'], ['Nejo'], ['Nerft'], ['Neuschwanstein'], ['New Concord'], ['New Halfa'], ['New Orleans'], ['Ngawi'], ["N'Goureyma"], ['Nicorps'], ['Niger (L6)'], ['Niger (LL6)'], ['Nikolaevka'], ['Nikolskoe'], ['Ningbo'], ['Ningqiang'], ['Nio'], ["N'Kandhla"], ['Nobleborough'], ['Noblesville'], ['Nogata'], ['Nogoya'], ['Norfork'], ['Norton County'], ['Noventa Vicentina'], ['Novo-Urei'], ['Novy-Ergi'], ['Novy-Projekt'], ['Noyan-Bogdo'], ['Nuevo Mercurio'], ['Nulles'], ['Numakai'], ['Nyaung'], ['Nyirábrany'], ['Ochansk'], ['Oesede'], ['Oesel'], ['Ofehértó'], ['Ogi'], ['Ohaba'], ['Ohuma'], ['Ojuelos Altos'], ['Okabe'], ['Okano'], ['Okniny'], ['Oldenburg (1930)'], ['Oliva-Gandia'], ['Olivenza'], ['Olmedilla de Alarcón'], ['Omolon'], ['Orgueil'], ['Orlando'], ['Ornans'], ['Ortenau'], ['Orvinio'], ['Oterøy'], ['Otomi'], ['Ottawa'], ['Ouadangou'], ['Oued el Hadjar'], ['Oum Dreyga'], ['Ourique'], ['Ovambo'], ['Oviedo'], ['Owrucz'], ['Pacula'], ['Padvarninkai'], ['Paitan'], ['Palahatchie'], ['Palca de Aparzo'], ['Palinshih'], ['Palmyra'], ['Palolo Valley'], ['Pampanga'], ['Pantar'], ['Paragould'], ['Parambu'], ['Paranaiba'], ['Park Forest'], ['Parnallee'], ['Parsa'], ['Pasamonte'], ['Patora'], ['Patrimonio'], ['Patti'], ['Patwar'], ['Pavel'], ['Pavlodar (stone)'], ['Pavlograd'], ['Pavlovka'], ['Pê'], ['Peace River'], ['Peckelsheim'], ['Peekskill'], ['Peña Blanca Spring'], ['Peramiho'], ['Perpeti'], ['Perth'], ['Pervomaisky'], ['Pesyanoe'], ['Pétèlkolé'], ['Petersburg'], ['Pettiswood'], ['Phillips County (stone)'], ['Phu Hong'], ['Phum Sambo'], ['Phuoc-Binh'], ['Piancaldoli'], ['Picote'], ['Pillistfer'], ['Piplia Kalan'], ['Piquetberg'], ['Pirgunje'], ['Pirthalla'], ['Pitts'], ['Plantersville'], ['Pleşcoi'], ['Ploschkovitz'], ['Pnompehn'], ['Pohlitz'], ['Pokhra'], ['Pollen'], ['Pontlyfni'], ['Portales Valley'], ['Portugal'], ['Po-wang Chen'], ['Prambachkirchen'], ['Pribram'], ['Pricetown'], ['Puerto Lápice'], ['Pulsora'], ['Pultusk'], ['Punganaru'], ['Putinga'], ['Qidong'], ['Qingzhen'], ["Queen's Mercy"], ['Quenggouk'], ['Quesa'], ['Quija'], ['Quincay'], ['Raco'], ['Raghunathpura'], ['Rahimyar Khan'], ['Rakovka'], ['Ramnagar'], ['Rampurhat'], ['Ramsdorf'], ['Ranchapur'], ['Rancho de la Presa'], ['Rangala'], ['Raoyang'], ['Ras Tanura'], ['Rasgrad'], ['Ratyn'], ['Red Canyon Lake'], ['Reliegos'], ['Rembang'], ['Renazzo'], ['Renca'], ['Renqiu'], ['Repeev Khutor'], ['Revelstoke'], ['Rewari'], ['Rich Mountain'], ['Richardton'], ['Richland Springs'], ['Richmond'], ['Rio Negro'], ['Rivolta de Bassi'], ['Rochester'], ['Rockhampton'], ['Roda'], ['Rodach'], ['Rose City'], ['Rowton'], ['Ruhobobo'], ['Rumuruti'], ['Rupota'], ['Ryechki'], ['Sabetmahet'], ['Sabrum'], ['Sagan'], ['Saint-Sauveur'], ['Saint-Séverin'], ['Sakauchi'], ['Salem'], ['Salles'], ['Salzwedel'], ['Samelia'], ['San Juan Capistrano'], ['San Michele'], ['San Pedro de Quiles'], ['San Pedro Jacuaro'], ['Santa Barbara'], ['Santa Cruz'], ['Santa Isabel'], ['Santa Lucia (2008)'], ['São Jose do Rio Preto'], ['Saratov'], ['Sasagase'], ['Sauguis'], ['Savtschenskoje'], ['Sayama'], ['Sazovice'], ['Schellin'], ['Schenectady'], ['Schönenberg'], ['Searsmont'], ['Sediköy'], ['Segowlie'], ['Selakopi'], ['Seldebourak'], ['Semarkona'], ['Sena'], ['Senboku'], ['Seoni'], ['Seres'], ['Serra de Magé'], ['Sete Lagoas'], ['Sevilla'], ['Sevrukovo'], ['Sfax'], ['Shalka'], ['Sharps'], ['Shelburne'], ['Shergotty'], ['Sheyang'], ['Shikarpur'], ['Shuangyang'], ['Shupiyan'], ['Shytal'], ['Siena'], ['Sikhote-Alin'], ['Silao'], ['Silistra'], ['Simmern'], ['Sinai'], ['Sindhri'], ['Sinnai'], ['Sioux County'], ['Sitathali'], ['Sivas'], ['Sixiangkou'], ['Ski'], ['Slavetic'], ['Slobodka'], ['Soheria'], ['Soko-Banja'], ['Sologne'], ['Sołtmany'], ['Sone'], ['Songyuan'], ['Sopot'], ['Soroti'], ['St. Caprais-de-Quinsac'], ['St. Christophe-la-Chartreuse'], ['St. Denis Westrem'], ['St. Germain-du-Pinel'], ['St. Louis'], ["St. Mark's"], ["St. Mary's County"], ['St. Mesmin'], ['St. Michel'], ['St.-Chinian'], ['Ställdalen'], ['Stannern'], ['Stavropol'], ['Ste. Marguerite'], ['Sterlitamak'], ['Stolzenau'], ['Stratford'], ['Strathmore'], ['Stretchleigh'], ['St-Robert'], ['Success'], ['Suchy Dul'], ['Suizhou'], ['Sulagiri'], ['Sultanpur'], ['Sungach'], ['Supuhee'], ["Sutter's Mill"], ['Sylacauga'], ['Tabor'], ['Tadjera'], ['Tagish Lake'], ['Tahara'], ['Takenouchi'], ['Talampaya'], ['Tambakwatu'], ['Tamdakht'], ['Tané'], ['Taonan'], ['Tatahouine'], ['Tathlith'], ['Tauk'], ['Tauti'], ['Tenham'], ['Tennasilm'], ['Thal'], ['Thika'], ['Thuathe'], ['Tianzhang'], ['Tieschitz'], ['Tilden'], ['Tillaberi'], ['Timochin'], ['Tirupati'], ['Tissint'], ['Tjabe'], ['Tjerebon'], ['Tomakovka'], ['Tomatlan'], ['Tomita'], ['Tomiya'], ['Tonk'], ...]
df.cols.names()
['name', 'id', 'nametype', 'recclass', 'mass (g)', 'fall', 'year', 'reclat', 'reclong', 'GeoLocation']
df.rows.to_list("name")
[['Aachen'], ['Aarhus'], ['Abee'], ['Acapulco'], ['Achiras'], ['Adhi Kot'], ['Adzhi-Bogdo (stone)'], ['Agen'], ['Aguada'], ['Aguila Blanca'], ['Aioun el Atrouss'], ['Aïr'], ['Aire-sur-la-Lys'], ['Akaba'], ['Akbarpur'], ['Akwanga'], ['Akyumak'], ['Al Rais'], ['Al Zarnkh'], ['Alais'], ['Albareto'], ['Alberta'], ['Alby sur Chéran'], ['Aldsworth'], ['Aleppo'], ['Alessandria'], ['Alexandrovsky'], ['Alfianello'], ['Allegan'], ['Allende'], ['Almahata Sitta'], ["Alta'ameem"], ['Ambapur Nagla'], ['Andhara'], ['Andover'], ['Andreevka'], ['Andura'], ['Northwest Africa 5815'], ['Angers'], ['Angra dos Reis (stone)'], ['Ankober'], ['Anlong'], ['Aomori'], ['Appley Bridge'], ['Apt'], ['Arbol Solo'], ['Archie'], ['Arroyo Aguiar'], ['Asco'], ['Ash Creek'], ['Ashdon'], ['Assisi'], ['Atarra'], ['Atemajac'], ['Athens'], ['Atoka'], ['Aubres'], ['Aumale'], ['Aumieres'], ['Ausson'], ['Avanhandava'], ['Avce'], ['Avilez'], ['Awere'], ['Aztec'], ['Bachmut'], ['Bahjoi'], ['Bald Mountain'], ['Baldwyn'], ['Bali'], ['Ban Rong Du'], ['Bandong'], ['Bansur'], ['Banswal'], ['Banten'], ['Barbotan'], ['Barcelona (stone)'], ['Barea'], ['Barnaul'], ['Barntrup'], ['Baroti'], ['Barwell'], ['Bassikounou'], ['Baszkówka'], ['Bath'], ['Bath Furnace'], ['Battle Mountain'], ['Bawku'], ['Baxter'], ['Beardsley'], ['Beaver Creek'], ['Beddgelert'], ['Bells'], ['Belville'], ['Benares (a)'], ['Benguerir'], ["Beni M'hira"], ['Benld'], ['Benoni'], ['Bensour'], ['Benton'], ['Berduc'], ['Béréba'], ['Berlanguillas'], ['Berthoud'], ['Bethlehem'], ['Beuste'], ['Beyrout'], ['Bhagur'], ['Bhawad'], ['Bherai'], ['Bhola'], ['Bholghati'], ['Bialystok'], ['Bielokrynitschie'], ['Bilanga'], ['Binningup'], ["Birni N'konni"], ['Bishopville'], ['Bishunpur'], ['Bjelaja Zerkov'], ['Bjurböle'], ['Black Moshannan Park'], ['Blackwell'], ['Blanket'], ['Blansko'], ['Bloomington'], ['Bo Xian'], ['Bocas'], ['Bogou'], ['Boguslavka'], ['Borgo San Donino'], ['Bori'], ['Boriskino'], ['Borkut'], ['Borodino'], ['Botschetschki'], ['Boumdeid (2003)'], ['Boumdeid (2011)'], ['Bovedy'], ['Bradford Woods'], ['Braunau'], ['Breitscheid'], ['Bremervörde'], ['Brient'], ['Bruderheim'], ['Bukhara'], ['Bulls Run'], ['Bunburra Rockhole'], ['Bununu'], ['Bur-Gheluai'], ['Burnwell'], ['Bursa'], ['Buschhof'], ['Bustee'], ['Butsura'], ['Buzzard Coulee'], ['Cabezo de Mayo'], ['Cabin Creek'], ['Cacak'], ['Cali'], ['Calivo'], ['Campos Sales'], ['Çanakkale'], ['Cañellas'], ['Cangas de Onis'], ['Canon City'], ['Cape Girardeau'], ['Capilla del Monte'], ['Carancas'], ['Caratash'], ['Castalia'], ['Castel Berardenga'], ['Castine'], ['Castrovillari'], ['Caswell County'], ['Ceniceros'], ['Centerville'], ['Cereseto'], ['Chadong'], ['Chail'], ['Chainpur'], ['Chajari'], ['Chandakapur'], ['Chandpur'], ['Changde'], ['Chantonnay'], ['Charlotte'], ['Charsonville'], ['Charwallas'], ['Chassigny'], ['Château-Renard'], ['Chaves'], ['Chela'], ['Chelyabinsk'], ['Chergach '], ['Chernyi Bor'], ['Cherokee Springs'], ['Chervettaz'], ['Chervony Kut'], ['Chetrinahatti'], ['Chiang Khan'], ['Chicora'], ['Chisenga'], ['Chitado'], ['Chitenay'], ['Cilimus'], ['Claxton'], ['Clohars'], ['Colby (Wisconsin)'], ['Cold Bokkeveld'], ['Coleman'], ['Collescipoli'], ['Conquista'], ['Cosina'], ['Cranganore'], ['Crescent'], ['Cronstad'], ['Cross Roads'], ['Crumlin'], ['Cumberland Falls'], ['Cynthiana'], ['Dahmani'], ['Dandapur'], ["Daniel's Kuil"], ['Danville'], ['Darmstadt'], ['Dashoguz'], ['Daule'], ['De Cewsville'], ['Deal'], ['Delhi'], ['Demina'], ['Denver'], ['Dergaon'], ['Desuri'], ['Devgaon'], ['Devri-Khera'], ['Dhajala'], ['Dharwar'], ['Dhurmsala'], ['Didim'], ['Diep River'], ['Distrito Quebracho'], ['Djati-Pengilon'], ['Djermaia'], ['Djoumine'], ['Dokachi'], ['Dolgovoli'], ['Domanitch'], ['Dong Ujimqin Qi'], ['Donga Kohrod'], ['Dongtai'], ['Doroninsk'], ['Dosso'], ['Douar Mghila'], ['Dowa'], ['Drake Creek'], ['Dresden (Ontario)'], ['Dubrovnik'], ['Dunbogan'], ['Dundrum'], ['Dunhua'], ['Durala'], ['Duruma'], ['Duwun'], ['Dwaleni'], ['Dyalpur'], ['Dyarrl Island'], ['Eagle'], ['Ehole'], ['Eichstädt'], ['Ekeby'], ['Ekh Khera'], ['El Idrissia'], ['El Paso de Aguila'], ['El Tigre'], ['Elbert'], ['Elbogen'], ['Elenovka'], ['Ellemeet'], ['Emmaville'], ['Enshi'], ['Ensisheim'], ['Épinal'], ['Erakot'], ['Erevan'], ['Ergheo'], ['Erxleben'], ['Esnandes'], ['Essebi'], ['Estherville'], ['Farmington'], ['Farmville'], ['Favars'], ['Fayetteville'], ['Feid Chair'], ['Felix'], ['Fenghsien-Ku'], ['Ferguson'], ['Fermo'], ['Fisher'], ['Florence'], ['Forest City'], ['Forest Vale'], ['Forksville'], ['Forsbach'], ['Forsyth'], ['Fort Flatters'], ['Frankfort (stone)'], ['Fuhe'], ['Fukutomi'], ['Fünen'], ['Futtehpur'], ['Fuyang'], ['Galapian'], ['Galim (a)'], ['Galim (b)'], ['Galkiv'], ['Gambat'], ['Gao-Guenie'], ['Garhi Yasin'], ['Garland'], ['Gashua'], ['Gasseltepaoua'], ['Geidam'], ['Gifu'], ['Girgenti'], ['Git-Git'], ['Glanerbrug'], ['Glanggang'], ['Glasatovo'], ['Glatton'], ['Gnadenfrei'], ['Gopalpur'], ['Gorlovka'], ['Granes'], ['Grefsheim'], ['Grimsby'], ['Grosnaja'], ['Gross-Divina'], ['Grossliebenthal'], ['Grüneberg'], ['Grzempach'], ['Gualeguaychú'], ['Guangmingshan'], ['Guangnan'], ['Guangrao'], ['Guareña'], ['Guêa'], ['Guibga'], ['Guidder'], ['Gujargaon'], ['Gujba'], ['Gumoschnik'], ['Gurram Konda'], ['Gursum'], ['Gütersloh'], ['Gyokukei'], ['Hachi-oji'], ['Hainaut'], ['Hallingeberg'], ['Hamlet'], ['Haraiya'], ['Haripura'], ['Harleton'], ['Harrison County'], ['Hashima'], ['Hassi-Jekna'], ['Hatford'], ['Haverö'], ['Hedeskoga'], ['Hedjaz'], ['Heredia'], ['Hessle'], ['Higashi-koen'], ['High Possil'], ['Hiroshima'], ['Hoima'], ['Hökmark'], ['Holbrook'], ['Holetta'], ['Homestead'], ['Honolulu'], ['Hotse'], ['Hoxie'], ['Hraschina'], ['Huaxi'], ['Hungen'], ['Hvittis'], ['Ibbenbüren'], ['Ibitira'], ['Ibrisim'], ['Ichkala'], ['Idutywa'], ['Iguaracu'], ['Ijopega'], ['Indarch'], ['Independence'], ['Inner Mongolia'], ['Innisfree'], ['Ipiranga'], ['Ishinga'], ['Isthilart'], ['Itapicuru-Mirim'], ['Itqiy'], ['Ivuna'], ['Jackalsfontein'], ['Jajh deh Kot Lalu'], ['Jalanash'], ['Jalandhar'], ['Jamkheir'], ['Jartai'], ['Jelica'], ['Jemlapur'], ['Jesenice'], ['Jhung'], ['Jiange'], ['Jianshi'], ['Jilin'], ['Jodiya'], ['Jodzie'], ['Johnstown'], ['Jolomba'], ['Jonzac'], ['Juancheng'], ['Judesegeri'], ['Jumapalo'], ['Junan'], ['Juromenha'], ['Juvinas'], ['Kaba'], ['Kabo'], ['Kadonah'], ['Kaee'], ['Kagarlyk'], ['Kaidun'], ['Kainsaz'], ['Kakangari'], ['Kakowa'], ['Kalaba'], ['Kalumbi'], ['Kamalpur'], ['Kamiomi'], ['Kamsagar'], ['Kandahar (Afghanistan)'], ['Kangean'], ['Kangra Valley'], ['Kapoeta'], ['Kaprada'], ['Kaptal-Aryk'], ['Karakol'], ['Karatu'], ['Karewar'], ['Karkh'], ['Karloowala'], ['Karoonda'], ['Kasamatsu'], ['Kasauli'], ['Katagum'], ['Kavarpura'], ['Kayakent'], ['Kediri'], ['Kemer'], ['Kendleton'], ['Kendrapara'], ['Kerilis'], ['Kernouve'], ['Kesen'], ['Khairpur'], ['Khanpur'], ['Kharkov'], ['Kheragur'], ['Khetri'], ['Khmelevka'], ['Khohar'], ['Khor Temiki'], ['Kidairat'], ['Kiel'], ['Kiffa'], ['Kijima (1906)'], ['Kikino'], ['Kilabo'], ['Kilbourn'], ['Killeter'], ['Kingai'], ['Kirbyville'], ['Kisvarsány'], ['Kitchener'], ['Klein-Wenden'], ['Knyahinya'], ['Kobe'], ['Kokubunji'], ['Komagome'], ['Konovo'], ['Košice'], ['Krähenberg'], ['Krasnoi-Ugol'], ['Krasnyi Klyuch'], ['Krutikha'], ['Krymka'], ['Kukschin'], ['Kulak'], ['Kuleschovka'], ['Kulp'], ['Kunashak'], ['Kunya-Urgench'], ['Kushiike'], ['Kusiali'], ['Kutais'], ['Kuttippuram'], ['Kuznetzovo'], ['Kyushu'], ['La Bécasse'], ['La Charca'], ['La Colina'], ['La Criolla'], ['Laborel'], ['Lahrauli'], ["L'Aigle"], ['Lakangaon'], ['Lalitpur'], ['Lancé'], ['Lancon'], ['Långhalsen'], ['Lanxi'], ['Lanzenkirchen'], ['Laochenzhen'], ['Launton'], ['Lavrentievka'], ['Le Pressoir'], ['Le Teilleul'], ['Leedey'], ['Leeuwfontein'], ['Leighlinbridge'], ['Leighton'], ['Leonovka'], ['Les Ormes'], ['Lesves'], ['Lichtenberg'], ['Lillaverke'], ['Limerick'], ['Linum'], ['Lishui'], ['Lissa'], ['Little Piney'], ['Lixna'], ['Lodran'], ['Lohawat'], ['Lorton'], ['Los Martinez'], ['Lost City'], ['Louisville'], ['Łowicz'], ['Lua'], ['Lucé'], ['Lumpkin'], ['Lunan'], ['Lundsgård'], ['Luotolax'], ['Luponnas'], ['Lusaka'], ['Mabwe-Khoywa'], ['Macau'], ['Machinga'], ['Macibini'], ['Madhipura'], ['Madiun'], ['Madrid'], ['Mafra'], ['Magnesia'], ['Magombedze'], ['Mahadevpur'], ['Maigatari-Danduma'], ['Malaga'], ['Malakal'], ['Malampaka'], ['Malotas'], ['Malvern'], ['Mamra Springs'], ['Manbhoom'], ['Manegaon'], ['Mangwendi'], ['Manych'], ['Mardan'], ['Maria Linden'], ['Mariaville'], ['Maribo'], ['Maridi'], ['Marilia'], ['Marion (Iowa)'], ['Marjalahti'], ['Marmande'], ['Maromandia'], ['Maryville'], ['Mascombes'], ['Mason Gully'], ['Mässing'], ['Mauerkirchen'], ['Mauritius'], ['Mayo Belwa'], ['Mazapil'], ['Maziba'], ['Mbale'], ['Medanitos'], ['Meerut'], ['Meester-Cornelis'], ['Menow'], ['Menziswyl'], ['Mern'], ['Meru'], ['Merua'], ['Messina'], ['Meuselbach'], ['Mezel'], ['Mezö-Madaras'], ['Mhow'], ['Mianchi'], ['Middlesbrough'], ['Mifflin'], ['Mighei'], ['Mihonoseki'], ['Mike'], ['Milena'], ['Millbillillie'], ['Miller (Arkansas)'], ['Minamino'], ['Mineo'], ['Min-Fan-Zhun'], ['Minnichhof'], ['Mirzapur'], ['Misshof'], ['Mjelleim'], ['Mocs'], ['Modoc (1905)'], ['Mokoia'], ['Molina'], ['Molteno'], ['Monahans (1998)'], ['Monroe'], ['Monte das Fortes'], ['Monte Milone'], ['Montferré'], ['Montlivault'], ['Monze'], ['Moore County'], ['Mooresfort'], ['Moorleah'], ['Moradabad'], ['Morávka'], ['Mornans'], ['Moss'], ['Moti-ka-nagla'], ['Motta di Conti'], ['Mount Browne'], ['Mount Tazerzait'], ['Mount Vaisi'], ['Mtola'], ['Muddoor'], ['Mulletiwu'], ['Muraid'], ['Murchison'], ['Murray'], ['Muzaffarpur'], ['Myhee Caunta'], ['Nadiabondi'], ['Nagai'], ['Nagaria'], ['Nagy-Borové'], ['Nakhla'], ['Nakhon Pathom'], ['Nammianthal'], ['Nan Yang Pao'], ['Nanjemoy'], ['Nantong'], ['Naoki'], ['Naragh'], ['Narellan'], ['Narni'], ['Nassirah'], ['Natal'], ['Nawapali'], ['Neagari'], ['Nedagolla'], ['Nejo'], ['Nerft'], ['Neuschwanstein'], ['New Concord'], ['New Halfa'], ['New Orleans'], ['Ngawi'], ["N'Goureyma"], ['Nicorps'], ['Niger (L6)'], ['Niger (LL6)'], ['Nikolaevka'], ['Nikolskoe'], ['Ningbo'], ['Ningqiang'], ['Nio'], ["N'Kandhla"], ['Nobleborough'], ['Noblesville'], ['Nogata'], ['Nogoya'], ['Norfork'], ['Norton County'], ['Noventa Vicentina'], ['Novo-Urei'], ['Novy-Ergi'], ['Novy-Projekt'], ['Noyan-Bogdo'], ['Nuevo Mercurio'], ['Nulles'], ['Numakai'], ['Nyaung'], ['Nyirábrany'], ['Ochansk'], ['Oesede'], ['Oesel'], ['Ofehértó'], ['Ogi'], ['Ohaba'], ['Ohuma'], ['Ojuelos Altos'], ['Okabe'], ['Okano'], ['Okniny'], ['Oldenburg (1930)'], ['Oliva-Gandia'], ['Olivenza'], ['Olmedilla de Alarcón'], ['Omolon'], ['Orgueil'], ['Orlando'], ['Ornans'], ['Ortenau'], ['Orvinio'], ['Oterøy'], ['Otomi'], ['Ottawa'], ['Ouadangou'], ['Oued el Hadjar'], ['Oum Dreyga'], ['Ourique'], ['Ovambo'], ['Oviedo'], ['Owrucz'], ['Pacula'], ['Padvarninkai'], ['Paitan'], ['Palahatchie'], ['Palca de Aparzo'], ['Palinshih'], ['Palmyra'], ['Palolo Valley'], ['Pampanga'], ['Pantar'], ['Paragould'], ['Parambu'], ['Paranaiba'], ['Park Forest'], ['Parnallee'], ['Parsa'], ['Pasamonte'], ['Patora'], ['Patrimonio'], ['Patti'], ['Patwar'], ['Pavel'], ['Pavlodar (stone)'], ['Pavlograd'], ['Pavlovka'], ['Pê'], ['Peace River'], ['Peckelsheim'], ['Peekskill'], ['Peña Blanca Spring'], ['Peramiho'], ['Perpeti'], ['Perth'], ['Pervomaisky'], ['Pesyanoe'], ['Pétèlkolé'], ['Petersburg'], ['Pettiswood'], ['Phillips County (stone)'], ['Phu Hong'], ['Phum Sambo'], ['Phuoc-Binh'], ['Piancaldoli'], ['Picote'], ['Pillistfer'], ['Piplia Kalan'], ['Piquetberg'], ['Pirgunje'], ['Pirthalla'], ['Pitts'], ['Plantersville'], ['Pleşcoi'], ['Ploschkovitz'], ['Pnompehn'], ['Pohlitz'], ['Pokhra'], ['Pollen'], ['Pontlyfni'], ['Portales Valley'], ['Portugal'], ['Po-wang Chen'], ['Prambachkirchen'], ['Pribram'], ['Pricetown'], ['Puerto Lápice'], ['Pulsora'], ['Pultusk'], ['Punganaru'], ['Putinga'], ['Qidong'], ['Qingzhen'], ["Queen's Mercy"], ['Quenggouk'], ['Quesa'], ['Quija'], ['Quincay'], ['Raco'], ['Raghunathpura'], ['Rahimyar Khan'], ['Rakovka'], ['Ramnagar'], ['Rampurhat'], ['Ramsdorf'], ['Ranchapur'], ['Rancho de la Presa'], ['Rangala'], ['Raoyang'], ['Ras Tanura'], ['Rasgrad'], ['Ratyn'], ['Red Canyon Lake'], ['Reliegos'], ['Rembang'], ['Renazzo'], ['Renca'], ['Renqiu'], ['Repeev Khutor'], ['Revelstoke'], ['Rewari'], ['Rich Mountain'], ['Richardton'], ['Richland Springs'], ['Richmond'], ['Rio Negro'], ['Rivolta de Bassi'], ['Rochester'], ['Rockhampton'], ['Roda'], ['Rodach'], ['Rose City'], ['Rowton'], ['Ruhobobo'], ['Rumuruti'], ['Rupota'], ['Ryechki'], ['Sabetmahet'], ['Sabrum'], ['Sagan'], ['Saint-Sauveur'], ['Saint-Séverin'], ['Sakauchi'], ['Salem'], ['Salles'], ['Salzwedel'], ['Samelia'], ['San Juan Capistrano'], ['San Michele'], ['San Pedro de Quiles'], ['San Pedro Jacuaro'], ['Santa Barbara'], ['Santa Cruz'], ['Santa Isabel'], ['Santa Lucia (2008)'], ['São Jose do Rio Preto'], ['Saratov'], ['Sasagase'], ['Sauguis'], ['Savtschenskoje'], ['Sayama'], ['Sazovice'], ['Schellin'], ['Schenectady'], ['Schönenberg'], ['Searsmont'], ['Sediköy'], ['Segowlie'], ['Selakopi'], ['Seldebourak'], ['Semarkona'], ['Sena'], ['Senboku'], ['Seoni'], ['Seres'], ['Serra de Magé'], ['Sete Lagoas'], ['Sevilla'], ['Sevrukovo'], ['Sfax'], ['Shalka'], ['Sharps'], ['Shelburne'], ['Shergotty'], ['Sheyang'], ['Shikarpur'], ['Shuangyang'], ['Shupiyan'], ['Shytal'], ['Siena'], ['Sikhote-Alin'], ['Silao'], ['Silistra'], ['Simmern'], ['Sinai'], ['Sindhri'], ['Sinnai'], ['Sioux County'], ['Sitathali'], ['Sivas'], ['Sixiangkou'], ['Ski'], ['Slavetic'], ['Slobodka'], ['Soheria'], ['Soko-Banja'], ['Sologne'], ['Sołtmany'], ['Sone'], ['Songyuan'], ['Sopot'], ['Soroti'], ['St. Caprais-de-Quinsac'], ['St. Christophe-la-Chartreuse'], ['St. Denis Westrem'], ['St. Germain-du-Pinel'], ['St. Louis'], ["St. Mark's"], ["St. Mary's County"], ['St. Mesmin'], ['St. Michel'], ['St.-Chinian'], ['Ställdalen'], ['Stannern'], ['Stavropol'], ['Ste. Marguerite'], ['Sterlitamak'], ['Stolzenau'], ['Stratford'], ['Strathmore'], ['Stretchleigh'], ['St-Robert'], ['Success'], ['Suchy Dul'], ['Suizhou'], ['Sulagiri'], ['Sultanpur'], ['Sungach'], ['Supuhee'], ["Sutter's Mill"], ['Sylacauga'], ['Tabor'], ['Tadjera'], ['Tagish Lake'], ['Tahara'], ['Takenouchi'], ['Talampaya'], ['Tambakwatu'], ['Tamdakht'], ['Tané'], ['Taonan'], ['Tatahouine'], ['Tathlith'], ['Tauk'], ['Tauti'], ['Tenham'], ['Tennasilm'], ['Thal'], ['Thika'], ['Thuathe'], ['Tianzhang'], ['Tieschitz'], ['Tilden'], ['Tillaberi'], ['Timochin'], ['Tirupati'], ['Tissint'], ['Tjabe'], ['Tjerebon'], ['Tomakovka'], ['Tomatlan'], ['Tomita'], ['Tomiya'], ['Tonk'], ...]
# df.cols.hist()
df.cols.frequency()
{'frequency': {'name': {'values': [{'value': 'Święcany', 'count': 1}, {'value': 'Hammadah al Hamra 012', 'count': 1}, {'value': 'Hammadah al Hamra 045', 'count': 1}, {'value': 'Hammadah al Hamra 046', 'count': 1}, {'value': 'Hammadah al Hamra 047', 'count': 1}, {'value': 'Hammadah al Hamra 048', 'count': 1}, {'value': 'Hammadah al Hamra 049', 'count': 1}, {'value': 'Hammadah al Hamra 050', 'count': 1}, {'value': 'Hammadah al Hamra 051', 'count': 1}, {'value': 'Hammadah al Hamra 043', 'count': 1}, {'value': 'Hammadah al Hamra 032', 'count': 1}, {'value': 'Hammadah al Hamra 031', 'count': 1}, {'value': 'Hammadah al Hamra 030', 'count': 1}, {'value': 'Hammadah al Hamra 009', 'count': 1}, {'value': 'Hammadah al Hamra 010', 'count': 1}, {'value': 'Hammadah al Hamra 011', 'count': 1}, {'value': 'Hammadah al Hamra 013', 'count': 1}, {'value': 'Hammadah al Hamra 007', 'count': 1}, {'value': 'Hammadah al Hamra 014', 'count': 1}, {'value': 'Hammadah al Hamra 017', 'count': 1}, {'value': 'Hammadah al Hamra 018', 'count': 1}, {'value': 'Hammadah al Hamra 019', 'count': 1}, {'value': 'Hammadah al Hamra 020', 'count': 1}, {'value': 'Hammadah al Hamra 021', 'count': 1}, {'value': 'Hammadah al Hamra 022', 'count': 1}, {'value': 'Hammadah al Hamra 023', 'count': 1}, {'value': 'Hammadah al Hamra 024', 'count': 1}, {'value': 'Hammadah al Hamra 025', 'count': 1}, {'value': 'Hammadah al Hamra 026', 'count': 1}, {'value': 'Hammadah al Hamra 027', 'count': 1}, {'value': 'Hammadah al Hamra 028', 'count': 1}, {'value': 'Hammadah al Hamra 029', 'count': 1}, {'value': 'Hammadah al Hamra 044', 'count': 1}]}, 'id': {'values': [{'value': '9999', 'count': 1}, {'value': '23983', 'count': 1}, {'value': '24014', 'count': 1}, {'value': '24015', 'count': 1}, {'value': '24016', 'count': 1}, {'value': '24017', 'count': 1}, {'value': '24018', 'count': 1}, {'value': '24019', 'count': 1}, {'value': '24020', 'count': 1}, {'value': '24012', 'count': 1}, {'value': '240', 'count': 1}, {'value': '24', 'count': 1}, {'value': '23999', 'count': 1}, {'value': '23980', 'count': 1}, {'value': '23981', 'count': 1}, {'value': '23982', 'count': 1}, {'value': '23984', 'count': 1}, {'value': '23978', 'count': 1}, {'value': '23985', 'count': 1}, {'value': '23986', 'count': 1}, {'value': '23987', 'count': 1}, {'value': '23988', 'count': 1}, {'value': '23989', 'count': 1}, {'value': '23990', 'count': 1}, {'value': '23991', 'count': 1}, {'value': '23992', 'count': 1}, {'value': '23993', 'count': 1}, {'value': '23994', 'count': 1}, {'value': '23995', 'count': 1}, {'value': '23996', 'count': 1}, {'value': '23997', 'count': 1}, {'value': '23998', 'count': 1}, {'value': '24013', 'count': 1}]}, 'nametype': {'values': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}]}, 'recclass': {'values': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}]}, 'mass (g)': {'values': [{'value': '1.3', 'count': 171}, {'value': '1.2', 'count': 140}, {'value': '1.4', 'count': 138}, {'value': '', 'count': 131}, {'value': '2.1', 'count': 130}, {'value': '2.4', 'count': 126}, {'value': '1.6', 'count': 120}, {'value': '0.5', 'count': 119}, {'value': '1.1', 'count': 116}, {'value': '3.8', 'count': 114}, {'value': '0.7', 'count': 111}, {'value': '1.5', 'count': 111}, {'value': '3.2', 'count': 109}, {'value': '3.1', 'count': 109}, {'value': '1.7', 'count': 109}, {'value': '3', 'count': 108}, {'value': '0.9', 'count': 108}, {'value': '0.6', 'count': 108}, {'value': '0.8', 'count': 107}, {'value': '1.8', 'count': 104}, {'value': '2.5', 'count': 103}, {'value': '2.7', 'count': 102}, {'value': '3.6', 'count': 96}, {'value': '2', 'count': 96}, {'value': '1', 'count': 96}, {'value': '4.2', 'count': 95}, {'value': '2.8', 'count': 93}, {'value': '2.9', 'count': 93}, {'value': '2.2', 'count': 92}, {'value': '2.6', 'count': 91}, {'value': '3.3', 'count': 88}, {'value': '4.6', 'count': 86}, {'value': '1.9', 'count': 86}]}, 'fall': {'values': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}]}, 'year': {'values': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}]}, 'reclat': {'values': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6438}, {'value': '-71.500000', 'count': 4761}, {'value': '-84.000000', 'count': 3040}, {'value': '-72.000000', 'count': 1506}, {'value': '-79.683330', 'count': 1130}, {'value': '-76.716670', 'count': 680}, {'value': '-76.183330', 'count': 539}, {'value': '-84.216670', 'count': 263}, {'value': '-86.366670', 'count': 226}, {'value': '-86.716670', 'count': 217}, {'value': '-85.666670', 'count': 185}, {'value': '-24.850000', 'count': 178}, {'value': '-85.633330', 'count': 108}, {'value': '-72.954880', 'count': 74}, {'value': '-72.778890', 'count': 69}, {'value': '-72.983890', 'count': 67}, {'value': '58.583330', 'count': 64}, {'value': '-72.775000', 'count': 57}, {'value': '-72.778330', 'count': 52}, {'value': '-72.998890', 'count': 41}, {'value': '-72.779170', 'count': 40}, {'value': '34.083330', 'count': 40}, {'value': '-72.782500', 'count': 39}, {'value': '-72.983889', 'count': 37}, {'value': '29.916670', 'count': 35}, {'value': '-72.778610', 'count': 35}, {'value': '-83.250000', 'count': 35}, {'value': '-82.500000', 'count': 32}, {'value': '-25.233330', 'count': 32}, {'value': '-72.774720', 'count': 31}, {'value': '-72.773610', 'count': 31}, {'value': '-72.989720', 'count': 31}]}, 'reclong': {'values': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6214}, {'value': '35.666670', 'count': 4985}, {'value': '168.000000', 'count': 3040}, {'value': '26.000000', 'count': 1506}, {'value': '159.750000', 'count': 657}, {'value': '159.666670', 'count': 637}, {'value': '157.166670', 'count': 542}, {'value': '155.750000', 'count': 473}, {'value': '160.500000', 'count': 263}, {'value': '-70.000000', 'count': 228}, {'value': '-141.500000', 'count': 217}, {'value': '175.000000', 'count': 185}, {'value': '-70.533330', 'count': 178}, {'value': '-68.700000', 'count': 105}, {'value': '160.473280', 'count': 74}, {'value': '13.433330', 'count': 64}, {'value': '159.333330', 'count': 44}, {'value': '75.313610', 'count': 42}, {'value': '75.246390', 'count': 42}, {'value': '-103.500000', 'count': 35}, {'value': '157.000000', 'count': 34}, {'value': '-5.583330', 'count': 33}, {'value': '75.187220', 'count': 33}, {'value': '155.500000', 'count': 32}, {'value': '-69.716670', 'count': 32}, {'value': '75.200000', 'count': 32}, {'value': '75.340000', 'count': 31}, {'value': '161.500000', 'count': 30}, {'value': '75.246389', 'count': 30}, {'value': '161.083330', 'count': 29}, {'value': '156.383330', 'count': 27}, {'value': '-69.766670', 'count': 24}]}, 'GeoLocation': {'values': [{'value': '', 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(27.166670, -9.500000)', 'count': 24}]}}}
df.ext.to_pandas()
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | |
---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21 | Fell | 01/01/1880 12:00:00 AM | 50.775000 | 6.083330 | (50.775000, 6.083330) |
1 | Aarhus | 2 | Valid | H6 | 720 | Fell | 01/01/1951 12:00:00 AM | 56.183330 | 10.233330 | (56.183330, 10.233330) |
2 | Abee | 6 | Valid | EH4 | 107000 | Fell | 01/01/1952 12:00:00 AM | 54.216670 | -113.000000 | (54.216670, -113.000000) |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 01/01/1976 12:00:00 AM | 16.883330 | -99.900000 | (16.883330, -99.900000) |
4 | Achiras | 370 | Valid | L6 | 780 | Fell | 01/01/1902 12:00:00 AM | -33.166670 | -64.950000 | (-33.166670, -64.950000) |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
45711 | Zillah 002 | 31356 | Valid | Eucrite | 172 | Found | 01/01/1990 12:00:00 AM | 29.037000 | 17.018500 | (29.037000, 17.018500) |
45712 | Zinder | 30409 | Valid | Pallasite, ungrouped | 46 | Found | 01/01/1999 12:00:00 AM | 13.783330 | 8.966670 | (13.783330, 8.966670) |
45713 | Zlin | 30410 | Valid | H4 | 3.3 | Found | 01/01/1939 12:00:00 AM | 49.250000 | 17.666670 | (49.250000, 17.666670) |
45714 | Zubkovsky | 31357 | Valid | L6 | 2167 | Found | 01/01/2003 12:00:00 AM | 49.789170 | 41.504600 | (49.789170, 41.504600) |
45715 | Zulu Queen | 30414 | Valid | L3.7 | 200 | Found | 01/01/1976 12:00:00 AM | 33.983330 | -115.683330 | (33.983330, -115.683330) |
45716 rows × 10 columns
df.ext.set_buffer()
df.ext.buffer_window("*",0,10)
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | |
---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21.0 | Fell | 01/01/1880 12:00:00 AM | 50.77500 | 6.08333 | (50.775000, 6.083330) |
1 | Aarhus | 2 | Valid | H6 | 720.0 | Fell | 01/01/1951 12:00:00 AM | 56.18333 | 10.23333 | (56.183330, 10.233330) |
2 | Abee | 6 | Valid | EH4 | 107000.0 | Fell | 01/01/1952 12:00:00 AM | 54.21667 | -113.00000 | (54.216670, -113.000000) |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914.0 | Fell | 01/01/1976 12:00:00 AM | 16.88333 | -99.90000 | (16.883330, -99.900000) |
4 | Achiras | 370 | Valid | L6 | 780.0 | Fell | 01/01/1902 12:00:00 AM | -33.16667 | -64.95000 | (-33.166670, -64.950000) |
5 | Adhi Kot | 379 | Valid | EH4 | 4239.0 | Fell | 01/01/1919 12:00:00 AM | 32.10000 | 71.80000 | (32.100000, 71.800000) |
6 | Adzhi-Bogdo (stone) | 390 | Valid | LL3-6 | 910.0 | Fell | 01/01/1949 12:00:00 AM | 44.83333 | 95.16667 | (44.833330, 95.166670) |
7 | Agen | 392 | Valid | H5 | 30000.0 | Fell | 01/01/1814 12:00:00 AM | 44.21667 | 0.61667 | (44.216670, 0.616670) |
8 | Aguada | 398 | Valid | L6 | 1620.0 | Fell | 01/01/1930 12:00:00 AM | -31.60000 | -65.23333 | (-31.600000, -65.233330) |
9 | Aguila Blanca | 417 | Valid | L | 1440.0 | Fell | 01/01/1920 12:00:00 AM | -30.86667 | -64.55000 | (-30.866670, -64.550000) |
df.ext.profile()
INFO:optimus:Using 'column_exp' to process column 'id' with function to_string INFO:optimus:Using 'column_exp' to process column 'id' with function _match INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_string INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _match INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'year' with function to_string INFO:optimus:Using 'column_exp' to process column 'year' with function _match INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_string INFO:optimus:Using 'column_exp' to process column 'reclat' with function _match INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_string INFO:optimus:Using 'column_exp' to process column 'reclong' with function _match INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to
{'columns': {'name': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Achiras', 'count': 1}, {'value': 'Adhi Kot', 'count': 1}, {'value': 'Aguada', 'count': 1}, {'value': 'Aguila Blanca', 'count': 1}, {'value': 'Aïr', 'count': 1}, {'value': 'Akaba', 'count': 1}, {'value': 'Akwanga', 'count': 1}, {'value': 'Alais', 'count': 1}, {'value': 'Alberta', 'count': 1}, {'value': 'Alby sur Chéran', 'count': 1}, {'value': 'Aleppo', 'count': 1}, {'value': 'Alessandria', 'count': 1}, {'value': 'Allegan', 'count': 1}, {'value': 'Allende', 'count': 1}, {'value': 'Almahata Sitta', 'count': 1}, {'value': "Alta'ameem", 'count': 1}, {'value': 'Andhara', 'count': 1}, {'value': 'Andover', 'count': 1}, {'value': 'Andreevka', 'count': 1}, {'value': 'Andura', 'count': 1}, {'value': 'Anlong', 'count': 1}, {'value': 'Appley Bridge', 'count': 1}, {'value': 'Apt', 'count': 1}, {'value': 'Arbol Solo', 'count': 1}, {'value': 'Archie', 'count': 1}, {'value': 'Ash Creek', 'count': 1}, {'value': 'Assisi', 'count': 1}, {'value': 'Atarra', 'count': 1}, {'value': 'Athens', 'count': 1}, {'value': 'Aumale', 'count': 1}, {'value': 'Aumieres', 'count': 1}, {'value': 'Avanhandava', 'count': 1}, {'value': 'Avce', 'count': 1}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'id': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': 1, 'count': 1}, {'value': 379, 'count': 1}, {'value': 417, 'count': 1}, {'value': 423, 'count': 1}, {'value': 425, 'count': 1}, {'value': 427, 'count': 1}, {'value': 433, 'count': 1}, {'value': 447, 'count': 1}, {'value': 453, 'count': 1}, {'value': 461, 'count': 1}, {'value': 463, 'count': 1}, {'value': 465, 'count': 1}, {'value': 48915, 'count': 1}, {'value': 2295, 'count': 1}, {'value': 50693, 'count': 1}, {'value': 2301, 'count': 1}, {'value': 2305, 'count': 1}, {'value': 2313, 'count': 1}, {'value': 2325, 'count': 1}, {'value': 2329, 'count': 1}, {'value': 2345, 'count': 1}, {'value': 2353, 'count': 1}, {'value': 4883, 'count': 1}, {'value': 4885, 'count': 1}, {'value': 4893, 'count': 1}, {'value': 4899, 'count': 1}, {'value': 4903, 'count': 1}, {'value': 4905, 'count': 1}, {'value': 4907, 'count': 1}, {'value': 4913, 'count': 1}, {'value': 4917, 'count': 1}, {'value': 4925, 'count': 1}, {'value': 4935, 'count': 1}], 'count_uniques': 'N/A'}, 'dtype': 'int', 'profiler_dtype': {'dtype': 'int'}}, 'nametype': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'recclass': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'mass (g)': {'stats': {'match': 0, 'missing': 131, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': 1.3, 'count': 171}, {'value': 1.2, 'count': 140}, {'value': 1.4, 'count': 138}, {'value': None, 'count': 131}, {'value': 2.1, 'count': 130}, {'value': 2.4, 'count': 126}, {'value': 1.6, 'count': 120}, {'value': 0.5, 'count': 119}, {'value': 1.1, 'count': 116}, {'value': 3.8, 'count': 114}, {'value': 1.5, 'count': 111}, {'value': 0.7, 'count': 111}, {'value': 1.7, 'count': 109}, {'value': 3.1, 'count': 109}, {'value': 3.2, 'count': 109}, {'value': 3.0, 'count': 108}, {'value': 0.6, 'count': 108}, {'value': 0.9, 'count': 108}, {'value': 0.8, 'count': 107}, {'value': 1.8, 'count': 104}, {'value': 2.5, 'count': 103}, {'value': 2.7, 'count': 102}, {'value': 1.0, 'count': 96}, {'value': 3.6, 'count': 96}, {'value': 2.0, 'count': 96}, {'value': 4.2, 'count': 95}, {'value': 2.9, 'count': 93}, {'value': 2.8, 'count': 93}, {'value': 2.2, 'count': 92}, {'value': 2.6, 'count': 91}, {'value': 3.3, 'count': 88}, {'value': 1.9, 'count': 86}, {'value': 4.1, 'count': 86}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'fall': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'year': {'stats': {'match': 0, 'missing': 288, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}, 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}}, 'reclat': {'stats': {'match': 0, 'missing': 7315, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6438}, {'value': -71.5, 'count': 4761}, {'value': -84.0, 'count': 3040}, {'value': -72.0, 'count': 1506}, {'value': -79.68333, 'count': 1130}, {'value': -76.71667, 'count': 680}, {'value': -76.18333, 'count': 539}, {'value': -84.21667, 'count': 263}, {'value': -86.36667, 'count': 226}, {'value': -86.71667, 'count': 217}, {'value': -85.66667, 'count': 185}, {'value': -24.85, 'count': 178}, {'value': -85.63333, 'count': 108}, {'value': -72.95488, 'count': 74}, {'value': -72.77889, 'count': 69}, {'value': -72.98389, 'count': 67}, {'value': 58.58333, 'count': 64}, {'value': -72.775, 'count': 57}, {'value': -72.77833, 'count': 52}, {'value': -72.99889, 'count': 41}, {'value': 34.08333, 'count': 40}, {'value': -72.77917, 'count': 40}, {'value': -72.7825, 'count': 39}, {'value': -72.983889, 'count': 37}, {'value': -72.77861, 'count': 35}, {'value': 29.91667, 'count': 35}, {'value': -83.25, 'count': 35}, {'value': -82.5, 'count': 32}, {'value': -25.23333, 'count': 32}, {'value': -72.98972, 'count': 31}, {'value': -72.77361, 'count': 31}, {'value': -72.77472, 'count': 31}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'reclong': {'stats': {'match': 0, 'missing': 7315, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6214}, {'value': 35.66667, 'count': 4985}, {'value': 168.0, 'count': 3040}, {'value': 26.0, 'count': 1506}, {'value': 159.75, 'count': 657}, {'value': 159.66667, 'count': 637}, {'value': 157.16667, 'count': 542}, {'value': 155.75, 'count': 473}, {'value': 160.5, 'count': 263}, {'value': -70.0, 'count': 228}, {'value': -141.5, 'count': 217}, {'value': 175.0, 'count': 185}, {'value': -70.53333, 'count': 178}, {'value': -68.7, 'count': 105}, {'value': 160.47328, 'count': 74}, {'value': 13.43333, 'count': 64}, {'value': 159.33333, 'count': 44}, {'value': 75.24639, 'count': 42}, {'value': 75.31361, 'count': 42}, {'value': -103.5, 'count': 35}, {'value': 157.0, 'count': 34}, {'value': 75.18722, 'count': 33}, {'value': -5.58333, 'count': 33}, {'value': -69.71667, 'count': 32}, {'value': 155.5, 'count': 32}, {'value': 75.2, 'count': 32}, {'value': 75.34, 'count': 31}, {'value': 75.246389, 'count': 30}, {'value': 161.5, 'count': 30}, {'value': 161.08333, 'count': 29}, {'value': 156.38333, 'count': 27}, {'value': -9.5, 'count': 24}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'GeoLocation': {'stats': {'match': 38401, 'missing': 7315, 'mismatch': 7315, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': None, 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(-24.683330, -69.766670)', 'count': 24}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}}, 'file_name': 'data/Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 45716, 'dtypes_list': ['int', 'string', 'double'], 'total_count_dtypes': 3, 'missing_count': 0}}
df.ext.display()
name
1 (string)
not nullable
|
id
2 (int)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
df.cols.select("id").cols.to_string().ext.display()
# df.ext.profile("id")
func_return_type1 string
INFO:optimus:Using 'column_exp' to process column 'id' with function to_string
id
1 (string)
not nullable
|
---|
1
|
2
|
6
|
10
|
370
|
379
|
390
|
392
|
398
|
417
|
df.cols.remove_accents("name").data.show()
df
name
1 (string)
not nullable
|
id
2 (int)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.ext.head()
[Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)'), Row(name='Aarhus', id=2, nametype='Valid', recclass='H6', mass (g)=720.0, fall='Fell', year='01/01/1951 12:00:00 AM', reclat=56.18333, reclong=10.23333, GeoLocation='(56.183330, 10.233330)'), Row(name='Abee', id=6, nametype='Valid', recclass='EH4', mass (g)=107000.0, fall='Fell', year='01/01/1952 12:00:00 AM', reclat=54.21667, reclong=-113.0, GeoLocation='(54.216670, -113.000000)'), Row(name='Acapulco', id=10, nametype='Valid', recclass='Acapulcoite', mass (g)=1914.0, fall='Fell', year='01/01/1976 12:00:00 AM', reclat=16.88333, reclong=-99.9, GeoLocation='(16.883330, -99.900000)'), Row(name='Achiras', id=370, nametype='Valid', recclass='L6', mass (g)=780.0, fall='Fell', year='01/01/1902 12:00:00 AM', reclat=-33.16667, reclong=-64.95, GeoLocation='(-33.166670, -64.950000)'), Row(name='Adhi Kot', id=379, nametype='Valid', recclass='EH4', mass (g)=4239.0, fall='Fell', year='01/01/1919 12:00:00 AM', reclat=32.1, reclong=71.8, GeoLocation='(32.100000, 71.800000)'), Row(name='Adzhi-Bogdo (stone)', id=390, nametype='Valid', recclass='LL3-6', mass (g)=910.0, fall='Fell', year='01/01/1949 12:00:00 AM', reclat=44.83333, reclong=95.16667, GeoLocation='(44.833330, 95.166670)'), Row(name='Agen', id=392, nametype='Valid', recclass='H5', mass (g)=30000.0, fall='Fell', year='01/01/1814 12:00:00 AM', reclat=44.21667, reclong=0.61667, GeoLocation='(44.216670, 0.616670)'), Row(name='Aguada', id=398, nametype='Valid', recclass='L6', mass (g)=1620.0, fall='Fell', year='01/01/1930 12:00:00 AM', reclat=-31.6, reclong=-65.23333, GeoLocation='(-31.600000, -65.233330)'), Row(name='Aguila Blanca', id=417, nametype='Valid', recclass='L', mass (g)=1440.0, fall='Fell', year='01/01/1920 12:00:00 AM', reclat=-30.86667, reclong=-64.55, GeoLocation='(-30.866670, -64.550000)')]
df.ext.set_buffer()
df.ext.get_buffer()
{'name': ['Aachen', 'Aarhus', 'Abee', 'Acapulco', 'Achiras', 'Adhi Kot', 'Adzhi-Bogdo (stone)', 'Agen', 'Aguada', 'Aguila Blanca', 'Aioun el Atrouss', 'Aïr', 'Aire-sur-la-Lys', 'Akaba', 'Akbarpur', 'Akwanga', 'Akyumak', 'Al Rais', 'Al Zarnkh', 'Alais', 'Albareto', 'Alberta', 'Alby sur Chéran', 'Aldsworth', 'Aleppo', 'Alessandria', 'Alexandrovsky', 'Alfianello', 'Allegan', 'Allende', 'Almahata Sitta', "Alta'ameem", 'Ambapur Nagla', 'Andhara', 'Andover', 'Andreevka', 'Andura', 'Northwest Africa 5815', 'Angers', 'Angra dos Reis (stone)', 'Ankober', 'Anlong', 'Aomori', 'Appley Bridge', 'Apt', 'Arbol Solo', 'Archie', 'Arroyo Aguiar', 'Asco', 'Ash Creek', 'Ashdon', 'Assisi', 'Atarra', 'Atemajac', 'Athens', 'Atoka', 'Aubres', 'Aumale', 'Aumieres', 'Ausson', 'Avanhandava', 'Avce', 'Avilez', 'Awere', 'Aztec', 'Bachmut', 'Bahjoi', 'Bald Mountain', 'Baldwyn', 'Bali', 'Ban Rong Du', 'Bandong', 'Bansur', 'Banswal', 'Banten', 'Barbotan', 'Barcelona (stone)', 'Barea', 'Barnaul', 'Barntrup', 'Baroti', 'Barwell', 'Bassikounou', 'Baszkówka', 'Bath', 'Bath Furnace', 'Battle Mountain', 'Bawku', 'Baxter', 'Beardsley', 'Beaver Creek', 'Beddgelert', 'Bells', 'Belville', 'Benares (a)', 'Benguerir', "Beni M'hira", 'Benld', 'Benoni', 'Bensour', 'Benton', 'Berduc', 'Béréba', 'Berlanguillas', 'Berthoud', 'Bethlehem', 'Beuste', 'Beyrout', 'Bhagur', 'Bhawad', 'Bherai', 'Bhola', 'Bholghati', 'Bialystok', 'Bielokrynitschie', 'Bilanga', 'Binningup', "Birni N'konni", 'Bishopville', 'Bishunpur', 'Bjelaja Zerkov', 'Bjurböle', 'Black Moshannan Park', 'Blackwell', 'Blanket', 'Blansko', 'Bloomington', 'Bo Xian', 'Bocas', 'Bogou', 'Boguslavka', 'Borgo San Donino', 'Bori', 'Boriskino', 'Borkut', 'Borodino', 'Botschetschki', 'Boumdeid (2003)', 'Boumdeid (2011)', 'Bovedy', 'Bradford Woods', 'Braunau', 'Breitscheid', 'Bremervörde', 'Brient', 'Bruderheim', 'Bukhara', 'Bulls Run', 'Bunburra Rockhole', 'Bununu', 'Bur-Gheluai', 'Burnwell', 'Bursa', 'Buschhof', 'Bustee', 'Butsura', 'Buzzard Coulee', 'Cabezo de Mayo', 'Cabin Creek', 'Cacak', 'Cali', 'Calivo', 'Campos Sales', 'Çanakkale', 'Cañellas', 'Cangas de Onis', 'Canon City', 'Cape Girardeau', 'Capilla del Monte', 'Carancas', 'Caratash', 'Castalia', 'Castel Berardenga', 'Castine', 'Castrovillari', 'Caswell County', 'Ceniceros', 'Centerville', 'Cereseto', 'Chadong', 'Chail', 'Chainpur', 'Chajari', 'Chandakapur', 'Chandpur', 'Changde', 'Chantonnay', 'Charlotte', 'Charsonville', 'Charwallas', 'Chassigny', 'Château-Renard', 'Chaves', 'Chela', 'Chelyabinsk', 'Chergach ', 'Chernyi Bor', 'Cherokee Springs', 'Chervettaz', 'Chervony Kut', 'Chetrinahatti', 'Chiang Khan', 'Chicora', 'Chisenga', 'Chitado', 'Chitenay', 'Cilimus', 'Claxton', 'Clohars', 'Colby (Wisconsin)', 'Cold Bokkeveld', 'Coleman', 'Collescipoli', 'Conquista', 'Cosina', 'Cranganore', 'Crescent', 'Cronstad', 'Cross Roads', 'Crumlin', 'Cumberland Falls', 'Cynthiana', 'Dahmani', 'Dandapur', "Daniel's Kuil", 'Danville', 'Darmstadt', 'Dashoguz', 'Daule', 'De Cewsville', 'Deal', 'Delhi', 'Demina', 'Denver', 'Dergaon', 'Desuri', 'Devgaon', 'Devri-Khera', 'Dhajala', 'Dharwar', 'Dhurmsala', 'Didim', 'Diep River', 'Distrito Quebracho', 'Djati-Pengilon', 'Djermaia', 'Djoumine', 'Dokachi', 'Dolgovoli', 'Domanitch', 'Dong Ujimqin Qi', 'Donga Kohrod', 'Dongtai', 'Doroninsk', 'Dosso', 'Douar Mghila', 'Dowa', 'Drake Creek', 'Dresden (Ontario)', 'Dubrovnik', 'Dunbogan', 'Dundrum', 'Dunhua', 'Durala', 'Duruma', 'Duwun', 'Dwaleni', 'Dyalpur', 'Dyarrl Island', 'Eagle', 'Ehole', 'Eichstädt', 'Ekeby', 'Ekh Khera', 'El Idrissia', 'El Paso de Aguila', 'El Tigre', 'Elbert', 'Elbogen', 'Elenovka', 'Ellemeet', 'Emmaville', 'Enshi', 'Ensisheim', 'Épinal', 'Erakot', 'Erevan', 'Ergheo', 'Erxleben', 'Esnandes', 'Essebi', 'Estherville', 'Farmington', 'Farmville', 'Favars', 'Fayetteville', 'Feid Chair', 'Felix', 'Fenghsien-Ku', 'Ferguson', 'Fermo', 'Fisher', 'Florence', 'Forest City', 'Forest Vale', 'Forksville', 'Forsbach', 'Forsyth', 'Fort Flatters', 'Frankfort (stone)', 'Fuhe', 'Fukutomi', 'Fünen', 'Futtehpur', 'Fuyang', 'Galapian', 'Galim (a)', 'Galim (b)', 'Galkiv', 'Gambat', 'Gao-Guenie', 'Garhi Yasin', 'Garland', 'Gashua', 'Gasseltepaoua', 'Geidam', 'Gifu', 'Girgenti', 'Git-Git', 'Glanerbrug', 'Glanggang', 'Glasatovo', 'Glatton', 'Gnadenfrei', 'Gopalpur', 'Gorlovka', 'Granes', 'Grefsheim', 'Grimsby', 'Grosnaja', 'Gross-Divina', 'Grossliebenthal', 'Grüneberg', 'Grzempach', 'Gualeguaychú', 'Guangmingshan', 'Guangnan', 'Guangrao', 'Guareña', 'Guêa', 'Guibga', 'Guidder', 'Gujargaon', 'Gujba', 'Gumoschnik', 'Gurram Konda', 'Gursum', 'Gütersloh', 'Gyokukei', 'Hachi-oji', 'Hainaut', 'Hallingeberg', 'Hamlet', 'Haraiya', 'Haripura', 'Harleton', 'Harrison County', 'Hashima', 'Hassi-Jekna', 'Hatford', 'Haverö', 'Hedeskoga', 'Hedjaz', 'Heredia', 'Hessle', 'Higashi-koen', 'High Possil', 'Hiroshima', 'Hoima', 'Hökmark', 'Holbrook', 'Holetta', 'Homestead', 'Honolulu', 'Hotse', 'Hoxie', 'Hraschina', 'Huaxi', 'Hungen', 'Hvittis', 'Ibbenbüren', 'Ibitira', 'Ibrisim', 'Ichkala', 'Idutywa', 'Iguaracu', 'Ijopega', 'Indarch', 'Independence', 'Inner Mongolia', 'Innisfree', 'Ipiranga', 'Ishinga', 'Isthilart', 'Itapicuru-Mirim', 'Itqiy', 'Ivuna', 'Jackalsfontein', 'Jajh deh Kot Lalu', 'Jalanash', 'Jalandhar', 'Jamkheir', 'Jartai', 'Jelica', 'Jemlapur', 'Jesenice', 'Jhung', 'Jiange', 'Jianshi', 'Jilin', 'Jodiya', 'Jodzie', 'Johnstown', 'Jolomba', 'Jonzac', 'Juancheng', 'Judesegeri', 'Jumapalo', 'Junan', 'Juromenha', 'Juvinas', 'Kaba', 'Kabo', 'Kadonah', 'Kaee', 'Kagarlyk', 'Kaidun', 'Kainsaz', 'Kakangari', 'Kakowa', 'Kalaba', 'Kalumbi', 'Kamalpur', 'Kamiomi', 'Kamsagar', 'Kandahar (Afghanistan)', 'Kangean', 'Kangra Valley', 'Kapoeta', 'Kaprada', 'Kaptal-Aryk', 'Karakol', 'Karatu', 'Karewar', 'Karkh', 'Karloowala', 'Karoonda', 'Kasamatsu', 'Kasauli', 'Katagum', 'Kavarpura', 'Kayakent', 'Kediri', 'Kemer', 'Kendleton', 'Kendrapara', 'Kerilis', 'Kernouve', 'Kesen', 'Khairpur', 'Khanpur', 'Kharkov', 'Kheragur', 'Khetri', 'Khmelevka', 'Khohar', 'Khor Temiki', 'Kidairat', 'Kiel', 'Kiffa', 'Kijima (1906)', 'Kikino', 'Kilabo', 'Kilbourn', 'Killeter', 'Kingai', 'Kirbyville', 'Kisvarsány', 'Kitchener', 'Klein-Wenden', 'Knyahinya', 'Kobe', 'Kokubunji', 'Komagome', 'Konovo', 'Košice', 'Krähenberg', 'Krasnoi-Ugol', 'Krasnyi Klyuch', 'Krutikha', 'Krymka', 'Kukschin', 'Kulak', 'Kuleschovka', 'Kulp', 'Kunashak', 'Kunya-Urgench', 'Kushiike', 'Kusiali', 'Kutais', 'Kuttippuram', 'Kuznetzovo', 'Kyushu', 'La Bécasse', 'La Charca', 'La Colina', 'La Criolla', 'Laborel', 'Lahrauli', "L'Aigle", 'Lakangaon', 'Lalitpur', 'Lancé', 'Lancon', 'Långhalsen', 'Lanxi', 'Lanzenkirchen', 'Laochenzhen', 'Launton', 'Lavrentievka', 'Le Pressoir', 'Le Teilleul', 'Leedey', 'Leeuwfontein', 'Leighlinbridge', 'Leighton', 'Leonovka', 'Les Ormes', 'Lesves', 'Lichtenberg', 'Lillaverke', 'Limerick', 'Linum', 'Lishui', 'Lissa', 'Little Piney', 'Lixna', 'Lodran', 'Lohawat', 'Lorton', 'Los Martinez', 'Lost City', 'Louisville', 'Łowicz', 'Lua', 'Lucé', 'Lumpkin', 'Lunan', 'Lundsgård', 'Luotolax', 'Luponnas', 'Lusaka', 'Mabwe-Khoywa', 'Macau', 'Machinga', 'Macibini', 'Madhipura', 'Madiun', 'Madrid', 'Mafra', 'Magnesia', 'Magombedze', 'Mahadevpur', 'Maigatari-Danduma', 'Malaga', 'Malakal', 'Malampaka', 'Malotas', 'Malvern', 'Mamra Springs', 'Manbhoom', 'Manegaon', 'Mangwendi', 'Manych', 'Mardan', 'Maria Linden', 'Mariaville', 'Maribo', 'Maridi', 'Marilia', 'Marion (Iowa)', 'Marjalahti', 'Marmande', 'Maromandia', 'Maryville', 'Mascombes', 'Mason Gully', 'Mässing', 'Mauerkirchen', 'Mauritius', 'Mayo Belwa', 'Mazapil', 'Maziba', 'Mbale', 'Medanitos', 'Meerut', 'Meester-Cornelis', 'Menow', 'Menziswyl', 'Mern', 'Meru', 'Merua', 'Messina', 'Meuselbach', 'Mezel', 'Mezö-Madaras', 'Mhow', 'Mianchi', 'Middlesbrough', 'Mifflin', 'Mighei', 'Mihonoseki', 'Mike', 'Milena', 'Millbillillie', 'Miller (Arkansas)', 'Minamino', 'Mineo', 'Min-Fan-Zhun', 'Minnichhof', 'Mirzapur', 'Misshof', 'Mjelleim', 'Mocs', 'Modoc (1905)', 'Mokoia', 'Molina', 'Molteno', 'Monahans (1998)', 'Monroe', 'Monte das Fortes', 'Monte Milone', 'Montferré', 'Montlivault', 'Monze', 'Moore County', 'Mooresfort', 'Moorleah', 'Moradabad', 'Morávka', 'Mornans', 'Moss', 'Moti-ka-nagla', 'Motta di Conti', 'Mount Browne', 'Mount Tazerzait', 'Mount Vaisi', 'Mtola', 'Muddoor', 'Mulletiwu', 'Muraid', 'Murchison', 'Murray', 'Muzaffarpur', 'Myhee Caunta', 'Nadiabondi', 'Nagai', 'Nagaria', 'Nagy-Borové', 'Nakhla', 'Nakhon Pathom', 'Nammianthal', 'Nan Yang Pao', 'Nanjemoy', 'Nantong', 'Naoki', 'Naragh', 'Narellan', 'Narni', 'Nassirah', 'Natal', 'Nawapali', 'Neagari', 'Nedagolla', 'Nejo', 'Nerft', 'Neuschwanstein', 'New Concord', 'New Halfa', 'New Orleans', 'Ngawi', "N'Goureyma", 'Nicorps', 'Niger (L6)', 'Niger (LL6)', 'Nikolaevka', 'Nikolskoe', 'Ningbo', 'Ningqiang', 'Nio', "N'Kandhla", 'Nobleborough', 'Noblesville', 'Nogata', 'Nogoya', 'Norfork', 'Norton County', 'Noventa Vicentina', 'Novo-Urei', 'Novy-Ergi', 'Novy-Projekt', 'Noyan-Bogdo', 'Nuevo Mercurio', 'Nulles', 'Numakai', 'Nyaung', 'Nyirábrany', 'Ochansk', 'Oesede', 'Oesel', 'Ofehértó', 'Ogi', 'Ohaba', 'Ohuma', 'Ojuelos Altos', 'Okabe', 'Okano', 'Okniny', 'Oldenburg (1930)', 'Oliva-Gandia', 'Olivenza', 'Olmedilla de Alarcón', 'Omolon', 'Orgueil', 'Orlando', 'Ornans', 'Ortenau', 'Orvinio', 'Oterøy', 'Otomi', 'Ottawa', 'Ouadangou', 'Oued el Hadjar', 'Oum Dreyga', 'Ourique', 'Ovambo', 'Oviedo', 'Owrucz', 'Pacula', 'Padvarninkai', 'Paitan', 'Palahatchie', 'Palca de Aparzo', 'Palinshih', 'Palmyra', 'Palolo Valley', 'Pampanga', 'Pantar', 'Paragould', 'Parambu', 'Paranaiba', 'Park Forest', 'Parnallee', 'Parsa', 'Pasamonte', 'Patora', 'Patrimonio', 'Patti', 'Patwar', 'Pavel', 'Pavlodar (stone)', 'Pavlograd', 'Pavlovka', 'Pê', 'Peace River', 'Peckelsheim', 'Peekskill', 'Peña Blanca Spring', 'Peramiho', 'Perpeti', 'Perth', 'Pervomaisky', 'Pesyanoe', 'Pétèlkolé', 'Petersburg', 'Pettiswood', 'Phillips County (stone)', 'Phu Hong', 'Phum Sambo', 'Phuoc-Binh', 'Piancaldoli', 'Picote', 'Pillistfer', 'Piplia Kalan', 'Piquetberg', 'Pirgunje', 'Pirthalla', 'Pitts', 'Plantersville', 'Pleşcoi', 'Ploschkovitz', 'Pnompehn', 'Pohlitz', 'Pokhra', 'Pollen', 'Pontlyfni', 'Portales Valley', 'Portugal', 'Po-wang Chen', 'Prambachkirchen', 'Pribram', 'Pricetown', 'Puerto Lápice', 'Pulsora', 'Pultusk', 'Punganaru', 'Putinga', 'Qidong', 'Qingzhen', "Queen's Mercy", 'Quenggouk', 'Quesa', 'Quija', 'Quincay', 'Raco', 'Raghunathpura', 'Rahimyar Khan', 'Rakovka', 'Ramnagar', 'Rampurhat', 'Ramsdorf', 'Ranchapur', 'Rancho de la Presa', 'Rangala', 'Raoyang', 'Ras Tanura', 'Rasgrad', 'Ratyn', 'Red Canyon Lake', 'Reliegos', 'Rembang', 'Renazzo', 'Renca', 'Renqiu', 'Repeev Khutor', 'Revelstoke', 'Rewari', 'Rich Mountain', 'Richardton', 'Richland Springs', 'Richmond', 'Rio Negro', 'Rivolta de Bassi', 'Rochester', 'Rockhampton', 'Roda', 'Rodach', 'Rose City', 'Rowton', 'Ruhobobo', 'Rumuruti', 'Rupota', 'Ryechki', 'Sabetmahet', 'Sabrum', 'Sagan', 'Saint-Sauveur', 'Saint-Séverin', 'Sakauchi', 'Salem', 'Salles', 'Salzwedel', 'Samelia', 'San Juan Capistrano', 'San Michele', 'San Pedro de Quiles', 'San Pedro Jacuaro', 'Santa Barbara', 'Santa Cruz', 'Santa Isabel', 'Santa Lucia (2008)', 'São Jose do Rio Preto', 'Saratov', 'Sasagase', 'Sauguis', 'Savtschenskoje', 'Sayama', 'Sazovice', 'Schellin', 'Schenectady', 'Schönenberg', 'Searsmont', 'Sediköy', 'Segowlie', 'Selakopi', 'Seldebourak', 'Semarkona', 'Sena', 'Senboku', 'Seoni', 'Seres', 'Serra de Magé', 'Sete Lagoas', 'Sevilla', 'Sevrukovo', 'Sfax', 'Shalka', 'Sharps', 'Shelburne', 'Shergotty', 'Sheyang', 'Shikarpur', 'Shuangyang', 'Shupiyan', 'Shytal', 'Siena', 'Sikhote-Alin', 'Silao', 'Silistra', 'Simmern', 'Sinai', 'Sindhri', 'Sinnai', 'Sioux County', 'Sitathali', 'Sivas', 'Sixiangkou', 'Ski', 'Slavetic', 'Slobodka', 'Soheria', 'Soko-Banja', 'Sologne', 'Sołtmany', 'Sone', 'Songyuan', 'Sopot', 'Soroti', 'St. Caprais-de-Quinsac', 'St. Christophe-la-Chartreuse', 'St. Denis Westrem', 'St. Germain-du-Pinel', 'St. Louis', "St. Mark's", "St. Mary's County", 'St. Mesmin', 'St. Michel', 'St.-Chinian', 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'(52.566670, -8.783330)', '(52.750000, 12.900000)', '(31.633330, 118.983330)', '(50.200000, 14.850000)', '(37.916670, -92.083330)', '(56.000000, 26.433330)', '(29.533330, 71.800000)', '(26.965560, 72.626670)', '(38.700660, -77.211630)', '(38.000000, -0.833330)', '(36.008330, -95.150000)', '(38.250000, -85.750000)', '(52.000000, 19.916670)', '(24.950000, 75.150000)', '(47.850000, 0.483330)', '(32.033330, -84.766670)', '(24.800000, 103.300000)', '(56.216670, 13.033330)', '(61.200000, 27.700000)', '(46.216670, 5.000000)', '(-7.216670, 29.433330)', '(19.000000, 97.000000)', '(-5.200000, -36.666670)', '(-15.212220, 35.242220)', '(-28.833330, 31.950000)', '(25.916670, 86.366670)', '(-7.750000, 111.533330)', '(40.416670, -3.716670)', '(-26.166670, -49.933330)', '(37.866670, 27.516670)', '(-19.483330, 31.650000)', '(27.666670, 95.783330)', '(12.833330, 9.383330)', '(32.216670, -104.000000)', '(9.500000, 31.750000)', '(-3.133330, 33.516670)', '(-28.933330, -63.233330)', '(-29.450000, 26.766670)', '(45.216670, 62.083330)', '(23.050000, 86.700000)', '(20.966670, 76.100000)', '(-17.650000, 31.600000)', '(45.816670, 44.633330)', '(34.233330, 72.083330)', None, '(42.716670, -99.383330)', '(54.761830, 11.467450)', '(4.666670, 29.250000)', '(-22.250000, -49.933330)', '(41.900000, -91.600000)', '(61.500000, 30.500000)', '(44.500000, 0.150000)', '(-14.200000, 48.100000)', '(35.800000, -84.100000)', '(45.366670, 1.866670)', '(0.000000, 0.000000)', '(48.133330, 12.616670)', '(48.183330, 13.133330)', '(-20.000000, 57.000000)', '(8.966670, 12.083330)', '(24.683330, -101.683330)', '(-1.216670, 30.000000)', '(1.066670, 34.166670)', '(-27.250000, -67.500000)', '(29.016670, 77.800000)', '(-6.233330, 106.883330)', '(53.183330, 13.150000)', '(46.818670, 7.218170)', '(55.050000, 12.066670)', '(0.000000, 37.666670)', '(25.483330, 81.983330)', '(38.183330, 15.566670)', '(50.583330, 11.100000)', '(45.766670, 3.250000)', '(46.500000, 25.733330)', '(25.900000, 83.616670)', '(34.800000, 111.700000)', '(54.566670, -1.166670)', '(42.907500, -90.365560)', '(48.066670, 30.966670)', '(35.568330, 133.220000)', '(46.233330, 17.533330)', '(46.183330, 16.100000)', '(-26.450000, 120.366670)', '(35.400000, -92.050000)', '(35.078330, 136.933330)', '(37.283330, 14.700000)', '(32.333330, 120.666670)', '(47.700000, 16.600000)', '(25.683330, 83.250000)', '(56.666670, 23.000000)', '(61.733330, 5.933330)', '(46.800000, 24.033330)', '(38.500000, -101.100000)', '(-39.633330, 174.400000)', '(38.116670, -1.166670)', '(-31.250000, 26.466670)', '(31.608330, -102.858330)', '(35.250000, -80.500000)', '(38.016670, -8.250000)', '(43.266670, 13.350000)', '(43.390560, 1.962500)', '(47.633330, 1.583330)', '(-15.966670, 27.350000)', '(35.416670, -79.383330)', '(52.450000, -8.333330)', '(-40.975000, 145.600000)', '(28.783330, 78.833330)', '(49.600000, 18.533330)', '(44.600000, 5.133330)', '(59.433330, 10.700000)', '(26.833330, 77.333330)', '(45.200000, 8.500000)', '(-29.800000, 141.700000)', '(18.700000, 4.800000)', '(44.083330, 6.866670)', '(-11.500000, 33.500000)', '(12.633330, 77.016670)', '(9.333330, 80.833330)', '(24.500000, 90.216670)', '(-36.616670, 145.200000)', '(36.600000, -88.100000)', '(26.133330, 85.533330)', '(23.050000, 72.633330)', '(12.000000, 1.000000)', '(38.121670, 140.061670)', '(26.983330, 78.216670)', '(49.166670, 19.500000)', '(31.316670, 30.350000)', '(13.733330, 100.083330)', '(12.283330, 79.200000)', '(35.666670, 103.500000)', '(38.416670, -77.166670)', '(32.116670, 121.800000)', '(19.250000, 77.000000)', '(33.750000, 51.500000)', '(-34.050000, 150.688890)', '(42.516670, 12.516670)', '(-21.733330, 165.900000)', None, '(21.250000, 83.666670)', '(36.449170, 136.465280)', '(18.683330, 83.483330)', '(9.500000, 35.333330)', '(56.500000, 21.500000)', '(47.525000, 10.808330)', '(40.000000, -81.766670)', '(15.366670, 35.683330)', '(29.947180, -90.109760)', '(-7.450000, 111.416670)', '(13.850000, -4.383330)', '(49.033330, -1.433330)', None, None, '(52.450000, 78.633330)', '(56.116670, 37.333330)', '(29.866670, 121.483330)', '(32.925000, 105.906670)', '(34.200000, 131.566670)', '(-28.566670, 30.700000)', '(44.083330, -69.483330)', '(40.085280, -86.055000)', '(33.725000, 130.750000)', '(-32.366670, -59.833330)', '(36.216670, -92.266670)', '(39.683330, -99.866670)', '(45.291670, 11.527220)', '(54.816670, 46.000000)', '(58.550000, 31.333330)', '(56.000000, 22.000000)', '(42.916670, 102.466670)', '(24.300000, -102.133330)', '(41.633330, 0.750000)', '(43.333330, 141.866670)', '(21.208330, 94.916670)', '(47.550000, 22.025000)', '(57.783330, 55.266670)', '(52.283330, 8.050000)', '(58.500000, 23.000000)', '(47.883330, 22.033330)', '(33.283330, 130.200000)', '(46.066670, 23.583330)', '(6.750000, 8.500000)', '(38.183330, -5.400000)', '(36.183330, 139.216670)', '(35.083330, 135.200000)', '(50.833330, 25.500000)', '(52.950000, 8.166670)', '(39.000000, -0.033330)', '(38.716670, -7.066670)', '(39.566670, -2.100000)', '(64.020000, 161.808330)', '(43.883330, 1.383330)', '(28.547500, -81.362220)', '(47.116670, 6.150000)', '(48.500000, 8.000000)', '(42.133330, 12.933330)', '(58.883330, 9.400000)', '(38.400000, 140.350000)', '(38.600000, -95.216670)', '(12.900000, 0.080000)', '(30.180000, -6.577170)', '(24.300000, -13.100000)', '(37.608330, -8.280000)', '(-18.000000, 16.000000)', '(43.400000, -5.866670)', '(51.333330, 28.833330)', '(21.050000, -99.300000)', '(55.666670, 25.000000)', '(17.743330, 120.455830)', '(32.316670, -89.716670)', '(-23.116670, -65.100000)', '(43.483330, 118.616670)', '(39.800000, -91.500000)', '(21.300000, -157.783330)', '(15.083330, 120.700000)', '(8.066670, 124.283330)', '(36.066670, -90.500000)', '(-6.233330, -40.700000)', '(-19.133330, -51.666670)', '(41.484720, -87.679170)', '(9.233330, 78.350000)', '(26.200000, 85.400000)', '(36.216670, -103.400000)', '(20.936940, 82.050000)', '(-19.533330, -48.566670)', '(38.133330, 14.966670)', '(23.150000, 91.183330)', '(43.466670, 25.516670)', '(52.300000, 77.033330)', '(48.533330, 35.983330)', '(52.033330, 43.000000)', '(11.333670, -3.542170)', '(56.133330, -117.933330)', '(51.666670, 9.250000)', '(41.283330, -73.916670)', '(30.125000, -103.116670)', '(-10.666670, 35.500000)', '(23.325000, 91.000000)', '(56.400000, -3.433330)', '(56.633330, 39.433330)', '(55.500000, 66.083330)', '(14.052000, 0.420000)', '(35.300000, -86.633330)', '(53.533330, -7.333330)', '(40.000000, -99.250000)', '(11.250000, 108.583330)', '(12.000000, 105.483330)', '(15.716670, 108.100000)', '(44.244170, 11.502220)', '(41.366670, -6.233330)', '(58.666670, 25.733330)', '(26.034720, 73.941670)', '(-32.866670, 18.716670)', '(25.800000, 88.450000)', '(29.583330, 76.000000)', '(31.950000, -83.516670)', '(30.700000, -96.116670)', '(45.275000, 26.709720)', '(50.533330, 14.116670)', '(11.583330, 104.916670)', '(50.933330, 12.133330)', '(26.716670, 82.666670)', '(66.348330, 14.015000)', '(53.036390, -4.319440)', '(34.175000, -103.295000)', '(38.500000, -8.000000)', '(31.416670, 118.500000)', '(48.302500, 13.940830)', '(49.666670, 14.033330)', '(39.116670, -83.850000)', '(39.350000, -3.516670)', '(23.366670, 75.183330)', '(52.766670, 21.266670)', '(13.333330, 78.950000)', '(-29.033330, -53.050000)', '(32.083330, 121.500000)', '(26.533330, 106.466670)', '(-30.116670, 28.700000)', '(17.766670, 95.183330)', '(39.000000, -0.666670)', '(44.616670, 126.133330)', '(46.600000, 0.250000)', '(-26.666670, -65.450000)', '(27.725280, 76.465000)', '(28.225000, 70.200000)', '(52.983330, 37.033330)', '(26.450000, 82.900000)', '(24.166670, 87.766670)', '(51.883330, 6.933330)', '(23.983330, 87.083330)', '(19.866670, -100.816670)', '(25.383330, 72.016670)', '(38.200000, 115.700000)', '(26.666670, 50.150000)', '(43.500000, 26.533330)', '(52.200000, 17.983330)', '(38.137420, -119.758120)', '(42.475000, -5.333330)', '(-6.733330, 111.366670)', '(44.766670, 11.283330)', '(-32.750000, -65.283330)', '(38.666670, 116.133330)', '(48.600000, 45.666670)', '(51.333330, -118.950000)', '(28.200000, 76.666670)', '(35.033330, -83.033330)', '(46.883330, -102.316670)', '(31.250000, -99.033330)', '(37.466670, -77.500000)', '(-26.100000, -49.800000)', '(45.483330, 9.516670)', '(41.083330, -86.283330)', '(-23.383330, 150.516670)', '(42.300000, 0.550000)', '(50.350000, 10.800000)', '(44.516670, -83.950000)', '(52.766670, -2.516670)', '(-1.450000, 29.833330)', '(0.266670, 36.533330)', '(-10.266670, 38.766670)', '(51.133330, 34.500000)', '(27.433330, 82.083330)', '(23.083330, 91.666670)', '(51.533330, 14.883330)', '(43.733330, 1.383330)', '(45.300000, 0.233330)', '(35.666670, 136.300000)', '(44.979170, -122.969440)', '(46.050000, 4.633330)', '(52.750000, 11.050000)', '(25.666670, 74.866670)', '(33.484720, -117.662500)', '(43.666670, 13.000000)', '(-31.016670, -71.400000)', '(19.766670, -100.650000)', '(-29.200000, -51.866670)', '(24.166670, -99.333330)', '(-33.900000, -61.700000)', '(-31.535556, -68.489444)', '(-20.810000, -49.380560)', '(52.550000, 46.550000)', '(34.716670, 137.783330)', '(43.150000, -0.850000)', '(47.216670, 29.866670)', '(35.866670, 139.400000)', '(49.233330, 17.566670)', '(53.350000, 15.050000)', '(42.860830, -73.950280)', '(48.116670, 10.466670)', '(44.366670, -69.200000)', '(38.300000, 27.133330)', '(26.750000, 84.783330)', '(-7.233330, 107.333330)', '(22.833330, 4.983330)', '(22.250000, 79.000000)', '(41.716670, -0.050000)', '(39.438330, 140.511670)', '(21.683890, 79.500830)', '(41.050000, 23.566670)', '(-8.383330, -36.766670)', '(-19.466670, -44.216670)', '(37.416670, -6.000000)', '(50.616670, 36.600000)', '(34.750000, 10.716670)', '(23.100000, 87.300000)', '(37.833330, -76.700000)', '(44.050000, -80.166670)', '(24.550000, 84.833330)', '(33.650000, 120.066670)', '(25.850000, 87.577500)', '(43.500000, 125.666670)', '(33.716670, 74.833330)', '(24.333330, 90.166670)', '(43.116670, 11.600000)', '(46.160000, 134.653330)', '(20.933330, -101.383330)', '(44.116670, 27.266670)', '(49.983330, 7.533330)', '(30.900000, 32.483330)', '(26.216670, 69.550000)', '(39.300000, 9.200000)', '(42.583330, -103.666670)', '(20.916670, 82.583330)', '(39.824670, 36.135830)', '(32.433330, 119.866670)', '(59.733330, 10.866670)', '(45.683330, 15.600000)', '(55.000000, 35.000000)', '(27.133330, 84.066670)', '(43.666670, 21.866670)', '(47.366670, 1.733330)', '(54.008830, 22.005000)', '(35.166670, 135.333330)', '(45.250000, 125.000000)', '(44.416670, 23.500000)', '(1.700000, 33.633330)', '(44.750000, 0.050000)', '(46.950000, -1.500000)', '(51.050000, 3.750000)', '(48.016670, -1.150000)', '(38.700000, -90.233330)', '(-32.016670, 27.416670)', '(38.166670, -76.383330)', '(48.450000, 3.933330)', '(61.650000, 27.200000)', '(43.433330, 2.950000)', '(59.933330, 14.950000)', '(49.283330, 15.566670)', '(45.050000, 41.983330)', '(50.766670, 3.000000)', '(53.666670, 55.983330)', '(52.533330, 9.050000)', '(41.200000, -73.133330)', '(56.583330, -3.250000)', '(50.383330, -3.950000)', '(45.968610, -72.978060)', '(36.483330, -90.666670)', '(50.538060, 16.263330)', '(31.616670, 113.466670)', '(12.666670, 78.033330)', '(25.933330, 84.283330)', '(44.866670, 133.166670)', '(26.716670, 84.216670)', '(38.803890, -120.908060)', '(33.188360, -86.294500)', '(49.400000, 14.650000)', '(36.183330, 5.416670)', '(59.704440, -134.201390)', '(34.720000, 137.305000)', '(35.383330, 134.900000)', None, '(-7.750000, 112.766670)', '(31.163330, -7.015000)', '(35.433330, 136.233330)', '(45.400000, 122.900000)', '(32.950000, 10.416670)', '(19.383330, 43.733330)', '(35.133330, 44.450000)', '(46.716670, 23.500000)', '(-25.733330, 142.950000)', '(58.033330, 26.950000)', '(33.400000, 70.600000)', '(-1.002780, 37.150280)', '(-29.333330, 27.583330)', '(32.946670, 118.990000)', '(49.600000, 17.116670)', '(38.200000, -89.683330)', '(14.250000, 1.533330)', '(54.500000, 35.200000)', '(13.633330, 79.416670)', '(29.481950, -7.611230)', '(-7.083330, 111.533330)', '(-6.666670, 106.583330)', '(47.850000, 34.766670)', '(20.166670, -105.216670)', '(34.500000, 133.750000)', '(38.366670, 140.865560)', '(24.650000, 76.866670)', ...]}
df.cols.frequency("id", n=10, count_uniques=True, compute=True)
INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to
45716
{'frequency': {'id': {'values': [{'value': '2', 'count': 1}, {'value': '6', 'count': 1}, {'value': '379', 'count': 1}, {'value': '392', 'count': 1}, {'value': '398', 'count': 1}, {'value': '423', 'count': 1}, {'value': '424', 'count': 1}, {'value': '425', 'count': 1}, {'value': '427', 'count': 1}, {'value': '432', 'count': 1}], 'count_uniques': 'N/A'}}}
(df.data.select("*").rdd
.flatMap(lambda x: x.asDict().items())
.map(lambda x: (x, 1))
.reduceByKey(lambda a, b: a + b)
.groupBy(lambda x: x[0][0])
).collect()
[('year', <pyspark.resultiterable.ResultIterable at 0x18cc1604588>), ('reclat', <pyspark.resultiterable.ResultIterable at 0x18cc1626188>), ('reclong', <pyspark.resultiterable.ResultIterable at 0x18cc1a5b7c8>), ('GeoLocation', <pyspark.resultiterable.ResultIterable at 0x18cc1609b08>), ('recclass', <pyspark.resultiterable.ResultIterable at 0x18cc1604708>), ('name', <pyspark.resultiterable.ResultIterable at 0x18cc2013608>), ('fall', <pyspark.resultiterable.ResultIterable at 0x18cc26fbec8>), ('id', <pyspark.resultiterable.ResultIterable at 0x18cc202b4c8>), ('mass (g)', <pyspark.resultiterable.ResultIterable at 0x18cc20136c8>), ('nametype', <pyspark.resultiterable.ResultIterable at 0x18cc3181308>)]
df.cols.count()
10
df.cols.remove_accents("*")
func_return_type0 str func_return_type1 str func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'name' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'id' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'nametype' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'recclass' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'mass (g)' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'fall' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'year' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'reclat' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'reclong' with function _remove_accents
func_return_type str pandas_udf
INFO:optimus:Using 'pandas_udf' to process column 'GeoLocation' with function _remove_accents
--------------------------------------------------------------------------- Py4JJavaError Traceback (most recent call last) ~\AppData\Roaming\Python\Python37\site-packages\IPython\core\formatters.py in __call__(self, obj) 700 type_pprinters=self.type_printers, 701 deferred_pprinters=self.deferred_printers) --> 702 printer.pretty(obj) 703 printer.flush() 704 return stream.getvalue() ~\AppData\Roaming\Python\Python37\site-packages\IPython\lib\pretty.py in pretty(self, obj) 400 if cls is not object \ 401 and callable(cls.__dict__.get('__repr__')): --> 402 return _repr_pprint(obj, self, cycle) 403 404 return _default_pprint(obj, self, cycle) ~\AppData\Roaming\Python\Python37\site-packages\IPython\lib\pretty.py in _repr_pprint(obj, p, cycle) 695 """A pprint that just redirects to the normal repr function.""" 696 # Find newlines and replace them with p.break_() --> 697 output = repr(obj) 698 for idx,output_line in enumerate(output.splitlines()): 699 if idx: ~\Documents\Optimus\optimus\new_optimus.py in __repr__(self) 51 52 def __repr__(self): ---> 53 self.ext.display() 54 return str(type(self)) 55 ~\Documents\Optimus\optimus\engines\base\extension.py in display(self, limit, columns, title, truncate) 503 def display(self, limit=None, columns=None, title=None, truncate=True): 504 # TODO: limit, columns, title, truncate --> 505 self.table(limit, columns, title, truncate) 506 507 def table(self, limit=None, columns=None, title=None, truncate=True): ~\Documents\Optimus\optimus\engines\base\extension.py in table(self, limit, columns, title, truncate) 510 if __IPYTHON__: 511 # TODO: move the html param to the ::: if __IPYTHON__ and engine.output is "html": --> 512 result = df.ext.table_html(title=title, limit=limit, columns=columns, truncate=truncate) 513 print_html(result) 514 else: ~\Documents\Optimus\optimus\engines\base\extension.py in table_html(self, limit, columns, title, full, truncate, count) 463 data = df.cols.select(columns).ext.to_dict() 464 else: --> 465 data = df.cols.select(columns).rows.limit(limit).ext.to_dict() 466 # Load the Jinja template 467 template_loader = jinja2.FileSystemLoader(searchpath=absolute_path("/templates/out")) ~\Documents\Optimus\optimus\engines\base\extension.py in to_dict(self, limit) 75 :return: 76 """ ---> 77 return self.parent.ext.to_pandas().to_dict("records") 78 # dict_result = {} 79 # re =[] ~\Documents\Optimus\optimus\engines\spark\extension.py in to_pandas(self) 256 def to_pandas(self): 257 df = self.parent.data --> 258 return df.toPandas() 259 260 def partitions(self): ~\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in toPandas(self) 2153 2154 # Below is toPandas without Arrow optimization. -> 2155 pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns) 2156 column_counter = Counter(self.columns) 2157 ~\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in collect(self) 533 """ 534 with SCCallSiteSync(self._sc) as css: --> 535 sock_info = self._jdf.collectToPython() 536 return list(_load_from_socket(sock_info, BatchedSerializer(PickleSerializer()))) 537 ~\Anaconda3\lib\site-packages\py4j\java_gateway.py in __call__(self, *args) 1255 answer = self.gateway_client.send_command(command) 1256 return_value = get_return_value( -> 1257 answer, self.gateway_client, self.target_id, self.name) 1258 1259 for temp_arg in temp_args: ~\Anaconda3\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw) 61 def deco(*a, **kw): 62 try: ---> 63 return f(*a, **kw) 64 except py4j.protocol.Py4JJavaError as e: 65 s = e.java_exception.toString() ~\Anaconda3\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name) 326 raise Py4JJavaError( 327 "An error occurred while calling {0}{1}{2}.\n". --> 328 format(target_id, ".", name), value) 329 else: 330 raise Py4JError( Py4JJavaError: An error occurred while calling o5009.collectToPython. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 192.0 failed 1 times, most recent failure: Lost task 0.0 in stage 192.0 (TID 294, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 377, in main File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 372, in process File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py", line 290, in dump_stream for series in iterator: File "<string>", line 1, in <lambda> File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 101, in <lambda> File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 92, in verify_result_length File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\util.py", line 99, in wrapper return f(*args, **kwargs) File "..\optimus\audf.py", line 78, in to_serie return apply_to_series(value, args) File "..\optimus\audf.py", line 75, in apply_to_series return value.apply(func, args=args) File "C:\Users\argenisleon\Anaconda3\lib\site-packages\pandas\core\series.py", line 4200, in apply mapped = lib.map_infer(values, f, convert=convert_dtype) File "pandas\_libs\lib.pyx", line 2401, in pandas._libs.lib.map_infer File "C:\Users\argenisleon\Anaconda3\lib\site-packages\pandas\core\series.py", line 4185, in f return func(x, *args, **kwds) TypeError: _remove_accents() takes 1 positional argument but 2 were given at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:102) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1925) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1912) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1912) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2146) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2095) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2084) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3263) at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3260) at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370) at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withAction(Dataset.scala:3369) at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3260) at sun.reflect.GeneratedMethodAccessor114.invoke(Unknown Source) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 377, in main File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 372, in process File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\serializers.py", line 290, in dump_stream for series in iterator: File "<string>", line 1, in <lambda> File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 101, in <lambda> File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\worker.py", line 92, in verify_result_length File "C:\opt\spark\spark-2.4.7-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\util.py", line 99, in wrapper return f(*args, **kwargs) File "..\optimus\audf.py", line 78, in to_serie return apply_to_series(value, args) File "..\optimus\audf.py", line 75, in apply_to_series return value.apply(func, args=args) File "C:\Users\argenisleon\Anaconda3\lib\site-packages\pandas\core\series.py", line 4200, in apply mapped = lib.map_infer(values, f, convert=convert_dtype) File "pandas\_libs\lib.pyx", line 2401, in pandas._libs.lib.map_infer File "C:\Users\argenisleon\Anaconda3\lib\site-packages\pandas\core\series.py", line 4185, in f return func(x, *args, **kwds) TypeError: _remove_accents() takes 1 positional argument but 2 were given at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:456) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:172) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:102) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) ... 1 more
df.cols.frequency("id", n=10, count_uniques=True, compute=True)
INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to
45716
{'frequency': {'id': {'count_uniques': 'N/A', 'values': [{'value': '2', 'count': 1}, {'value': '6', 'count': 1}, {'value': '379', 'count': 1}, {'value': '392', 'count': 1}, {'value': '398', 'count': 1}, {'value': '423', 'count': 1}, {'value': '424', 'count': 1}, {'value': '425', 'count': 1}, {'value': '427', 'count': 1}, {'value': '432', 'count': 1}]}}}
type(df.cols.select("*").rows.limit(10).data)
pyspark.sql.dataframe.DataFrame
df.data.head()
Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)')
# df.ext.display()
# df.ext.display()
df.cols.select("*").rows.limit(10).ext.to_dict()
[{'name': 'Aachen', 'id': 1, 'nametype': 'Valid', 'recclass': 'L5', 'mass (g)': 21.0, 'fall': 'Fell', 'year': '01/01/1880 12:00:00 AM', 'reclat': 50.775, 'reclong': 6.08333, 'GeoLocation': '(50.775000, 6.083330)'}, {'name': 'Aarhus', 'id': 2, 'nametype': 'Valid', 'recclass': 'H6', 'mass (g)': 720.0, 'fall': 'Fell', 'year': '01/01/1951 12:00:00 AM', 'reclat': 56.18333, 'reclong': 10.23333, 'GeoLocation': '(56.183330, 10.233330)'}, {'name': 'Abee', 'id': 6, 'nametype': 'Valid', 'recclass': 'EH4', 'mass (g)': 107000.0, 'fall': 'Fell', 'year': '01/01/1952 12:00:00 AM', 'reclat': 54.21667, 'reclong': -113.0, 'GeoLocation': '(54.216670, -113.000000)'}, {'name': 'Acapulco', 'id': 10, 'nametype': 'Valid', 'recclass': 'Acapulcoite', 'mass (g)': 1914.0, 'fall': 'Fell', 'year': '01/01/1976 12:00:00 AM', 'reclat': 16.88333, 'reclong': -99.9, 'GeoLocation': '(16.883330, -99.900000)'}, {'name': 'Achiras', 'id': 370, 'nametype': 'Valid', 'recclass': 'L6', 'mass (g)': 780.0, 'fall': 'Fell', 'year': '01/01/1902 12:00:00 AM', 'reclat': -33.16667, 'reclong': -64.95, 'GeoLocation': '(-33.166670, -64.950000)'}, {'name': 'Adhi Kot', 'id': 379, 'nametype': 'Valid', 'recclass': 'EH4', 'mass (g)': 4239.0, 'fall': 'Fell', 'year': '01/01/1919 12:00:00 AM', 'reclat': 32.1, 'reclong': 71.8, 'GeoLocation': '(32.100000, 71.800000)'}, {'name': 'Adzhi-Bogdo (stone)', 'id': 390, 'nametype': 'Valid', 'recclass': 'LL3-6', 'mass (g)': 910.0, 'fall': 'Fell', 'year': '01/01/1949 12:00:00 AM', 'reclat': 44.83333, 'reclong': 95.16667, 'GeoLocation': '(44.833330, 95.166670)'}, {'name': 'Agen', 'id': 392, 'nametype': 'Valid', 'recclass': 'H5', 'mass (g)': 30000.0, 'fall': 'Fell', 'year': '01/01/1814 12:00:00 AM', 'reclat': 44.21667, 'reclong': 0.61667, 'GeoLocation': '(44.216670, 0.616670)'}, {'name': 'Aguada', 'id': 398, 'nametype': 'Valid', 'recclass': 'L6', 'mass (g)': 1620.0, 'fall': 'Fell', 'year': '01/01/1930 12:00:00 AM', 'reclat': -31.6, 'reclong': -65.23333, 'GeoLocation': '(-31.600000, -65.233330)'}, {'name': 'Aguila Blanca', 'id': 417, 'nametype': 'Valid', 'recclass': 'L', 'mass (g)': 1440.0, 'fall': 'Fell', 'year': '01/01/1920 12:00:00 AM', 'reclat': -30.86667, 'reclong': -64.55, 'GeoLocation': '(-30.866670, -64.550000)'}]
len(df.data.get_partition(0).sample(frac=0.1))
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-10-b92b64a2c97b> in <module> ----> 1 len(df.data.get_partition(0).sample(frac=0.1)) ~\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in __getattr__(self, name) 1303 if name not in self.columns: 1304 raise AttributeError( -> 1305 "'%s' object has no attribute '%s'" % (self.__class__.__name__, name)) 1306 jc = self._jdf.apply(name) 1307 return Column(jc) AttributeError: 'DataFrame' object has no attribute 'get_partition'
df.cols.frequency()
INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to
{'year': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}], 'reclat': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6438}, {'value': -71.5, 'count': 4761}, {'value': -84.0, 'count': 3040}, {'value': -72.0, 'count': 1506}, {'value': -79.68333, 'count': 1130}, {'value': -76.71667, 'count': 680}, {'value': -76.18333, 'count': 539}, {'value': -84.21667, 'count': 263}, {'value': -86.36667, 'count': 226}], 'reclong': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6214}, {'value': 35.66667, 'count': 4985}, {'value': 168.0, 'count': 3040}, {'value': 26.0, 'count': 1506}, {'value': 159.75, 'count': 657}, {'value': 159.66667, 'count': 637}, {'value': 157.16667, 'count': 542}, {'value': 155.75, 'count': 473}, {'value': 160.5, 'count': 263}], 'GeoLocation': [{'value': None, 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}], 'recclass': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}], 'name': [{'value': 'Achiras', 'count': 1}, {'value': 'Adhi Kot', 'count': 1}, {'value': 'Aguada', 'count': 1}, {'value': 'Aguila Blanca', 'count': 1}, {'value': 'Aïr', 'count': 1}, {'value': 'Akaba', 'count': 1}, {'value': 'Akwanga', 'count': 1}, {'value': 'Alais', 'count': 1}, {'value': 'Alberta', 'count': 1}, {'value': 'Alby sur Chéran', 'count': 1}], 'fall': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'id': [{'value': 1, 'count': 1}, {'value': 379, 'count': 1}, {'value': 417, 'count': 1}, {'value': 423, 'count': 1}, {'value': 425, 'count': 1}, {'value': 427, 'count': 1}, {'value': 433, 'count': 1}, {'value': 447, 'count': 1}, {'value': 453, 'count': 1}, {'value': 461, 'count': 1}], 'mass (g)': [{'value': 1.3, 'count': 171}, {'value': 1.2, 'count': 140}, {'value': 1.4, 'count': 138}, {'value': None, 'count': 131}, {'value': 2.1, 'count': 130}, {'value': 2.4, 'count': 126}, {'value': 1.6, 'count': 120}, {'value': 0.5, 'count': 119}, {'value': 1.1, 'count': 116}, {'value': 3.8, 'count': 114}], 'nametype': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}]}
df
name
1 (string)
not nullable
|
id
2 (int)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.ext.profile()
func[dtype] ^\d+$ int
INFO:optimus:Using 'column_exp' to process column 'id' with function to_string INFO:optimus:Using 'column_exp' to process column 'id' with function _match INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to
func[dtype] ^\d+\.\d$ decimal
INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_string INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _match INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to
func[dtype] . date
INFO:optimus:Using 'column_exp' to process column 'year' with function to_string INFO:optimus:Using 'column_exp' to process column 'year' with function _match INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to
func[dtype] ^\d+\.\d$ decimal
INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_string INFO:optimus:Using 'column_exp' to process column 'reclat' with function _match INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to
func[dtype] ^\d+\.\d$ decimal
INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_string INFO:optimus:Using 'column_exp' to process column 'reclong' with function _match INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to
45716
{'columns': {'name': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Achiras', 'count': 1}, {'value': 'Adhi Kot', 'count': 1}, {'value': 'Aguada', 'count': 1}, {'value': 'Aguila Blanca', 'count': 1}, {'value': 'Aïr', 'count': 1}, {'value': 'Akaba', 'count': 1}, {'value': 'Akwanga', 'count': 1}, {'value': 'Alais', 'count': 1}, {'value': 'Alberta', 'count': 1}, {'value': 'Alby sur Chéran', 'count': 1}, {'value': 'Aleppo', 'count': 1}, {'value': 'Alessandria', 'count': 1}, {'value': 'Allegan', 'count': 1}, {'value': 'Allende', 'count': 1}, {'value': 'Almahata Sitta', 'count': 1}, {'value': "Alta'ameem", 'count': 1}, {'value': 'Andhara', 'count': 1}, {'value': 'Andover', 'count': 1}, {'value': 'Andreevka', 'count': 1}, {'value': 'Andura', 'count': 1}, {'value': 'Anlong', 'count': 1}, {'value': 'Appley Bridge', 'count': 1}, {'value': 'Apt', 'count': 1}, {'value': 'Arbol Solo', 'count': 1}, {'value': 'Archie', 'count': 1}, {'value': 'Ash Creek', 'count': 1}, {'value': 'Assisi', 'count': 1}, {'value': 'Atarra', 'count': 1}, {'value': 'Athens', 'count': 1}, {'value': 'Aumale', 'count': 1}, {'value': 'Aumieres', 'count': 1}, {'value': 'Avanhandava', 'count': 1}, {'value': 'Avce', 'count': 1}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'id': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': 1, 'count': 1}, {'value': 379, 'count': 1}, {'value': 417, 'count': 1}, {'value': 423, 'count': 1}, {'value': 425, 'count': 1}, {'value': 427, 'count': 1}, {'value': 433, 'count': 1}, {'value': 447, 'count': 1}, {'value': 453, 'count': 1}, {'value': 461, 'count': 1}, {'value': 463, 'count': 1}, {'value': 465, 'count': 1}, {'value': 48915, 'count': 1}, {'value': 2295, 'count': 1}, {'value': 50693, 'count': 1}, {'value': 2301, 'count': 1}, {'value': 2305, 'count': 1}, {'value': 2313, 'count': 1}, {'value': 2325, 'count': 1}, {'value': 2329, 'count': 1}, {'value': 2345, 'count': 1}, {'value': 2353, 'count': 1}, {'value': 4883, 'count': 1}, {'value': 4885, 'count': 1}, {'value': 4893, 'count': 1}, {'value': 4899, 'count': 1}, {'value': 4903, 'count': 1}, {'value': 4905, 'count': 1}, {'value': 4907, 'count': 1}, {'value': 4913, 'count': 1}, {'value': 4917, 'count': 1}, {'value': 4925, 'count': 1}, {'value': 4935, 'count': 1}], 'count_uniques': 'N/A'}, 'dtype': 'int', 'profiler_dtype': {'dtype': 'int'}}, 'nametype': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'recclass': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'mass (g)': {'stats': {'match': 0, 'missing': 131, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': 1.3, 'count': 171}, {'value': 1.2, 'count': 140}, {'value': 1.4, 'count': 138}, {'value': None, 'count': 131}, {'value': 2.1, 'count': 130}, {'value': 2.4, 'count': 126}, {'value': 1.6, 'count': 120}, {'value': 0.5, 'count': 119}, {'value': 1.1, 'count': 116}, {'value': 3.8, 'count': 114}, {'value': 1.5, 'count': 111}, {'value': 0.7, 'count': 111}, {'value': 1.7, 'count': 109}, {'value': 3.1, 'count': 109}, {'value': 3.2, 'count': 109}, {'value': 3.0, 'count': 108}, {'value': 0.6, 'count': 108}, {'value': 0.9, 'count': 108}, {'value': 0.8, 'count': 107}, {'value': 1.8, 'count': 104}, {'value': 2.5, 'count': 103}, {'value': 2.7, 'count': 102}, {'value': 1.0, 'count': 96}, {'value': 3.6, 'count': 96}, {'value': 2.0, 'count': 96}, {'value': 4.2, 'count': 95}, {'value': 2.9, 'count': 93}, {'value': 2.8, 'count': 93}, {'value': 2.2, 'count': 92}, {'value': 2.6, 'count': 91}, {'value': 3.3, 'count': 88}, {'value': 1.9, 'count': 86}, {'value': 4.1, 'count': 86}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'fall': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'year': {'stats': {'match': 0, 'missing': 288, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}, 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}}, 'reclat': {'stats': {'match': 0, 'missing': 7315, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6438}, {'value': -71.5, 'count': 4761}, {'value': -84.0, 'count': 3040}, {'value': -72.0, 'count': 1506}, {'value': -79.68333, 'count': 1130}, {'value': -76.71667, 'count': 680}, {'value': -76.18333, 'count': 539}, {'value': -84.21667, 'count': 263}, {'value': -86.36667, 'count': 226}, {'value': -86.71667, 'count': 217}, {'value': -85.66667, 'count': 185}, {'value': -24.85, 'count': 178}, {'value': -85.63333, 'count': 108}, {'value': -72.95488, 'count': 74}, {'value': -72.77889, 'count': 69}, {'value': -72.98389, 'count': 67}, {'value': 58.58333, 'count': 64}, {'value': -72.775, 'count': 57}, {'value': -72.77833, 'count': 52}, {'value': -72.99889, 'count': 41}, {'value': 34.08333, 'count': 40}, {'value': -72.77917, 'count': 40}, {'value': -72.7825, 'count': 39}, {'value': -72.983889, 'count': 37}, {'value': -72.77861, 'count': 35}, {'value': 29.91667, 'count': 35}, {'value': -83.25, 'count': 35}, {'value': -82.5, 'count': 32}, {'value': -25.23333, 'count': 32}, {'value': -72.98972, 'count': 31}, {'value': -72.77361, 'count': 31}, {'value': -72.77472, 'count': 31}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'reclong': {'stats': {'match': 0, 'missing': 7315, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6214}, {'value': 35.66667, 'count': 4985}, {'value': 168.0, 'count': 3040}, {'value': 26.0, 'count': 1506}, {'value': 159.75, 'count': 657}, {'value': 159.66667, 'count': 637}, {'value': 157.16667, 'count': 542}, {'value': 155.75, 'count': 473}, {'value': 160.5, 'count': 263}, {'value': -70.0, 'count': 228}, {'value': -141.5, 'count': 217}, {'value': 175.0, 'count': 185}, {'value': -70.53333, 'count': 178}, {'value': -68.7, 'count': 105}, {'value': 160.47328, 'count': 74}, {'value': 13.43333, 'count': 64}, {'value': 159.33333, 'count': 44}, {'value': 75.24639, 'count': 42}, {'value': 75.31361, 'count': 42}, {'value': -103.5, 'count': 35}, {'value': 157.0, 'count': 34}, {'value': 75.18722, 'count': 33}, {'value': -5.58333, 'count': 33}, {'value': -69.71667, 'count': 32}, {'value': 155.5, 'count': 32}, {'value': 75.2, 'count': 32}, {'value': 75.34, 'count': 31}, {'value': 75.246389, 'count': 30}, {'value': 161.5, 'count': 30}, {'value': 161.08333, 'count': 29}, {'value': 156.38333, 'count': 27}, {'value': -9.5, 'count': 24}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'GeoLocation': {'stats': {'match': 38401, 'missing': 7315, 'mismatch': 7315, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': None, 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(-24.683330, -69.766670)', 'count': 24}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}}, 'file_name': 'data/Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 45716, 'dtypes_list': ['double', 'string', 'int'], 'total_count_dtypes': 3, 'missing_count': 0}}
# df.rows.limit(10)
df.ext.profile()
func[dtype] ^\d+$ int
INFO:optimus:Using 'column_exp' to process column 'id' with function to_string INFO:optimus:Using 'column_exp' to process column 'id' with function _match INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to
func[dtype] ^\d+\.\d$ decimal
INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_string INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _match INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to
func[dtype] . date
INFO:optimus:Using 'column_exp' to process column 'year' with function to_string INFO:optimus:Using 'column_exp' to process column 'year' with function _match INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to
func[dtype] ^\d+\.\d$ decimal
INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_string INFO:optimus:Using 'column_exp' to process column 'reclat' with function _match INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to
func[dtype] ^\d+\.\d$ decimal
INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_string INFO:optimus:Using 'column_exp' to process column 'reclong' with function _match INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to
45716
{'columns': {'name': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Achiras', 'count': 1}, {'value': 'Adhi Kot', 'count': 1}, {'value': 'Aguada', 'count': 1}, {'value': 'Aguila Blanca', 'count': 1}, {'value': 'Aïr', 'count': 1}, {'value': 'Akaba', 'count': 1}, {'value': 'Akwanga', 'count': 1}, {'value': 'Alais', 'count': 1}, {'value': 'Alberta', 'count': 1}, {'value': 'Alby sur Chéran', 'count': 1}, {'value': 'Aleppo', 'count': 1}, {'value': 'Alessandria', 'count': 1}, {'value': 'Allegan', 'count': 1}, {'value': 'Allende', 'count': 1}, {'value': 'Almahata Sitta', 'count': 1}, {'value': "Alta'ameem", 'count': 1}, {'value': 'Andhara', 'count': 1}, {'value': 'Andover', 'count': 1}, {'value': 'Andreevka', 'count': 1}, {'value': 'Andura', 'count': 1}, {'value': 'Anlong', 'count': 1}, {'value': 'Appley Bridge', 'count': 1}, {'value': 'Apt', 'count': 1}, {'value': 'Arbol Solo', 'count': 1}, {'value': 'Archie', 'count': 1}, {'value': 'Ash Creek', 'count': 1}, {'value': 'Assisi', 'count': 1}, {'value': 'Atarra', 'count': 1}, {'value': 'Athens', 'count': 1}, {'value': 'Aumale', 'count': 1}, {'value': 'Aumieres', 'count': 1}, {'value': 'Avanhandava', 'count': 1}, {'value': 'Avce', 'count': 1}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'id': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': 1, 'count': 1}, {'value': 379, 'count': 1}, {'value': 417, 'count': 1}, {'value': 423, 'count': 1}, {'value': 425, 'count': 1}, {'value': 427, 'count': 1}, {'value': 433, 'count': 1}, {'value': 447, 'count': 1}, {'value': 453, 'count': 1}, {'value': 461, 'count': 1}, {'value': 463, 'count': 1}, {'value': 465, 'count': 1}, {'value': 48915, 'count': 1}, {'value': 2295, 'count': 1}, {'value': 50693, 'count': 1}, {'value': 2301, 'count': 1}, {'value': 2305, 'count': 1}, {'value': 2313, 'count': 1}, {'value': 2325, 'count': 1}, {'value': 2329, 'count': 1}, {'value': 2345, 'count': 1}, {'value': 2353, 'count': 1}, {'value': 4883, 'count': 1}, {'value': 4885, 'count': 1}, {'value': 4893, 'count': 1}, {'value': 4899, 'count': 1}, {'value': 4903, 'count': 1}, {'value': 4905, 'count': 1}, {'value': 4907, 'count': 1}, {'value': 4913, 'count': 1}, {'value': 4917, 'count': 1}, {'value': 4925, 'count': 1}, {'value': 4935, 'count': 1}], 'count_uniques': 'N/A'}, 'dtype': 'int', 'profiler_dtype': {'dtype': 'int'}}, 'nametype': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'recclass': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'mass (g)': {'stats': {'match': 0, 'missing': 131, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': 1.3, 'count': 171}, {'value': 1.2, 'count': 140}, {'value': 1.4, 'count': 138}, {'value': None, 'count': 131}, {'value': 2.1, 'count': 130}, {'value': 2.4, 'count': 126}, {'value': 1.6, 'count': 120}, {'value': 0.5, 'count': 119}, {'value': 1.1, 'count': 116}, {'value': 3.8, 'count': 114}, {'value': 1.5, 'count': 111}, {'value': 0.7, 'count': 111}, {'value': 1.7, 'count': 109}, {'value': 3.1, 'count': 109}, {'value': 3.2, 'count': 109}, {'value': 3.0, 'count': 108}, {'value': 0.6, 'count': 108}, {'value': 0.9, 'count': 108}, {'value': 0.8, 'count': 107}, {'value': 1.8, 'count': 104}, {'value': 2.5, 'count': 103}, {'value': 2.7, 'count': 102}, {'value': 1.0, 'count': 96}, {'value': 3.6, 'count': 96}, {'value': 2.0, 'count': 96}, {'value': 4.2, 'count': 95}, {'value': 2.9, 'count': 93}, {'value': 2.8, 'count': 93}, {'value': 2.2, 'count': 92}, {'value': 2.6, 'count': 91}, {'value': 3.3, 'count': 88}, {'value': 1.9, 'count': 86}, {'value': 4.1, 'count': 86}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'fall': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}, 'year': {'stats': {'match': 0, 'missing': 288, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}, 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}}, 'reclat': {'stats': {'match': 0, 'missing': 7315, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6438}, {'value': -71.5, 'count': 4761}, {'value': -84.0, 'count': 3040}, {'value': -72.0, 'count': 1506}, {'value': -79.68333, 'count': 1130}, {'value': -76.71667, 'count': 680}, {'value': -76.18333, 'count': 539}, {'value': -84.21667, 'count': 263}, {'value': -86.36667, 'count': 226}, {'value': -86.71667, 'count': 217}, {'value': -85.66667, 'count': 185}, {'value': -24.85, 'count': 178}, {'value': -85.63333, 'count': 108}, {'value': -72.95488, 'count': 74}, {'value': -72.77889, 'count': 69}, {'value': -72.98389, 'count': 67}, {'value': 58.58333, 'count': 64}, {'value': -72.775, 'count': 57}, {'value': -72.77833, 'count': 52}, {'value': -72.99889, 'count': 41}, {'value': 34.08333, 'count': 40}, {'value': -72.77917, 'count': 40}, {'value': -72.7825, 'count': 39}, {'value': -72.983889, 'count': 37}, {'value': -72.77861, 'count': 35}, {'value': 29.91667, 'count': 35}, {'value': -83.25, 'count': 35}, {'value': -82.5, 'count': 32}, {'value': -25.23333, 'count': 32}, {'value': -72.98972, 'count': 31}, {'value': -72.77361, 'count': 31}, {'value': -72.77472, 'count': 31}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'reclong': {'stats': {'match': 0, 'missing': 7315, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6214}, {'value': 35.66667, 'count': 4985}, {'value': 168.0, 'count': 3040}, {'value': 26.0, 'count': 1506}, {'value': 159.75, 'count': 657}, {'value': 159.66667, 'count': 637}, {'value': 157.16667, 'count': 542}, {'value': 155.75, 'count': 473}, {'value': 160.5, 'count': 263}, {'value': -70.0, 'count': 228}, {'value': -141.5, 'count': 217}, {'value': 175.0, 'count': 185}, {'value': -70.53333, 'count': 178}, {'value': -68.7, 'count': 105}, {'value': 160.47328, 'count': 74}, {'value': 13.43333, 'count': 64}, {'value': 159.33333, 'count': 44}, {'value': 75.24639, 'count': 42}, {'value': 75.31361, 'count': 42}, {'value': -103.5, 'count': 35}, {'value': 157.0, 'count': 34}, {'value': 75.18722, 'count': 33}, {'value': -5.58333, 'count': 33}, {'value': -69.71667, 'count': 32}, {'value': 155.5, 'count': 32}, {'value': 75.2, 'count': 32}, {'value': 75.34, 'count': 31}, {'value': 75.246389, 'count': 30}, {'value': 161.5, 'count': 30}, {'value': 161.08333, 'count': 29}, {'value': 156.38333, 'count': 27}, {'value': -9.5, 'count': 24}], 'count_uniques': 'N/A'}, 'dtype': 'double', 'profiler_dtype': {'dtype': 'decimal'}}, 'GeoLocation': {'stats': {'match': 38401, 'missing': 7315, 'mismatch': 7315, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': None, 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(-24.683330, -69.766670)', 'count': 24}], 'count_uniques': 'N/A'}, 'dtype': 'string', 'profiler_dtype': {'dtype': 'string'}}}, 'file_name': 'data/Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 45716, 'dtypes_list': ['double', 'string', 'int'], 'total_count_dtypes': 3, 'missing_count': 0}}
df.constants
<optimus.engines.spark.constants.Constants at 0x29a5db04748>
df.cols.match("nametype",regex="a").data.head()
nametype | |
---|---|
0 | False |
1 | False |
2 | False |
3 | False |
4 | False |
df.cols.names()
invalid escape sequence \d
['name', 'id', 'nametype', 'recclass', 'mass (g)', 'fall', 'year', 'reclat', 'reclong', 'GeoLocation']
df.cols.names()
['name', 'id', 'nametype', 'recclass', 'mass (g)', 'fall', 'year', 'reclat', 'reclong', 'GeoLocation']
df.cols.to_string("id")
INFO:optimus:Using 'column_exp' to process column 'id' with function to_string
name
1 (string)
not nullable
|
id
2 (string)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.cols.match("id")
['Column', 'DataFrame', 'DataType', 'PandasUDFType', 'PythonEvalType', 'SparkContext', 'StringType', 'UserDefinedFunction', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_binary_mathfunctions', '_collect_list_doc', '_collect_set_doc', '_create_binary_mathfunction', '_create_column_from_literal', '_create_function', '_create_udf', '_create_window_function', '_functions', '_functions_1_4', '_functions_1_6', '_functions_2_1', '_functions_2_4', '_functions_deprecated', '_lit_doc', '_message', '_string_functions', '_test', '_to_java_column', '_to_seq', '_window_functions', '_wrap_deprecated_function', 'abs', 'acos', 'add_months', 'approxCountDistinct', 'approx_count_distinct', 'array', 'array_contains', 'array_distinct', 'array_except', 'array_intersect', 'array_join', 'array_max', 'array_min', 'array_position', 'array_remove', 'array_repeat', 'array_sort', 'array_union', 'arrays_overlap', 'arrays_zip', 'asc', 'asc_nulls_first', 'asc_nulls_last', 'ascii', 'asin', 'atan', 'atan2', 'avg', 'base64', 'basestring', 'bin', 'bitwiseNOT', 'blacklist', 'broadcast', 'bround', 'cbrt', 'ceil', 'coalesce', 'col', 'collect_list', 'collect_set', 'column', 'concat', 'concat_ws', 'conv', 'corr', 'cos', 'cosh', 'count', 'countDistinct', 'covar_pop', 'covar_samp', 'crc32', 'create_map', 'cume_dist', 'current_date', 'current_timestamp', 'date_add', 'date_format', 'date_sub', 'date_trunc', 'datediff', 'dayofmonth', 'dayofweek', 'dayofyear', 'decode', 'degrees', 'dense_rank', 'desc', 'desc_nulls_first', 'desc_nulls_last', 'element_at', 'encode', 'exp', 'explode', 'explode_outer', 'expm1', 'expr', 'factorial', 'first', 'flatten', 'floor', 'format_number', 'format_string', 'from_json', 'from_unixtime', 'from_utc_timestamp', 'functools', 'get_json_object', 'greatest', 'grouping', 'grouping_id', 'hash', 'hex', 'hour', 'hypot', 'ignore_unicode_prefix', 'initcap', 'input_file_name', 'instr', 'isnan', 'isnull', 'json_tuple', 'kurtosis', 'lag', 'last', 'last_day', 'lead', 'least', 'length', 'levenshtein', 'lit', 'locate', 'log', 'log10', 'log1p', 'log2', 'lower', 'lpad', 'ltrim', 'map_concat', 'map_from_arrays', 'map_from_entries', 'map_keys', 'map_values', 'max', 'md5', 'mean', 'min', 'minute', 'monotonically_increasing_id', 'month', 'months_between', 'nanvl', 'next_day', 'ntile', 'pandas_udf', 'percent_rank', 'posexplode', 'posexplode_outer', 'pow', 'quarter', 'radians', 'rand', 'randn', 'rank', 'regexp_extract', 'regexp_replace', 'repeat', 'reverse', 'rint', 'round', 'row_number', 'rpad', 'rtrim', 'schema_of_json', 'second', 'sequence', 'sha1', 'sha2', 'shiftLeft', 'shiftRight', 'shiftRightUnsigned', 'shuffle', 'signum', 'sin', 'since', 'sinh', 'size', 'skewness', 'slice', 'sort_array', 'soundex', 'spark_partition_id', 'split', 'sqrt', 'stddev', 'stddev_pop', 'stddev_samp', 'struct', 'substring', 'substring_index', 'sum', 'sumDistinct', 'sys', 'tan', 'tanh', 'toDegrees', 'toRadians', 'to_date', 'to_json', 'to_timestamp', 'to_utc_timestamp', 'translate', 'trim', 'trunc', 'udf', 'unbase64', 'unhex', 'unix_timestamp', 'upper', 'var_pop', 'var_samp', 'variance', 'warnings', 'weekofyear', 'when', 'window', 'year']
INFO:optimus:Using 'column_exp' to process column 'id' with function func_regex
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-48-4732577fee11> in <module> ----> 1 df.cols.match("id") ~\Documents\Optimus\optimus\engines\spark\columns.py in match(self, input_cols, regex, value, output_cols) 966 # df = self.parent 967 return self.apply(input_cols, func=func_regex, args=[regex, value], output_cols=output_cols, --> 968 meta_action=Actions.REPLACE_REGEX.value) 969 970 def replace(self, input_cols, search=None, replace_by=None, search_by="chars", output_cols=None): ~\Documents\Optimus\optimus\engines\spark\columns.py in apply(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, default, mode) 220 df = odf.data 221 for input_col, output_col in columns: --> 222 df = df.withColumn(output_col, expr(when)) 223 self.parent.meta.preserve(self, meta_action, output_col) 224 df = self.parent.new(df) ~\Documents\Optimus\optimus\engines\spark\columns.py in expr(_when) 211 212 def expr(_when): --> 213 main_query = audf(input_col, func, func_return_type, args, func_type) 214 if when is not None: 215 # Use the data type to filter the query ~\Documents\Optimus\optimus\audf.py in abstract_udf(col, func, func_return_type, args, func_type) 49 50 # print("-----------------df_func(_func, args)(col)", df_func(_func, args)(col)) ---> 51 return df_func(_func, args)(col) 52 53 ~\Documents\Optimus\optimus\audf.py in inner(col_name) 87 return func(col_name) 88 else: ---> 89 return func(col_name, *args) 90 91 return inner ~\Documents\Optimus\optimus\engines\spark\columns.py in func_regex(_input_cols, attr) 961 _replace = attr[1] 962 dir(F) --> 963 return F.match(_input_cols, _search, _replace) 964 965 print(dir(F)) AttributeError: module 'pyspark.sql.functions' has no attribute 'match'
df.cols.frequency("*")
INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'nametype' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'recclass' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'fall' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'year' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclat' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'reclong' with function _cast_to INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function _cast_to
{'year': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}], 'reclat': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6438}, {'value': -71.5, 'count': 4761}, {'value': -84.0, 'count': 3040}, {'value': -72.0, 'count': 1506}, {'value': -79.68333, 'count': 1130}, {'value': -76.71667, 'count': 680}, {'value': -76.18333, 'count': 539}, {'value': -84.21667, 'count': 263}, {'value': -86.36667, 'count': 226}], 'reclong': [{'value': None, 'count': 7315}, {'value': 0.0, 'count': 6214}, {'value': 35.66667, 'count': 4985}, {'value': 168.0, 'count': 3040}, {'value': 26.0, 'count': 1506}, {'value': 159.75, 'count': 657}, {'value': 159.66667, 'count': 637}, {'value': 157.16667, 'count': 542}, {'value': 155.75, 'count': 473}, {'value': 160.5, 'count': 263}], 'GeoLocation': [{'value': None, 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}], 'recclass': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}], 'name': [{'value': 'Achiras', 'count': 1}, {'value': 'Adhi Kot', 'count': 1}, {'value': 'Aguada', 'count': 1}, {'value': 'Aguila Blanca', 'count': 1}, {'value': 'Aïr', 'count': 1}, {'value': 'Akaba', 'count': 1}, {'value': 'Akwanga', 'count': 1}, {'value': 'Alais', 'count': 1}, {'value': 'Alberta', 'count': 1}, {'value': 'Alby sur Chéran', 'count': 1}], 'fall': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'id': [{'value': 1, 'count': 1}, {'value': 379, 'count': 1}, {'value': 417, 'count': 1}, {'value': 423, 'count': 1}, {'value': 425, 'count': 1}, {'value': 427, 'count': 1}, {'value': 433, 'count': 1}, {'value': 447, 'count': 1}, {'value': 453, 'count': 1}, {'value': 461, 'count': 1}], 'mass (g)': [{'value': 1.3, 'count': 171}, {'value': 1.2, 'count': 140}, {'value': 1.4, 'count': 138}, {'value': None, 'count': 131}, {'value': 2.1, 'count': 130}, {'value': 2.4, 'count': 126}, {'value': 1.6, 'count': 120}, {'value': 0.5, 'count': 119}, {'value': 1.1, 'count': 116}, {'value': 3.8, 'count': 114}], 'nametype': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}]}
# df.ext.head()
# df.rows.limit(10)
# df.ext.profile()
# df.cols.count_na()
# df.ext.to_dict()
df.ext.profile("id")
# df.cols.schema_dtype("*")
INFO:optimus:Using 'column_exp' to process column 'id' with function to_string INFO:optimus:Using 'column_exp' to process column 'id' with function _match
data_type str
INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to
data_type str
INFO:optimus:Using 'column_exp' to process column 'id' with function _cast_to
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-57-35fd30bcd6a3> in <module> 4 # df.cols.count_na() 5 # df.ext.to_dict() ----> 6 df.ext.profile("id") 7 # df.cols.schema_dtype("*") ~\Documents\Optimus\optimus\engines\base\extension.py in profile(self, columns, bins, output, flush, size) 659 if compute is True: 660 hist, freq, mismatch, freq_uniques = dd.compute(hist, freq, mismatch, freq_uniques) --> 661 updated_columns = merge(cols_to_profile, hist, freq, mismatch, dtypes, freq_uniques) 662 profiler_data = update_dict(profiler_data, updated_columns) 663 ~\Documents\Optimus\optimus\engines\base\extension.py in merge(_columns, _hist, _freq, _mismatch, _dtypes, _freq_uniques) 646 647 if _freq is not None: --> 648 for _col_name, f in _freq["frequency"].items(): 649 _f[_col_name]["stats"]["frequency"] = f["values"] 650 _f[_col_name]["stats"]["count_uniques"] = f["count_uniques"] KeyError: 'frequency'
df.data.tocount()
name 45716 id 45716 nametype 45716 recclass 45716 mass (g) 45585 fall 45716 year 45428 reclat 38401 reclong 38401 GeoLocation 38401 dtype: int64
# import fastnumbers
# # fastnumbers.fast_float("1")
# import numpy as np
# fastnumbers.fast_float("1.2", default=np.nan)
# df.cols.to_float("id")
# df.cols.to_integer("id")
df.cols.to_float("id")
# df.cols.to_string("id")
# df.cols.to_integer("id")
INFO:optimus:Using 'column_exp' to process column 'id' with function to_float
name
1 (string)
not nullable
|
id
2 (float)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1.0
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2.0
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6.0
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10.0
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370.0
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379.0
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390.0
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392.0
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398.0
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417.0
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.cols.max("*")
{'name': 'Święcany', 'id': 57458, 'nametype': 'Valid', 'recclass': 'Winonaite', 'mass (g)': 60000000.0, 'fall': 'Found', 'year': '12/28/0860 12:00:00 AM', 'reclat': 81.16667, 'reclong': 354.47333, 'GeoLocation': '(9.916670, 13.983330)'}
df.data.limit(10).toPandas()
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | |
---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21.0 | Fell | 01/01/1880 12:00:00 AM | 50.77500 | 6.08333 | (50.775000, 6.083330) |
1 | Aarhus | 2 | Valid | H6 | 720.0 | Fell | 01/01/1951 12:00:00 AM | 56.18333 | 10.23333 | (56.183330, 10.233330) |
2 | Abee | 6 | Valid | EH4 | 107000.0 | Fell | 01/01/1952 12:00:00 AM | 54.21667 | -113.00000 | (54.216670, -113.000000) |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914.0 | Fell | 01/01/1976 12:00:00 AM | 16.88333 | -99.90000 | (16.883330, -99.900000) |
4 | Achiras | 370 | Valid | L6 | 780.0 | Fell | 01/01/1902 12:00:00 AM | -33.16667 | -64.95000 | (-33.166670, -64.950000) |
5 | Adhi Kot | 379 | Valid | EH4 | 4239.0 | Fell | 01/01/1919 12:00:00 AM | 32.10000 | 71.80000 | (32.100000, 71.800000) |
6 | Adzhi-Bogdo (stone) | 390 | Valid | LL3-6 | 910.0 | Fell | 01/01/1949 12:00:00 AM | 44.83333 | 95.16667 | (44.833330, 95.166670) |
7 | Agen | 392 | Valid | H5 | 30000.0 | Fell | 01/01/1814 12:00:00 AM | 44.21667 | 0.61667 | (44.216670, 0.616670) |
8 | Aguada | 398 | Valid | L6 | 1620.0 | Fell | 01/01/1930 12:00:00 AM | -31.60000 | -65.23333 | (-31.600000, -65.233330) |
9 | Aguila Blanca | 417 | Valid | L | 1440.0 | Fell | 01/01/1920 12:00:00 AM | -30.86667 | -64.55000 | (-30.866670, -64.550000) |
df.data.show(10)
+-------------------+---+--------+-----------+--------+----+--------------------+---------+---------+--------------------+ | name| id|nametype| recclass|mass (g)|fall| year| reclat| reclong| GeoLocation| +-------------------+---+--------+-----------+--------+----+--------------------+---------+---------+--------------------+ | Aachen| 1| Valid| L5| 21.0|Fell|01/01/1880 12:00:...| 50.775| 6.08333|(50.775000, 6.083...| | Aarhus| 2| Valid| H6| 720.0|Fell|01/01/1951 12:00:...| 56.18333| 10.23333|(56.183330, 10.23...| | Abee| 6| Valid| EH4|107000.0|Fell|01/01/1952 12:00:...| 54.21667| -113.0|(54.216670, -113....| | Acapulco| 10| Valid|Acapulcoite| 1914.0|Fell|01/01/1976 12:00:...| 16.88333| -99.9|(16.883330, -99.9...| | Achiras|370| Valid| L6| 780.0|Fell|01/01/1902 12:00:...|-33.16667| -64.95|(-33.166670, -64....| | Adhi Kot|379| Valid| EH4| 4239.0|Fell|01/01/1919 12:00:...| 32.1| 71.8|(32.100000, 71.80...| |Adzhi-Bogdo (stone)|390| Valid| LL3-6| 910.0|Fell|01/01/1949 12:00:...| 44.83333| 95.16667|(44.833330, 95.16...| | Agen|392| Valid| H5| 30000.0|Fell|01/01/1814 12:00:...| 44.21667| 0.61667|(44.216670, 0.616...| | Aguada|398| Valid| L6| 1620.0|Fell|01/01/1930 12:00:...| -31.6|-65.23333|(-31.600000, -65....| | Aguila Blanca|417| Valid| L| 1440.0|Fell|01/01/1920 12:00:...|-30.86667| -64.55|(-30.866670, -64....| +-------------------+---+--------+-----------+--------+----+--------------------+---------+---------+--------------------+ only showing top 10 rows
NoneType
df.rows.limit(10)
DataFrame[name: string, id: int, nametype: string, recclass: string, mass (g): double, fall: string, year: string, reclat: double, reclong: double, GeoLocation: string]
%%time
df.ext.profile()
# df.ext.profile()
type(df.rows.limit(10).ext.to_pandas())
pandas.core.frame.DataFrame
df.ext.head()
[Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)'), Row(name='Aarhus', id=2, nametype='Valid', recclass='H6', mass (g)=720.0, fall='Fell', year='01/01/1951 12:00:00 AM', reclat=56.18333, reclong=10.23333, GeoLocation='(56.183330, 10.233330)'), Row(name='Abee', id=6, nametype='Valid', recclass='EH4', mass (g)=107000.0, fall='Fell', year='01/01/1952 12:00:00 AM', reclat=54.21667, reclong=-113.0, GeoLocation='(54.216670, -113.000000)'), Row(name='Acapulco', id=10, nametype='Valid', recclass='Acapulcoite', mass (g)=1914.0, fall='Fell', year='01/01/1976 12:00:00 AM', reclat=16.88333, reclong=-99.9, GeoLocation='(16.883330, -99.900000)'), Row(name='Achiras', id=370, nametype='Valid', recclass='L6', mass (g)=780.0, fall='Fell', year='01/01/1902 12:00:00 AM', reclat=-33.16667, reclong=-64.95, GeoLocation='(-33.166670, -64.950000)'), Row(name='Adhi Kot', id=379, nametype='Valid', recclass='EH4', mass (g)=4239.0, fall='Fell', year='01/01/1919 12:00:00 AM', reclat=32.1, reclong=71.8, GeoLocation='(32.100000, 71.800000)'), Row(name='Adzhi-Bogdo (stone)', id=390, nametype='Valid', recclass='LL3-6', mass (g)=910.0, fall='Fell', year='01/01/1949 12:00:00 AM', reclat=44.83333, reclong=95.16667, GeoLocation='(44.833330, 95.166670)'), Row(name='Agen', id=392, nametype='Valid', recclass='H5', mass (g)=30000.0, fall='Fell', year='01/01/1814 12:00:00 AM', reclat=44.21667, reclong=0.61667, GeoLocation='(44.216670, 0.616670)'), Row(name='Aguada', id=398, nametype='Valid', recclass='L6', mass (g)=1620.0, fall='Fell', year='01/01/1930 12:00:00 AM', reclat=-31.6, reclong=-65.23333, GeoLocation='(-31.600000, -65.233330)'), Row(name='Aguila Blanca', id=417, nametype='Valid', recclass='L', mass (g)=1440.0, fall='Fell', year='01/01/1920 12:00:00 AM', reclat=-30.86667, reclong=-64.55, GeoLocation='(-30.866670, -64.550000)')]
df.rows.count()
# len(df.data)
45716
from dask import dataframe as dd
dd.compute(df[["id"]].data.min())[0][0]
# df["id"].data.min().head()
1
df.cols.lower("nametype")
name
1 (object)
not nullable
|
id
2 (int64)
not nullable
|
nametype
3 (object)
not nullable
|
recclass
4 (object)
not nullable
|
mass (g)
5 (object)
not nullable
|
fall
6 (object)
not nullable
|
year
7 (object)
not nullable
|
reclat
8 (object)
not nullable
|
reclong
9 (object)
not nullable
|
GeoLocation
10 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
valid
|
L5
|
21
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775000
|
6.083330
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
valid
|
H6
|
720
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.183330
|
10.233330
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
valid
|
EH4
|
107000
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.216670
|
-113.000000
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
valid
|
Acapulcoite
|
1914
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.883330
|
-99.900000
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
valid
|
L6
|
780
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.166670
|
-64.950000
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
valid
|
EH4
|
4239
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.100000
|
71.800000
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
valid
|
LL3-6
|
910
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.833330
|
95.166670
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
valid
|
H5
|
30000
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.216670
|
0.616670
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
valid
|
L6
|
1620
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.600000
|
-65.233330
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
valid
|
L
|
1440
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.866670
|
-64.550000
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.DaskDataFrame'>
df[["id"]].data.min().compute()
id 1 dtype: int64
list({"a":1}.values())[0]
1
%%time
df.ext.profile("*")
# df.cols.max("id")
Wall time: 6.94 s
{'columns': {'name': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Święcany', 'count': 1}, {'value': 'Hammadah al Hamra 012', 'count': 1}, {'value': 'Hammadah al Hamra 045', 'count': 1}, {'value': 'Hammadah al Hamra 046', 'count': 1}, {'value': 'Hammadah al Hamra 047', 'count': 1}, {'value': 'Hammadah al Hamra 048', 'count': 1}, {'value': 'Hammadah al Hamra 049', 'count': 1}, {'value': 'Hammadah al Hamra 050', 'count': 1}, {'value': 'Hammadah al Hamra 051', 'count': 1}, {'value': 'Hammadah al Hamra 043', 'count': 1}, {'value': 'Hammadah al Hamra 032', 'count': 1}, {'value': 'Hammadah al Hamra 031', 'count': 1}, {'value': 'Hammadah al Hamra 030', 'count': 1}, {'value': 'Hammadah al Hamra 009', 'count': 1}, {'value': 'Hammadah al Hamra 010', 'count': 1}, {'value': 'Hammadah al Hamra 011', 'count': 1}, {'value': 'Hammadah al Hamra 013', 'count': 1}, {'value': 'Hammadah al Hamra 007', 'count': 1}, {'value': 'Hammadah al Hamra 014', 'count': 1}, {'value': 'Hammadah al Hamra 017', 'count': 1}, {'value': 'Hammadah al Hamra 018', 'count': 1}, {'value': 'Hammadah al Hamra 019', 'count': 1}, {'value': 'Hammadah al Hamra 020', 'count': 1}, {'value': 'Hammadah al Hamra 021', 'count': 1}, {'value': 'Hammadah al Hamra 022', 'count': 1}, {'value': 'Hammadah al Hamra 023', 'count': 1}, {'value': 'Hammadah al Hamra 024', 'count': 1}, {'value': 'Hammadah al Hamra 025', 'count': 1}, {'value': 'Hammadah al Hamra 026', 'count': 1}, {'value': 'Hammadah al Hamra 027', 'count': 1}, {'value': 'Hammadah al Hamra 028', 'count': 1}, {'value': 'Hammadah al Hamra 029', 'count': 1}, {'value': 'Hammadah al Hamra 044', 'count': 1}], 'count_uniques': 45716}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'id': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '9999', 'count': 1}, {'value': '23983', 'count': 1}, {'value': '24014', 'count': 1}, {'value': '24015', 'count': 1}, {'value': '24016', 'count': 1}, {'value': '24017', 'count': 1}, {'value': '24018', 'count': 1}, {'value': '24019', 'count': 1}, {'value': '24020', 'count': 1}, {'value': '24012', 'count': 1}, {'value': '240', 'count': 1}, {'value': '24', 'count': 1}, {'value': '23999', 'count': 1}, {'value': '23980', 'count': 1}, {'value': '23981', 'count': 1}, {'value': '23982', 'count': 1}, {'value': '23984', 'count': 1}, {'value': '23978', 'count': 1}, {'value': '23985', 'count': 1}, {'value': '23986', 'count': 1}, {'value': '23987', 'count': 1}, {'value': '23988', 'count': 1}, {'value': '23989', 'count': 1}, {'value': '23990', 'count': 1}, {'value': '23991', 'count': 1}, {'value': '23992', 'count': 1}, {'value': '23993', 'count': 1}, {'value': '23994', 'count': 1}, {'value': '23995', 'count': 1}, {'value': '23996', 'count': 1}, {'value': '23997', 'count': 1}, {'value': '23998', 'count': 1}, {'value': '24013', 'count': 1}], 'count_uniques': 45716}, 'dtype': 'int64', 'profiler_dtype': {'dtype': 'int'}}, 'nametype': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'recclass': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}], 'count_uniques': 466}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'mass (g)': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '1.3', 'count': 171}, {'value': '1.2', 'count': 140}, {'value': '1.4', 'count': 138}, {'value': '', 'count': 131}, {'value': '2.1', 'count': 130}, {'value': '2.4', 'count': 126}, {'value': '1.6', 'count': 120}, {'value': '0.5', 'count': 119}, {'value': '1.1', 'count': 116}, {'value': '3.8', 'count': 114}, {'value': '0.7', 'count': 111}, {'value': '1.5', 'count': 111}, {'value': '3.2', 'count': 109}, {'value': '3.1', 'count': 109}, {'value': '1.7', 'count': 109}, {'value': '3', 'count': 108}, {'value': '0.9', 'count': 108}, {'value': '0.6', 'count': 108}, {'value': '0.8', 'count': 107}, {'value': '1.8', 'count': 104}, {'value': '2.5', 'count': 103}, {'value': '2.7', 'count': 102}, {'value': '3.6', 'count': 96}, {'value': '2', 'count': 96}, {'value': '1', 'count': 96}, {'value': '4.2', 'count': 95}, {'value': '2.8', 'count': 93}, {'value': '2.9', 'count': 93}, {'value': '2.2', 'count': 92}, {'value': '2.6', 'count': 91}, {'value': '3.3', 'count': 88}, {'value': '4.6', 'count': 86}, {'value': '1.9', 'count': 86}], 'count_uniques': 12577}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'fall': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'year': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}, 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}], 'count_uniques': 270}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}}, 'reclat': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6438}, {'value': '-71.500000', 'count': 4761}, {'value': '-84.000000', 'count': 3040}, {'value': '-72.000000', 'count': 1506}, {'value': '-79.683330', 'count': 1130}, {'value': '-76.716670', 'count': 680}, {'value': '-76.183330', 'count': 539}, {'value': '-84.216670', 'count': 263}, {'value': '-86.366670', 'count': 226}, {'value': '-86.716670', 'count': 217}, {'value': '-85.666670', 'count': 185}, {'value': '-24.850000', 'count': 178}, {'value': '-85.633330', 'count': 108}, {'value': '-72.954880', 'count': 74}, {'value': '-72.778890', 'count': 69}, {'value': '-72.983890', 'count': 67}, {'value': '58.583330', 'count': 64}, {'value': '-72.775000', 'count': 57}, {'value': '-72.778330', 'count': 52}, {'value': '-72.998890', 'count': 41}, {'value': '-72.779170', 'count': 40}, {'value': '34.083330', 'count': 40}, {'value': '-72.782500', 'count': 39}, {'value': '-72.983889', 'count': 37}, {'value': '29.916670', 'count': 35}, {'value': '-72.778610', 'count': 35}, {'value': '-83.250000', 'count': 35}, {'value': '-82.500000', 'count': 32}, {'value': '-25.233330', 'count': 32}, {'value': '-72.774720', 'count': 31}, {'value': '-72.773610', 'count': 31}, {'value': '-72.989720', 'count': 31}], 'count_uniques': 12739}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'reclong': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6214}, {'value': '35.666670', 'count': 4985}, {'value': '168.000000', 'count': 3040}, {'value': '26.000000', 'count': 1506}, {'value': '159.750000', 'count': 657}, {'value': '159.666670', 'count': 637}, {'value': '157.166670', 'count': 542}, {'value': '155.750000', 'count': 473}, {'value': '160.500000', 'count': 263}, {'value': '-70.000000', 'count': 228}, {'value': '-141.500000', 'count': 217}, {'value': '175.000000', 'count': 185}, {'value': '-70.533330', 'count': 178}, {'value': '-68.700000', 'count': 105}, {'value': '160.473280', 'count': 74}, {'value': '13.433330', 'count': 64}, {'value': '159.333330', 'count': 44}, {'value': '75.313610', 'count': 42}, {'value': '75.246390', 'count': 42}, {'value': '-103.500000', 'count': 35}, {'value': '157.000000', 'count': 34}, {'value': '-5.583330', 'count': 33}, {'value': '75.187220', 'count': 33}, {'value': '155.500000', 'count': 32}, {'value': '-69.716670', 'count': 32}, {'value': '75.200000', 'count': 32}, {'value': '75.340000', 'count': 31}, {'value': '161.500000', 'count': 30}, {'value': '75.246389', 'count': 30}, {'value': '161.083330', 'count': 29}, {'value': '156.383330', 'count': 27}, {'value': '-69.766670', 'count': 24}], 'count_uniques': 14641}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'GeoLocation': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(27.166670, -9.500000)', 'count': 24}], 'count_uniques': 17101}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'file_name': 'data/Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 45716, 'dtypes_list': ['object', 'int64'], 'total_count_dtypes': 2, 'missing_count': 0}}
from optimus.engines.dask.functions import DaskFunctions
DaskFunctions().min(df["id"])
id
1 (int64)
not nullable
|
---|
1
|
2
|
6
|
10
|
370
|
379
|
390
|
392
|
398
|
417
|
<class 'optimus.new_optimus.DaskDataFrame'> 1
nan
df.cols.hist()
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-43-770c3f45d27c> in <module> ----> 1 df.cols.hist() ~\Documents\Optimus\optimus\engines\base\columns.py in hist(self, columns, buckets, compute) 1591 _columns} 1592 -> 1593 _min = df.cols.min(columns, compute=False, tidy=False) 1594 _max = df.cols.max(columns, compute=False, tidy=False) 1595 _bins = _bins_col(columns, _min, _max) ~\Documents\Optimus\optimus\engines\base\columns.py in min(self, columns, tidy, compute) 761 def min(self, columns, tidy=True, compute=True): 762 df = self.parent --> 763 return df.cols.agg_exprs(columns, self.F.min, compute=compute, tidy=tidy) 764 765 def max(self, columns, tidy=True, compute=True): ~\Documents\Optimus\optimus\engines\base\columns.py in agg_exprs(self, columns, funcs, compute, tidy, *args) 740 741 funcs = val_to_list(funcs) --> 742 funcs = [{func.__name__: {col_name: func(df[col_name], *args)}} for col_name in columns for func in funcs] 743 a = self.exec_agg(funcs, compute) 744 ~\Documents\Optimus\optimus\engines\base\columns.py in <listcomp>(.0) 740 741 funcs = val_to_list(funcs) --> 742 funcs = [{func.__name__: {col_name: func(df[col_name], *args)}} for col_name in columns for func in funcs] 743 a = self.exec_agg(funcs, compute) 744 ~\Documents\Optimus\optimus\engines\base\functions.py in min(series) 16 @staticmethod 17 def min(series): ---> 18 return series.ext.to_float().min() 19 20 @staticmethod AttributeError: 'Series' object has no attribute 'ext'
# df.functions
# from pyspark.sql import functions as F
# dir(F)
df.data.replace("name","A","a").show()
df
name
1 (string)
not nullable
|
id
2 (int)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.ext.head()
[Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)'), Row(name='Aarhus', id=2, nametype='Valid', recclass='H6', mass (g)=720.0, fall='Fell', year='01/01/1951 12:00:00 AM', reclat=56.18333, reclong=10.23333, GeoLocation='(56.183330, 10.233330)'), Row(name='Abee', id=6, nametype='Valid', recclass='EH4', mass (g)=107000.0, fall='Fell', year='01/01/1952 12:00:00 AM', reclat=54.21667, reclong=-113.0, GeoLocation='(54.216670, -113.000000)'), Row(name='Acapulco', id=10, nametype='Valid', recclass='Acapulcoite', mass (g)=1914.0, fall='Fell', year='01/01/1976 12:00:00 AM', reclat=16.88333, reclong=-99.9, GeoLocation='(16.883330, -99.900000)'), Row(name='Achiras', id=370, nametype='Valid', recclass='L6', mass (g)=780.0, fall='Fell', year='01/01/1902 12:00:00 AM', reclat=-33.16667, reclong=-64.95, GeoLocation='(-33.166670, -64.950000)'), Row(name='Adhi Kot', id=379, nametype='Valid', recclass='EH4', mass (g)=4239.0, fall='Fell', year='01/01/1919 12:00:00 AM', reclat=32.1, reclong=71.8, GeoLocation='(32.100000, 71.800000)'), Row(name='Adzhi-Bogdo (stone)', id=390, nametype='Valid', recclass='LL3-6', mass (g)=910.0, fall='Fell', year='01/01/1949 12:00:00 AM', reclat=44.83333, reclong=95.16667, GeoLocation='(44.833330, 95.166670)'), Row(name='Agen', id=392, nametype='Valid', recclass='H5', mass (g)=30000.0, fall='Fell', year='01/01/1814 12:00:00 AM', reclat=44.21667, reclong=0.61667, GeoLocation='(44.216670, 0.616670)'), Row(name='Aguada', id=398, nametype='Valid', recclass='L6', mass (g)=1620.0, fall='Fell', year='01/01/1930 12:00:00 AM', reclat=-31.6, reclong=-65.23333, GeoLocation='(-31.600000, -65.233330)'), Row(name='Aguila Blanca', id=417, nametype='Valid', recclass='L', mass (g)=1440.0, fall='Fell', year='01/01/1920 12:00:00 AM', reclat=-30.86667, reclong=-64.55, GeoLocation='(-30.866670, -64.550000)')]
%%time
df.ext.profile()
# df.cols.frequency()
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <timed eval> in <module> ~\Documents\Optimus\optimus\engines\base\extension.py in profile(self, columns, bins, output, flush, size) 591 numeric_cols = [] 592 string_cols = [] --> 593 cols_and_inferred_dtype = odf.cols.infer_profiler_dtypes(cols_to_profile) 594 compute = True 595 mismatch = odf.cols.count_mismatch(cols_and_inferred_dtype, compute=compute) ~\Documents\Optimus\optimus\engines\base\columns.py in infer_profiler_dtypes(self, columns) 1710 # Infer the data type from every element in a Series. 1711 # FIX: could this be vectorized -> 1712 sample = df.ext.head(columns, total_preview_rows).ext.to_pandas() 1713 pdf = sample.applymap(Infer.parse_pandas) 1714 AttributeError: 'list' object has no attribute 'ext'
df.cols.cast("nametype","int")
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-128-b0fa04256dc8> in <module> ----> 1 df.ext.cast("nametype","int") AttributeError: 'Ext' object has no attribute 'cast'
df.cols.frequency()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-109-bb94dd60d9bb> in <module> ----> 1 df.cols.frequency() ~\Documents\Optimus\optimus\engines\spark\columns.py in frequency(self, columns, n, percentage, total_rows) 1632 1633 if non_compatible_columns is not None: -> 1634 df = self.cast(non_compatible_columns, "str") 1635 1636 freq = (df.select(columns).rdd ~\Documents\Optimus\optimus\engines\spark\columns.py in cast(input_cols, dtype, output_cols, columns) 353 # Parse params 354 if columns is None: --> 355 input_cols = parse_columns(self, input_cols) 356 if is_list(input_cols) or is_one_element(input_cols): 357 NameError: name 'self' is not defined
# df.cols.lower("name")
# from pyspark.sql import functions as F
# df.data.withColumn("name", F.col("name"))
df.cols.replace("name", ["3", 2], 10)
_search_and_replace_by {'3': '10', '2': '10'} multiple_replace <function Cols.replace.<locals>.func_chars_words.<locals>.multiple_replace at 0x0000027178962A68>
INFO:optimus:Using 'column_exp' to process column 'name' with function multiple_replace
expression_func <function func_factory.<locals>.expression_func at 0x0000027178330A68> inner <function func_factory.<locals>.expression_func.<locals>.inner at 0x00000271791243A8> AAAAAAAAAAAA ({'3': '10', '2': '10'},) func <function Cols.replace.<locals>.func_chars_words.<locals>.multiple_replace at 0x0000027178962A68> __search_and_replace_by11111111111 {'3': '10', '2': '10'} result11111111----- name -----------------df_func(_func, args)(col) name inner <function func_factory.<locals>.expression_func.<locals>.inner at 0x0000027178962288> AAAAAAAAAAAA ({'3': '10', '2': '10'},) func <function Cols.replace.<locals>.func_chars_words.<locals>.multiple_replace at 0x0000027178962A68> __search_and_replace_by11111111111 {'3': '10', '2': '10'} result11111111----- name main_query name
--------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-102-5f9c16bee1fe> in <module> 2 # from pyspark.sql import functions as F 3 # df.data.withColumn("name", F.col("name")) ----> 4 df.cols.replace("name", ["3", 2], 10) ~\Documents\Optimus\optimus\engines\spark\columns.py in replace(self, input_cols, search, replace_by, search_by, output_cols) 1031 df = odf.data 1032 for input_col, output_col in columns: -> 1033 df = func(df, input_col, output_col, search, replace_by) 1034 1035 df = df.meta.preserve(self, Actions.REPLACE.value, output_col) ~\Documents\Optimus\optimus\engines\spark\columns.py in func_chars_words(_df, _input_col, _output_col, _search, _replace_by) 993 print("_search_and_replace_by",_search_and_replace_by) 994 print("multiple_replace",multiple_replace) --> 995 return self.apply(_input_col, multiple_replace, "str", (_search_and_replace_by,), output_cols=_output_col) 996 997 def func_full(_df, _input_col, _output_col, _search, _replace_by): ~\Documents\Optimus\optimus\engines\spark\columns.py in apply(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, default, mode) 225 # print("expr(when)",type(expr(when)),expr(when)) 226 --> 227 df = df.withColumn(output_col, expr(when)) 228 self.parent.meta.preserve(self, meta_action, output_col) 229 df = self.parent.new(df) ~\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in withColumn(self, colName, col) 1995 1996 """ -> 1997 assert isinstance(col, Column), "col should be Column" 1998 return DataFrame(self._jdf.withColumn(colName, col._jc), self.sql_ctx) 1999 AssertionError: col should be Column
df.data.withColumn("name","name")
--------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-59-75c6bffac50a> in <module> ----> 1 df.data.withColumn("name","name") ~\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in withColumn(self, colName, col) 1995 1996 """ -> 1997 assert isinstance(col, Column), "col should be Column" 1998 return DataFrame(self._jdf.withColumn(colName, col._jc), self.sql_ctx) 1999 AssertionError: col should be Column
df.cols.replace("name", "A","a")
# df.cols.lower("name")
_search_and_replace_by {'A': 'a'} input_col, output_col name name <function Cols.apply.<locals>.expr at 0x00000271789A1AF8>
--------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-55-d0f06bc65c58> in <module> ----> 1 df.cols.replace("name", "A","a") 2 # df.cols.lower("name") ~\Documents\Optimus\optimus\engines\spark\columns.py in replace(self, input_cols, search, replace_by, search_by, output_cols) 1025 df = odf.data 1026 for input_col, output_col in columns: -> 1027 df = func(df, input_col, output_col, search, replace_by) 1028 1029 df = df.meta.preserve(self, Actions.REPLACE.value, output_col) ~\Documents\Optimus\optimus\engines\spark\columns.py in func_chars_words(_df, _input_col, _output_col, _search, _replace_by) 987 return result 988 print("_search_and_replace_by",_search_and_replace_by) --> 989 return self.apply(_input_col, multiple_replace, "str", (_search_and_replace_by,), output_cols=_output_col) 990 991 def func_full(_df, _input_col, _output_col, _search, _replace_by): ~\Documents\Optimus\optimus\engines\spark\columns.py in apply(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, default, mode) 221 for input_col, output_col in columns: 222 print("input_col, output_col", input_col, output_col, expr) --> 223 df = df.withColumn(output_col, input_col) 224 self.parent.meta.preserve(self, meta_action, output_col) 225 df = self.parent.new(df) ~\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in withColumn(self, colName, col) 1995 1996 """ -> 1997 assert isinstance(col, Column), "col should be Column" 1998 return DataFrame(self._jdf.withColumn(colName, col._jc), self.sql_ctx) 1999 AssertionError: col should be Column
df.cols.to_integer()
input_col, output_col name name <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'name' with function to_integer
input_col, output_col id id <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'id' with function to_integer
input_col, output_col nametype nametype <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'nametype' with function to_integer
input_col, output_col recclass recclass <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'recclass' with function to_integer
input_col, output_col mass (g) mass (g) <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function to_integer
input_col, output_col fall fall <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'fall' with function to_integer
input_col, output_col year year <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'year' with function to_integer
input_col, output_col reclat reclat <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'reclat' with function to_integer
input_col, output_col reclong reclong <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'reclong' with function to_integer
input_col, output_col GeoLocation GeoLocation <function Cols.apply.<locals>.expr at 0x000002717888EEE8>
INFO:optimus:Using 'column_exp' to process column 'GeoLocation' with function to_integer
name
1 (int)
not nullable
|
id
2 (int)
not nullable
|
nametype
3 (int)
not nullable
|
recclass
4 (int)
not nullable
|
mass (g)
5 (int)
not nullable
|
fall
6 (int)
not nullable
|
year
7 (int)
not nullable
|
reclat
8 (int)
not nullable
|
reclong
9 (int)
not nullable
|
GeoLocation
10 (int)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
None
|
1
|
None
|
None
|
21
|
None
|
None
|
50
|
6
|
None
|
None
|
2
|
None
|
None
|
720
|
None
|
None
|
56
|
10
|
None
|
None
|
6
|
None
|
None
|
107000
|
None
|
None
|
54
|
-113
|
None
|
None
|
10
|
None
|
None
|
1914
|
None
|
None
|
16
|
-99
|
None
|
None
|
370
|
None
|
None
|
780
|
None
|
None
|
-33
|
-64
|
None
|
None
|
379
|
None
|
None
|
4239
|
None
|
None
|
32
|
71
|
None
|
None
|
390
|
None
|
None
|
910
|
None
|
None
|
44
|
95
|
None
|
None
|
392
|
None
|
None
|
30000
|
None
|
None
|
44
|
0
|
None
|
None
|
398
|
None
|
None
|
1620
|
None
|
None
|
-31
|
-65
|
None
|
None
|
417
|
None
|
None
|
1440
|
None
|
None
|
-30
|
-64
|
None
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.cols.replace("name","a","b")
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-49-0c74d5199f16> in <module> ----> 1 df.cols.replace("name","a","b") ~\Documents\Optimus\optimus\engines\spark\columns.py in replace(self, input_cols, search, replace_by, search_by, output_cols) 1020 filter_dtype = [odf.constants.NUMERIC_TYPES] 1021 -> 1022 input_cols = parse_columns(odf, input_cols, filter_by_column_dtypes=filter_dtype) 1023 1024 check_column_numbers(input_cols, "*") ~\Documents\Optimus\optimus\helpers\columns.py in parse_columns(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert) 167 # if columns value is * get all dataframes columns 168 attrs = None --> 169 df_columns = columns_names(df) 170 171 if is_regex is True: ~\Documents\Optimus\optimus\helpers\columns.py in columns_names(df) 137 elif is_pandas_dataframe(df) or is_dask_dataframe(df): 138 columns_names = list(df.columns) --> 139 elif is_cudf_dataframe(df): 140 141 columns_names = list(df.columns) ~\Documents\Optimus\optimus\helpers\check.py in is_cudf_dataframe(value) 73 74 def is_cudf_dataframe(value): ---> 75 from cudf.core import DataFrame as CUDFDataFrame 76 return isinstance(value, CUDFDataFrame) 77 ModuleNotFoundError: No module named 'cudf'
# df.cols.lower("name").cols.upper("nametype").ext.display()
df.cols.percentile("id")
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-44-f582e378cbca> in <module> 1 # df.cols.lower("name").cols.upper("nametype").ext.display() ----> 2 df.cols.percentile("id") ~\Documents\Optimus\optimus\engines\base\columns.py in percentile(self, columns, values, relative_error, tidy, compute) 779 if values is None: 780 values = [0.25, 0.5, 0.75] --> 781 return df.cols.agg_exprs(columns, self.F.percentile, values, relative_error, tidy=tidy, compute=True) 782 783 def median(self, columns, relative_error=RELATIVE_ERROR, tidy=True, compute=True): ~\Documents\Optimus\optimus\engines\spark\columns.py in agg_exprs(self, columns, funcs, compute, tidy, *args) 562 :return: 563 """ --> 564 return self.exec_agg(self.create_exprs(columns, funcs, *args)) 565 566 def exec_agg(self, exprs): ~\Documents\Optimus\optimus\engines\spark\columns.py in create_exprs(self, columns, funcs, *args) 548 return _exprs 549 --> 550 r = _agg_exprs(exprs) 551 552 return r ~\Documents\Optimus\optimus\engines\spark\columns.py in _agg_exprs(_funcs) 541 542 if not _filter(_col_name, _func): --> 543 agg = _func(*_args) 544 if agg is not None: 545 func_name = _beautify_col_names(_func) TypeError: percentile() missing 1 required positional argument: 'relative_error'
df.ext.display()
name
1 (string)
not nullable
|
id
2 (int)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
df.meta.get()
{'file_name': 'data/Meteorite_Landings.csv'}
df.cols.select("name")
name
1 (string)
not nullable
|
---|
Aachen
|
Aarhus
|
Abee
|
Acapulco
|
Achiras
|
Adhi⋅Kot
|
Adzhi-Bogdo⋅(stone)
|
Agen
|
Aguada
|
Aguila⋅Blanca
|
<class 'optimus.new_optimus.SparkDataFrame'>
df
name
1 (string)
not nullable
|
id
2 (int)
not nullable
|
nametype
3 (string)
not nullable
|
recclass
4 (string)
not nullable
|
mass (g)
5 (double)
not nullable
|
fall
6 (string)
not nullable
|
year
7 (string)
not nullable
|
reclat
8 (double)
not nullable
|
reclong
9 (double)
not nullable
|
GeoLocation
10 (string)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21.0
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775
|
6.08333
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720.0
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.18333
|
10.23333
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000.0
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.21667
|
-113.0
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914.0
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.88333
|
-99.9
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780.0
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.16667
|
-64.95
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239.0
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.1
|
71.8
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910.0
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.83333
|
95.16667
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000.0
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.21667
|
0.61667
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620.0
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.6
|
-65.23333
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440.0
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.86667
|
-64.55
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.SparkDataFrame'>
df.cols.names()
['name', 'id', 'nametype', 'recclass', 'mass (g)', 'fall', 'year', 'reclat', 'reclong', 'GeoLocation']
df.cols.remove_accents("name").data.show()
INFO:optimus:Using 'pandas_udf' to process column 'name' with function _remove_accents
--------------------------------------------------------------------------- Py4JJavaError Traceback (most recent call last) <ipython-input-18-6d581b074b5b> in <module> ----> 1 df.cols.remove_accents("name").data.show() ~\Anaconda3\lib\site-packages\pyspark\sql\dataframe.py in show(self, n, truncate, vertical) 379 """ 380 if isinstance(truncate, bool) and truncate: --> 381 print(self._jdf.showString(n, 20, vertical)) 382 else: 383 print(self._jdf.showString(n, int(truncate), vertical)) ~\Anaconda3\lib\site-packages\py4j\java_gateway.py in __call__(self, *args) 1255 answer = self.gateway_client.send_command(command) 1256 return_value = get_return_value( -> 1257 answer, self.gateway_client, self.target_id, self.name) 1258 1259 for temp_arg in temp_args: ~\Anaconda3\lib\site-packages\pyspark\sql\utils.py in deco(*a, **kw) 61 def deco(*a, **kw): 62 try: ---> 63 return f(*a, **kw) 64 except py4j.protocol.Py4JJavaError as e: 65 s = e.java_exception.toString() ~\Anaconda3\lib\site-packages\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name) 326 raise Py4JJavaError( 327 "An error occurred while calling {0}{1}{2}.\n". --> 328 format(target_id, ".", name), value) 329 else: 330 raise Py4JError( Py4JJavaError: An error occurred while calling o301.showString. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 11.0 failed 1 times, most recent failure: Lost task 0.0 in stage 11.0 (TID 16, localhost, executor driver): java.lang.IllegalArgumentException at java.nio.ByteBuffer.allocate(ByteBuffer.java:334) at org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543) at org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58) at org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132) at org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181) at org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172) at org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:102) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1925) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1913) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1912) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1912) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:948) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:948) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2146) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2095) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2084) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:759) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:3389) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2550) at org.apache.spark.sql.Dataset$$anonfun$52.apply(Dataset.scala:3370) at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:80) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:127) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:75) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withAction(Dataset.scala:3369) at org.apache.spark.sql.Dataset.head(Dataset.scala:2550) at org.apache.spark.sql.Dataset.take(Dataset.scala:2764) at org.apache.spark.sql.Dataset.getRows(Dataset.scala:254) at org.apache.spark.sql.Dataset.showString(Dataset.scala:291) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:282) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:238) at java.lang.Thread.run(Thread.java:748) Caused by: java.lang.IllegalArgumentException at java.nio.ByteBuffer.allocate(ByteBuffer.java:334) at org.apache.arrow.vector.ipc.message.MessageSerializer.readMessage(MessageSerializer.java:543) at org.apache.arrow.vector.ipc.message.MessageChannelReader.readNext(MessageChannelReader.java:58) at org.apache.arrow.vector.ipc.ArrowStreamReader.readSchema(ArrowStreamReader.java:132) at org.apache.arrow.vector.ipc.ArrowReader.initialize(ArrowReader.java:181) at org.apache.arrow.vector.ipc.ArrowReader.ensureInitialized(ArrowReader.java:172) at org.apache.arrow.vector.ipc.ArrowReader.getVectorSchemaRoot(ArrowReader.java:65) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:162) at org.apache.spark.sql.execution.python.ArrowPythonRunner$$anon$1.read(ArrowPythonRunner.scala:122) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:410) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec$$anon$2.<init>(ArrowEvalPythonExec.scala:102) at org.apache.spark.sql.execution.python.ArrowEvalPythonExec.evaluate(ArrowEvalPythonExec.scala:100) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:127) at org.apache.spark.sql.execution.python.EvalPythonExec$$anonfun$doExecute$1.apply(EvalPythonExec.scala:89) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:823) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:346) at org.apache.spark.rdd.RDD.iterator(RDD.scala:310) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) ... 1 more
# df.cols.to_datetime("%Y/%m/%d")
df.data.head()
Row(name='Aachen', id=1, nametype='Valid', recclass='L5', mass (g)=21.0, fall='Fell', year='01/01/1880 12:00:00 AM', reclat=50.775, reclong=6.08333, GeoLocation='(50.775000, 6.083330)')
# df.cols.to_datetime("birth", "%Y/%m/%d")
# df.cols.lower()
# df.cols.to_datetime("birth", format= "%Y/%m/%d").cols.year("birth")
# df.cols.to_datetime("birth", format= "%Y/%m/%d")
# df.cols.year("birth", format= "%Y/%m/%d")
# df.cols.lower("firstName")
# df.cols.select("firstName").cols.to_string().cols.remove_accents().cols.replace(
# search="L", replace_by="aaa")
df["name"]
name
1 (object)
not nullable
|
---|
Aachen
|
Aarhus
|
Abee
|
Acapulco
|
Achiras
|
Adhi⋅Kot
|
Adzhi-Bogdo⋅(stone)
|
Agen
|
Aguada
|
Aguila⋅Blanca
|
<class 'optimus.new_optimus.DaskDataFrame'>
%%time
df.cols.pattern_counts("name")
Wall time: 2.76 s
{'name': {'values': [{'value': 'Ulllll ######', 'count': 5105}, {'value': 'Ullllllll Ulllll ####', 'count': 4064}, {'value': 'Ullll Ullllllll Ullll #####', 'count': 3393}, {'value': 'Ullll Ullll #####', 'count': 2682}, {'value': 'Ullll Ullllllll ######', 'count': 2462}, {'value': 'Ulllll #####', 'count': 2304}, {'value': 'Ulllllll Ullllll #####', 'count': 2177}, {'value': 'Ullll ######', 'count': 1263}, {'value': 'UlUll Ulllllll #####', 'count': 1152}, {'value': 'Ulllll ###', 'count': 1076}], 'more': True, 'updated': 1604903513.3142393}}
df.meta.get()
{'file_name': 'data/Meteorite_Landings.csv', 'profile': {'columns': {'name': {'patterns': {'values': [{'value': 'Ulllll ######', 'count': 5105}, {'value': 'Ullllllll Ulllll ####', 'count': 4064}, {'value': 'Ullll Ullllllll Ullll #####', 'count': 3393}, {'value': 'Ullll Ullll #####', 'count': 2682}, {'value': 'Ullll Ullllllll ######', 'count': 2462}, {'value': 'Ulllll #####', 'count': 2304}, {'value': 'Ulllllll Ullllll #####', 'count': 2177}, {'value': 'Ullll ######', 'count': 1263}, {'value': 'UlUll Ulllllll #####', 'count': 1152}, {'value': 'Ulllll ###', 'count': 1076}], 'more': True, 'updated': 1604903513.3142393}}}}}
df.cols.select("name").cols.to_string().data["name"]
Dask Series Structure: npartitions=1 object ... Name: name, dtype: object Dask Name: getitem, 7 tasks
df.cols.frequency()
{'frequency': {'name': {'values': [{'value': 'Święcany', 'count': 1}, {'value': 'Hammadah al Hamra 012', 'count': 1}, {'value': 'Hammadah al Hamra 045', 'count': 1}, {'value': 'Hammadah al Hamra 046', 'count': 1}, {'value': 'Hammadah al Hamra 047', 'count': 1}, {'value': 'Hammadah al Hamra 048', 'count': 1}, {'value': 'Hammadah al Hamra 049', 'count': 1}, {'value': 'Hammadah al Hamra 050', 'count': 1}, {'value': 'Hammadah al Hamra 051', 'count': 1}, {'value': 'Hammadah al Hamra 043', 'count': 1}, {'value': 'Hammadah al Hamra 032', 'count': 1}, {'value': 'Hammadah al Hamra 031', 'count': 1}, {'value': 'Hammadah al Hamra 030', 'count': 1}, {'value': 'Hammadah al Hamra 009', 'count': 1}, {'value': 'Hammadah al Hamra 010', 'count': 1}, {'value': 'Hammadah al Hamra 011', 'count': 1}, {'value': 'Hammadah al Hamra 013', 'count': 1}, {'value': 'Hammadah al Hamra 007', 'count': 1}, {'value': 'Hammadah al Hamra 014', 'count': 1}, {'value': 'Hammadah al Hamra 017', 'count': 1}, {'value': 'Hammadah al Hamra 018', 'count': 1}, {'value': 'Hammadah al Hamra 019', 'count': 1}, {'value': 'Hammadah al Hamra 020', 'count': 1}, {'value': 'Hammadah al Hamra 021', 'count': 1}, {'value': 'Hammadah al Hamra 022', 'count': 1}, {'value': 'Hammadah al Hamra 023', 'count': 1}, {'value': 'Hammadah al Hamra 024', 'count': 1}, {'value': 'Hammadah al Hamra 025', 'count': 1}, {'value': 'Hammadah al Hamra 026', 'count': 1}, {'value': 'Hammadah al Hamra 027', 'count': 1}, {'value': 'Hammadah al Hamra 028', 'count': 1}, {'value': 'Hammadah al Hamra 029', 'count': 1}, {'value': 'Hammadah al Hamra 044', 'count': 1}]}, 'id': {'values': [{'value': '9999', 'count': 1}, {'value': '23983', 'count': 1}, {'value': '24014', 'count': 1}, {'value': '24015', 'count': 1}, {'value': '24016', 'count': 1}, {'value': '24017', 'count': 1}, {'value': '24018', 'count': 1}, {'value': '24019', 'count': 1}, {'value': '24020', 'count': 1}, {'value': '24012', 'count': 1}, {'value': '240', 'count': 1}, {'value': '24', 'count': 1}, {'value': '23999', 'count': 1}, {'value': '23980', 'count': 1}, {'value': '23981', 'count': 1}, {'value': '23982', 'count': 1}, {'value': '23984', 'count': 1}, {'value': '23978', 'count': 1}, {'value': '23985', 'count': 1}, {'value': '23986', 'count': 1}, {'value': '23987', 'count': 1}, {'value': '23988', 'count': 1}, {'value': '23989', 'count': 1}, {'value': '23990', 'count': 1}, {'value': '23991', 'count': 1}, {'value': '23992', 'count': 1}, {'value': '23993', 'count': 1}, {'value': '23994', 'count': 1}, {'value': '23995', 'count': 1}, {'value': '23996', 'count': 1}, {'value': '23997', 'count': 1}, {'value': '23998', 'count': 1}, {'value': '24013', 'count': 1}]}, 'nametype': {'values': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}]}, 'recclass': {'values': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}]}, 'mass (g)': {'values': [{'value': '1.3', 'count': 171}, {'value': '1.2', 'count': 140}, {'value': '1.4', 'count': 138}, {'value': '', 'count': 131}, {'value': '2.1', 'count': 130}, {'value': '2.4', 'count': 126}, {'value': '1.6', 'count': 120}, {'value': '0.5', 'count': 119}, {'value': '1.1', 'count': 116}, {'value': '3.8', 'count': 114}, {'value': '0.7', 'count': 111}, {'value': '1.5', 'count': 111}, {'value': '3.2', 'count': 109}, {'value': '3.1', 'count': 109}, {'value': '1.7', 'count': 109}, {'value': '3', 'count': 108}, {'value': '0.9', 'count': 108}, {'value': '0.6', 'count': 108}, {'value': '0.8', 'count': 107}, {'value': '1.8', 'count': 104}, {'value': '2.5', 'count': 103}, {'value': '2.7', 'count': 102}, {'value': '3.6', 'count': 96}, {'value': '2', 'count': 96}, {'value': '1', 'count': 96}, {'value': '4.2', 'count': 95}, {'value': '2.8', 'count': 93}, {'value': '2.9', 'count': 93}, {'value': '2.2', 'count': 92}, {'value': '2.6', 'count': 91}, {'value': '3.3', 'count': 88}, {'value': '4.6', 'count': 86}, {'value': '1.9', 'count': 86}]}, 'fall': {'values': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}]}, 'year': {'values': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}]}, 'reclat': {'values': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6438}, {'value': '-71.500000', 'count': 4761}, {'value': '-84.000000', 'count': 3040}, {'value': '-72.000000', 'count': 1506}, {'value': '-79.683330', 'count': 1130}, {'value': '-76.716670', 'count': 680}, {'value': '-76.183330', 'count': 539}, {'value': '-84.216670', 'count': 263}, {'value': '-86.366670', 'count': 226}, {'value': '-86.716670', 'count': 217}, {'value': '-85.666670', 'count': 185}, {'value': '-24.850000', 'count': 178}, {'value': '-85.633330', 'count': 108}, {'value': '-72.954880', 'count': 74}, {'value': '-72.778890', 'count': 69}, {'value': '-72.983890', 'count': 67}, {'value': '58.583330', 'count': 64}, {'value': '-72.775000', 'count': 57}, {'value': '-72.778330', 'count': 52}, {'value': '-72.998890', 'count': 41}, {'value': '-72.779170', 'count': 40}, {'value': '34.083330', 'count': 40}, {'value': '-72.782500', 'count': 39}, {'value': '-72.983889', 'count': 37}, {'value': '29.916670', 'count': 35}, {'value': '-72.778610', 'count': 35}, {'value': '-83.250000', 'count': 35}, {'value': '-82.500000', 'count': 32}, {'value': '-25.233330', 'count': 32}, {'value': '-72.774720', 'count': 31}, {'value': '-72.773610', 'count': 31}, {'value': '-72.989720', 'count': 31}]}, 'reclong': {'values': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6214}, {'value': '35.666670', 'count': 4985}, {'value': '168.000000', 'count': 3040}, {'value': '26.000000', 'count': 1506}, {'value': '159.750000', 'count': 657}, {'value': '159.666670', 'count': 637}, {'value': '157.166670', 'count': 542}, {'value': '155.750000', 'count': 473}, {'value': '160.500000', 'count': 263}, {'value': '-70.000000', 'count': 228}, {'value': '-141.500000', 'count': 217}, {'value': '175.000000', 'count': 185}, {'value': '-70.533330', 'count': 178}, {'value': '-68.700000', 'count': 105}, {'value': '160.473280', 'count': 74}, {'value': '13.433330', 'count': 64}, {'value': '159.333330', 'count': 44}, {'value': '75.313610', 'count': 42}, {'value': '75.246390', 'count': 42}, {'value': '-103.500000', 'count': 35}, {'value': '157.000000', 'count': 34}, {'value': '-5.583330', 'count': 33}, {'value': '75.187220', 'count': 33}, {'value': '155.500000', 'count': 32}, {'value': '-69.716670', 'count': 32}, {'value': '75.200000', 'count': 32}, {'value': '75.340000', 'count': 31}, {'value': '161.500000', 'count': 30}, {'value': '75.246389', 'count': 30}, {'value': '161.083330', 'count': 29}, {'value': '156.383330', 'count': 27}, {'value': '-69.766670', 'count': 24}]}, 'GeoLocation': {'values': [{'value': '', 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(27.166670, -9.500000)', 'count': 24}]}}}
df.meta.get()
{'file_name': 'data/Meteorite_Landings.csv'}
col_name = "name"
df.cols.select(col_name).cols.to_string().cols.match(col_name, r"^\d+\.\d$").cols.frequency()
# df.cols.select(col_name).cols.to_string().data[col_name].str.match(func[dtype]).value_counts()
{'frequency': {'name': {'values': [{'value': 'False', 'count': 45716}]}}}
df.ext.profile()
{'columns': {'name': {'patterns': {'values': [{'value': 'Ulllll ######', 'count': 5105}, {'value': 'Ullllllll Ulllll ####', 'count': 4064}, {'value': 'Ullll Ullllllll Ullll #####', 'count': 3393}, {'value': 'Ullll Ullll #####', 'count': 2682}, {'value': 'Ullll Ullllllll ######', 'count': 2462}, {'value': 'Ulllll #####', 'count': 2304}, {'value': 'Ulllllll Ullllll #####', 'count': 2177}, {'value': 'Ullll ######', 'count': 1263}, {'value': 'UlUll Ulllllll #####', 'count': 1152}, {'value': 'Ulllll ###', 'count': 1076}], 'more': True, 'updated': 1604903513.3142393}, 'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Święcany', 'count': 1}, {'value': 'Hammadah al Hamra 012', 'count': 1}, {'value': 'Hammadah al Hamra 045', 'count': 1}, {'value': 'Hammadah al Hamra 046', 'count': 1}, {'value': 'Hammadah al Hamra 047', 'count': 1}, {'value': 'Hammadah al Hamra 048', 'count': 1}, {'value': 'Hammadah al Hamra 049', 'count': 1}, {'value': 'Hammadah al Hamra 050', 'count': 1}, {'value': 'Hammadah al Hamra 051', 'count': 1}, {'value': 'Hammadah al Hamra 043', 'count': 1}, {'value': 'Hammadah al Hamra 032', 'count': 1}, {'value': 'Hammadah al Hamra 031', 'count': 1}, {'value': 'Hammadah al Hamra 030', 'count': 1}, {'value': 'Hammadah al Hamra 009', 'count': 1}, {'value': 'Hammadah al Hamra 010', 'count': 1}, {'value': 'Hammadah al Hamra 011', 'count': 1}, {'value': 'Hammadah al Hamra 013', 'count': 1}, {'value': 'Hammadah al Hamra 007', 'count': 1}, {'value': 'Hammadah al Hamra 014', 'count': 1}, {'value': 'Hammadah al Hamra 017', 'count': 1}, {'value': 'Hammadah al Hamra 018', 'count': 1}, {'value': 'Hammadah al Hamra 019', 'count': 1}, {'value': 'Hammadah al Hamra 020', 'count': 1}, {'value': 'Hammadah al Hamra 021', 'count': 1}, {'value': 'Hammadah al Hamra 022', 'count': 1}, {'value': 'Hammadah al Hamra 023', 'count': 1}, {'value': 'Hammadah al Hamra 024', 'count': 1}, {'value': 'Hammadah al Hamra 025', 'count': 1}, {'value': 'Hammadah al Hamra 026', 'count': 1}, {'value': 'Hammadah al Hamra 027', 'count': 1}, {'value': 'Hammadah al Hamra 028', 'count': 1}, {'value': 'Hammadah al Hamra 029', 'count': 1}, {'value': 'Hammadah al Hamra 044', 'count': 1}], 'count_uniques': 45716}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'id': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '9999', 'count': 1}, {'value': '23983', 'count': 1}, {'value': '24014', 'count': 1}, {'value': '24015', 'count': 1}, {'value': '24016', 'count': 1}, {'value': '24017', 'count': 1}, {'value': '24018', 'count': 1}, {'value': '24019', 'count': 1}, {'value': '24020', 'count': 1}, {'value': '24012', 'count': 1}, {'value': '240', 'count': 1}, {'value': '24', 'count': 1}, {'value': '23999', 'count': 1}, {'value': '23980', 'count': 1}, {'value': '23981', 'count': 1}, {'value': '23982', 'count': 1}, {'value': '23984', 'count': 1}, {'value': '23978', 'count': 1}, {'value': '23985', 'count': 1}, {'value': '23986', 'count': 1}, {'value': '23987', 'count': 1}, {'value': '23988', 'count': 1}, {'value': '23989', 'count': 1}, {'value': '23990', 'count': 1}, {'value': '23991', 'count': 1}, {'value': '23992', 'count': 1}, {'value': '23993', 'count': 1}, {'value': '23994', 'count': 1}, {'value': '23995', 'count': 1}, {'value': '23996', 'count': 1}, {'value': '23997', 'count': 1}, {'value': '23998', 'count': 1}, {'value': '24013', 'count': 1}], 'count_uniques': 45716}, 'dtype': 'int64', 'profiler_dtype': {'dtype': 'int'}}, 'nametype': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'recclass': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}], 'count_uniques': 466}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'mass (g)': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '1.3', 'count': 171}, {'value': '1.2', 'count': 140}, {'value': '1.4', 'count': 138}, {'value': '', 'count': 131}, {'value': '2.1', 'count': 130}, {'value': '2.4', 'count': 126}, {'value': '1.6', 'count': 120}, {'value': '0.5', 'count': 119}, {'value': '1.1', 'count': 116}, {'value': '3.8', 'count': 114}, {'value': '0.7', 'count': 111}, {'value': '1.5', 'count': 111}, {'value': '3.2', 'count': 109}, {'value': '3.1', 'count': 109}, {'value': '1.7', 'count': 109}, {'value': '3', 'count': 108}, {'value': '0.9', 'count': 108}, {'value': '0.6', 'count': 108}, {'value': '0.8', 'count': 107}, {'value': '1.8', 'count': 104}, {'value': '2.5', 'count': 103}, {'value': '2.7', 'count': 102}, {'value': '3.6', 'count': 96}, {'value': '2', 'count': 96}, {'value': '1', 'count': 96}, {'value': '4.2', 'count': 95}, {'value': '2.8', 'count': 93}, {'value': '2.9', 'count': 93}, {'value': '2.2', 'count': 92}, {'value': '2.6', 'count': 91}, {'value': '3.3', 'count': 88}, {'value': '4.6', 'count': 86}, {'value': '1.9', 'count': 86}], 'count_uniques': 12577}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'fall': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'year': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}, 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}], 'count_uniques': 270}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}}, 'reclat': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6438}, {'value': '-71.500000', 'count': 4761}, {'value': '-84.000000', 'count': 3040}, {'value': '-72.000000', 'count': 1506}, {'value': '-79.683330', 'count': 1130}, {'value': '-76.716670', 'count': 680}, {'value': '-76.183330', 'count': 539}, {'value': '-84.216670', 'count': 263}, {'value': '-86.366670', 'count': 226}, {'value': '-86.716670', 'count': 217}, {'value': '-85.666670', 'count': 185}, {'value': '-24.850000', 'count': 178}, {'value': '-85.633330', 'count': 108}, {'value': '-72.954880', 'count': 74}, {'value': '-72.778890', 'count': 69}, {'value': '-72.983890', 'count': 67}, {'value': '58.583330', 'count': 64}, {'value': '-72.775000', 'count': 57}, {'value': '-72.778330', 'count': 52}, {'value': '-72.998890', 'count': 41}, {'value': '-72.779170', 'count': 40}, {'value': '34.083330', 'count': 40}, {'value': '-72.782500', 'count': 39}, {'value': '-72.983889', 'count': 37}, {'value': '29.916670', 'count': 35}, {'value': '-72.778610', 'count': 35}, {'value': '-83.250000', 'count': 35}, {'value': '-82.500000', 'count': 32}, {'value': '-25.233330', 'count': 32}, {'value': '-72.774720', 'count': 31}, {'value': '-72.773610', 'count': 31}, {'value': '-72.989720', 'count': 31}], 'count_uniques': 12739}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'reclong': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6214}, {'value': '35.666670', 'count': 4985}, {'value': '168.000000', 'count': 3040}, {'value': '26.000000', 'count': 1506}, {'value': '159.750000', 'count': 657}, {'value': '159.666670', 'count': 637}, {'value': '157.166670', 'count': 542}, {'value': '155.750000', 'count': 473}, {'value': '160.500000', 'count': 263}, {'value': '-70.000000', 'count': 228}, {'value': '-141.500000', 'count': 217}, {'value': '175.000000', 'count': 185}, {'value': '-70.533330', 'count': 178}, {'value': '-68.700000', 'count': 105}, {'value': '160.473280', 'count': 74}, {'value': '13.433330', 'count': 64}, {'value': '159.333330', 'count': 44}, {'value': '75.313610', 'count': 42}, {'value': '75.246390', 'count': 42}, {'value': '-103.500000', 'count': 35}, {'value': '157.000000', 'count': 34}, {'value': '-5.583330', 'count': 33}, {'value': '75.187220', 'count': 33}, {'value': '155.500000', 'count': 32}, {'value': '-69.716670', 'count': 32}, {'value': '75.200000', 'count': 32}, {'value': '75.340000', 'count': 31}, {'value': '161.500000', 'count': 30}, {'value': '75.246389', 'count': 30}, {'value': '161.083330', 'count': 29}, {'value': '156.383330', 'count': 27}, {'value': '-69.766670', 'count': 24}], 'count_uniques': 14641}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'GeoLocation': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(27.166670, -9.500000)', 'count': 24}], 'count_uniques': 17101}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'file_name': 'data/Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 45716, 'dtypes_list': ['object', 'int64'], 'total_count_dtypes': 2, 'missing_count': 0}}
df.meta.get()
{'file_name': 'data/Meteorite_Landings.csv', 'profile': {'columns': {'name': {'patterns': {'values': [{'value': 'Ulllll ######', 'count': 5105}, {'value': 'Ullllllll Ulllll ####', 'count': 4064}, {'value': 'Ullll Ullllllll Ullll #####', 'count': 3393}, {'value': 'Ullll Ullll #####', 'count': 2682}, {'value': 'Ullll Ullllllll ######', 'count': 2462}, {'value': 'Ulllll #####', 'count': 2304}, {'value': 'Ulllllll Ullllll #####', 'count': 2177}, {'value': 'Ullll ######', 'count': 1263}, {'value': 'UlUll Ulllllll #####', 'count': 1152}, {'value': 'Ulllll ###', 'count': 1076}], 'more': True, 'updated': 1604903513.3142393}, 'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Święcany', 'count': 1}, {'value': 'Hammadah al Hamra 012', 'count': 1}, {'value': 'Hammadah al Hamra 045', 'count': 1}, {'value': 'Hammadah al Hamra 046', 'count': 1}, {'value': 'Hammadah al Hamra 047', 'count': 1}, {'value': 'Hammadah al Hamra 048', 'count': 1}, {'value': 'Hammadah al Hamra 049', 'count': 1}, {'value': 'Hammadah al Hamra 050', 'count': 1}, {'value': 'Hammadah al Hamra 051', 'count': 1}, {'value': 'Hammadah al Hamra 043', 'count': 1}, {'value': 'Hammadah al Hamra 032', 'count': 1}, {'value': 'Hammadah al Hamra 031', 'count': 1}, {'value': 'Hammadah al Hamra 030', 'count': 1}, {'value': 'Hammadah al Hamra 009', 'count': 1}, {'value': 'Hammadah al Hamra 010', 'count': 1}, {'value': 'Hammadah al Hamra 011', 'count': 1}, {'value': 'Hammadah al Hamra 013', 'count': 1}, {'value': 'Hammadah al Hamra 007', 'count': 1}, {'value': 'Hammadah al Hamra 014', 'count': 1}, {'value': 'Hammadah al Hamra 017', 'count': 1}, {'value': 'Hammadah al Hamra 018', 'count': 1}, {'value': 'Hammadah al Hamra 019', 'count': 1}, {'value': 'Hammadah al Hamra 020', 'count': 1}, {'value': 'Hammadah al Hamra 021', 'count': 1}, {'value': 'Hammadah al Hamra 022', 'count': 1}, {'value': 'Hammadah al Hamra 023', 'count': 1}, {'value': 'Hammadah al Hamra 024', 'count': 1}, {'value': 'Hammadah al Hamra 025', 'count': 1}, {'value': 'Hammadah al Hamra 026', 'count': 1}, {'value': 'Hammadah al Hamra 027', 'count': 1}, {'value': 'Hammadah al Hamra 028', 'count': 1}, {'value': 'Hammadah al Hamra 029', 'count': 1}, {'value': 'Hammadah al Hamra 044', 'count': 1}], 'count_uniques': 45716}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'id': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '9999', 'count': 1}, {'value': '23983', 'count': 1}, {'value': '24014', 'count': 1}, {'value': '24015', 'count': 1}, {'value': '24016', 'count': 1}, {'value': '24017', 'count': 1}, {'value': '24018', 'count': 1}, {'value': '24019', 'count': 1}, {'value': '24020', 'count': 1}, {'value': '24012', 'count': 1}, {'value': '240', 'count': 1}, {'value': '24', 'count': 1}, {'value': '23999', 'count': 1}, {'value': '23980', 'count': 1}, {'value': '23981', 'count': 1}, {'value': '23982', 'count': 1}, {'value': '23984', 'count': 1}, {'value': '23978', 'count': 1}, {'value': '23985', 'count': 1}, {'value': '23986', 'count': 1}, {'value': '23987', 'count': 1}, {'value': '23988', 'count': 1}, {'value': '23989', 'count': 1}, {'value': '23990', 'count': 1}, {'value': '23991', 'count': 1}, {'value': '23992', 'count': 1}, {'value': '23993', 'count': 1}, {'value': '23994', 'count': 1}, {'value': '23995', 'count': 1}, {'value': '23996', 'count': 1}, {'value': '23997', 'count': 1}, {'value': '23998', 'count': 1}, {'value': '24013', 'count': 1}], 'count_uniques': 45716}, 'dtype': 'int64', 'profiler_dtype': {'dtype': 'int'}}, 'nametype': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'recclass': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}], 'count_uniques': 466}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'mass (g)': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '1.3', 'count': 171}, {'value': '1.2', 'count': 140}, {'value': '1.4', 'count': 138}, {'value': '', 'count': 131}, {'value': '2.1', 'count': 130}, {'value': '2.4', 'count': 126}, {'value': '1.6', 'count': 120}, {'value': '0.5', 'count': 119}, {'value': '1.1', 'count': 116}, {'value': '3.8', 'count': 114}, {'value': '0.7', 'count': 111}, {'value': '1.5', 'count': 111}, {'value': '3.2', 'count': 109}, {'value': '3.1', 'count': 109}, {'value': '1.7', 'count': 109}, {'value': '3', 'count': 108}, {'value': '0.9', 'count': 108}, {'value': '0.6', 'count': 108}, {'value': '0.8', 'count': 107}, {'value': '1.8', 'count': 104}, {'value': '2.5', 'count': 103}, {'value': '2.7', 'count': 102}, {'value': '3.6', 'count': 96}, {'value': '2', 'count': 96}, {'value': '1', 'count': 96}, {'value': '4.2', 'count': 95}, {'value': '2.8', 'count': 93}, {'value': '2.9', 'count': 93}, {'value': '2.2', 'count': 92}, {'value': '2.6', 'count': 91}, {'value': '3.3', 'count': 88}, {'value': '4.6', 'count': 86}, {'value': '1.9', 'count': 86}], 'count_uniques': 12577}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'fall': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'year': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}, 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}], 'count_uniques': 270}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'date', 'format': '%m/%d/%Y %I:%M:%S %p'}}, 'reclat': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6438}, {'value': '-71.500000', 'count': 4761}, {'value': '-84.000000', 'count': 3040}, {'value': '-72.000000', 'count': 1506}, {'value': '-79.683330', 'count': 1130}, {'value': '-76.716670', 'count': 680}, {'value': '-76.183330', 'count': 539}, {'value': '-84.216670', 'count': 263}, {'value': '-86.366670', 'count': 226}, {'value': '-86.716670', 'count': 217}, {'value': '-85.666670', 'count': 185}, {'value': '-24.850000', 'count': 178}, {'value': '-85.633330', 'count': 108}, {'value': '-72.954880', 'count': 74}, {'value': '-72.778890', 'count': 69}, {'value': '-72.983890', 'count': 67}, {'value': '58.583330', 'count': 64}, {'value': '-72.775000', 'count': 57}, {'value': '-72.778330', 'count': 52}, {'value': '-72.998890', 'count': 41}, {'value': '-72.779170', 'count': 40}, {'value': '34.083330', 'count': 40}, {'value': '-72.782500', 'count': 39}, {'value': '-72.983889', 'count': 37}, {'value': '29.916670', 'count': 35}, {'value': '-72.778610', 'count': 35}, {'value': '-83.250000', 'count': 35}, {'value': '-82.500000', 'count': 32}, {'value': '-25.233330', 'count': 32}, {'value': '-72.774720', 'count': 31}, {'value': '-72.773610', 'count': 31}, {'value': '-72.989720', 'count': 31}], 'count_uniques': 12739}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'reclong': {'stats': {'match': 0, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'decimal'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '0.000000', 'count': 6214}, {'value': '35.666670', 'count': 4985}, {'value': '168.000000', 'count': 3040}, {'value': '26.000000', 'count': 1506}, {'value': '159.750000', 'count': 657}, {'value': '159.666670', 'count': 637}, {'value': '157.166670', 'count': 542}, {'value': '155.750000', 'count': 473}, {'value': '160.500000', 'count': 263}, {'value': '-70.000000', 'count': 228}, {'value': '-141.500000', 'count': 217}, {'value': '175.000000', 'count': 185}, {'value': '-70.533330', 'count': 178}, {'value': '-68.700000', 'count': 105}, {'value': '160.473280', 'count': 74}, {'value': '13.433330', 'count': 64}, {'value': '159.333330', 'count': 44}, {'value': '75.313610', 'count': 42}, {'value': '75.246390', 'count': 42}, {'value': '-103.500000', 'count': 35}, {'value': '157.000000', 'count': 34}, {'value': '-5.583330', 'count': 33}, {'value': '75.187220', 'count': 33}, {'value': '155.500000', 'count': 32}, {'value': '-69.716670', 'count': 32}, {'value': '75.200000', 'count': 32}, {'value': '75.340000', 'count': 31}, {'value': '161.500000', 'count': 30}, {'value': '75.246389', 'count': 30}, {'value': '161.083330', 'count': 29}, {'value': '156.383330', 'count': 27}, {'value': '-69.766670', 'count': 24}], 'count_uniques': 14641}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'decimal'}}, 'GeoLocation': {'stats': {'match': 45716, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': '', 'count': 7315}, {'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(27.166670, -9.500000)', 'count': 24}], 'count_uniques': 17101}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'file_name': 'data/Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 45716, 'dtypes_list': ['object', 'int64'], 'total_count_dtypes': 2, 'missing_count': 0}}, 'transformations': {'actions': []}}
df.data
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | |
---|---|---|---|---|---|---|---|---|---|---|
npartitions=1 | ||||||||||
object | int64 | object | object | object | object | object | object | object | object | |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
df
name
1 (object)
not nullable
|
id
2 (int64)
not nullable
|
nametype
3 (object)
not nullable
|
recclass
4 (object)
not nullable
|
mass (g)
5 (object)
not nullable
|
fall
6 (object)
not nullable
|
year
7 (object)
not nullable
|
reclat
8 (object)
not nullable
|
reclong
9 (object)
not nullable
|
GeoLocation
10 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775000
|
6.083330
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.183330
|
10.233330
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.216670
|
-113.000000
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.883330
|
-99.900000
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.166670
|
-64.950000
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.100000
|
71.800000
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.833330
|
95.166670
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.216670
|
0.616670
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.600000
|
-65.233330
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.866670
|
-64.550000
|
(-30.866670,⋅-64.550000)
|
<class 'optimus.new_optimus.DaskDataFrame'>
df.ext.head("*",10)
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | |
---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21 | Fell | 01/01/1880 12:00:00 AM | 50.775000 | 6.083330 | (50.775000, 6.083330) |
1 | Aarhus | 2 | Valid | H6 | 720 | Fell | 01/01/1951 12:00:00 AM | 56.183330 | 10.233330 | (56.183330, 10.233330) |
2 | Abee | 6 | Valid | EH4 | 107000 | Fell | 01/01/1952 12:00:00 AM | 54.216670 | -113.000000 | (54.216670, -113.000000) |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 01/01/1976 12:00:00 AM | 16.883330 | -99.900000 | (16.883330, -99.900000) |
4 | Achiras | 370 | Valid | L6 | 780 | Fell | 01/01/1902 12:00:00 AM | -33.166670 | -64.950000 | (-33.166670, -64.950000) |
5 | Adhi Kot | 379 | Valid | EH4 | 4239 | Fell | 01/01/1919 12:00:00 AM | 32.100000 | 71.800000 | (32.100000, 71.800000) |
6 | Adzhi-Bogdo (stone) | 390 | Valid | LL3-6 | 910 | Fell | 01/01/1949 12:00:00 AM | 44.833330 | 95.166670 | (44.833330, 95.166670) |
7 | Agen | 392 | Valid | H5 | 30000 | Fell | 01/01/1814 12:00:00 AM | 44.216670 | 0.616670 | (44.216670, 0.616670) |
8 | Aguada | 398 | Valid | L6 | 1620 | Fell | 01/01/1930 12:00:00 AM | -31.600000 | -65.233330 | (-31.600000, -65.233330) |
9 | Aguila Blanca | 417 | Valid | L | 1440 | Fell | 01/01/1920 12:00:00 AM | -30.866670 | -64.550000 | (-30.866670, -64.550000) |
df.ext.profile()
df <class 'dask.dataframe.core.Series'> Dask Series Structure: npartitions=1 GeoLocation int64 year ... dtype: int64 Dask Name: dataframe-sum-agg, 4 tasks
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) <ipython-input-39-6e469caec322> in <module> ----> 1 df.ext.profile() ~\Documents\Optimus\optimus\engines\base\extension.py in profile(self, columns, bins, output, flush, size) 598 cols_and_inferred_dtype = odf.cols.infer_profiler_dtypes(cols_to_profile) 599 compute = True --> 600 mismatch = odf.cols.count_mismatch(cols_and_inferred_dtype, compute=compute) 601 602 # Get with columns are numerical and does not have mismatch so we can calculate the histogram ~\Documents\Optimus\optimus\engines\base\columns.py in count_mismatch(self, columns_type, **kwargs) 1623 1624 result = {} -> 1625 nulls = self.parent.new(df.isnull().sum()).ext.to_dict() 1626 total_rows = len(df) 1627 # TODO: Test this cudf.Series(cudf.core.column.string.cpp_is_integer(a["A"]._column)) and fast_numbers ~\Documents\Optimus\optimus\engines\base\extension.py in to_dict(self) 77 df = self.parent.data 78 print("df",type(df),df) ---> 79 return collect_as_dict(df) 80 81 @staticmethod ~\Documents\Optimus\optimus\helpers\functions.py in collect_as_dict(df, limit) 55 dict_result = [] 56 ---> 57 df = any_dataframe_to_pandas(df) 58 59 # if there is only an element in the dict just return the value ~\Documents\Optimus\optimus\helpers\converter.py in any_dataframe_to_pandas(df) 112 elif is_dask_dataframe(df): 113 result = dask_dataframe_to_pandas(df) --> 114 elif is_cudf_dataframe(df) or is_cudf_series(df): 115 result = cudf_to_pandas(df) 116 elif is_dask_cudf_dataframe(df): ~\Documents\Optimus\optimus\helpers\check.py in is_cudf_dataframe(value) 72 73 def is_cudf_dataframe(value): ---> 74 from cudf.core import DataFrame as CUDFDataFrame 75 return isinstance(value, CUDFDataFrame) 76 ModuleNotFoundError: No module named 'cudf'
df.cols.year("birth", "%Y/%m/%d")
distributed.worker - WARNING - Compute Failed Function: getitem args: ( name id nametype recclass mass (g) fall \ 0 Aachen 1 Valid L5 21 Fell 1 Aarhus 2 Valid H6 720 Fell 2 Abee 6 Valid EH4 107000 Fell 3 Acapulco 10 Valid Acapulcoite 1914 Fell 4 Achiras 370 Valid L6 780 Fell ... ... ... ... ... ... ... 45711 Zillah 002 31356 Valid Eucrite 172 Found 45712 Zinder 30409 Valid Pallasite, ungrouped 46 Found 45713 Zlin 30410 Valid H4 3.3 Found 45714 Zubkovsky 31357 Valid L6 2167 Found 45715 Zulu Queen 30414 Valid L3.7 200 Found year reclat reclong \ 0 01/01/1880 12:00:00 AM 50.77500 kwargs: {} Exception: KeyError('birth')
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) ~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 2890 try: -> 2891 return self._engine.get_loc(casted_key) 2892 except KeyError as err: pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'birth' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) <ipython-input-20-1b8355d304a6> in <module> ----> 1 df.cols.year("birth", "%Y/%m/%d") ~\Documents\Optimus\optimus\engines\base\columns.py in year(self, input_cols, format, output_cols) 1220 1221 return self.apply(input_cols, F().year, args=format, output_cols=output_cols, meta_action=Actions.YEAR.value, -> 1222 mode="pandas", set_index=True) 1223 1224 def month(self, input_cols, format=None, output_cols=None): ~\Documents\Optimus\optimus\engines\base\columns.py in apply(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, mode, set_index, default) 208 delayed_parts = [dask.delayed(func)(part[input_col], *args) for part in partitions] 209 --> 210 kw_columns[output_col] = from_delayed(delayed_parts) 211 212 elif mode == "vectorized" or mode == "pandas": ~\Anaconda3\lib\site-packages\dask\dataframe\io\io.py in from_delayed(dfs, meta, divisions, prefix, verify_meta) 584 585 if meta is None: --> 586 meta = delayed(make_meta)(dfs[0]).compute() 587 else: 588 meta = make_meta(meta) ~\Anaconda3\lib\site-packages\dask\base.py in compute(self, **kwargs) 165 dask.base.compute 166 """ --> 167 (result,) = compute(self, traverse=False, **kwargs) 168 return result 169 ~\Anaconda3\lib\site-packages\dask\base.py in compute(*args, **kwargs) 450 postcomputes.append(x.__dask_postcompute__()) 451 --> 452 results = schedule(dsk, keys, **kwargs) 453 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)]) 454 ~\Anaconda3\lib\site-packages\distributed\client.py in get(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs) 2710 should_rejoin = False 2711 try: -> 2712 results = self.gather(packed, asynchronous=asynchronous, direct=direct) 2713 finally: 2714 for f in futures.values(): ~\Anaconda3\lib\site-packages\distributed\client.py in gather(self, futures, errors, direct, asynchronous) 1989 direct=direct, 1990 local_worker=local_worker, -> 1991 asynchronous=asynchronous, 1992 ) 1993 ~\Anaconda3\lib\site-packages\distributed\client.py in sync(self, func, asynchronous, callback_timeout, *args, **kwargs) 832 else: 833 return sync( --> 834 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs 835 ) 836 ~\Anaconda3\lib\site-packages\distributed\utils.py in sync(loop, func, callback_timeout, *args, **kwargs) 337 if error[0]: 338 typ, exc, tb = error[0] --> 339 raise exc.with_traceback(tb) 340 else: 341 return result[0] ~\Anaconda3\lib\site-packages\distributed\utils.py in f() 321 if callback_timeout is not None: 322 future = asyncio.wait_for(future, callback_timeout) --> 323 result[0] = yield future 324 except Exception as exc: 325 error[0] = sys.exc_info() ~\Anaconda3\lib\site-packages\tornado\gen.py in run(self) 733 734 try: --> 735 value = future.result() 736 except Exception: 737 exc_info = sys.exc_info() ~\Anaconda3\lib\site-packages\distributed\client.py in _gather(self, futures, errors, direct, local_worker) 1848 exc = CancelledError(key) 1849 else: -> 1850 raise exception.with_traceback(traceback) 1851 raise exc 1852 if errors == "skip": ~\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key) 2900 if self.columns.nlevels > 1: 2901 return self._getitem_multilevel(key) -> 2902 indexer = self.columns.get_loc(key) 2903 if is_integer(indexer): 2904 indexer = [indexer] ~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 2891 return self._engine.get_loc(casted_key) 2892 except KeyError as err: -> 2893 raise KeyError(key) from err 2894 2895 if tolerance is not None: KeyError: 'birth'
# df.cols.lower("firstName").data.head()
df.cols.lower("firstName").cols.upper("lastName").cols.lower().cols.left(input_cols="*", n=5).cols.right(input_cols="*", n=3).ext.display()
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) ~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 2890 try: -> 2891 return self._engine.get_loc(casted_key) 2892 except KeyError as err: pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'firstName' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) <ipython-input-21-a87903fce6c3> in <module> 1 # df.cols.lower("firstName").data.head() ----> 2 df.cols.lower("firstName").cols.upper("lastName").cols.lower().cols.left(input_cols="*", n=5).cols.right(input_cols="*", n=3).ext.display() ~\Documents\Optimus\optimus\engines\base\columns.py in lower(self, input_cols, output_cols) 1101 def lower(self, input_cols="*", output_cols=None): 1102 return self.apply(input_cols, F.lower, func_return_type=str, -> 1103 output_cols=output_cols, meta_action=Actions.LOWER.value, mode="vectorized") 1104 1105 def upper(self, input_cols="*", output_cols=None): ~\Documents\Optimus\optimus\engines\base\columns.py in apply(self, input_cols, func, func_return_type, args, func_type, when, filter_col_by_dtypes, output_cols, skip_output_cols_processing, meta_action, mode, set_index, default) 211 212 elif mode == "vectorized" or mode == "pandas": --> 213 _ddf = func(df[input_col], *args) 214 215 if is_dask_dataframe(_ddf): ~\Anaconda3\lib\site-packages\dask\dataframe\core.py in __getitem__(self, key) 3529 3530 # error is raised from pandas -> 3531 meta = self._meta[_extract_meta(key)] 3532 dsk = partitionwise_graph(operator.getitem, name, self, key) 3533 graph = HighLevelGraph.from_collections(name, dsk, dependencies=[self]) ~\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key) 2900 if self.columns.nlevels > 1: 2901 return self._getitem_multilevel(key) -> 2902 indexer = self.columns.get_loc(key) 2903 if is_integer(indexer): 2904 indexer = [indexer] ~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance) 2891 return self._engine.get_loc(casted_key) 2892 except KeyError as err: -> 2893 raise KeyError(key) from err 2894 2895 if tolerance is not None: KeyError: 'firstName'
df
id
1 (int64)
not nullable
|
firstName
2 (object)
not nullable
|
lastName
3 (object)
not nullable
|
billingId
4 (int64)
not nullable
|
product
5 (object)
not nullable
|
price
6 (int64)
not nullable
|
birth
7 (object)
not nullable
|
dummyCol
8 (object)
not nullable
|
---|---|---|---|---|---|---|---|
1
|
Luis
|
Alvarez$$%!
|
123
|
Cake
|
10
|
1980/07/07
|
never
|
2
|
André
|
Ampère
|
423
|
piza
|
8
|
1950/07/08
|
gonna
|
3
|
NiELS
|
Böhr//((%%
|
551
|
pizza
|
8
|
1990/07/09
|
give
|
4
|
PAUL
|
dirac$
|
521
|
pizza
|
8
|
1954/07/10
|
you
|
5
|
Albert
|
Einstein
|
634
|
pizza
|
8
|
1990/07/11
|
up
|
6
|
Galileo
|
⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
672
|
arepa
|
5
|
1930/08/12
|
never
|
7
|
CaRL
|
Ga%%%uss
|
323
|
taco
|
3
|
1970/07/13
|
gonna
|
8
|
David
|
H$$$ilbert
|
624
|
taaaccoo
|
3
|
1950/07/14
|
let
|
9
|
Johannes
|
KEPLER
|
735
|
taco
|
3
|
1920/04/22
|
you
|
10
|
JaMES
|
M$$ax%%well
|
875
|
taco
|
3
|
1923/03/12
|
down
|
<class 'optimus.new_optimus.DaskDataFrame'>
df.ext.display()
id
1 (int64)
not nullable
|
firstName
2 (object)
not nullable
|
lastName
3 (object)
not nullable
|
billingId
4 (int64)
not nullable
|
product
5 (object)
not nullable
|
price
6 (int64)
not nullable
|
birth
7 (object)
not nullable
|
dummyCol
8 (object)
not nullable
|
---|---|---|---|---|---|---|---|
1
|
Luis
|
Alvarez$$%!
|
123
|
Cake
|
10
|
1980/07/07
|
never
|
2
|
André
|
Ampère
|
423
|
piza
|
8
|
1950/07/08
|
gonna
|
3
|
NiELS
|
Böhr//((%%
|
551
|
pizza
|
8
|
1990/07/09
|
give
|
4
|
PAUL
|
dirac$
|
521
|
pizza
|
8
|
1954/07/10
|
you
|
5
|
Albert
|
Einstein
|
634
|
pizza
|
8
|
1990/07/11
|
up
|
6
|
Galileo
|
⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
672
|
arepa
|
5
|
1930/08/12
|
never
|
7
|
CaRL
|
Ga%%%uss
|
323
|
taco
|
3
|
1970/07/13
|
gonna
|
8
|
David
|
H$$$ilbert
|
624
|
taaaccoo
|
3
|
1950/07/14
|
let
|
9
|
Johannes
|
KEPLER
|
735
|
taco
|
3
|
1920/04/22
|
you
|
10
|
JaMES
|
M$$ax%%well
|
875
|
taco
|
3
|
1923/03/12
|
down
|
from optimus import OptimusDaskDataframe
odd = OptimusDaskDataframe(df)
# odd.cols.names()
print(type(odd))
odd.cols.lower("lastName").cols.upper("lastName").ext.display()
# .cols.upper("lastName").df.head()
# odd.cols.proper("product").compute()
# odd.cols.replace("product","C","c").compute()
odd.ext.profile()
df <class 'optimus.engines.dask.extension.ext.<locals>.Ext'> ['__abstractmethods__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__slots__', '__str__', '__subclasshook__', '__weakref__', '_abc_impl', '_name', 'buffer_json', 'buffer_window', 'cache', 'calculate_cols_to_profile', 'cast_and_profile', 'compute', 'create_id', 'debug', 'delayed', 'df', 'display', 'export', 'get_buffer', 'get_name', 'head', 'is_cached', 'melt', 'optimize', 'partitioner', 'partitions', 'pivot', 'profile', 'query', 'repartition', 'reset', 'run', 'sample', 'send', 'set_buffer', 'set_name', 'show', 'size', 'stratified_sample', 'table', 'table_html', 'table_image', 'to_delayed', 'to_dict', 'to_json', 'to_pandas']
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-15-edd857bf4cea> in <module> ----> 1 odd.ext.profile() ~\Documents\Optimus\optimus\engines\base\extension.py in profile(self, columns, bins, output, flush, size) 593 print("df",type(self),dir(self)) 594 if flush is False: --> 595 cols_to_profile = df.ext.calculate_cols_to_profile(df, columns) 596 else: 597 cols_to_profile = parse_columns(df, columns) ~\Anaconda3\lib\site-packages\dask\dataframe\core.py in __getattr__(self, key) 3606 return self[key] 3607 else: -> 3608 raise AttributeError("'DataFrame' object has no attribute %r" % key) 3609 3610 def __dir__(self): AttributeError: 'DataFrame' object has no attribute 'ext'
odd.cols.select().rows.limit(10)
odd.cols.select().rows.limit(10).df
self <optimus.engines.dask.rows.rows.<locals>.Rows object at 0x000001D44B5EC6C8>
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 2 | André | Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
2 | 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
3 | 4 | PAUL | dirac$ | 521 | pizza | 8 | 1954/07/10 | you |
4 | 5 | Albert | Einstein | 634 | pizza | 8 | 1990/07/11 | up |
5 | 6 | Galileo | GALiLEI | 672 | arepa | 5 | 1930/08/12 | never |
6 | 7 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna |
7 | 8 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let |
8 | 9 | Johannes | KEPLER | 735 | taco | 3 | 1920/04/22 | you |
9 | 10 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down |
import pandas as pd
df = pd.read_csv("data/foo.csv")
# df = "dataframe"
a = A(df)
# a.cols.replace().cols.replace()
# a.cols.get_meta()
# a.meta.get()
# a.cols.split().cols.replace()
# a.cols.split("firstName","i").df
a.cols.lower("firstName").cols.upper("firstName")
# a.cols.mix("firstName")
a.meta.set_value(1)
a.meta.set_value(2)
a.meta.set_value(3)
b = a
print(b.meta.get_value())
print(b.data)
Cols init Cols init [1, 2, 3] id firstName lastName billingId product \ 0 1 LUIS Alvarez$$%! 123 Cake 1 2 ANDRÉ Ampère 423 piza 2 3 NIELS Böhr//((%% 551 pizza 3 4 PAUL dirac$ 521 pizza 4 5 ALBERT Einstein 634 pizza 5 6 GALILEO GALiLEI 672 arepa 6 7 CARL Ga%%%uss 323 taco 7 8 DAVID H$$$ilbert 624 taaaccoo 8 9 JOHANNES KEPLER 735 taco 9 10 JAMES M$$ax%%well 875 taco 10 11 ISAAC Newton 992 pasta 11 12 EMMY%% Nöether$ 234 pasta 12 13 MAX!!! Planck!!! 111 hamburguer 13 14 FRED Hoy&&&le 553 pizzza 14 15 ((( HEINRICH ))))) Hertz 116 pizza 15 16 WILLIAM Gilbert### 886 BEER 16 17 MARIE CURIE 912 Rice 17 18 ARTHUR COM%%%pton 812 110790 18 19 JAMES Chadwick 467 NaN price birth dummyCol 0 10 1980/07/07 never 1 8 1950/07/08 gonna 2 8 1990/07/09 give 3 8 1954/07/10 you 4 8 1990/07/11 up 5 5 1930/08/12 never 6 3 1970/07/13 gonna 7 3 1950/07/14 let 8 3 1920/04/22 you 9 3 1923/03/12 down 10 9 1999/02/15 never 11 9 1993/12/08 gonna 12 4 1994/01/04 run 13 8 1997/06/27 around 14 8 1956/11/30 and 15 2 1958/03/26 desert 16 1 2000/03/22 you 17 5 1899/01/01 # 18 10 1921/05/03 #
op = Optimus("dask", n_workers=1, threads_per_worker=8, silence_logs=30, memory_limit="1G", comm=True)
# df1 = op.load.file("data/github_stargazers.json").ext.cache()
df = op.load.file("data/Meteorite_Landings.csv").ext.cache()
df = df.ext.repartition(8).ext.cache()
# import pandas as pd
# import numpy as np
# df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'], columns=['one', 'two', 'three'])
# df.loc[1,2] =""
# df.astype(str)
df.cols.frequency("mass (g)")
{'frequency': {'mass (g)': {'values': [{'value': '1.3', 'count': 171}, {'value': '1.2', 'count': 140}, {'value': '1.4', 'count': 138}, {'value': '', 'count': 131}, {'value': '2.1', 'count': 130}, {'value': '2.4', 'count': 126}, {'value': '1.6', 'count': 120}, {'value': '0.5', 'count': 119}, {'value': '1.1', 'count': 116}, {'value': '3.8', 'count': 114}, {'value': '0.7', 'count': 111}, {'value': '1.5', 'count': 111}, {'value': '3.2', 'count': 109}, {'value': '3.1', 'count': 109}, {'value': '1.7', 'count': 109}, {'value': '3', 'count': 108}, {'value': '0.9', 'count': 108}, {'value': '0.6', 'count': 108}, {'value': '0.8', 'count': 107}, {'value': '1.8', 'count': 104}, {'value': '2.5', 'count': 103}, {'value': '2.7', 'count': 102}, {'value': '3.6', 'count': 96}, {'value': '2', 'count': 96}, {'value': '1', 'count': 96}, {'value': '4.2', 'count': 95}, {'value': '2.8', 'count': 93}, {'value': '2.9', 'count': 93}, {'value': '2.2', 'count': 92}, {'value': '2.6', 'count': 91}, {'value': '3.3', 'count': 88}, {'value': '4.6', 'count': 86}, {'value': '1.9', 'count': 86}]}}}
# requests.get(f"https://api.github.com/users/argenisleon").json()
print(result)
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-11-b2d61f7d3118> in <module> 1 # requests.get(f"https://api.github.com/users/argenisleon").json() ----> 2 print(result) NameError: name 'result' is not defined
count=0
#!pip install PyGithub
import time
import requests
from github import Github
g = Github("argenisleon", "Uz7J4O9wmCM4")
result =[]
def _request(cell):
time.sleep(0.1)
result.append({"login":g.get_user(cell).login,"bio":g.get_user(cell).bio})
print(len(result))
df1.cols.select("login").compute()["login"].apply(_request)
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--------------------------------------------------------------------------- Error Traceback (most recent call last) <ipython-input-87-ecff4903dc74> in <module> 15 16 ---> 17 df1.cols.select("login").compute()["login"].apply(_request) 18 ~\Anaconda3\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds) 4198 else: 4199 values = self.astype(object)._values -> 4200 mapped = lib.map_infer(values, f, convert=convert_dtype) 4201 4202 if len(mapped) and isinstance(mapped[0], Series): pandas\_libs\lib.pyx in pandas._libs.lib.map_infer() <ipython-input-87-ecff4903dc74> in _request(cell) 11 def _request(cell): 12 time.sleep(0.1) ---> 13 result.append({"login":g.get_user(cell).login,"bio":g.get_user(cell).bio}) 14 print(len(result)) 15 ~\Anaconda3\lib\site-packages\github\MainClass.py in get_user(self, login) 270 else: 271 headers, data = self.__requester.requestJsonAndCheck( --> 272 "GET", "/users/" + login 273 ) 274 return github.NamedUser.NamedUser( ~\Anaconda3\lib\site-packages\github\Requester.py in requestJsonAndCheck(self, verb, url, parameters, headers, input) 317 return self.__check( 318 *self.requestJson( --> 319 verb, url, parameters, headers, input, self.__customConnection(url) 320 ) 321 ) ~\Anaconda3\lib\site-packages\github\Requester.py in requestJson(self, verb, url, parameters, headers, input, cnx) 408 return "application/json", json.dumps(input) 409 --> 410 return self.__requestEncode(cnx, verb, url, parameters, headers, input, encode) 411 412 def requestMultipart( ~\Anaconda3\lib\site-packages\github\Requester.py in __requestEncode(self, cnx, verb, url, parameters, requestHeaders, input, encode) 485 486 status, responseHeaders, output = self.__requestRaw( --> 487 cnx, verb, url, requestHeaders, encoded_input 488 ) 489 ~\Anaconda3\lib\site-packages\github\Requester.py in __requestRaw(self, cnx, verb, url, requestHeaders, input) 511 cnx = self.__createConnection() 512 cnx.request(verb, url, input, requestHeaders) --> 513 response = cnx.getresponse() 514 515 status = response.status ~\Anaconda3\lib\site-packages\github\Requester.py in getresponse(self) 114 timeout=self.timeout, 115 verify=self.verify, --> 116 allow_redirects=False, 117 ) 118 return RequestsResponse(r) ~\AppData\Roaming\Python\Python37\site-packages\requests\sessions.py in get(self, url, **kwargs) 541 542 kwargs.setdefault('allow_redirects', True) --> 543 return self.request('GET', url, **kwargs) 544 545 def options(self, url, **kwargs): ~\AppData\Roaming\Python\Python37\site-packages\requests\sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json) 528 } 529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs) 531 532 return resp ~\AppData\Roaming\Python\Python37\site-packages\requests\sessions.py in send(self, request, **kwargs) 641 642 # Send the request --> 643 r = adapter.send(request, **kwargs) 644 645 # Total elapsed time of the request (approximately) ~\AppData\Roaming\Python\Python37\site-packages\requests\adapters.py in send(self, request, stream, timeout, verify, cert, proxies) 447 decode_content=False, 448 retries=self.max_retries, --> 449 timeout=timeout 450 ) 451 ~\Anaconda3\lib\site-packages\urllib3\connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw) 598 timeout=timeout_obj, 599 body=body, headers=headers, --> 600 chunked=chunked) 601 602 # If we're going to release the connection in ``finally:``, then ~\Anaconda3\lib\site-packages\urllib3\connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 382 # Remove the TypeError from the exception chain in Python 3; 383 # otherwise it looks like a programming error was the cause. --> 384 six.raise_from(e, None) 385 except (SocketTimeout, BaseSSLError, SocketError) as e: 386 self._raise_timeout(err=e, url=url, timeout_value=read_timeout) ~\Anaconda3\lib\site-packages\urllib3\packages\six.py in raise_from(value, from_value) ~\Anaconda3\lib\site-packages\urllib3\connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw) 378 except TypeError: # Python 3 379 try: --> 380 httplib_response = conn.getresponse() 381 except Exception as e: 382 # Remove the TypeError from the exception chain in Python 3; ~\Anaconda3\lib\http\client.py in getresponse(self) 1342 try: 1343 try: -> 1344 response.begin() 1345 except ConnectionError: 1346 self.close() ~\Anaconda3\lib\http\client.py in begin(self) 304 # read until we get a non-100 response 305 while True: --> 306 version, status, reason = self._read_status() 307 if status != CONTINUE: 308 break ~\Anaconda3\lib\http\client.py in _read_status(self) 265 266 def _read_status(self): --> 267 line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1") 268 if len(line) > _MAXLINE: 269 raise LineTooLong("status line") ~\Anaconda3\lib\socket.py in readinto(self, b) 587 while True: 588 try: --> 589 return self._sock.recv_into(b) 590 except timeout: 591 self._timeout_occurred = True ~\Anaconda3\lib\site-packages\urllib3\contrib\pyopenssl.py in recv_into(self, *args, **kwargs) 310 raise timeout('The read operation timed out') 311 else: --> 312 return self.recv_into(*args, **kwargs) 313 314 def settimeout(self, timeout): ~\Anaconda3\lib\site-packages\urllib3\contrib\pyopenssl.py in recv_into(self, *args, **kwargs) 295 def recv_into(self, *args, **kwargs): 296 try: --> 297 return self.connection.recv_into(*args, **kwargs) 298 except OpenSSL.SSL.SysCallError as e: 299 if self.suppress_ragged_eofs and e.args == (-1, 'Unexpected EOF'): ~\Anaconda3\lib\site-packages\OpenSSL\SSL.py in recv_into(self, buffer, nbytes, flags) 1838 else: 1839 result = _lib.SSL_read(self._ssl, buf, nbytes) -> 1840 self._raise_ssl_error(self._ssl, result) 1841 1842 # This strange line is all to avoid a memory copy. The buffer protocol ~\Anaconda3\lib\site-packages\OpenSSL\SSL.py in _raise_ssl_error(self, ssl, result) 1669 pass 1670 else: -> 1671 _raise_current_error() 1672 1673 def get_context(self): ~\Anaconda3\lib\site-packages\OpenSSL\_util.py in exception_from_error_queue(exception_type) 52 text(lib.ERR_reason_error_string(error)))) 53 ---> 54 raise exception_type(errors) 55 56 Error: [('SSL routines', 'ssl3_read_bytes', 'sslv3 alert bad record mac')]
import json
import pandas as pd
from io import StringIO
j =json.dumps(result)
pd.read_json(StringIO(j)).to_csv("github_profile.csv")
pdf = pd.read_json(StringIO(j))
pdf.dropna()
login | bio | |
---|---|---|
1 | hugounavez | Electrical Engineer. MSc in Biomedical Enginee... |
2 | juliomonagas | UX/UI Designer, Frontend Developer, Graphic De... |
3 | MarcoPortillo | I like to code, that's all. Venezuelan. |
4 | Bonsanto | I studied Electrical and Computer Engineering.... |
6 | vinta | I failed the Turing Test. |
... | ... | ... |
1009 | ikedaosushi | I love 🍣, ♨️and ✈️. |
1010 | shenxiangzhuang | Life isn’t about waiting for the storm to pass... |
1011 | devendraap | Avid programmer |
1013 | rsohlot | Big data + ML |
1015 | kitoki | i want to start my adventure, new kind challen... |
456 rows × 2 columns
def print_full(x):
pd.set_option('display.max_rows', len(x))
print(x)
pd.reset_option('display.max_rows')
ppdf = pdf.dropna(subset=["bio"])
print_full(pdf)
# print_full(pdf["bio"].str.lower())
login \ 1 hugounavez 2 juliomonagas 3 MarcoPortillo 4 Bonsanto 6 vinta 7 pythonjokeun 9 belgacea 11 emptyr1 12 bigdatavik 13 FavioVazquez 14 tspannhw 16 papagunit 18 dustyny 19 Diego999 21 mutabazi 27 MiguelPeralvo 31 ChrisCompton 33 leoluyi 36 razeone 37 iht 38 anooptp 39 zaratsian 42 silverstone1903 43 MrPowers 45 manuzhang 46 donelianc 49 talegari 51 daniel-sali 53 mdagost 54 esafak 56 travisbrady 57 gjreda 58 adriantorrie 60 fly51fly 68 uptonking 69 luismayta 73 denisarnaud 74 bveliqi 76 dazajuandaniel 78 datorresb 80 ramanan-r 82 vidalalcala 84 DuyguA 85 NumPoet 87 EKami 88 sbarman-mi9 90 Jaimeardp 91 danielgrijalva 94 evmisu 95 aayushshah15 96 floresfdev 97 diansheng92 98 ibrahimsharaf 99 hahmed988 101 hugounavez 102 juliomonagas 103 MarcoPortillo 104 Bonsanto 106 vinta 107 pythonjokeun 109 belgacea 111 emptyr1 112 bigdatavik 113 FavioVazquez 114 tspannhw 116 papagunit 118 dustyny 119 Diego999 121 mutabazi 127 MiguelPeralvo 131 ChrisCompton 133 leoluyi 136 razeone 137 iht 138 anooptp 139 zaratsian 142 silverstone1903 143 MrPowers 145 manuzhang 146 donelianc 149 talegari 151 daniel-sali 153 mdagost 154 esafak 156 travisbrady 157 gjreda 158 adriantorrie 160 fly51fly 168 uptonking 169 luismayta 173 denisarnaud 174 bveliqi 176 dazajuandaniel 178 datorresb 180 ramanan-r 182 vidalalcala 184 DuyguA 185 NumPoet 187 EKami 188 sbarman-mi9 190 Jaimeardp 191 danielgrijalva 194 evmisu 195 aayushshah15 196 floresfdev 197 diansheng92 198 ibrahimsharaf 199 hahmed988 201 anktplwl91 202 seanreed1111 205 ohuarcaya 206 elchulito88 207 astrojuanlu 208 avannaldas 209 shalabhsingh 211 nightscape 212 kcnickerson 213 linekin 214 acrozes 215 shubhampachori12110095 216 ventouris 218 shukwong 219 supern92 220 JamieShelley 221 mmortazavi 222 miike 225 qiulin 226 yuseferi 228 paulochf 229 WI-KIWI 232 franzantejm 234 thisisashukla 235 t3hSeb 238 GCPBigData 240 Guillermogsjc 247 satyaborg 248 hzitoun 257 jeremyjordan 258 pratos 259 treymerkley 268 wguillema 270 gmartinezramirez-old 272 alexrigler 273 akasranjan005 275 nick5435 278 dgopsq 280 thdaraujo 282 vutsalsinghal 283 fenandosr 285 bilalaslamseattle 287 rcitterio 288 suvayu 289 jeanmidevacc 290 spaghettifunk 293 Vjais 296 jamesacampbell 298 plliao 303 yutingyw 304 smrjan 306 Neuw84 308 ozcanyarimdunya 310 ronaldfalcao 311 liangdabiao 314 oliviernguyenquoc 315 o9812 317 david-gero-cp 321 kevinsegal 323 rbramwell 324 clarksun 325 praveensastry 326 marius92mc 328 Gaarv 337 chaoyue729 339 masumsoft 342 ShreyasFadnavis 343 bitcoinfaf 344 ishantbansal 346 mpolatcan 347 hussainasghar 351 nfsrules 353 raank 355 5hirish 358 xuexuemiao 360 asmith26 361 naushadS 362 bnerDY 364 mentecuantica 365 mithunmanohar 366 VictorDataEngineer 368 iflores12 369 zmughal 370 hughcameron 372 ptmaroct 373 githubcyc 376 beautifulNow1992 380 gaving 384 ozlerhakan 386 javogranda 389 PyarakaSrikanth 392 etng 394 Florents-Tselai 396 fjsj 399 thnery 405 gperakis 409 jake-aft 410 rosscdh 412 BigDataRPG 415 joaorafaelm 417 pbamotra 422 timkofu 424 eyadsibai 426 levytskyi 427 wenzhihong2003 428 pingjiang 429 emptymalei 436 NP-compete 437 noleto 438 drkostas 442 gmiretti 443 ereynrs 444 bgarcial 447 jkgjoye 448 rogues-gallery 450 radu941208 451 aoelvp94 452 shafaypro 454 sinemhasircioglu 455 upendrak 456 valware 457 marioBD 458 shravik 460 eryk 465 agladstein 466 carterrees 467 andreaschandra 469 TsukiZombina 470 manugarri 472 carlosevi94 475 antoniomdk 477 junqueira 481 gregwchase 482 Sehaba95 485 devendrasr 487 luo1994 492 vivek2319 493 munael 495 alimasri 496 hskang9 498 Aymericr 500 renatocf 502 JoseCage 503 abenpy 505 absognety 507 rajagurunath 508 Vitiell0 510 michaelmior 512 Aylr 513 qytz 514 joelkim 520 hmelberg 522 josefigueredo 524 pavank 526 johndpope 527 LiutongZhou 528 leslyarun 529 timetobye 530 rdempsey 533 markjbaker 534 MCaviezel 535 huyhoang17 537 cjen07 545 shadowkun 550 otrenav 551 HackyRoot 552 quillan86 553 souo 554 zhenyulin 556 danielsobrado 557 harryprince 559 gavinzhu1127 563 HaithamMaya 564 lw334 567 dbalabka 568 nicolasfguillaume 572 brandhaug 573 franchb 577 vlukiyanov 578 Y-oHr-N 590 hichMEN 591 bramvds 592 wuya2357 595 sylviaxxy 597 mzntaka0 598 foxan 600 capncodewash 602 JBalloonist 603 mohitatgithub 604 mgc26 607 aljabr0 609 bahattincinic 612 foowaa 613 tomstesco 615 luiscruz 617 bavuongco10 618 ArchTaqi 620 lujin77 624 fyrk 626 zkn365 628 prashant-kikani 629 niakki 630 greird 631 spokerman12 632 TankMasterRL 636 pratikbarjatya 643 DataEngg 646 zhiruiwang 647 kuoteng 648 apsamuel 650 s0kil 651 Siva227 652 senthilnayagam 658 bsc-ampiant 659 OsirisXTLS 667 settinghead 674 siansiansu 677 carsondahlberg 679 zyuanlim 685 giacomolanciano 689 jm-gutierrezh 693 jhermann 694 baifengbai 695 djfan 696 garciacaleb904 699 viktorchukhantsev 701 sidhu177 706 Skyblueballykid 709 nyartsgnaw 713 burness 714 STHSF 717 RafaelZarate 720 MariusMez 721 JLuisRojas 725 RodolfoFerro 728 da-edra 729 jdayllon 731 krzjoa 736 shaboi 737 arpit1997 738 crew102 740 TomMonkeyMan 742 asdf247 750 pauldevos 751 elwarren 753 brunowego 757 JamesLoong 762 pbadeer 763 RodrigoVillatoro 764 jhuangtw-dev 765 umer7 767 sainide 772 analyticalmonk 778 javiergodoy 789 cvd 790 muhammetenes 792 wasi0013 793 wieshka 797 PraneethKarnena 799 eyaltrabelsi 803 josephwinston 806 Frans06 811 senthilmk 815 sarikayamerts 816 ariosramirez 817 joseangel-sc 818 huaji1992 819 jospaqui 823 hmatalonga 826 shauryashaurya 827 p-gourseaud 829 bryanyang0528 831 diegotony 834 eschizoid 835 adamnietopfizer 843 foxgem 844 argenisleon 850 dblue0406 851 georgeblu1 852 volpatto 854 neeksor 856 liuzhenqi77 859 souradeepta 861 rachaeltay 863 nachoalvarez 864 salotz 867 Shifu-Engineer 869 r4um 870 ettorerizza 875 mcolebrook 876 aleksihakli 880 ecogit 883 alfonso777 885 wtznc 886 franccesco 888 vivekmishra369 892 rohithsrinivaas 897 dogrdon 898 dentarg 899 RainFung 900 sarikayamehmet 903 sogasg 908 yennanliu 910 bt3gl 911 NishantBhavsar 913 JustEdro 914 williamsne2 915 hujinghaoabcd 922 deepdreamer0 925 andrassy 926 algunion 929 zephoon 931 rafpyprog 932 VirTek 934 shangzixie 941 ae86208 944 asears 948 DataBoyTX 951 hangtwenty 952 ParzivalWins 953 aliisakroe 960 ALEXKIRNAS 961 truskovskiyk 962 J0hnG4lt 966 mjschock 967 gvvynplaine 971 pdeguzman96 974 rhymiz 975 adamnieto 981 ansegundo 982 philMarius 985 cnnrrss 988 The-Gupta 989 yangjue-han 991 ankur-gupta 993 atgmello 999 rschlaefli 1000 prabha6kar 1003 dcgithub 1004 stjordanis 1005 romanofficial 1006 SongshGeo 1008 LuiGGi629 1009 ikedaosushi 1010 shenxiangzhuang 1011 devendraap 1013 rsohlot 1015 kitoki bio 1 Electrical Engineer. MSc in Biomedical Enginee... 2 UX/UI Designer, Frontend Developer, Graphic De... 3 I like to code, that's all. Venezuelan. 4 I studied Electrical and Computer Engineering.... 6 I failed the Turing Test. 7 Data Engineer 9 TryHarder 11 Data Science Engineer 12 (graphs) - [:ARE] - > (everywhere) 13 Physicist and computational engineer. I have a... 14 DataFlow Engineer, Java developer, Cloud Anal... 16 Solutions Architect at DemandGen.\r\nMinister ... 18 I'm a polygot engineer who codes when he can. ... 19 Ph.D. Student in NLP & ML. Interests in artifi... 21 (big|small|open) #data #nerd ≡ #AI, #datascien... 27 Solutions Architect at Databricks 31 Health Informatics and Technology\r\n 33 Data Scientist, Engineer 36 I like the web, arts and traveling 37 Strategic Cloud Engineer @GoogleCloudPlatform 38 I am a Software Professional. I have a passion... 39 AI/Machine Learning Cloud Engineer @ Google. S... 42 Data Scientist & DevOps,\r\nB.Sc. & M.Sc. Stat... 43 Data engineer at prognos.ai. Like Scala, Spar... 45 Fault Tolerant; Eventually Consistent; Not Ava... 46 Data hacker (level 3). Actuary with mathematic... 49 Code and explorations in Statistical Machine L... 51 Data Engineer @ Alphacruncher 53 Director of Data Science @ShopRunner; \r\nForm... 54 Data scientist, machine learning engineer 56 Machine learning, whole page optimization, sea... 57 Machine Learning Engineer / Data Scientist 58 Likes AI, autonomous vehicles, and finance. Re... 60 新浪微博 @爱可可-爱生活 68 HelloWord 69 Hermit | Passionate Coder | Blockchain, IOTA, ... 73 Scientist, dreamer, data lover 74 Co-Founder & CTO @layer7ai 76 All things Data - Master of Information Techno... 78 I am a; Mathematical physicist, Data Scientist... 80 Big Data, NLP and Machine Learning enthusiast 82 Applied Mathematician working as a Data Scientist 84 Senior NLP developer, working on Conversationa... 85 An ordinary person that on a command line ever... 87 A freelancer passionate about everything revol... 88 Data Scientist | WPI Grad | \r\nFocused on Mac... 90 Apasionado por los datos me gusta poder verlos... 91 Freelancer @ Upwork 94 a data passionate from financial industry 95 software developer @cockroachdb. 96 Data scientist and Consultant in data science ... 97 Android Software Developer 98 NLP Research Engineer 99 A curious and detail oriented professional who... 101 Electrical Engineer. MSc in Biomedical Enginee... 102 UX/UI Designer, Frontend Developer, Graphic De... 103 I like to code, that's all. Venezuelan. 104 I studied Electrical and Computer Engineering.... 106 I failed the Turing Test. 107 Data Engineer 109 TryHarder 111 Data Science Engineer 112 (graphs) - [:ARE] - > (everywhere) 113 Physicist and computational engineer. I have a... 114 DataFlow Engineer, Java developer, Cloud Anal... 116 Solutions Architect at DemandGen.\r\nMinister ... 118 I'm a polygot engineer who codes when he can. ... 119 Ph.D. Student in NLP & ML. Interests in artifi... 121 (big|small|open) #data #nerd ≡ #AI, #datascien... 127 Solutions Architect at Databricks 131 Health Informatics and Technology\r\n 133 Data Scientist, Engineer 136 I like the web, arts and traveling 137 Strategic Cloud Engineer @GoogleCloudPlatform 138 I am a Software Professional. I have a passion... 139 AI/Machine Learning Cloud Engineer @ Google. S... 142 Data Scientist & DevOps,\r\nB.Sc. & M.Sc. Stat... 143 Data engineer at prognos.ai. Like Scala, Spar... 145 Fault Tolerant; Eventually Consistent; Not Ava... 146 Data hacker (level 3). Actuary with mathematic... 149 Code and explorations in Statistical Machine L... 151 Data Engineer @ Alphacruncher 153 Director of Data Science @ShopRunner; \r\nForm... 154 Data scientist, machine learning engineer 156 Machine learning, whole page optimization, sea... 157 Machine Learning Engineer / Data Scientist 158 Likes AI, autonomous vehicles, and finance. Re... 160 新浪微博 @爱可可-爱生活 168 HelloWord 169 Hermit | Passionate Coder | Blockchain, IOTA, ... 173 Scientist, dreamer, data lover 174 Co-Founder & CTO @layer7ai 176 All things Data - Master of Information Techno... 178 I am a; Mathematical physicist, Data Scientist... 180 Big Data, NLP and Machine Learning enthusiast 182 Applied Mathematician working as a Data Scientist 184 Senior NLP developer, working on Conversationa... 185 An ordinary person that on a command line ever... 187 A freelancer passionate about everything revol... 188 Data Scientist | WPI Grad | \r\nFocused on Mac... 190 Apasionado por los datos me gusta poder verlos... 191 Freelancer @ Upwork 194 a data passionate from financial industry 195 software developer @cockroachdb. 196 Data scientist and Consultant in data science ... 197 Android Software Developer 198 NLP Research Engineer 199 A curious and detail oriented professional who... 201 PostGrad in Machine Intelligence, love to expl... 202 I write picture books for kids about science a... 205 Bsc. Computer Science, UNI\r\n 206 Python Hacker!!! 207 Astrodynamics enthusiast, open source software... 208 abhijitannaldas.com 209 Previously Undergraduate at @IIT_Ropar. Into M... 211 Big fan of Functional Programming, Scala, Idri... 212 Maker, Coder, dTaz, Magniware, i6, OmersV, CDL... 213 doing enterprise software 214 CTO: Data Scientist / Big Data ; Mobile : iOS... 215 Learning to extract signal from noise. 216 Data Scientist @ Hattrick Ltd 218 Genomic Data Scientist 219 Playing with Random data. 220 Software developer at Linx Printing 221 A T-shaped data scientist passionate about dat... 222 @poplindata 225 A coder focus on big data 226 Senior BackEnd(Symfony,Drupal,Django) Engineer... 228 Data Science, Python, R, LaTeX 229 Data Scientist, Python & open source evangelist 232 Systems Engineer - BI & Big Data 234 ML & Web Enthusiast | CSRE, IIT Bombay | KNIT ... 235 Data Scientist (Milan, ITA) 238 Data Scientist | (56) 990542812 | web2ajax@gma... 240 A monkey hitting keys at random on a keyboard... 247 Coding is an art and just another conduit for ... 248 Machine Learning Engineer | Deep Learning & Te... 257 Machine learning engineer. Broadly curious. \r... 258 Learning to build\r\n 259 DevOps Engineer. He/Him 268 I work with models ;) 270 This is a old account. 272 Founder/CEO of Raedan Technologies. Supporting... 273 Full stack Python Developer | Machine Learning... 275 Mathematics Graduate Student\r\nUniversity of ... 278 Full stack engineer @efficiam 280 Software Engineer, interested in Artificial In... 282 The more I learn, the more I realize how much ... 283 \r\n mx\r\n 285 Databricks. Previously DocuSign, Appuri and Mi... 287 Desarrollador web, Scrum y Devops. 288 A former experimental particle physicist @ CER... 289 Data scientist and R&D engineer who like to te... 290 I love le coding so much 293 I am grad student in CS dept at University of ... 296 Artist working in Technology.\r\n 298 Data Scientist, Data Engineer, Software Engineer 303 implementations and experiments during my leis... 304 AI/ML researcher 306 Artificial Intelligence team leader at Ikerlan 308 A python developer. 310 Product Owner! Sempre no meio de clientes e de... 311 programmer in shenzhen\r\nhttps://www.liangdab... 314 Data Scientist @ L'Oréal 315 Use the Source, Luke! 317 also @davidpgero 321 Interstellar Real Estate Developer 323 Senior DevOps Engineer at Pythian :: Intereste... 324 programmer, father. 325 FullStack/Cloud/SRE, open source contributor a... 326 Computer Science Student, Former Data Engineer... 328 Developer of data applications / pipelines in ... 337 DL&ML with awesome products 339 Co-Founder, CTO at CodeMarshal 342 \r\n PhD Student IU Bloomington\r\n 343 bitcoinfaf = BitcoinFastasFuk 344 Computer Vision Engineer 346 Senior Data Engineer 347 Big Data Enthusiastic 351 Deep Learner | Machine Learner 353 Full Stack Developer; PHP, Python and NodeJS 355 Automating Automation 358 Director of Big Data Engineering 360 Love Python | Distributed Computing | Data Sci... 361 Data Science | Machine Learning | Python 362 @UQComputingSociety, Australia\r\n@UoMCS, Unit... 364 From Russia with love to Reverse Engineering, ... 365 Data Engineer | Python Dev 366 DataEngineer/DataAnalyst 368 Currently working in political tech but I'd ra... 369 biomedical image analysis (@CBL-ORION), scient... 370 Data @lexerdev 372 React Native and React JS developer 373 fintech 376 代码搬运工,进入了一个新的层次,庖丁解牛,从各种开源框架中提取设计,提取核心代码算法,搬运参... 380 comin' to you 1000bpm 384 Search Engine Developer at n11.com. Duke's Cho... 386 Data Manager, Customer Cluster 389 I m Data science consultant, ML/DL practitione... 392 PHP==>Python==>Go==>Anything Possible! 394 Data Strategist & Engineer • Think, Code, Read... 396 Partner at @vintasoftware 399 I'm a software engineer from Recife who likes ... 405 Business-minded data scientist with a demonstr... 409 Stuff with Data, Products, Agile, and Block-Ch... 410 2x Startup founder, Software Engineer, Dev-ops... 412 I'm just ordinary Data Scientist who love in A... 415 \r\n https://joaorafaelm.github.io\r\n 417 pythonista, machine learning, numbers, basicml... 422 Software Engineer 424 Software engineer building reliable machine le... 426 Just a regular guy. 427 java&python&golang程序猿一枚 428 站在巨人的肩膀上开发卓越软件 429 I make data speak in Germany. Data Scientist, ... 436 The full "stuck" Developer | The Dev-Oops Engi... 437 I'm a data scientist and software engineer wit... 438 I enjoy making Bots and using Machine Learning... 442 Computer Scientist as Data Scientist / Enginee... 443 Researcher. Professor. Data Architect. 444 Guy in constant learning. 447 Ciencia de Datos 448 I'm a loner, Dottie. A rebel. 450 ML/NLP//Energy Efficiency/ Market 451 Data Engineer at Mutt Data 452 Muhammad Shafay Amjad\r\nProgressing as Data ... 454 Computer engineer 455 I am a Data Scientist at Greenlight Bioscience... 456 Computer Science - UNMSM\r\n 457 Candidato a Maestro de Ciencias de Datos, Inge... 458 Computer Science graduate student 460 微信公众号: pyquant 465 Postdoc in Genetics.\r\nMachine learning in po... 466 Sr. Manager, Data Science 467 I love NLP, ASR, and Deep Learning | Indonesia 469 I'm a MSc in Computer Science and a Chemistry ... 470 Send bitcoin plz https://manugarri.bit.ac/ 472 Data Scientist.\r\n\r\nPython Lover & Developer. 475 Strathclyde University Postgraduate Student.\r... 477 Software engineers: ... 481 Machine learning engineer in pursuit of creati... 482 Junior Machine Learning Researcher. I am inter... 485 Building something awesome @47Billion , @gotuk... 487 no bb,just do it 492 Data Scientist 💡 Curious, 🔍 skeptic, humble, ... 493 Eternally confuzzled. CS apprentice, currently... 495 I am a Research and Development Engineer dedic... 496 🤷 498 Co-founder, CTO at Ledger Investing 500 Principal Machine Learning Engineer @elo7 | Ma... 502 Showing to the world how Open Source can chang... 503 Master Student at NYU Center for DataScience\r... 505 I am a Data enthusiast and a Coder. Interested... 507 Pythonist ,Data Engineer, Deep learning 508 Founder at Cooklist and Handground 510 \r\n Assistant Professor in the Department ... 512 Data Scientist. Engineer. Maker. Endlessly cur... 513 云在青天水在瓶 514 Quant, Data Scientist 520 Health economist, analyzing data from hospital... 522 Developer since forever. Always learning and t... 524 I work with Data 526 swift / crypto / ai / ml / quant 527 Data Scientist @ AWS Machine Learning Solution... 528 For Christ - NLP/AI/Deep Learning/Data Science 529 Jr.Data Analyst // 데이터 분석 및 개발 시작 - 2018년 06월 530 I build machine learning systems, teach data s... 533 nice kid 534 I love data! 535 #HUST #SAMI #BTN #PYMI #SUN* #PHH \r\nhttps:... 537 Christian 545 https://shadowkun.github.io/ 550 I help data-intensive companies be smarter and... 551 A dynamic Data Scientist with expertise in Mac... 552 Data Scientist 553 Big data hacker, do stuff with python and scal... 554 λ => { :ok, data |> model.predict } 556 Worked in New York, London, Tokyo, Madrid and ... 557 A Simmer/Keras/Stan/Spark player 559 Data Scientist | CRM Expert | Digital Analytic... 563 Senior Data Scientist @Kumanu 564 Looking for a spacetime singularity 567 M.Sc. in Computer Science, +10 years experienc... 568 Independent Python Data Engineer 572 Full-stack Software Engineer and Entrepreneur ... 573 Go at work 577 Data-focused software engineer. 578 ML Researcher 590 Innovation Addict \r\nIoT, Big Data, Cloud & F... 591 Bioinformatician in Life Sciences 592 i can't tell 595 Data Engineer, former Data Scientist 597 Machine Learning Engineer\r\nSpeech Processing... 598 Data Engineer @smartnews // ex Software Engine... 600 Data science, ML, NLP, and more. 602 Data Scientist 603 Data Science Enthusiast 604 Otolaryngologist, Author & Machine Learning Re... 607 Freelancer | Math | Machine Learning | C++ | P... 609 Software Developer, Computer Engineer, Travel,... 612 Frustra fit per plura quod potest fieri per pa... 613 Data scientist 615 Postdoc 617 Your regular dev, talk much, code much, hide i... 618 Tᴇᴄʜ ᴇɴᴛʜᴜsɪᴀsᴛɪᴄ ᴡʜᴏ ʟᴏᴠᴇs ᴛᴏ ʟᴇᴀʀɴ ɴᴇᴡ ᴛʜɪɴɢs! 620 Data Mining Engineer 624 Games, AI, and random widgetry. 626 learn and practice 628 Data scientist | Kaggle 1x Master, 2x Expert 629 Data Science Engineer 630 I'm a Product Manager from Paris 🥐, now living... 631 Information is finite because attention span i... 632 Electronic engineer, with an interest in embed... 636 ML Practitioner | Data Science | Data Engineer... 643 Researcher of Database and on Data 646 Senior Data Scientist at BCG 647 still not a software developer 648 I'm an engineer 650 Ψ ∞ Φ - μαθητής 651 Data Scientist at Quadratyx, Hyderabad 652 Blockchain and Crypto Currency Consultant and ... 658 Boston Scientific account 659 Long time SysAdmin, aspiring DevOps with a hin... 667 FREEDOMIZE ALL TEH CODE 674 Weeks of coding can save you hours of planning. 677 Data Scientist as Wells Fargo 679 Data Scientist 685 Computer Engineer | Ph.D. Student in Data Science 689 Machine Learning Engineer, Piano player, Yoga ... 693 Pythonista. Writer of docs. DevOps practitione... 694 python NLP R 695 "The purpose of computing is insight, not numb... 696 Physics undergrad at the University of Oklahom... 699 Ruby on Rails 5Y+\r\nPostgreSQL 4Y+\r\nReact 3... 701 Student of Science | Exploring Knowledge Work 706 Data Scientist @ Dynamo Software 709 Go suck the data ;) 713 code player 714 NLP, Machine learning, DeepLearning, AI 717 Software Engineer @aplijobs 720 Co-founder startup studio Hola-Up\r\n\r\nGeek ... 721 UASLP, Ing. en Sistemas Inteligentes 725 Community lead @CodeandoMexico 👨🏻💻 · Co-found... 728 main = putStrLn "Hello, World!" 729 Statician, Marketeer and Developer 😃\r\nPython... 731 Data Scientist & Software Developer 736 Data Scientist 737 Data Engineer 738 Data Scientist @GEICO 740 aHa, I found you! 742 Python enthusiast 750 Data Scientist, Data Engineer, AWS Data Archit... 751 meh. 753 I'm a passionate and highly opinionated softwa... 757 AIops 762 Data Science, Product Management, and building... 763 Data Scientist @ Skyscanner 764 lobster at @lob 765 Certified Salesforce.com Consultant || Data En... 767 I'm passionate about problem-solving, learning... 772 Wanderer - previously @atlanhq @socialcopsdev ... 778 Digital Analytics Development & Innovation. BB... 789 Passionate about Python, Machine Learning, Jav... 790 Python, Golang, Javascript 792 Freelance Full-Stack Software Engineer 793 Head of Video @ PlayTech.com 797 Python & Django Developer. Proficient in MySQL... 799 Enthusiastic Software Engineer 👷 with big pass... 803 I clearly remember programming on computers th... 806 Mechatronic trying to level up my knowledge fr... 811 Go/Python/Java + Cloud = Jack of all trades 815 Data Scientist 816 Data Scientist and Machine Learning Developer 817 Professional Tetris player \r\n 818 study hard and make progress every day 819 Computer and Systems Engineering Student 823 🚀 Data scientist and Web developer. 826 Over 17 years of solution architecture, consul... 827 Data Scientist & Big Data Engineer 829 https://www.linkedin.com/in/bryanyang0528/ 831 :computer: :alien: :ghost: :question: \r\n:1... 834 Happy Hacking! 835 Data Scientist and Software Developer 843 iiot + eth + ml 844 Venezuelan in Mexico @argenisleon 850 personal GitHub from Allison. 851 Learning new stuff, keeping good stuffs privat... 852 Numerical Developer @ESSS. MSc and DSc (ongoin... 854 teamwork makes the dream work. 856 neuroscience, machine learning, python and more 859 coding = 💛 861 Tech + Data + Design 863 eCommerce, Payments & Bots 864 PhD Student in Biochemistry & Molecular Biolog... 867 Just a Typical Engineer trying to automate the... 869 systems guy • curious and intrusive • 99.999% ... 870 Researcher & PhD student in Information Scienc... 875 Associate Professor 876 Software Engineer @Vincit 880 Independent ecopreneur who is supporting proje... 883 Computer Scientist | \r\nMsc at IME at Univers... 885 Product Data Scientist.\r\nComputer science gr... 886 Author of CodingDose a programming blog about ... 888 Data Scientist with interests in Complex Syste... 892 I am studying B.Tech in Metallurgical and Mate... 897 absurdist data hound 898 Software developer with interest in security, ... 899 Game Algorithm Engineer in Timi Studio| \r\n\r... 900 Researcher of Artificial Intelligence at ITU, ... 903 Blockchain and cryptocurrency enthusiast, dApp... 908 Data dev | Python | Scala | Spark | Distribut... 910 Ph.D. in astrophysics, senior software enginee... 911 Data Science Enthusiast. 913 Data Engineer @ Miro 914 Recent PhD graduate in PChem with experience i... 915 Life is short, you need python 922 data analysis 925 EM for Search at Cookpad - building the world'... 926 "What I cannot create, I do not understand." -... 929 Python Dev 931 Making life easier with Python. 932 Business Intelligence Developer , Data Enginee... 934 Master of Science in Columbia University 941 CV & ML 944 I star things... 948 https://www.linkedin.com/in/thomasccook/ 951 Independent software developer in Portland, OR... 952 Living the Oasis 953 Currently: Data Science Engineer 960 Researcher at Computer Vision domain 961 Machine Learning Engineer 🇺🇦🇨🇦 962 \r\n A computer scientist who is interested... 966 Machine Learning Engineer 967 NULL VOID NONE 971 MS in Business Analytics at UCLA | Aspiring Da... 974 Can be bribed with good food and coffee. 🇸🇷🇸🇽🇨🇼🇺🇸 975 Data Scientist interested in engineering softw... 981 Master's student on Applied Computing at USP 982 \r\n Data Scientist, spend my time in Edinb... 985 (╯°□°)╯︵ ┻━┻ 988 AI Software Engineer, Bosch R&D [ARiSE, RBEI] ... 989 Macro-finance Economist 991 Lead Data Scientist @ Salesforce AI Research. ... 993 Passionate about all things FLOS. Special inte... 999 \r\n Studying data science @uzh - \r\nWeb d... 1000 a techno freak of Industry 4.0 1003 leaning.. 1004 ML/Data Scientist 1005 Front-End Developer 1006 Beijing Normal University\r\n 1008 Pythonista 🐍\r\nDataScientist 📊\r\nBayesian 🧙 1009 I love 🍣, ♨️and ✈️. 1010 Life isn’t about waiting for the storm to pass... 1011 Avid programmer 1013 Big data + ML 1015 i want to start my adventure, new kind challen...
words = pdf['bio'].str.replace(".","").str.replace(",","").str.lower().str.split()
word_counts = pd.value_counts(words.apply(pd.Series).stack())
print_full(word_counts)
data 194 and 150 engineer 94 in 87 scientist 76 learning 73 | 68 a 63 i 53 of 53 machine 52 science 49 developer 47 at 46 software 44 python 40 & 35 to 31 with 28 the 26 big 25 computer 24 on 23 ai 21 for 21 - 19 @ 19 engineering 17 ml 17 student 16 about 15 systems 14 i'm 14 nlp 14 passionate 14 from 13 intelligence 13 development 13 my 13 is 13 / 12 code 12 more 12 analytics 12 phd 11 am 11 who 11 interested 10 web 10 enthusiast 10 working 10 cloud 10 artificial 10 msc 10 programming 10 love 9 not 9 researcher 9 deep 9 technology 9 devops 9 all 9 open 8 consultant 8 me 8 master 8 full 8 senior 8 professional 8 like 8 an 8 business 7 work 7 source 7 research 7 co-founder 7 time 7 architect 7 can 7 dev 7 stuff 7 university 7 distributed 7 stack 7 that 6 lover 6 go 6 scala 6 computing 6 + 6 have 6 curious 6 as 6 things 6 ruby 5 studying 5 information 5 coder 5 former 5 solutions 5 search 5 coding 5 freelancer 5 do 5 vision 5 de 5 experience 5 much 5 en 5 manager 5 models 5 specializing 5 r 5 physics 5 learner 5 aws 5 physicist 5 ex 5 // 5 passion 5 natural 4 looking 4 new 4 just 4 language 4 bsc 4 hacker 4 gusta 4 datos 4 interests 4 person 4 biomedical 4 programmer 4 technologies 4 building 4 make 4 designer 4 expert 4 test 4 guy 4 knowledge 4 mathematician 4 blockchain 4 • 4 statistical 4 processing 4 product 4 computational 4 you 4 electrical 4 build 4 statistics 4 = 4 enthusiastic 4 life 4 health 4 previously 4 por 4 cto 4 practitioner 4 pythonista 4 player 4 functional 4 products 4 be 4 graduate 4 always 4 focused 4 problems 4 likes 4 scientific 4 everything 4 verlos 4 own 3 mining 3 r&d 3 since 3 learn 3 trying 3 || 3 math 3 iot 3 solving 3 professor 3 using 3 industry 3 write 3 architecture 3 team 3 databricks 3 focus 3 also 3 great 3 intern 3 digital 3 director 3 react 3 interest 3 spark 3 or 3 related 3 years 3 independent 3 automation 3 venezuelan 3 fan 3 kubernetes 3 financial 3 text 3 loves 3 applied 3 c++ 3 cosmology 3 ordinary 3 mathematical 3 random 3 robotics 3 musician 3 grad 3 -> 3 · 3 into 3 complex 3 full-stack 3 available 3 linux 3 enchiladas 2 fanboy 2 insights 2 #data 2 startup 2 every 2 scale 2 data! 2 arts 2 are 2 eos 2 neural 2 member 2 help 2 faster 2 designing 2 #globaldev 2 #policy 2 optimization 2 microservices 2 autonomous 2 @googlecloudplatform 2 hard 2 books 2 it 2 games 2 structures 2 been 2 author 2 per 2 @worldbank 2 sistemas 2 codes 2 up 2 sciences 2 flank 2 postgresql 2 entrepreneur 2 (big|small|open) 2 engine 2 command 2 deployment 2 ai/ml 2 coffee 2 because 2 ux/ui 2 (graphs) 2 > 2 ello 2 this 2 opinion 2 mobile 2 apache 2 agile 2 (level 2 (c) 2 informática 2 analyzing 2 ocaml 2 anything 2 boy 2 donde 2 android 2 apasiona 2 la 2 crypto 2 marketo 2 psychoanalysis 2 #foss 2 vehicles 2 cs 2 real 2 terraform 2 civis 2 nyu 2 finance 2 desde 2 #datascience 2 oriented 2 data-driven 2 minister 2 solution 2 (everywhere) 2 fault 2 upwork 2 awesome 2 traveling 2 supporting 2 personal 2 wind-turbine 2 failed 2 very 2 dart 2 living 2 probabilistic 2 design 2 ci/cd 2 applications 2 alphacruncher 2 a; 2 新浪微博 2 studied 2 that's 2 marketing 2 forecasting 2 twitter: 2 bots 2 demandgen 2 but 2 consistent; 2 @layer7ai 2 currently 2 helloword 2 actuary 2 recommender 2 serverless 2 conversational 2 maracaibo 2 spent 2 analysis 2 venezuela 2 lacanian 2 account 2 haskell 2 world 2 trades 2 : 2 detail 2 relevance 2 trading 2 los 2 wrangling 2 front-end 2 e 2 lead 2 aspiring 2 graphql 2 #p2p 2 explorations 2 @hadootoo 2 philosophy 2 good 2 london 2 was 2 methods 2 lifelong 2 creating 2 networks 2 maker 2 analyst 2 revolving 2 recently 2 finite 2 golang 2 turing 2 kagglecom/silverstone1903 2 chris 2 rp 2 so 2 etc 2 he 2 strategic 2 prognosai 2 advanced 2 writer 2 formerly 2 hermit 2 materials 2 dream 2 eng 2 3) 2 want 2 verdes 2 2012 2 economist 2 @cockroachdb 2 🚀 2 [:are] 2 eventually 2 page 2 frontend 2 #nerd 2 todas 2 @shoprunner; 2 founder 2 @爱可可-爱生活 2 modelling 2 around 2 modeling 2 eloqua 2 ai/machine 2 iit 2 probability 2 most 2 graphic 2 nifi 2 dataflow 2 tolerant; 2 when 2 informatics 2 line 2 postdoc 2 quant 2 innovation 2 ≡ 2 ramas 2 neuroscience 2 speak 2 obama 2 consulting/analytics/data 2 github 2 java 2 dreamer 2 #ai 2 google 2 jack 2 #unam 2 self 2 poder 2 projects 2 apasionado 2 sus 2 tech 2 ledger 2 assistant 2 todo 2 javascript 2 git 2 regular 2 whole 2 2x 2 polygot 2 wpi 2 💻♟️🏃☕️🎧✈🚘 2 y 2 short 2 i've 2 ;) 2 ; 2 no 2 statistician 2 how 2 specializes 2 nacen 2 pipelines 2 aspecto 2 making 2 tryharder 2 🐍 2 machine/deep 2 braintree 2 does 2 iota 2 reinforcement 1 se 1 crushing 1 @uzh 1 problem-solving 1 world's 1 :alien: 1 comin' 1 @vintasoftware 1 game 1 μαθητής 1 field 1 owner! 1 vs 1 mutt 1 junior 1 currently: 1 algorithmic 1 playing 1 talk 1 urban 1 conferences 1 ᴡʜᴏ 1 eth 1 forever 1 langone 1 reliable 1 teamwork 1 0a83 1 freelance 1 biking 1 performance 1 associate 1 plz 1 endlessly 1 desarrollador 1 chemistry 1 some 1 quality 1 tenacious 1 pass 1 come 1 founder/ceo 1 fargo 1 people 1 control 1 during 1 matlab/python 1 choice 1 suck 1 atelierdeeplearningcom 1 @poliastro 1 fit 1 scrum 1 😊 1 tokyo 1 (@project-renard) 1 isn’t 1 muhammad 1 illinois 1 partner 1 which 1 blog 1 4y+ 1 worked 1 car 1 complete 1 🇺🇦🇨🇦 1 medical 1 sports 1 1000bpm 1 studio| 1 @gotuktuk-in 1 48a9 1 iiot 1 fullstack/cloud/sre 1 fraud 1 "what 1 tool 1 going 1 https://manugarribitac/ 1 study 1 🍣 1 sector 1 doing 1 mundo 1 carlistmy 1 speaker 1 sultanpur 1 ☕️ 1 pauciora 1 textile 1 bitcoinfaf 1 { 1 [arise 1 crushed 1 typical 1 wells 1 @davidpgero 1 ψ 1 supporter 1 isa 1 happier 1 behavioral 1 debt 1 recife 1 => 1 system 1 master's 1 given 1 ┻━┻ 1 pandas 1 pythonist 1 keeping 1 fog 1 create 1 bbva 1 0106 1 linx 1 egypt 1 playtechcom 1 c121 1 chatbot 1 3y+ 1 void 1 uaslp 1 lincoln 1 https://joaorafaelmgithubio 1 numerical 1 cannot 1 🥐 1 positive 1 need 1 tw 1 previous 1 purpose 1 conduit 1 part-time 1 there 1 (56) 1 optimisations🐍🤖🦸🏼 1 hacking! 1 idris 1 ⚡️ 1 ingeniero 1 loner 1 studio 1 entrepreneurs 1 ♨️and 1 advocate 1 1x 1 australia 1 🧙 1 codemarshal 1 strategist 1 estate 1 profile) 1 plura 1 hk 1 strathclyde 1 biosciences 1 @lexerdev 1 40 1 e-mail 1 management 1 xopencola 1 nia] 1 hattrick 1 deliver 1 kaodimcom 1 exploring 1 ginger 1 ita) 1 sysadmin 1 https://wwwlinkedincom/in/thomasccook/ 1 @uqcomputingsociety 1 everything** 1 enable 1 @etsy 1 devops) 1 ms 1 remember 1 release 1 em 1 type 1 detection 1 numbers 1 usp 1 ∞ 1 ecopreneur 1 sectors 1 i'd 1 highly 1 微信公众号: 1 save 1 代码搬运工,进入了一个新的层次,庖丁解牛,从各种开源框架中提取设计,提取核心代码算法,搬运参考,提取来实现自己的框架。复杂问题抽象分层可以极致简单。 1 cto: 1 bcg 1 :ecuador: 1 themes 1 xbanks 1 :+1: 1 云在青天水在瓶 1 postgrad 1 computación 1 leaning 1 tdd 1 customer 1 bi 1 people’s 1 by 1 normal 1 human 1 fieri 1 makes 1 pgp: 1 hexdevscom 1 genetics 1 hours 1 spacetime 1 #phh 1 implementations 1 meh 1 services 1 timi 1 christian 1 society 1 astrodynamics 1 picture 1 profile: 1 repeat 1 mongodb 1 technology-related 1 room 1 attention 1 fintech 1 sdet 1 @47billion 1 @aplijobs 1 economics 1 kids 1 send 1 :ghost: 1 javascript(ts) 1 ambos 1 tᴇᴄʜ 1 modelpredict 1 @vincit 1 geek 1 payment 1 signal 1 united 1 political 1 -- 1 m 1 dynamic 1 :question: 1 @lob 1 penetration 1 undergraduate 1 (personal 1 🎸 1 healthcare 1 smarter 1 #sami 1 postgraduate 1 statician 1 number" 1 salesforcecom 1 block-chain/distributed 1 courses 1 clientes 1 paris 1 tools 1 2xgsoc 1 quadratyx 1 addict 1 @jeremyjordan 1 sustainability 1 sempre 1 look 1 💛 1 latex 1 dev-ops 1 star 1 still 1 embedded 1 developer; 1 +10 1 if 1 madrid 1 tell 1 brains 1 8+ 1 proficient 1 level 1 food 1 분석 1 those 1 (data 1 superconductive 1 demonstrated 1 happy 1 -_- 1 ability 1 desenvolvedores 1 understand" 1 :computer: 1 17 1 são 1 juntos 1 prev: 1 🤷 1 group 1 conference 1 barely 1 budding 1 ? 1 eternally 1 @spark 1 gis 1 department 1 gates 1 💡 1 maths 1 🚩 1 such 1 teach 1 @pdlporters) 1 n11com 1 deeplearning 1 via 1 dynamo 1 database 1 dottie 1 ton 1 codingdose 1 mudarem 1 hound 1 it’s 1 :: 1 http://huyhoang17githubio 1 evangelist 1 raedan 1 columbia 1 extract 1 greenlight 1 lifes 1 change 1 ʟᴏᴠᴇs 1 https://twittercom/__phanhoang__ 1 👨🏻💻 1 990542812 1 "stuck" 1 policy 1 ml/dl 1 mathematics 1 datascientist 1 neutrino 1 ᴛᴏ 1 questions 1 macro-finance 1 @socialcopsdev 1 feynman 1 hacker!!! 1 become 1 meio 1 ai/ml/dl 1 portland 1 clearly 1 opinions 1 @usp 1 rochester 1 humble 1 scalability 1 none 1 's 1 stuffs 1 education 1 📈🔍 1 php==>python==>go==>anything 1 mind 1 miro 1 @zapatacomputing 1 french 1 swift 1 (makers 1 to) 1 yoga 1 @apple 1 (@cbl-orion) 1 c07d 1 taming 1 @rstats-gsoc 1 difference 1 payments 1 js 1 cooklist 1 @python-spain 1 } 1 datascience 1 ucla 1 adventure 1 others 1 (╯°□°)╯︵ 1 skyscanner 1 9147 1 main 1 leader 1 teh 1 video 1 :pizza: 1 @linkasoftwares 1 safe 1 hide 1 ✈️ 1 unmsm 1 had 1 astrophysics 1 futures/options 1 hands-on 1 theory 1 out 1 stand 1 @ml-gde 1 데이터 1 employer's 1 nlp/ai/deep 1 undergrad 1 gpg: 1 2018년 1 insight 1 onlywiththose 1 rails 1 business-minded 1 image 1 electronic 1 analysis+regression 1 ) 1 candidato 1 insanely 1 easier 1 lives 1 bioinformatician 1 russia 1 dsc 1 explore 1 climate 1 cybersecurity 1 ime 1 bayesian 1 👨🏫 1 😃 1 databases 1 @poplindata 1 csre 1 edinburgh 1 1461 1 iu 1 bitcoinfastasfuk 1 discovery 1 community 1 06월 1 marketeer 1 @uomcs 1 c742 1 ltd 1 cdl 1 infinite 1 creativity 1 ʟᴇᴀʀɴ 1 inteligentes 1 york 1 after 1 security 1 head 1 magniware 1 nebraska 1 horsepower 1 "hello 1 b05a 1 valuable 1 cluster 1 over 1 dataengineer/dataanalyst 1 fx 1 @givebloodorg 1 fun 1 long 1 challenge 1 techno 1 realize 1 ajudar 1 online 1 #geneticprogramming 1 ugly 1 @kumanu 1 pratik 1 now 1 germany 1 rest 1 progressing 1 @tinklabs 1 abu 1 testing 1 freedomize 1 blue 1 metallurgical 1 automating 1 shenzhen 1 dapp 1 quod 1 l'oréal 1 web2ajax@gmailcom 1 #hust 1 duke's 1 :ok 1 engineers: 1 keyboard 1 currency 1 #snns 1 medium 1 potest 1 oasis 1 aws/gcp 1 patterns 1 cern 1 @snagr 1 @geico 1 rates 1 digital! 1 @codeandomexico 1 ᴛʜɪɴɢs! 1 handground 1 any 1 molecular 1 recovering 1 frustra 1 pchem 1 https://twittercom/mezphotos 1 itu 1 everlasting 1 contributor 1 ciencia 1 ml/data 1 national 1 monkey 1 rebel 1 interests: 1 ᴇɴᴛʜᴜsɪᴀsᴛɪᴄ 1 null 1 go-getter 1 @yelp 1 campus 1 biochemistry 1 whether 1 🧠 1 european 1 (but 1 center 1 django 1 pyquant 1 allison 1 chicagomy 1 hack 1 lab 1 站在巨人的肩膀上开发卓越软件 1 infosec 1 answer 1 artist 1 📊 1 pride 1 cv 1 father 1 trainer 1 note: 1 t-shaped 1 nothing 1 salesforce 1 개발 1 http://tselaicom 1 biophysics 1 scientist! 1 sleep 1 ciencias 1 start 1 consulting 1 go/python/java 1 nodejs 1 in-memory 1 @scala 1 apprentice 1 pythian 1 processing/ocr 1 logic 1 madrasinterested 1 ɴᴇᴡ 1 possible! 1 backend(symfonydrupaldjango) 1 span 1 immigration 1 interstellar 1 your 1 simmer/keras/stan/spark 1 you! 1 knit 1 otolaryngologist 1 :100: 1 putstrln 1 dabbling 1 avid 1 indonesia 1 @diffblue 1 refine 1 citizen 1 dba 1 @shopify 1 flos 1 java&python&golang程序猿一枚 1 https://wwwliangdabiaocom/ 1 flixel 1 cloudy 1 maestro 1 φ 1 art 1 https://wwwlinkedincom/in/bryanyang0528/ 1 deploy 1 pizza 1 prescription 1 [icets 1 absurdist 1 laboratory 1 @uspcodelab 1 enjoy 1 docs 1 (@entropyorg 1 🔍 1 hospital 1 @esss 1 progress 1 beijing 1 dhabi 1 parts 1 brain-computer 1 wanderer 1 award 1 efficiency/ 1 population 1 @atlanhq 1 #sun* 1 rather 1 🇨🇦 1 python-ist 1 lies 1 appuri 1 improve 1 @uzh-bf 1 almost 1 weeks 1 kwnoledge 1 lean 1 developing 1 expectations) 1 @cloverhealth 1 mysql 1 b9d9 1 mexico 1 printing 1 634f 1 health; 1 hint 1 native 1 xkobo 1 read 1 🇸🇷🇸🇽🇨🇼🇺🇸 1 kid 1 @smartnews 1 particle 1 creator 1 specialization 1 uk/sg 1 cyclist 1 richard 1 https://gitlabcom/otrenav 1 🔁 1 bloomington 1 leisure 1 improvement 1 (brazil) 1 dl 1 nice 1 travel 1 its 1 bitcoin 1 noise 1 experimental 1 broadly 1 @argenisleon 1 data-focused 1 took 1 tea 1 cryptocurrency 1 hola-up 1 🇬🇧 1 graphs 1 special 1 freak 1 driving 1 us 1 decipher 1 algorithm 1 certified 1 think 1 sr 1 sql 1 get 1 @dask 1 chemical 1 institute 1 podcast 1 mx 1 6c0b 1 androïd 1 piano 1 abhijitannaldascom 1 producer 1 expertise 1 horror 1 rbei] 1 christ 1 pursuit 1 automate 1 infosys 1 transformations 1 agriculture 1 mathematica 1 php 1 has 1 faang 1 및 1 spend 1 market 1 skeptic 1 #pymi 1 snippets 1 @bnl 1 will 1 @geoperakis 1 aiops 1 1y+ 1 o 1 @python 1 incorporate 1 oklahoma 1 backend 1 orange 1 microsoft 1 3d53 1 dept 1 novel 1 gde 1 bombay 1 021a 1 ml/nlp//energy 1 it's 1 boston 1 @efficiam 1 @lanl 1 docusign 1 hitting 1 private 1 destroy 1 para 1 bribed 1 in: 1 😋 1 recipe 1 keys 1 role 1 le 1 tensorflow 1 compelling 1 thought 1 omersv 1 🔰 1 @alexrd 1 5y+ 1 cookpad 1 kaggle 1 better 1 skills 1 xmicrosoft 1 enterprise 1 bosch 1 @pratik007a 1 ethz 1 committed 1 dev-oops 1 back-end 1 constant 1 computers 1 asr 1 dev/dba 1 film 1 genomics 1 #btn 1 process 1 can't 1 amjad 1 bb,just 1 place 1 waiting 1 something 1 domain 1 company 1 640d 1 confuzzled 1 berlin 1 medium-size 1 rain 1 99999% 1 📜 1 @futurelabmx 1 spiking 1 storm 1 dl&ml 1 investing 1 chat! 1 kind 1 intrusive 1 basicmlcom 1 pipeline 1 windows 1 interaction 1 genomic 1 (usp) 1 singularity 1 showing 1 their 1 being 1 meditation 1 (milan 1 λ 1 shakespeare 1 use 1 inclinations 1 tetris 1 motion 1 dtaz 1 another 1 companies 1 widgetry 1 kingdom 1 surely 1 lobster 1 world!" 1 ios 1 principal 1 시작 1 journalist 1 @surfline 1 compreenderem 1 way 1 enough 1 db0b 1 "the 1 pharmacological 1 experiments 1 @aeropython 1 dedicated 1 speech 1 crm 1 uni 1 opinionated 1 snotra 1 6710 1 👷 1 @elo7 1 data-intensive 1 co-host 1 aha 1 ing 1 dfb8 1 paulo 1 day 1 found 1 luke! 1 practice 1 interplanetary 1 |> 1 old 1 topics 1 ikerlan 1 auc 1 best 1 in-person 1 planning 1 sometimes 1 changed 1 reverse 1 jrdata 1 dance 1 i6 1 mechatronic 1 shafay 1 mountain 1 hyderabad 1 learning/data 1 entire 1 d8f4 1 finding 1 job 1 biology 1 frustrated 1 btech 1 header 1 e-commerce 1 📊🎨📉 1 (ongoing) 1 cool 1 https://shadowkungithubio/ 1 xrogers 1 amount 1 neuron 1 gigs 1 he/him 1 nosql 1 works 1 recent 1 @iit_ropar 1 a910 1 ecommerce 1 dtype: int64
print(result)
[<Response [200]>, <Response [200]>, <Response [200]>, <Response [200]>, <Response [200]>, <Response [200]>, <Response [200]>, <Response [200]>]
# df1.cols.pattern_counts("firstName", n=5, mode=0)
df1.cols.pad("firstName",10, "both","0").compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | 000Luis000 | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 2 | 00André000 | Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
2 | 3 | 00NiELS000 | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
3 | 4 | 000PAUL000 | dirac$ | 521 | pizza | 8 | 1954/07/10 | you |
4 | 5 | 00Albert00 | Einstein | 634 | pizza | 8 | 1990/07/11 | up |
5 | 6 | 0Galileo00 | GALiLEI | 672 | arepa | 5 | 1930/08/12 | never |
6 | 7 | 000CaRL000 | Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna |
7 | 8 | 00David000 | H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let |
8 | 9 | 0Johannes0 | KEPLER | 735 | taco | 3 | 1920/04/22 | you |
9 | 10 | 00JaMES000 | M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down |
10 | 11 | 00Isaac000 | Newton | 992 | pasta | 9 | 1999/02/15 | never |
11 | 12 | 00Emmy%%00 | Nöether$ | 234 | pasta | 9 | 1993/12/08 | gonna |
12 | 13 | 00Max!!!00 | Planck!!! | 111 | hamburguer | 4 | 1994/01/04 | run |
13 | 14 | 000Fred000 | Hoy&&&le | 553 | pizzza | 8 | 1997/06/27 | around |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956/11/30 | and |
15 | 16 | 0William00 | Gilbert### | 886 | BEER | 2 | 1958/03/26 | desert |
16 | 17 | 00Marie000 | CURIE | 912 | Rice | 1 | 2000/03/22 | you |
17 | 18 | 00Arthur00 | COM%%%pton | 812 | 110790 | 5 | 1899/01/01 | # |
18 | 19 | 00JAMES000 | Chadwick | 467 | NaN | 10 | 1921/05/03 | # |
# import sys,os,os.path
# os.environ
# import string_grouper
# string_grouper.__file__
from optimus import Optimus
op = Optimus("dask", n_workers=1, threads_per_worker=8, processes=False, memory_limit="3G", comm=True)
df = op.load.file("data/crime.csv").ext.cache()
df = df.ext.repartition(8).ext.cache()
df.ext.display()
INCIDENT_NUMBER
1 (object)
not nullable
|
OFFENSE_CODE
2 (object)
not nullable
|
OFFENSE_CODE_GROUP
3 (object)
not nullable
|
OFFENSE_DESCRIPTION
4 (object)
not nullable
|
DISTRICT
5 (object)
not nullable
|
REPORTING_AREA
6 (object)
not nullable
|
SHOOTING
7 (object)
not nullable
|
OCCURRED_ON_DATE
8 (object)
not nullable
|
YEAR
9 (object)
not nullable
|
MONTH
10 (object)
not nullable
|
DAY_OF_WEEK
11 (object)
not nullable
|
HOUR
12 (object)
not nullable
|
UCR_PART
13 (object)
not nullable
|
STREET
14 (object)
not nullable
|
Lat
15 (object)
not nullable
|
Long
16 (object)
not nullable
|
Location
17 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
I182070945
|
00619
|
Larceny
|
LARCENY⋅ALL⋅OTHERS
|
D14
|
808
|
nan
|
2018-09-02⋅13:00:00
|
2018
|
9
|
Sunday
|
13
|
Part⋅One
|
LINCOLN⋅ST
|
42.35779134
|
-71.13937053
|
(42.35779134,⋅-71.13937053)
|
I182070943
|
01402
|
Vandalism
|
VANDALISM
|
C11
|
347
|
nan
|
2018-08-21⋅00:00:00
|
2018
|
8
|
Tuesday
|
0
|
Part⋅Two
|
HECLA⋅ST
|
42.30682138
|
-71.06030035
|
(42.30682138,⋅-71.06030035)
|
I182070941
|
03410
|
Towed
|
TOWED⋅MOTOR⋅VEHICLE
|
D4
|
151
|
nan
|
2018-09-03⋅19:27:00
|
2018
|
9
|
Monday
|
19
|
Part⋅Three
|
CAZENOVE⋅ST
|
42.34658879
|
-71.07242943
|
(42.34658879,⋅-71.07242943)
|
I182070940
|
03114
|
Investigate⋅Property
|
INVESTIGATE⋅PROPERTY
|
D4
|
272
|
nan
|
2018-09-03⋅21:16:00
|
2018
|
9
|
Monday
|
21
|
Part⋅Three
|
NEWCOMB⋅ST
|
42.33418175
|
-71.07866441
|
(42.33418175,⋅-71.07866441)
|
I182070938
|
03114
|
Investigate⋅Property
|
INVESTIGATE⋅PROPERTY
|
B3
|
421
|
nan
|
2018-09-03⋅21:05:00
|
2018
|
9
|
Monday
|
21
|
Part⋅Three
|
DELHI⋅ST
|
42.27536542
|
-71.09036101
|
(42.27536542,⋅-71.09036101)
|
I182070936
|
03820
|
Motor⋅Vehicle⋅Accident⋅Response
|
M/V⋅ACCIDENT⋅INVOLVING⋅PEDESTRIAN⋅-⋅INJURY
|
C11
|
398
|
nan
|
2018-09-03⋅21:09:00
|
2018
|
9
|
Monday
|
21
|
Part⋅Three
|
TALBOT⋅AVE
|
42.29019621
|
-71.07159012
|
(42.29019621,⋅-71.07159012)
|
I182070933
|
00724
|
Auto⋅Theft
|
AUTO⋅THEFT
|
B2
|
330
|
nan
|
2018-09-03⋅21:25:00
|
2018
|
9
|
Monday
|
21
|
Part⋅One
|
NORMANDY⋅ST
|
42.30607218
|
-71.08273260
|
(42.30607218,⋅-71.08273260)
|
I182070932
|
03301
|
Verbal⋅Disputes
|
VERBAL⋅DISPUTE
|
B2
|
584
|
nan
|
2018-09-03⋅20:39:37
|
2018
|
9
|
Monday
|
20
|
Part⋅Three
|
LAWN⋅ST
|
42.32701648
|
-71.10555088
|
(42.32701648,⋅-71.10555088)
|
I182070931
|
00301
|
Robbery
|
ROBBERY⋅-⋅STREET
|
C6
|
177
|
nan
|
2018-09-03⋅20:48:00
|
2018
|
9
|
Monday
|
20
|
Part⋅One
|
MASSACHUSETTS⋅AVE
|
42.33152148
|
-71.07085307
|
(42.33152148,⋅-71.07085307)
|
I182070929
|
03301
|
Verbal⋅Disputes
|
VERBAL⋅DISPUTE
|
C11
|
364
|
nan
|
2018-09-03⋅20:38:00
|
2018
|
9
|
Monday
|
20
|
Part⋅Three
|
LESLIE⋅ST
|
42.29514664
|
-71.05860832
|
(42.29514664,⋅-71.05860832)
|
# df.cols.remove()
# df.cols.replace_regex("OFFENSE_CODE_GROUP", "L").head()
df.cols.pad("OFFENSE_DESCRIPTION",15,side="both",fillchar="0").head()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | I182070945 | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | NaN | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) |
1 | I182070943 | 01402 | Vandalism | 000VANDALISM000 | C11 | 347 | NaN | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) |
2 | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | NaN | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) |
3 | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | NaN | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) |
4 | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | NaN | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
df["OFFENSE_CODE_GROUP"].str[1:5].head()
0 arce 1 anda 2 owed 3 nves 4 nves Name: OFFENSE_CODE_GROUP, dtype: object
# df["OFFENSE_CODE_GROUP"].str.extract("(L)").compute()
df.cols.extract("OFFENSE_CODE_GROUP", "(L)").compute()
output_ordered_columns ['INCIDENT_NUMBER', 'OFFENSE_CODE', 'OFFENSE_CODE_GROUP', 'OFFENSE_DESCRIPTION', 'DISTRICT', 'REPORTING_AREA', 'SHOOTING', 'OCCURRED_ON_DATE', 'YEAR', 'MONTH', 'DAY_OF_WEEK', 'HOUR', 'UCR_PART', 'STREET', 'Lat', 'Long', 'Location']
0 | INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | L | I182070945 | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | NaN | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) |
1 | NaN | I182070943 | 01402 | Vandalism | VANDALISM | C11 | 347 | NaN | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) |
2 | NaN | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | NaN | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) |
3 | NaN | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | NaN | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) |
4 | NaN | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | NaN | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
39880 | NaN | I050310906-00 | 03125 | Warrant Arrests | WARRANT ARREST | D4 | 285 | NaN | 2016-06-05 17:25:00 | 2016 | 6 | Sunday | 17 | Part Three | COVENTRY ST | 42.33695098 | -71.08574813 | (42.33695098, -71.08574813) |
39881 | NaN | I030217815-08 | 00111 | Homicide | MURDER, NON-NEGLIGIENT MANSLAUGHTER | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part One | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) |
39882 | NaN | I030217815-08 | 03125 | Warrant Arrests | WARRANT ARREST | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part Three | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) |
39883 | NaN | I010370257-00 | 03125 | Warrant Arrests | WARRANT ARREST | E13 | 569 | NaN | 2016-05-31 19:35:00 | 2016 | 5 | Tuesday | 19 | Part Three | NEW WASHINGTON ST | 42.30233307 | -71.11156487 | (42.30233307, -71.11156487) |
39884 | NaN | 142052550 | 03125 | Warrant Arrests | WARRANT ARREST | D4 | 903 | NaN | 2015-06-22 00:12:00 | 2015 | 6 | Monday | 0 | Part Three | WASHINGTON ST | 42.33383935 | -71.08029038 | (42.33383935, -71.08029038) |
319073 rows × 18 columns
df.cols.append(r).compute()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-43-3dd685da4d24> in <module> ----> 1 df.cols.append(r).compute() NameError: name 'r' is not defined
df.cols.names()
['id', 'firstName', 'lastName', 'billingId', 'product', 'price', 'birth', 'dummyCol']
%%time
df.cols.pattern_counts("firstName", 2)
Wall time: 997 µs
{'firstName': {'values': [{'value': 'Ullll', 'count': 4}, {'value': 'Ullllll', 'count': 2}], 'more': True, 'updated': 1603206510.6664248}}
import pandas as pd
df= pd.DataFrame(data={'col1': ["http://www.google.com:8080?param1=param_name"],"col2":["argenisleon@gmail.com"]})
# df.cols.port("col1")
# df.cols.domain_scheme("col1")
# df.cols.host("col1")
# df.cols.domain_params("col1")
# df.cols.host("col1")
#Email funcions
print(df.cols.email_user("col2"))
print(df.cols.email_domain("col2"))
print(df.cols.email_extension("col2"))
col1 col2 0 http://www.google.com:8080?param1=param_name argenisleon col1 col2 0 http://www.google.com:8080?param1=param_name gmail.com col1 col2 0 http://www.google.com:8080?param1=param_name gmail
df.cols.select("meta_key__wp_s2member_custom").cols.
File "<ipython-input-70-5fb6324d4692>", line 1 df.cols.select("meta_key__wp_s2member_custom").cols. ^ SyntaxError: invalid syntax
%%time
for col_name in df.cols.names():
df[col_name].astype(str).compute()
Wall time: 5.91 s
from urllib.parse import urlparse
# from urlparse import urlparse # Python 2
parsed_uri = urlparse('http://sub.stackoverflow.com:8080/questions/1234567/blah-blah-blah-blah?123=123&234=234' )
result = '{uri.scheme}://{uri.netloc}/'.format(uri=parsed_uri)
print(parsed_uri)
ParseResult(scheme='http', netloc='sub.stackoverflow.com:8080', path='/questions/1234567/blah-blah-blah-blah', params='', query='123=123&234=234', fragment='')
%%time
df.ext.profile("*", 5, flush=True)
Wall time: 41.8 s
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'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__screen_layout_surl': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__closedpostboxes_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__metaboxhidden_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__meta-box-order_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__screen_layout_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__community-events-location': {'stats': {'match': 6213, 'missing': 6212, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6212}, {'value': 'a:1:{s:2:"ip";s:21:"2600:6c51:7d7f:f8e7::";}', 'count': 1}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__as3cf_dismissed_notices': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__syntax_highlighting': {'stats': {'match': 4082, 'missing': 2131, 'mismatch': 2131, 'profiler_dtype': {'dtype': 'boolean'}, 'frequency': [{'value': 'true', 'count': 4082}, {'value': 'nan', 'count': 2131}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'boolean'}}, 'meta_key__instagram': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__myspace': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__pinterest': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__soundcloud': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tumblr': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wikipedia': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___edd_user_address': {'stats': {'match': 6213, 'missing': 5836, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 5836}, {'value': 'a:0:{}', 'count': 377}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__closedpostboxes_wbcr-snippets': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__metaboxhidden_wbcr-snippets': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__q_eud_exports': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_user': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_created': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_expire': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_token': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__show_welcome_panel': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_do_not_ask_again': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_do_not_ask_again_time': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_last_login': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_login_count': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wp_table_pixie_options': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wp_yoast_notifications': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_review_done': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_review_done_time': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___aal_elementor_install_notice': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__jetpack_tracks_anon_id': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__jetpack_tracks_wpcom_id': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}}, 'name': None, 'file_name': 'data/users-data(1)(1).csv', 'summary': {'cols_count': 183, 'rows_count': 6213, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}
df.astype("str")
ID | user_login | user_nicename | user_email | user_url | user_registered | user_activation_key | user_status | display_name | role | ccaps | meta_key__first_name | meta_key__last_name | meta_key__nickname | meta_key__description | meta_key__rich_editing | meta_key__comment_shortcuts | meta_key__admin_color | meta_key__use_ssl | meta_key__show_admin_bar_front | meta_key__wp_capabilities | meta_key__wp_user_level | meta_key__dismissed_wp_pointers | meta_key__wp_dashboard_quick_press_last_post_id | meta_key__wp_user-settings | meta_key__wp_user-settings-time | meta_key__AtD_options | meta_key__AtD_check_when | meta_key__AtD_guess_lang | meta_key__AtD_ignored_phrases | meta_key__aim | meta_key__yim | meta_key__jabber | meta_key__nav_menu_recently_edited | meta_key__managenav-menuscolumnshidden | meta_key__metaboxhidden_nav-menus | meta_key__closedpostboxes_post | meta_key__metaboxhidden_post | meta_key__pmpro_logins | meta_key__pmpro_visits | meta_key__pmpro_views | meta_key__wp_s2member_last_login_time | meta_key__wp_s2member_registration_ip | meta_key__wp_s2member_login_counter | meta_key__wp_s2member_paid_registration_times | meta_key__default_password_nag | meta_key__wp_s2member_subscr_gateway | meta_key__wp_s2member_subscr_id | meta_key__wp_s2member_custom | meta_key__wp_s2member_ipn_signup_vars | meta_key__wp_s2member_first_payment_txn_id | meta_key__wp_s2member_last_payment_time | meta_key__meta-box-order_dashboard | meta_key__screen_layout_dashboard | meta_key__wp_s2member_notes | meta_key__wp_s2member_auto_eot_time | meta_key___yoast_wpseo_profile_updated | meta_key__wpseo_title | meta_key__wpseo_metadesc | meta_key__wpseo_metakey | meta_key__googleplus | meta_key__twitter | meta_key__facebook | meta_key__meta-box-order_post | meta_key__screen_layout_post | meta_key__closedpostboxes_page | meta_key__metaboxhidden_page | meta_key__manageedit-postcolumnshidden | meta_key__easycontactforms_stat_pointer | meta_key__wp_s2member_capability_times | meta_key__wp_s2member_subscr_baid | meta_key__closedpostboxes_dashboard | meta_key__metaboxhidden_dashboard | meta_key__closedpostboxes_attachment | meta_key__metaboxhidden_attachment | meta_key__meta-box-order_attachment | meta_key__screen_layout_attachment | meta_key__session_tokens | meta_key___edd_htaccess_missing_dismissed | meta_key__wp_media_library_mode | meta_key__closedpostboxes_download | meta_key__metaboxhidden_download | meta_key__billing_country | meta_key__billing_first_name | meta_key__billing_last_name | meta_key__billing_company | meta_key__billing_address_1 | meta_key__billing_address_2 | meta_key__billing_city | meta_key__billing_state | meta_key__billing_postcode | meta_key__billing_email | meta_key__billing_phone | meta_key__shipping_country | meta_key__shipping_first_name | meta_key__shipping_last_name | meta_key__shipping_company | meta_key__shipping_address_1 | meta_key__shipping_address_2 | meta_key__shipping_city | meta_key__shipping_state | meta_key__shipping_postcode | meta_key__user_title | meta_key__linkedin | meta_key__digg | meta_key__flickr | meta_key__stumbleupon | meta_key__youtube | meta_key__yelp | meta_key__reddit | meta_key__delicious | meta_key__closedpostboxes_spucpt | meta_key__metaboxhidden_spucpt | meta_key__default_category_id_for_user | meta_key___edd_nginx_redirect_dismissed | meta_key__meta-box-order_download | meta_key__screen_layout_download | meta_key___edd_pending_verification | meta_key__ecae_premium_ignore_count_notice | meta_key__ecae_premium_ignore_notice | meta_key__ecae_premium_ignore_count_notice_total | meta_key__wpseo_ignore_tour | meta_key__wpseo_noindex_author | meta_key__wpseo_seen_about_version | meta_key__meta-box-order_page | meta_key__screen_layout_page | meta_key__amt_twitter_author_username | meta_key__amt_facebook_author_profile_url | meta_key__amt_googleplus_author_profile_url | meta_key__locale | meta_key__wp_s2member_access_cap_times | meta_key__wp_s2member_last_auto_eot_time | meta_key__manageedit-shop_ordercolumnshidden | meta_key__wp_s2member_subscr_cid | meta_key___woocommerce_persistent_cart | meta_key__last_update | meta_key__wp_s2member_coupon_codes | meta_key__wp_s2member_reminders_enable | meta_key__as3cfpro-dismiss-licence-notice | meta_key__edit_page_per_page | meta_key__wpseo_content_analysis_disable | meta_key__wpseo_keyword_analysis_disable | meta_key__closedpostboxes_surl | meta_key__metaboxhidden_surl | meta_key__edit_download_per_page | meta_key__edit_post_per_page | meta_key__closedpostboxes_acf-field-group | meta_key__metaboxhidden_acf-field-group | meta_key__meta-box-order_surl | meta_key__screen_layout_surl | meta_key__closedpostboxes_popupbuilder | meta_key__metaboxhidden_popupbuilder | meta_key__meta-box-order_popupbuilder | meta_key__screen_layout_popupbuilder | meta_key__community-events-location | meta_key__as3cf_dismissed_notices | meta_key__syntax_highlighting | meta_key__instagram | meta_key__myspace | meta_key__pinterest | meta_key__soundcloud | meta_key__tumblr | meta_key__wikipedia | meta_key___edd_user_address | meta_key__closedpostboxes_wbcr-snippets | meta_key__metaboxhidden_wbcr-snippets | meta_key__q_eud_exports | meta_key___wtlwp_user | meta_key___wtlwp_created | meta_key___wtlwp_expire | meta_key___wtlwp_token | meta_key__show_welcome_panel | meta_key__tlwp_feedback_do_not_ask_again | meta_key__tlwp_feedback_do_not_ask_again_time | meta_key___wtlwp_last_login | meta_key___wtlwp_login_count | meta_key__wp_table_pixie_options | meta_key__wp_yoast_notifications | meta_key__tlwp_feedback_review_done | meta_key__tlwp_feedback_review_done_time | meta_key___aal_elementor_install_notice | meta_key__jetpack_tracks_anon_id | meta_key__jetpack_tracks_wpcom_id | |
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... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
op.client.dashboard_link
Skipping line 852: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 996: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 2196: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 2711: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 4785: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead
'http://192.168.86.250:43766/status'
preview_df = op.load.file("https://bumblebee.nyc3.digitaloceanspaces.com/argenisleon/users-data%20%281%29%20%281%29-535c306e-d5fe-4b09-bd6a-0693e6db26e8.csv", n_rows=35).ext.cache()
_output = {**preview_df.ext.to_json("*"), "meta": preview_df.meta.get() if (preview_df.meta and preview_df.meta.get) else {} }
_output = preview_df.ext.profile(columns="*", output="json")
Skipping line 46: unexpected end of data Skipping line 852: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 996: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 2196: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 2711: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead Skipping line 4785: NULL byte detected. This byte cannot be processed in Python's native csv library at the moment, so please pass in engine='c' instead
import dask
dask.__version__
'2.27.0'
print(_output)
{"columns": {"ID": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "int"}, "frequency": [{"value": "98", "count": 1}, {"value": "261", "count": 1}, {"value": "276", "count": 1}, {"value": "288", "count": 1}, {"value": "31", "count": 1}, {"value": "32", "count": 1}, {"value": "271", "count": 1}, {"value": "33", "count": 1}, {"value": "44", "count": 1}, {"value": "45", "count": 1}, {"value": "48", "count": 1}, {"value": "61", "count": 1}, {"value": "66", "count": 1}, {"value": "71", "count": 1}, {"value": "42", "count": 1}, {"value": "75", "count": 1}, {"value": "251", "count": 1}, {"value": "208", "count": 1}, {"value": "131", "count": 1}, {"value": "132", "count": 1}, {"value": "139", "count": 1}, {"value": "152", "count": 1}, {"value": "155", "count": 1}, {"value": "166", "count": 1}, {"value": "254", "count": 1}, {"value": "187", "count": 1}, {"value": "211", "count": 1}, {"value": "233", "count": 1}, {"value": "235", "count": 1}, {"value": "236", "count": 1}, {"value": "243", "count": 1}, {"value": "249", "count": 1}, {"value": "273", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "int"}}, "user_login": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "zuriavvega", "count": 1}, {"value": "caralunapr", "count": 1}, {"value": "claudiacj", "count": 1}, {"value": "franciscaroni", "count": 1}, {"value": "gabette", "count": 1}, {"value": "georgi2608", "count": 1}, {"value": "cataguio", "count": 1}, {"value": "gloriag", "count": 1}, {"value": "mariana", "count": 1}, {"value": "silvyfalquez", "count": 1}, {"value": "soniasoglia", "count": 1}, {"value": "valentinac", "count": 1}, {"value": "vaneggiare", "count": 1}, {"value": "wenvillarruel", "count": 1}, {"value": "irenea", "count": 1}, {"value": "zeina gabriela", "count": 1}, {"value": "arimaui", "count": 1}, {"value": "Satoriv", "count": 1}, {"value": "CarlaECM", "count": 1}, {"value": "Clara", "count": 1}, {"value": "Itavaamonde", "count": 1}, {"value": "Luisa55", "count": 1}, {"value": "MINEMARQUEZ", "count": 1}, {"value": "Nina", "count": 1}, {"value": "brigittesaint", "count": 1}, {"value": "Rebe22222", "count": 1}, {"value": "SuperBela", "count": 1}, {"value": "Susanacfg", "count": 1}, {"value": "Suza", "count": 1}, {"value": "almarincon", "count": 1}, {"value": "altruisted", "count": 1}, {"value": "amaliaamoedo", "count": 1}, {"value": "cinthia7", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "user_nicename": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "zuriavvega", "count": 1}, {"value": "irenea", "count": 1}, {"value": "minemarquez", "count": 1}, {"value": "nina", "count": 1}, {"value": "rebe22222", "count": 1}, {"value": "satoriv", "count": 1}, {"value": "itavaamonde", "count": 1}, {"value": "silvyfalquez", "count": 1}, {"value": "superbela", "count": 1}, {"value": "susanacfg", "count": 1}, {"value": "suza", "count": 1}, {"value": "valentinac", "count": 1}, {"value": "vaneggiare", "count": 1}, {"value": "wenvillarruel", "count": 1}, {"value": "soniasoglia", "count": 1}, {"value": "zeina-gabriela", "count": 1}, {"value": "georgi2608", "count": 1}, {"value": "cataguio", "count": 1}, {"value": "almarincon", "count": 1}, {"value": "altruisted", "count": 1}, {"value": "amaliaamoedo", "count": 1}, {"value": "arimaui", "count": 1}, {"value": "brigittesaint", "count": 1}, {"value": "caralunapr", "count": 1}, {"value": "gloriag", "count": 1}, {"value": "carlaecm", "count": 1}, {"value": "cgarcia23", "count": 1}, {"value": "cinthia7", "count": 1}, {"value": "clara", "count": 1}, {"value": "claudiacj", "count": 1}, {"value": "franciscaroni", "count": 1}, {"value": "gabette", "count": 1}, {"value": "mariana", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "user_email": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "email"}, "frequency": [{"value": "zuriavega11@yahoo.com", "count": 1}, {"value": "glogon71@gmail.com", "count": 1}, {"value": "m.vaamonde@gmail.com", "count": 1}, {"value": "marianavq@hotmail.com.ar", "count": 1}, {"value": "minnemarquez@gmail.com", "count": 1}, {"value": "rebeca.omana@yahoo.com.mx", "count": 1}, {"value": "ireneabreu@gmail.com", "count": 1}, {"value": "satori.chi@gmail.com", "count": 1}, {"value": "soniasoglia@yahoo.com.ar", "count": 1}, {"value": "susana.fernandes@gmail.com", "count": 1}, {"value": "susanavasquez17@gmail.com", "count": 1}, {"value": "unmailparanina@gmail.com", "count": 1}, {"value": "valentinaisabel@gmail.com", "count": 1}, {"value": "vanem03@hotmail.com", "count": 1}, {"value": "sfalquez@hotmail.com", "count": 1}, {"value": "zeinaname@gmail.com", "count": 1}, {"value": "ganabela12@hotmail.com", "count": 1}, {"value": "catalinaguio@gmail.com", "count": 1}, {"value": "almarinconruiz@hotmail.com", "count": 1}, {"value": "altruisted@gmail.com", "count": 1}, {"value": "ama.amoedo@gmail.com", "count": 1}, {"value": "ariadnarome@gmail.com", "count": 1}, {"value": "awhernandez75@gmail.com", "count": 1}, {"value": "brigittesaint@hotmail.com", "count": 1}, {"value": "georgi2608@gmail.com", "count": 1}, {"value": "carlacastillom@gmail.com", "count": 1}, {"value": "cinthialarreategui@hotmail.com", "count": 1}, {"value": "claraalfaro@me.com", "count": 1}, {"value": "claudiajimenez@hotmail.com", "count": 1}, {"value": "cynthiagar64@hotmail.com", "count": 1}, {"value": "franciscaroni@gmail.com", "count": 1}, {"value": "gabrielablon@yahoo.com.ar", "count": 1}, {"value": "luisita.g.i@gmail.com", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "email"}}, "user_url": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "user_registered": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "date", "format": "%Y-%m-%d %H:%M:%S"}, "frequency": [{"value": "2013-11-06 18:46:21", "count": 1}, {"value": "2013-09-11 20:13:46", "count": 1}, {"value": "2013-10-06 16:54:35", "count": 1}, {"value": "2013-10-15 17:50:58", "count": 1}, {"value": "2013-10-15 22:57:50", "count": 1}, {"value": "2013-10-16 01:39:11", "count": 1}, {"value": "2013-09-16 16:54:45", "count": 1}, {"value": "2013-10-20 03:34:49", "count": 1}, {"value": "2013-10-23 04:07:46", "count": 1}, {"value": "2013-10-24 22:39:40", "count": 1}, {"value": "2013-10-29 02:40:17", "count": 1}, {"value": "2013-10-31 01:26:49", "count": 1}, {"value": "2013-10-31 21:57:25", "count": 1}, {"value": "2013-11-01 03:22:54", "count": 1}, {"value": "2013-10-22 19:28:11", "count": 1}, {"value": "2013-11-02 16:47:36", "count": 1}, {"value": "2013-09-02 18:39:50", "count": 1}, {"value": "2013-07-13 23:33:40", "count": 1}, {"value": "2013-07-07 14:28:03", "count": 1}, {"value": "2013-07-07 14:33:45", "count": 1}, {"value": "2013-07-08 14:12:40", "count": 1}, {"value": "2013-07-08 14:57:53", "count": 1}, {"value": "2013-07-08 16:46:56", "count": 1}, {"value": "2013-07-08 21:57:02", "count": 1}, {"value": "2013-09-11 20:09:00", "count": 1}, {"value": "2013-07-12 22:58:21", "count": 1}, {"value": "2013-07-15 19:06:34", "count": 1}, {"value": "2013-07-16 19:23:10", "count": 1}, {"value": "2013-07-30 15:05:31", "count": 1}, {"value": "2013-08-09 00:16:38", "count": 1}, {"value": "2013-08-28 06:43:41", "count": 1}, {"value": "2013-08-28 18:30:27", "count": 1}, {"value": "2013-10-04 09:46:13", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "date", "format": "%Y-%m-%d %H:%M:%S"}}, "user_activation_key": {"stats": {"match": 6, "missing": 29, "mismatch": 29, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "nan", "count": 29}, {"value": "iE9NCjqVrAJtHWHtMAUG", "count": 1}, {"value": "1551405832:$P$BZTT/wFqoQhPE8K9..9c/nRhewlGMp.", "count": 1}, {"value": "$P$BdT5dfJ/ya0i2YoprnjEf/JTkWfdsE0", "count": 1}, {"value": "$P$BKwfdu7aoH3uac8WSkMziuyef1.qvd.", "count": 1}, {"value": "$P$BKU7gDOauoNL1/ValTgGTIQ/T3UyFM.", "count": 1}, {"value": "$P$BIwpSjEYtbb0r2Yop4HvgbHSl6f0qZ/", "count": 1}], "count_uniques": 7}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "user_status": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "int"}, "frequency": [{"value": "0", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "int"}}, "display_name": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "zuria vega", "count": 1}, {"value": "Maria Luisa Gutierrez Ibarra", "count": 1}, {"value": "Susana Fernandes", "count": 1}, {"value": "Susana Vasquez", "count": 1}, {"value": "Valentina Cordero", "count": 1}, {"value": "Vanessa Mosqueda", "count": 1}, {"value": "Mariana Vergara Quindimil", "count": 1}, {"value": "Wendy Hernandez", "count": 1}, {"value": "alma rincon", "count": 1}, {"value": "ariadna romero", "count": 1}, {"value": "cinthia larreategui", "count": 1}, {"value": "francis suarez", "count": 1}, {"value": "gabriela Blondeau", "count": 1}, {"value": "silvana falquez", "count": 1}, {"value": "Zeina Nam\u00e9", "count": 1}, {"value": "sonia soglia", "count": 1}, {"value": "Margarita Vaamonde", "count": 1}, {"value": "Clara Alfaro", "count": 1}, {"value": "Amalia Amoedo", "count": 1}, {"value": "Anabela Gette", "count": 1}, {"value": "Brigitte Saint-Hilaire", "count": 1}, {"value": "Carla Castillo Mujica", "count": 1}, {"value": "Catalina Guio", "count": 1}, {"value": "Celi N", "count": 1}, {"value": "Maria Alejandra Perez Rincon", "count": 1}, {"value": "Cinthia Garcia", "count": 1}, {"value": "Claudia Jimenez", "count": 1}, {"value": "Georgina Perez", "count": 1}, {"value": "Gloria Gonzalez", "count": 1}, {"value": "Irene Abreu", "count": 1}, {"value": "Juliannette Reyes", "count": 1}, {"value": "MINERVA MARQUEZ", "count": 1}, {"value": "Rebeca Omana Bruguera", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "role": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "s2member_level4", "count": 32}, {"value": "subscriber", "count": 3}], "count_uniques": 2}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "ccaps": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__first_name": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "Susana", "count": 2}, {"value": "Maria Alejandra", "count": 1}, {"value": "gabriela", "count": 1}, {"value": "francis", "count": 1}, {"value": "cinthia", "count": 1}, {"value": "ariadna", "count": 1}, {"value": "alma", "count": 1}, {"value": "Zeina", "count": 1}, {"value": "Wendy", "count": 1}, {"value": "Vanessa", "count": 1}, {"value": "Valentina", "count": 1}, {"value": "Rebeca", "count": 1}, {"value": "Raquel", "count": 1}, {"value": "Mariana", "count": 1}, {"value": "Maria Luisa", "count": 1}, {"value": "sonia", "count": 1}, {"value": "MINERVA", "count": 1}, {"value": "zuria", "count": 1}, {"value": "Amalia", "count": 1}, {"value": "Anabela", "count": 1}, {"value": "Brigitte", "count": 1}, {"value": "Carla", "count": 1}, {"value": "Catalina", "count": 1}, {"value": "Celi", "count": 1}, {"value": "Margarita", "count": 1}, {"value": "Cinthia", "count": 1}, {"value": "Claudia", "count": 1}, {"value": "Georgina", "count": 1}, {"value": "Gloria", "count": 1}, {"value": "Irene", "count": 1}, {"value": "Juliannette", "count": 1}, {"value": "Clara", "count": 1}, {"value": "silvana", "count": 1}], "count_uniques": 34}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "meta_key__last_name": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "vega", "count": 1}, {"value": "Nam\u00e9", "count": 1}, {"value": "Reyes", "count": 1}, {"value": "Rocchini", "count": 1}, {"value": "Rojas", "count": 1}, {"value": "Saint-Hilaire", "count": 1}, {"value": "Omana Bruguera", "count": 1}, {"value": "Vaamonde", "count": 1}, {"value": "Vergara Quindimil", "count": 1}, {"value": "falquez", "count": 1}, {"value": "larreategui", "count": 1}, {"value": "rincon", "count": 1}, {"value": "romero", "count": 1}, {"value": "soglia", "count": 1}, {"value": "Vasquez", "count": 1}, {"value": "suarez", "count": 1}, {"value": "Mosqueda", "count": 1}, {"value": "Gette", "count": 1}, {"value": "Alfaro", "count": 1}, {"value": "Amoedo", "count": 1}, {"value": "Blondeau", "count": 1}, {"value": "Castillo Mujica", "count": 1}, {"value": "Cordero", "count": 1}, {"value": "Fernandes", "count": 1}, {"value": "N", "count": 1}, {"value": "Garcia", "count": 1}, {"value": "Gonzalez", "count": 1}, {"value": "Guio", "count": 1}, {"value": "Gutierrez Ibarra", "count": 1}, {"value": "Hernandez", "count": 1}, {"value": "Jimenez", "count": 1}, {"value": "MARQUEZ", "count": 1}, {"value": "Perez Rincon", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "meta_key__nickname": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "zuriavvega", "count": 1}, {"value": "caralunapr", "count": 1}, {"value": "claudiacj", "count": 1}, {"value": "franciscaroni", "count": 1}, {"value": "gabette", "count": 1}, {"value": "georgi2608", "count": 1}, {"value": "cataguio", "count": 1}, {"value": "gloriag", "count": 1}, {"value": "mariana", "count": 1}, {"value": "silvyfalquez", 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"frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__wp_table_pixie_options": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__wp_yoast_notifications": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__tlwp_feedback_review_done": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__tlwp_feedback_review_done_time": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key___aal_elementor_install_notice": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__jetpack_tracks_anon_id": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__jetpack_tracks_wpcom_id": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}}, "name": null, "file_name": "https://bumblebee.nyc3.digitaloceanspaces.com/argenisleon/users-data%20%281%29%20%281%29-535c306e-d5fe-4b09-bd6a-0693e6db26e8.csv", "summary": {"cols_count": 183, "rows_count": 35, "dtypes_list": ["object"], "total_count_dtypes": 1, "missing_count": 0, "p_missing": 0.0}}
!pip install dask==2.27.0
Collecting dask==2.27.0 Downloading dask-2.27.0-py3-none-any.whl (843 kB) Requirement already satisfied: pyyaml in c:\users\argenisleon\appdata\roaming\python\python37\site-packages (from dask==2.27.0) (5.3.1) Installing collected packages: dask Attempting uninstall: dask Found existing installation: dask 2.25.0 Uninstalling dask-2.25.0: Successfully uninstalled dask-2.25.0 Successfully installed dask-2.27.0
ERROR: datashader 0.8.0 requires datashape>=0.5.1, which is not installed. ERROR: datashader 0.8.0 requires scikit-image, which is not installed.
import dask
dask.__version__
'2.25.0'
print(_output)
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{"value": "Clara", "count": 1}, {"value": "Itavaamonde", "count": 1}, {"value": "Luisa55", "count": 1}, {"value": "MINEMARQUEZ", "count": 1}, {"value": "Nina", "count": 1}, {"value": "brigittesaint", "count": 1}, {"value": "Rebe22222", "count": 1}, {"value": "SuperBela", "count": 1}, {"value": "Susanacfg", "count": 1}, {"value": "Suza", "count": 1}, {"value": "almarincon", "count": 1}, {"value": "altruisted", "count": 1}, {"value": "amaliaamoedo", "count": 1}, {"value": "cinthia7", "count": 1}], "count_uniques": 35}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}, "user_nicename": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "zuriavvega", "count": 1}, {"value": "irenea", "count": 1}, {"value": "minemarquez", "count": 1}, {"value": "nina", "count": 1}, {"value": "rebe22222", "count": 1}, {"value": "satoriv", "count": 1}, {"value": "itavaamonde", "count": 1}, {"value": "silvyfalquez", "count": 1}, {"value": 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"dtype": "object", "profiler_dtype": {"dtype": "object"}}, "user_registered": {"stats": {"match": 35, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "date", "format": "%Y-%m-%d %H:%M:%S"}, "frequency": [{"value": "2013-11-06 18:46:21", "count": 1}, {"value": "2013-09-11 20:13:46", "count": 1}, {"value": "2013-10-06 16:54:35", "count": 1}, {"value": "2013-10-15 17:50:58", "count": 1}, {"value": "2013-10-15 22:57:50", "count": 1}, {"value": "2013-10-16 01:39:11", "count": 1}, {"value": "2013-09-16 16:54:45", "count": 1}, {"value": "2013-10-20 03:34:49", "count": 1}, {"value": "2013-10-23 04:07:46", "count": 1}, {"value": "2013-10-24 22:39:40", "count": 1}, {"value": "2013-10-29 02:40:17", "count": 1}, {"value": "2013-10-31 01:26:49", "count": 1}, {"value": "2013-10-31 21:57:25", "count": 1}, {"value": "2013-11-01 03:22:54", "count": 1}, {"value": "2013-10-22 19:28:11", "count": 1}, {"value": "2013-11-02 16:47:36", "count": 1}, {"value": "2013-09-02 18:39:50", "count": 1}, 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"frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__wp_table_pixie_options": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__wp_yoast_notifications": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__tlwp_feedback_review_done": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__tlwp_feedback_review_done_time": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key___aal_elementor_install_notice": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__jetpack_tracks_anon_id": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}, "meta_key__jetpack_tracks_wpcom_id": {"stats": {"match": 35, "missing": 35, "mismatch": 0, "profiler_dtype": {"dtype": "object"}, "frequency": [{"value": "nan", "count": 35}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": {"dtype": "object"}}}, "name": null, "file_name": "https://bumblebee.nyc3.digitaloceanspaces.com/argenisleon/users-data%20%281%29%20%281%29-535c306e-d5fe-4b09-bd6a-0693e6db26e8.csv", "summary": {"cols_count": 183, "rows_count": 35, "dtypes_list": ["object"], "total_count_dtypes": 1, "missing_count": 0, "p_missing": 0.0}}
_output = df.ext.buffer_window("*", 4, 34).ext.to_json("*")
print(_output)
{'sample': {'columns': [{'title': 'ID'}, {'title': 'user_login'}, {'title': 'user_nicename'}, {'title': 'user_email'}, {'title': 'user_url'}, {'title': 'user_registered'}, {'title': 'user_activation_key'}, {'title': 'user_status'}, {'title': 'display_name'}, {'title': 'role'}, {'title': 'ccaps'}, {'title': 'meta_key__first_name'}, {'title': 'meta_key__last_name'}, {'title': 'meta_key__nickname'}, {'title': 'meta_key__description'}, {'title': 'meta_key__rich_editing'}, {'title': 'meta_key__comment_shortcuts'}, {'title': 'meta_key__admin_color'}, {'title': 'meta_key__use_ssl'}, {'title': 'meta_key__show_admin_bar_front'}, {'title': 'meta_key__wp_capabilities'}, {'title': 'meta_key__wp_user_level'}, {'title': 'meta_key__dismissed_wp_pointers'}, {'title': 'meta_key__wp_dashboard_quick_press_last_post_id'}, {'title': 'meta_key__wp_user-settings'}, {'title': 'meta_key__wp_user-settings-time'}, {'title': 'meta_key__AtD_options'}, {'title': 'meta_key__AtD_check_when'}, {'title': 'meta_key__AtD_guess_lang'}, {'title': 'meta_key__AtD_ignored_phrases'}, {'title': 'meta_key__aim'}, {'title': 'meta_key__yim'}, {'title': 'meta_key__jabber'}, {'title': 'meta_key__nav_menu_recently_edited'}, {'title': 'meta_key__managenav-menuscolumnshidden'}, {'title': 'meta_key__metaboxhidden_nav-menus'}, {'title': 'meta_key__closedpostboxes_post'}, {'title': 'meta_key__metaboxhidden_post'}, {'title': 'meta_key__pmpro_logins'}, {'title': 'meta_key__pmpro_visits'}, {'title': 'meta_key__pmpro_views'}, {'title': 'meta_key__wp_s2member_last_login_time'}, {'title': 'meta_key__wp_s2member_registration_ip'}, {'title': 'meta_key__wp_s2member_login_counter'}, {'title': 'meta_key__wp_s2member_paid_registration_times'}, {'title': 'meta_key__default_password_nag'}, {'title': 'meta_key__wp_s2member_subscr_gateway'}, {'title': 'meta_key__wp_s2member_subscr_id'}, {'title': 'meta_key__wp_s2member_custom'}, {'title': 'meta_key__wp_s2member_ipn_signup_vars'}, {'title': 'meta_key__wp_s2member_first_payment_txn_id'}, {'title': 'meta_key__wp_s2member_last_payment_time'}, {'title': 'meta_key__meta-box-order_dashboard'}, {'title': 'meta_key__screen_layout_dashboard'}, {'title': 'meta_key__wp_s2member_notes'}, {'title': 'meta_key__wp_s2member_auto_eot_time'}, {'title': 'meta_key___yoast_wpseo_profile_updated'}, {'title': 'meta_key__wpseo_title'}, {'title': 'meta_key__wpseo_metadesc'}, {'title': 'meta_key__wpseo_metakey'}, {'title': 'meta_key__googleplus'}, {'title': 'meta_key__twitter'}, {'title': 'meta_key__facebook'}, {'title': 'meta_key__meta-box-order_post'}, {'title': 'meta_key__screen_layout_post'}, {'title': 'meta_key__closedpostboxes_page'}, {'title': 'meta_key__metaboxhidden_page'}, {'title': 'meta_key__manageedit-postcolumnshidden'}, {'title': 'meta_key__easycontactforms_stat_pointer'}, {'title': 'meta_key__wp_s2member_capability_times'}, {'title': 'meta_key__wp_s2member_subscr_baid'}, {'title': 'meta_key__closedpostboxes_dashboard'}, {'title': 'meta_key__metaboxhidden_dashboard'}, {'title': 'meta_key__closedpostboxes_attachment'}, {'title': 'meta_key__metaboxhidden_attachment'}, {'title': 'meta_key__meta-box-order_attachment'}, {'title': 'meta_key__screen_layout_attachment'}, {'title': 'meta_key__session_tokens'}, {'title': 'meta_key___edd_htaccess_missing_dismissed'}, {'title': 'meta_key__wp_media_library_mode'}, {'title': 'meta_key__closedpostboxes_download'}, {'title': 'meta_key__metaboxhidden_download'}, {'title': 'meta_key__billing_country'}, {'title': 'meta_key__billing_first_name'}, {'title': 'meta_key__billing_last_name'}, {'title': 'meta_key__billing_company'}, {'title': 'meta_key__billing_address_1'}, {'title': 'meta_key__billing_address_2'}, {'title': 'meta_key__billing_city'}, {'title': 'meta_key__billing_state'}, {'title': 'meta_key__billing_postcode'}, {'title': 'meta_key__billing_email'}, {'title': 'meta_key__billing_phone'}, {'title': 'meta_key__shipping_country'}, {'title': 'meta_key__shipping_first_name'}, {'title': 'meta_key__shipping_last_name'}, {'title': 'meta_key__shipping_company'}, {'title': 'meta_key__shipping_address_1'}, {'title': 'meta_key__shipping_address_2'}, {'title': 'meta_key__shipping_city'}, {'title': 'meta_key__shipping_state'}, {'title': 'meta_key__shipping_postcode'}, {'title': 'meta_key__user_title'}, {'title': 'meta_key__linkedin'}, {'title': 'meta_key__digg'}, {'title': 'meta_key__flickr'}, {'title': 'meta_key__stumbleupon'}, {'title': 'meta_key__youtube'}, {'title': 'meta_key__yelp'}, {'title': 'meta_key__reddit'}, {'title': 'meta_key__delicious'}, {'title': 'meta_key__closedpostboxes_spucpt'}, {'title': 'meta_key__metaboxhidden_spucpt'}, {'title': 'meta_key__default_category_id_for_user'}, {'title': 'meta_key___edd_nginx_redirect_dismissed'}, {'title': 'meta_key__meta-box-order_download'}, {'title': 'meta_key__screen_layout_download'}, {'title': 'meta_key___edd_pending_verification'}, {'title': 'meta_key__ecae_premium_ignore_count_notice'}, {'title': 'meta_key__ecae_premium_ignore_notice'}, {'title': 'meta_key__ecae_premium_ignore_count_notice_total'}, {'title': 'meta_key__wpseo_ignore_tour'}, {'title': 'meta_key__wpseo_noindex_author'}, {'title': 'meta_key__wpseo_seen_about_version'}, {'title': 'meta_key__meta-box-order_page'}, {'title': 'meta_key__screen_layout_page'}, {'title': 'meta_key__amt_twitter_author_username'}, {'title': 'meta_key__amt_facebook_author_profile_url'}, {'title': 'meta_key__amt_googleplus_author_profile_url'}, {'title': 'meta_key__locale'}, {'title': 'meta_key__wp_s2member_access_cap_times'}, {'title': 'meta_key__wp_s2member_last_auto_eot_time'}, {'title': 'meta_key__manageedit-shop_ordercolumnshidden'}, {'title': 'meta_key__wp_s2member_subscr_cid'}, {'title': 'meta_key___woocommerce_persistent_cart'}, {'title': 'meta_key__last_update'}, {'title': 'meta_key__wp_s2member_coupon_codes'}, {'title': 'meta_key__wp_s2member_reminders_enable'}, {'title': 'meta_key__as3cfpro-dismiss-licence-notice'}, {'title': 'meta_key__edit_page_per_page'}, {'title': 'meta_key__wpseo_content_analysis_disable'}, {'title': 'meta_key__wpseo_keyword_analysis_disable'}, {'title': 'meta_key__closedpostboxes_surl'}, {'title': 'meta_key__metaboxhidden_surl'}, {'title': 'meta_key__edit_download_per_page'}, {'title': 'meta_key__edit_post_per_page'}, {'title': 'meta_key__closedpostboxes_acf-field-group'}, {'title': 'meta_key__metaboxhidden_acf-field-group'}, {'title': 'meta_key__meta-box-order_surl'}, {'title': 'meta_key__screen_layout_surl'}, {'title': 'meta_key__closedpostboxes_popupbuilder'}, {'title': 'meta_key__metaboxhidden_popupbuilder'}, {'title': 'meta_key__meta-box-order_popupbuilder'}, {'title': 'meta_key__screen_layout_popupbuilder'}, {'title': 'meta_key__community-events-location'}, {'title': 'meta_key__as3cf_dismissed_notices'}, {'title': 'meta_key__syntax_highlighting'}, {'title': 'meta_key__instagram'}, {'title': 'meta_key__myspace'}, {'title': 'meta_key__pinterest'}, {'title': 'meta_key__soundcloud'}, {'title': 'meta_key__tumblr'}, {'title': 'meta_key__wikipedia'}, {'title': 'meta_key___edd_user_address'}, {'title': 'meta_key__closedpostboxes_wbcr-snippets'}, {'title': 'meta_key__metaboxhidden_wbcr-snippets'}, {'title': 'meta_key__q_eud_exports'}, {'title': 'meta_key___wtlwp_user'}, {'title': 'meta_key___wtlwp_created'}, {'title': 'meta_key___wtlwp_expire'}, {'title': 'meta_key___wtlwp_token'}, {'title': 'meta_key__show_welcome_panel'}, {'title': 'meta_key__tlwp_feedback_do_not_ask_again'}, {'title': 'meta_key__tlwp_feedback_do_not_ask_again_time'}, {'title': 'meta_key___wtlwp_last_login'}, {'title': 'meta_key___wtlwp_login_count'}, {'title': 'meta_key__wp_table_pixie_options'}, {'title': 'meta_key__wp_yoast_notifications'}, {'title': 'meta_key__tlwp_feedback_review_done'}, {'title': 'meta_key__tlwp_feedback_review_done_time'}, {'title': 'meta_key___aal_elementor_install_notice'}, {'title': 'meta_key__jetpack_tracks_anon_id'}, {'title': 'meta_key__jetpack_tracks_wpcom_id'}], 'value': [['44', 'Rebe22222', 'rebe22222', 'rebeca.omana@yahoo.com.mx', nan, '2013-07-08 14:57:53', nan, '0', 'Rebeca Omana Bruguera', 's2member_level4', nan, 'Rebeca', 'Omana Bruguera', 'Rebe22222', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Sun Jul 05 2020 11:50:35 GMT-0400', '69.174.87.140', '996', '{"level":1373295473,"level4":1373295473}', '1', 'paypal', 'I-GDDHAAPKNT4R', nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, nan, nan, '1379337854', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:1:{s:64:"1ab370b34ee335ca3db5979209ac1a1e1ff3c4458d04c9077f30a21f9f5270e9";a:4:{s:10:"expiration";i:1595173835;s:2:"ip";s:13:"187.237.25.96";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1";s:5:"login";i:1593964235;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['45', 'Clara', 'clara', 'claraalfaro@me.com', nan, '2013-07-08 16:46:56', '$P$BIwpSjEYtbb0r2Yop4HvgbHSl6f0qZ/', '0', 'Clara Alfaro', 's2member_level4', nan, 'Clara', 'Alfaro', 'Clara', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Mon Sep 07 2020 06:21:49 GMT-0400', '5.152.172.137', '323', '{"level":1373302016,"level4":1373302016}', '1', 'paypal', 'I-L9A7GPEKGRRV', 'miastral.com', '{"txn_type":"subscr_signup","subscr_id":"I-L9A7GPEKGRRV","last_name":"Alfaro Brujo","option_selection1":"miastral.com","option_selection2":"5.152.172.137","residence_country":"ES","mc_currency":"USD","item_name":"Aware Supervixen (Anual)","business":"miastral.servicios@gmail.com","amount3":"300.00","recurring":"300.00","verify_sign":"A1c3jw3C7NhiAhhhDN.T7m6uZtVRAvk8JRV-Hbtd-DUrq66jhwAW2KSy","payer_status":"verified","payer_email":"claraalfaro@mac.com","first_name":"Clara","receiver_email":"miastral.servicios@gmail.com","payer_id":"UEZW6R9TY2YMU","option_name1":"Originating Domain","invoice":"51daeba19084f~5.152.172.137","option_name2":"Customer IP Address","reattempt":"1","item_number":"4","subscr_date":"09:45:07 Jul 08, 2013 PDT","custom":"miastral.com","charset":"windows-1252","notify_version":"3.7","period3":"1 Y","mc_amount3":"300.00","ipn_track_id":"7fd3c38f4b7c7","subscr_gateway":"paypal","eotper":null,"ccaps":null,"level":"4","ip":"5.152.172.137","period1":"0 D","mc_amount1":"0.00","initial_term":"0 D","initial":"300.00","regular":"300.00","regular_term":"1 Y"}', '3AJ64428S53837158', 'Wed Jul 08 2020 08:17:38 GMT-0400', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:3:{s:64:"8086733ad3764358d56675287c9ba17a4163d16b8fe04346f2f4d80a4f341eae";a:4:{s:10:"expiration";i:1627326617;s:2:"ip";s:12:"62.83.77.152";s:2:"ua";s:126:"Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X) AppleWebKit/603.3.8 (KHTML, like Gecko) Version/10.0 Mobile/14G60 Safari/602.1";s:5:"login";i:1595790617;}s:64:"870a29d310585e24ae0865e26a38cca9bf0773b47fa74bbdb111de2bc8ad12a5";a:4:{s:10:"expiration";i:1627787670;s:2:"ip";s:12:"81.35.120.27";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_3_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.5 Mobile/15E148 Safari/604.1";s:5:"login";i:1596251670;}s:64:"e4f3b3cbca13877521098ba8464252ed4724dd83c378aadbeebcacfa09c134f0";a:4:{s:10:"expiration";i:1631010109;s:2:"ip";s:14:"37.222.204.122";s:2:"ua";s:126:"Mozilla/5.0 (iPad; CPU OS 10_3_3 like Mac OS X) AppleWebKit/603.3.8 (KHTML, like Gecko) Version/10.0 Mobile/14G60 Safari/602.1";s:5:"login";i:1599474109;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['48', 'wenvillarruel', 'wenvillarruel', 'awhernandez75@gmail.com', nan, '2013-07-08 21:57:02', 'iE9NCjqVrAJtHWHtMAUG', '0', 'Wendy Hernandez', 's2member_level4', nan, 'Wendy', 'Hernandez', 'wenvillarruel', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Sun Mar 11 2018 23:39:46 GMT-0400', '187.237.39.24', '178', '{"level":1373320622,"level4":1373320622}', nan, 'paypal', nan, nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, nan, nan, '1378743081', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:1:{s:64:"b440f9a42f9d9d5ffbd74daf09f13abfe5dab564098eba3932eaf64839025feb";a:4:{s:10:"expiration";i:1522035586;s:2:"ip";s:14:"189.216.99.177";s:2:"ua";s:136:"Mozilla/5.0 (iPhone; CPU iPhone OS 11_2_6 like Mac OS X) AppleWebKit/604.5.6 (KHTML, like Gecko) Version/11.0 Mobile/15D100 Safari/604.1";s:5:"login";i:1520825986;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['61', 'franciscaroni', 'franciscaroni', 'franciscaroni@gmail.com', nan, '2013-07-12 22:58:21', nan, '0', 'francis suarez', 's2member_level4', nan, 'francis', 'suarez', 'franciscaroni', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Tue Sep 15 2020 23:40:46 GMT-0400', '71.196.55.231', '1727', '{"level":1373669901,"level4":1373669901}', nan, 'paypal', nan, nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, 'Demoted by s2Member: Wed Jun 8, 2016 12:14 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-1449MNDVFHP8', nan, '1393386477', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:4:{i:1430669658;s:16:"-s2member_level4";i:1433951729;s:15:"s2member_level4";i:1465388059;s:16:"-s2member_level4";i:1468437306;s:15:"s2member_level4";}', nan, nan, nan, nan, nan, nan, nan, 'a:10:{s:64:"aeacffe39ae680d415194905c4468538103857b2e18b4032c1dae3ee95ee07a3";a:4:{s:10:"expiration";i:1626799639;s:2:"ip";s:39:"2607:fb90:92c4:6ad5:f5c9:d6c4:79ea:c29e";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1";s:5:"login";i:1595263639;}s:64:"f1e7b84d81f0d3261ef5645c4bb9ef01a2108f09080303c98ea6e4bb3a6140f9";a:4:{s:10:"expiration";i:1626802178;s:2:"ip";s:39:"2600:1700:13a0:4420:a85e:fea7:3fa3:89d1";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1";s:5:"login";i:1595266178;}s:64:"1100c4b2392ceb861a22c3371f7f4b3f0a98feb734a4b976b059cf65ca829da2";a:4:{s:10:"expiration";i:1626815837;s:2:"ip";s:39:"2600:1700:1cf0:86c1:ac37:15fc:9239:9c3e";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; 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ID @ time of demotion: paypal -› I-C8639KPWYEG9\nDemoted by s2Member: Fri May 22, 2015 1:39 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-URGT53F4WTPV\nDemoted by s2Member: Tue Jul 12, 2016 1:05 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-81ANA8RBJTED\nDemoted by s2Member: Wed Mar 22, 2017 1:31 pm UTC\nPaid Subscr. 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CPU iPhone OS 13_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.2 Mobile/15E148 Safari/604.1";s:5:"login";i:1600154390;}s:64:"927ff3d1a108731fe3b00c7621aec3866998b4023217be78072593a6e1897e8a";a:4:{s:10:"expiration";i:1631826845;s:2:"ip";s:15:"181.203.102.238";s:2:"ua";s:137:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.2 Mobile/15E148 Safari/604.1";s:5:"login";i:1600290845;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['115', 'gabette', 'gabette', 'gabrielablon@yahoo.com.ar', nan, '2013-08-09 00:16:38', nan, '0', 'gabriela Blondeau', 's2member_level4', nan, 'gabriela', 'Blondeau', 'gabette', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Wed Sep 04 2013 23:32:19 GMT-0430', '201.235.136.18', '2', '{"level":1376007398,"level4":1376007398}', nan, 'paypal', 'I-K1DTCMSNXDUU', nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, 'Demoted by s2Member: Mon Dec 9, 2013 12:29 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-8N1H5L8S4B47', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:1:{i:1482258226;s:15:"s2member_level4";}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['131', 'MINEMARQUEZ', 'minemarquez', 'minnemarquez@gmail.com', nan, '2013-08-28 06:43:41', nan, '0', 'MINERVA MARQUEZ', 's2member_level4', nan, 'MINERVA', 'MARQUEZ', 'MINEMARQUEZ', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Mon Sep 14 2020 12:11:48 GMT-0400', '201.110.140.80', '552', '{"level":1377672221,"level4":1377672221}', nan, nan, nan, nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, 'Demoted by s2Member: Mon Oct 28, 2013 2:59 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-W9DBUEX7NHS9\nDemoted by s2Member: Wed Jan 27, 2016 8:34 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-M6UGL1XS2BCU\nDemoted by s2Member: Mon Apr 4, 2016 12:04 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-MUM7AHAY3D59\nDemoted by s2Member: Thu Apr 19, 2018 11:24 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-WB7VVPXTC3Y9', nan, '1457029570', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:4:{i:1436277114;s:15:"s2member_level4";i:1453926859;s:16:"-s2member_level4";i:1457029570;s:15:"s2member_level4";i:1459771458;s:16:"-s2member_level4";}', nan, nan, nan, nan, nan, nan, nan, 'a:14:{s:64:"2e8de7d288ecc01b1316d978a3bf47a70e1ff9b8a55ea4da430d8bc4646483bc";a:4:{s:10:"expiration";i:1626491880;s:2:"ip";s:36:"2806:102e:a:dcdc:54ed:ee11:1622:ddcc";s:2:"ua";s:121:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Mobile Safari/537.36";s:5:"login";i:1594955880;}s:64:"c170c3d4a0cafcf77b8ddfb682c3e5e4f9759e1ac32312966cbcd25b97f7223c";a:4:{s:10:"expiration";i:1626701822;s:2:"ip";s:36:"2806:102e:a:dcdc:54ed:ee11:1622:ddcc";s:2:"ua";s:121:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Mobile Safari/537.36";s:5:"login";i:1595165822;}s:64:"f67af95eda4146dd1b1fc6d46b42a1cb099b4cb6ce38ddad9ef2e6ec17e5c090";a:4:{s:10:"expiration";i:1627828308;s:2:"ip";s:36:"2806:102e:a:86bc:2cb1:e604:dfcf:7a9f";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Mobile Safari/537.36";s:5:"login";i:1596292308;}s:64:"aaa17b527e312e5085d0e55707e5e19a8e5677f22a32ec9c4ad8525925010e85";a:4:{s:10:"expiration";i:1627914192;s:2:"ip";s:36:"2806:102e:a:86bc:2cb1:e604:dfcf:7a9f";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Mobile Safari/537.36";s:5:"login";i:1596378192;}s:64:"053ba1f00f6c7e7802256fa4f825ce9c34c8d2569b532769fa1ac83fd84183c0";a:4:{s:10:"expiration";i:1627935235;s:2:"ip";s:36:"2806:102e:a:86bc:b5d2:b776:17a4:39d2";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Mobile Safari/537.36";s:5:"login";i:1596399235;}s:64:"e7a6002c9d4fce6243b0a65563f91a0997f037128d58d1777c055c54107d1c1f";a:4:{s:10:"expiration";i:1627945833;s:2:"ip";s:36:"2806:102e:a:3425:b5d2:b776:17a4:39d2";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Mobile Safari/537.36";s:5:"login";i:1596409833;}s:64:"06173c3346500741e04c2d60325aa2a9a0febaff8ba826ff25c2e2cf11aa3abb";a:4:{s:10:"expiration";i:1628488893;s:2:"ip";s:36:"2806:102e:a:c5e9:ada3:dd68:e37e:44cb";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36";s:5:"login";i:1596952893;}s:64:"fcdc0e1aa6c3cf6e4fa0be69d0d51a0a52ba0b78dbda9d282660e43f53e009ed";a:4:{s:10:"expiration";i:1628610866;s:2:"ip";s:36:"2806:102e:a:c5e9:a457:e65d:f6fb:d3b0";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36";s:5:"login";i:1597074866;}s:64:"febfb93396f954c797c5f600f26d3ca43d5a2fc16d10f1823322f51e4c59db04";a:4:{s:10:"expiration";i:1628873810;s:2:"ip";s:36:"2806:102e:a:fb78:a457:e65d:f6fb:d3b0";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Mobile Safari/537.36";s:5:"login";i:1597337810;}s:64:"7ebf1c2b1c1c3f84227b7e5170bf85b7c6bcffa7601edb6df20d14abf467d80e";a:4:{s:10:"expiration";i:1629180113;s:2:"ip";s:36:"2806:102e:a:d74c:c1e7:c230:7109:59e9";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Mobile Safari/537.36";s:5:"login";i:1597644113;}s:64:"f1e1610ab3e0de458178aa98cdc4ca471fc7e9f21dc3e4158e7b47f4f2c12a96";a:4:{s:10:"expiration";i:1629185163;s:2:"ip";s:36:"2806:102e:a:d74c:c1e7:c230:7109:59e9";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Mobile Safari/537.36";s:5:"login";i:1597649163;}s:64:"96ac27ce1191bcdad85c8c2f09161c14b7986959a97c14e91aef2b4813a1b5ac";a:4:{s:10:"expiration";i:1631022505;s:2:"ip";s:36:"2806:102e:a:7900:6060:2893:15f3:a940";s:2:"ua";s:150:"Mozilla/5.0 (Linux; Android 10; SAMSUNG SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) SamsungBrowser/12.1 Chrome/79.0.3945.136 Mobile Safari/537.36";s:5:"login";i:1599486505;}s:64:"00f801a9385dd2b3e8e0d1369267f0c777c0afbdcf90ff69fd6d635db5835476";a:4:{s:10:"expiration";i:1631563961;s:2:"ip";s:36:"2806:102e:a:77a9:ed45:af81:2921:c4de";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.101 Mobile Safari/537.36";s:5:"login";i:1600027961;}s:64:"f5718238bfcee3a2e5ea1010891770bed96ee5af79fd4edd2761929cf135d808";a:4:{s:10:"expiration";i:1631635908;s:2:"ip";s:15:"201.175.159.182";s:2:"ua";s:122:"Mozilla/5.0 (Linux; Android 10; SM-A515F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.101 Mobile Safari/537.36";s:5:"login";i:1600099908;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, '{"1519134388.0001":"level1","1519134388.0002":"level2","1519134388.0003":"level3","1519134388.0004":"level4","1524137057.0001":"-level1","1524137057.0002":"-level2","1524137057.0003":"-level3","1524137057.0004":"-level4","1526672884.0001":"level1","1526672884.0002":"level2","1526672884.0003":"level3","1526672884.0004":"level4"}', '1524136675', nan, nan, nan, nan, 'a:0:{}', '1', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'true', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['132', 'SuperBela', 'superbela', 'ganabela12@hotmail.com', nan, '2013-08-28 18:30:27', nan, '0', 'Anabela Gette', 's2member_level4', nan, 'Anabela', 'Gette', 'SuperBela', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Mon Sep 07 2020 13:37:17 GMT-0400', '200.61.162.81', '869', '{"level":1377714627,"level4":1377714627}', '1', nan, nan, nan, nan, nan, 'Wed Dec 31 1969 20:00:00 GMT-0400', nan, nan, 'Arg. Anual. 12/08/16 // Cuenta venció el 12/08/17\n\nArg. Anual reactivada el 25 de agosto 2017 / Cuenta venció el 25 de agosto de 2018\n\nArg. Anual. Cuenta reactivada el 31 de agosto de 2018/ Cuenta venció el 31 de Agosto de 2019\n\nArg. Anual. Cuenta reactivada el 08 de abril de 2020', nan, '1589222571', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'a:4:{i:1437395984;s:16:"-s2member_level4";i:1438029027;s:15:"s2member_level4";i:1470838373;s:16:"-s2member_level4";i:1471031664;s:15:"s2member_level4";}', nan, nan, nan, nan, nan, nan, nan, 'a:2:{s:64:"18f501ddc6919dcb0043f0839530a6230d31ab49d3727b0aea8dcd3ebbef876d";a:4:{s:10:"expiration";i:1627288581;s:2:"ip";s:14:"90.170.101.179";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1";s:5:"login";i:1595752581;}s:64:"1b0b1356a237e82552f530bdf6f6cb74c2482e55296927e808bf28068476ed8e";a:4:{s:10:"expiration";i:1631036237;s:2:"ip";s:14:"90.170.101.179";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_6_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.2 Mobile/15E148 Safari/604.1";s:5:"login";i:1599500237;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, '{"1503603078.0001":"-level1","1503603078.0002":"-level2","1503603078.0003":"-level3","1503603078.0004":"-level4","1503689282.0001":"level1","1503689282.0002":"level2","1503689282.0003":"level3","1503689282.0004":"level4","1535385039.0001":"-level1","1535385039.0002":"-level2","1535385039.0003":"-level3","1535385039.0004":"-level4","1535733296.0001":"level1","1535733296.0002":"level2","1535733296.0003":"level3","1535733296.0004":"level4","1567442527.0001":"-level1","1567442527.0002":"-level2","1567442527.0003":"-level3","1567442527.0004":"-level4","1586383310.0001":"level1","1586383310.0002":"level2","1586383310.0003":"level3","1586383310.0004":"level4","1588978737.0001":"-level1","1588978737.0002":"-level2","1588978737.0003":"-level3","1588978737.0004":"-level4","1589222571.0001":"level1","1589222571.0002":"level2","1589222571.0003":"level3","1589222571.0004":"level4"}', nan, nan, nan, nan, nan, 'a:0:{}', '1', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'true', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['139', 'cgarcia23', 'cgarcia23', 'cynthiagar64@hotmail.com', nan, '2013-09-02 18:39:50', nan, '0', 'Cinthia Garcia', 's2member_level4', nan, 'Cinthia', 'Garcia', 'cgarcia23', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Mon Sep 14 2020 07:24:54 GMT-0400', '186.7.16.203', '816', '{"level":1378147190,"level2":1378147190,"level4":1522343971}', nan, 'paypal', 'I-VG1LUTKFP5DX', 'miastral.com', '{"txn_type":"subscr_signup","subscr_id":"I-VG1LUTKFP5DX","last_name":"Garcia","option_selection1":"139","option_selection2":"168.196.126.99","residence_country":"DO","mc_currency":"USD","item_name":"Aware Supervixen (Mensual)","business":"miastral.servicios@gmail.com","amount3":"30.00","recurring":"30.00","verify_sign":"A00ZnsNH5LtCdimPdJCzMdGJgE5JAwMUDcmJM7W9DXsB3u8RU9LAU6jj","payer_status":"verified","payer_email":"csfashionproject@live.com","first_name":"Cinthia","receiver_email":"miastral.servicios@gmail.com","option_name1":"Referencing Customer ID","payer_id":"AFSRT8N8VT4AN","invoice":"5f39a5e1a3e70~168.196.126.99","option_name2":"Customer IP Address","reattempt":"1","item_number":"4","payer_business_name":"Xcentriss By Cinthia Garcia","subscr_date":"14:33:35 Aug 16, 2020 PDT","custom":"miastral.com","charset":"UTF-8","notify_version":"3.9","period3":"1 M","mc_amount3":"30.00","ipn_track_id":"8d60b49de9500","option_selection":"139","amount":"30.00","option_name":"Referencing Customer ID","period":"1 M","mc_amount":"30.00","subscr_gateway":"paypal","subscr_baid":"I-VG1LUTKFP5DX","subscr_cid":"I-VG1LUTKFP5DX","level":"4","ccaps":null,"eotper":null,"ip":"168.196.126.99","period1":"0 D","mc_amount1":"0.00","initial_term":"0 D","initial":"30.00","regular":"30.00","regular_term":"1 M","currency":"USD","currency_symbol":"$"}', '6E234920B5860774C', 'Wed Sep 16 2020 07:15:40 GMT-0400', nan, nan, 'Demoted by s2Member: Thu Oct 3, 2013 6:49 pm UTC\nPaid Subscr. ID @ time of demotion: paypal -› I-3CJBPAY5PARY\nDemoted by s2Member: Mon Jul 9, 2018 11:28 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-E0S5BY671BJ2\nDemoted by s2Member: Mon Jun 1, 2020 12:51 pm UTC\nPaid Subscr. ID @ time of demotion: paypal → I-KYCJMVLNSV43\nDemoted by s2Member: Fri Aug 7, 2020 12:06 pm UTC\nPaid Subscr. 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CPU iPhone OS 13_7 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.2 Mobile/15E148 Safari/604.1";s:5:"login";i:1600288022;}}', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, None, None], ['235', 'arimaui', 'arimaui', 'ariadnarome@gmail.com', nan, '2013-10-15 22:57:50', nan, '0', 'ariadna romero', 's2member_level4', nan, 'ariadna', 'romero', 'arimaui', nan, 'true', 'false', 'fresh', '0', 'true', '{"s2member_level4":true}', '0', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'Sun Sep 06 2020 11:26:49 GMT-0400', '66.27.202.227', '428', '{"level":1381877870,"level2":1381877870,"level4":1393546969}', nan, 'paypal', 'I-GC81YMMN93NW', 'miastral.com', '{"txn_type":"subscr_signup","subscr_id":"I-GC81YMMN93NW","last_name":"Romero","option_selection1":"235","option_selection2":"50.209.80.217","residence_country":"US","mc_currency":"USD","item_name":"Aware Supervixen (Mensual)","business":"miastral.servicios@gmail.com","amount3":"30.00","recurring":"30.00","verify_sign":"AXowYGewk.OA42iWMyq7c-zQr0YnAOhUxvTHAr1GBvufehqTqAeo7VNs","payer_status":"unverified","payer_email":"ariadnarome@gmail.com","first_name":"Ariadna","receiver_email":"miastral.servicios@gmail.com","payer_id":"SP9TWEKQTUHFY","option_name1":"Referencing Customer ID","invoice":"5f4676c55029e~50.209.80.217","option_name2":"Customer IP Address","reattempt":"1","item_number":"4","subscr_date":"07:52:01 Aug 26, 2020 PDT","custom":"miastral.com","charset":"UTF-8","notify_version":"3.9","period3":"1 M","mc_amount3":"30.00","ipn_track_id":"cc2782297f8f6","option_selection":"235","amount":"30.00","option_name":"Referencing Customer ID","period":"1 M","mc_amount":"30.00","subscr_gateway":"paypal","subscr_baid":"I-GC81YMMN93NW","subscr_cid":"I-GC81YMMN93NW","level":"4","ccaps":null,"eotper":null,"ip":"50.209.80.217","period1":"0 D","mc_amount1":"0.00","initial_term":"0 D","initial":"30.00","regular":"30.00","regular_term":"1 M","currency":"USD","currency_symbol":"$"}', '9UU731185S702840K', 'Wed Aug 26 2020 10:53:27 GMT-0400', nan, nan, 'Demoted by s2Member: Sat Apr 6, 2019 11:32 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-1AUGHLXRBVE0\nDemoted by s2Member: Fri Oct 18, 2019 1:27 pm UTC\nPaid Subscr. ID @ time of demotion: paypal → I-2TERSRGX22RJ\nDemoted by s2Member: Thu Nov 28, 2019 10:36 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-71GKEVH8VMEJ\nDemoted by s2Member: Tue Jan 7, 2020 10:44 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-D3SF3CBLPSEE\nDemoted by s2Member: Thu Mar 19, 2020 11:10 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-6V4MTC4AXKB8\nDemoted by s2Member: Wed Jul 8, 2020 11:35 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-WAH4XBK1ULWT\nDemoted by s2Member: Sun Aug 23, 2020 11:59 am UTC\nPaid Subscr. ID @ time of demotion: paypal → I-YPV7WN4H0CRE', nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 'I-GC81YMMN93NW', nan, nan, nan, nan, nan, nan, 'a:9:{s:64:"b7ea494ee0288516eec7933f37d32a8aa2812b172089898cffbb4b74192bc295";a:4:{s:10:"expiration";i:1627451710;s:2:"ip";s:18:"2600:387:6:80d::ad";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1";s:5:"login";i:1595915710;}s:64:"60f99dd19177eae54c241496d44f1f078271abce78fe4938a6c1f8c70f0825df";a:4:{s:10:"expiration";i:1627563762;s:2:"ip";s:37:"2600:1700:6ba0:5740:f0c6:b62b:d9:527b";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; CPU iPhone OS 13_5_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Mobile/15E148 Safari/604.1";s:5:"login";i:1596027762;}s:64:"686862090a32cb589e101dbc7698d85a56a6773fadc1ff4baa0ae5fe55c67df0";a:4:{s:10:"expiration";i:1627627639;s:2:"ip";s:39:"2600:1700:6ba0:5740:d4a0:878b:fbf2:e1e5";s:2:"ua";s:139:"Mozilla/5.0 (iPhone; 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ID @ time of demotion: paypal -› I-FLKUYEUSN9SJ\nDemoted by s2Member: Thu May 21, 2020 12:15 pm UTC\nPaid Subscr. ID @ time of demotion: paypal → I-RSN61B6B87C0\nDemoted by s2Member: Tue Sep 29, 2020 4:30 am UTC\nPaid Subscr. 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C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 6213 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
df.ext.profile("*")
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'count': 1}, {'value': '{"1535898887.0001":"level0","1535898887.0002":"level1","1535898887.0003":"level2","1535898887.0004":"level3","1535898887.0005":"level4"}', 'count': 1}], 'count_uniques': 5598}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'meta_key__wp_s2member_last_auto_eot_time': {'stats': {'match': 2490, 'missing': 3723, 'mismatch': 3723, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': 'nan', 'count': 3723}, {'value': '1569324035', 'count': 1}, {'value': '1570208469', 'count': 1}, {'value': '1570271155', 'count': 1}, {'value': '1570531545', 'count': 1}, {'value': '1570564266', 'count': 1}, {'value': '1570617540', 'count': 1}, {'value': '1570621903', 'count': 1}, {'value': '1570705528', 'count': 1}, {'value': '1570710510', 'count': 1}, {'value': '1570713633', 'count': 1}, {'value': '1570789190', 'count': 1}, {'value': '1569932674', 'count': 1}, {'value': '1569929328', 'count': 1}, {'value': '1569841605', 'count': 1}, {'value': '1569755889', 'count': 1}, {'value': '1569325878', 'count': 1}, {'value': '1569515347', 'count': 1}, {'value': '1569400529', 'count': 1}, {'value': '1569408305', 'count': 1}, {'value': '1569419976', 'count': 1}, {'value': '1569494820', 'count': 1}, {'value': '1569498013', 'count': 1}, {'value': '1569081134', 'count': 1}, {'value': '1569503479', 'count': 1}, {'value': '1569585604', 'count': 1}, {'value': '1569665395', 'count': 1}, {'value': '1569667256', 'count': 1}, {'value': '1569668851', 'count': 1}, {'value': '1569672961', 'count': 1}, {'value': '1569673695', 'count': 1}, {'value': '1569751982', 'count': 1}, {'value': '1570188916', 'count': 1}], 'count_uniques': 2491}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'meta_key__manageedit-shop_ordercolumnshidden': {'stats': {'match': 6213, 'missing': 6208, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6208}, {'value': 'a:1:{i:0;s:15:"billing_address";}', 'count': 5}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wp_s2member_subscr_cid': {'stats': {'match': 1149, 'missing': 5064, 'mismatch': 5064, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'nan', 'count': 5064}, {'value': 'I-B215AJE9KF07', 'count': 1}, {'value': 'I-B1CKKEYYRK5R', 'count': 1}, {'value': 'I-AY0ANM3EVBSB', 'count': 1}, {'value': 'I-AXRECDYGH1F8', 'count': 1}, {'value': 'I-AWTUXY92MEY6', 'count': 1}, {'value': 'I-B1H4KUSWEVV5', 'count': 1}, {'value': 'I-AWTHD99CGEY6', 'count': 1}, {'value': 'I-AWGKW585C2G8', 'count': 1}, {'value': 'I-ATLU9WL7S1HM', 'count': 1}, {'value': 'I-ASBYNVCXX71D', 'count': 1}, {'value': 'I-ANN66CA41R4T', 'count': 1}, {'value': 'I-ANN52SKG9J4R', 'count': 1}, {'value': 'I-AN3B3NN777B4', 'count': 1}, {'value': 'I-AWJU65H1R25B', 'count': 1}, {'value': 'I-BT87TMDXKF9L', 'count': 1}, {'value': 'I-B2JHEBPFHFAF', 'count': 1}, {'value': 'I-BGKLNBHFFEU1', 'count': 1}, {'value': 'I-BSX1F50ND719', 'count': 1}, {'value': 'I-BSC92FAVR6WW', 'count': 1}, {'value': 'I-BSBTB4BCB9NN', 'count': 1}, {'value': 'I-BKF152HAPU38', 'count': 1}, {'value': 'I-BJLYWJE9RHY6', 'count': 1}, {'value': 'I-BJL9EKE32GY6', 'count': 1}, {'value': 'I-B2J5TXP9BEAF', 'count': 1}, {'value': 'I-BJL6AAEDKHY6', 'count': 1}, {'value': 'I-BEE0BHDF17AK', 'count': 1}, {'value': 'I-BE8TEL66URER', 'count': 1}, {'value': 'I-BE8R0B63WRER', 'count': 1}, {'value': 'I-B8112GJR38T3', 'count': 1}, {'value': 'I-B6XUW9MWD00D', 'count': 1}, {'value': 'I-B421J984LRUF', 'count': 1}, {'value': 'I-B1D3JU4EYXT0', 'count': 1}], 'count_uniques': 1150}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'meta_key___woocommerce_persistent_cart': {'stats': {'match': 6213, 'missing': 6199, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6199}, {'value': 'a:1:{s:4:"cart";a:0:{}}', 'count': 14}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__last_update': {'stats': {'match': 6213, 'missing': 6207, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6207}, {'value': '1486646617', 'count': 1}, {'value': '1486600864', 'count': 1}, {'value': '1486579404', 'count': 1}, {'value': '1486577280', 'count': 1}, {'value': '1486551198', 'count': 1}, {'value': '1486543330', 'count': 1}], 'count_uniques': 7}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wp_s2member_coupon_codes': {'stats': {'match': 951, 'missing': 5262, 'mismatch': 5262, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'nan', 'count': 5262}, {'value': 'a:0:{}', 'count': 951}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'meta_key__wp_s2member_reminders_enable': {'stats': {'match': 951, 'missing': 5262, 'mismatch': 5262, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': 'nan', 'count': 5262}, {'value': '1', 'count': 951}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'meta_key__as3cfpro-dismiss-licence-notice': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__edit_page_per_page': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wpseo_content_analysis_disable': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wpseo_keyword_analysis_disable': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__closedpostboxes_surl': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__metaboxhidden_surl': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__edit_download_per_page': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__edit_post_per_page': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__closedpostboxes_acf-field-group': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__metaboxhidden_acf-field-group': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__meta-box-order_surl': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__screen_layout_surl': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__closedpostboxes_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__metaboxhidden_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__meta-box-order_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__screen_layout_popupbuilder': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__community-events-location': {'stats': {'match': 6213, 'missing': 6212, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6212}, {'value': 'a:1:{s:2:"ip";s:21:"2600:6c51:7d7f:f8e7::";}', 'count': 1}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__as3cf_dismissed_notices': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__syntax_highlighting': {'stats': {'match': 4082, 'missing': 2131, 'mismatch': 2131, 'profiler_dtype': {'dtype': 'boolean'}, 'frequency': [{'value': 'true', 'count': 4082}, {'value': 'nan', 'count': 2131}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'boolean'}}, 'meta_key__instagram': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__myspace': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__pinterest': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__soundcloud': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tumblr': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wikipedia': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___edd_user_address': {'stats': {'match': 6213, 'missing': 5836, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 5836}, {'value': 'a:0:{}', 'count': 377}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__closedpostboxes_wbcr-snippets': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__metaboxhidden_wbcr-snippets': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__q_eud_exports': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_user': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_created': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_expire': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_token': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__show_welcome_panel': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_do_not_ask_again': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_do_not_ask_again_time': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_last_login': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___wtlwp_login_count': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wp_table_pixie_options': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__wp_yoast_notifications': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_review_done': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__tlwp_feedback_review_done_time': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key___aal_elementor_install_notice': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__jetpack_tracks_anon_id': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}, 'meta_key__jetpack_tracks_wpcom_id': {'stats': {'match': 6213, 'missing': 6213, 'mismatch': 0, 'profiler_dtype': {'dtype': 'object'}, 'frequency': [{'value': 'nan', 'count': 6213}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'object'}}}, 'name': None, 'file_name': 'data/users-data(1)(1).csv', 'summary': {'cols_count': 183, 'rows_count': 6213, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}
df.ext.display()
ID
1 (object)
not nullable
|
user_login
2 (object)
not nullable
|
user_nicename
3 (object)
not nullable
|
user_email
4 (object)
not nullable
|
user_url
5 (object)
not nullable
|
user_registered
6 (object)
not nullable
|
user_activation_key
7 (object)
not nullable
|
user_status
8 (object)
not nullable
|
display_name
9 (object)
not nullable
|
role
10 (object)
not nullable
|
ccaps
11 (object)
not nullable
|
meta_key__first_name
12 (object)
not nullable
|
meta_key__last_name
13 (object)
not nullable
|
meta_key__nickname
14 (object)
not nullable
|
meta_key__description
15 (object)
not nullable
|
meta_key__rich_editing
16 (object)
not nullable
|
meta_key__comment_shortcuts
17 (object)
not nullable
|
meta_key__admin_color
18 (object)
not nullable
|
meta_key__use_ssl
19 (object)
not nullable
|
meta_key__show_admin_bar_front
20 (object)
not nullable
|
meta_key__wp_capabilities
21 (object)
not nullable
|
meta_key__wp_user_level
22 (object)
not nullable
|
meta_key__dismissed_wp_pointers
23 (object)
not nullable
|
meta_key__wp_dashboard_quick_press_last_post_id
24 (object)
not nullable
|
meta_key__wp_user-settings
25 (object)
not nullable
|
meta_key__wp_user-settings-time
26 (object)
not nullable
|
meta_key__AtD_options
27 (object)
not nullable
|
meta_key__AtD_check_when
28 (object)
not nullable
|
meta_key__AtD_guess_lang
29 (object)
not nullable
|
meta_key__AtD_ignored_phrases
30 (object)
not nullable
|
meta_key__aim
31 (object)
not nullable
|
meta_key__yim
32 (object)
not nullable
|
meta_key__jabber
33 (object)
not nullable
|
meta_key__nav_menu_recently_edited
34 (object)
not nullable
|
meta_key__managenav-menuscolumnshidden
35 (object)
not nullable
|
meta_key__metaboxhidden_nav-menus
36 (object)
not nullable
|
meta_key__closedpostboxes_post
37 (object)
not nullable
|
meta_key__metaboxhidden_post
38 (object)
not nullable
|
meta_key__pmpro_logins
39 (object)
not nullable
|
meta_key__pmpro_visits
40 (object)
not nullable
|
meta_key__pmpro_views
41 (object)
not nullable
|
meta_key__wp_s2member_last_login_time
42 (object)
not nullable
|
meta_key__wp_s2member_registration_ip
43 (object)
not nullable
|
meta_key__wp_s2member_login_counter
44 (object)
not nullable
|
meta_key__wp_s2member_paid_registration_times
45 (object)
not nullable
|
meta_key__default_password_nag
46 (object)
not nullable
|
meta_key__wp_s2member_subscr_gateway
47 (object)
not nullable
|
meta_key__wp_s2member_subscr_id
48 (object)
not nullable
|
meta_key__wp_s2member_custom
49 (object)
not nullable
|
meta_key__wp_s2member_ipn_signup_vars
50 (object)
not nullable
|
meta_key__wp_s2member_first_payment_txn_id
51 (object)
not nullable
|
meta_key__wp_s2member_last_payment_time
52 (object)
not nullable
|
meta_key__meta-box-order_dashboard
53 (object)
not nullable
|
meta_key__screen_layout_dashboard
54 (object)
not nullable
|
meta_key__wp_s2member_notes
55 (object)
not nullable
|
meta_key__wp_s2member_auto_eot_time
56 (object)
not nullable
|
meta_key___yoast_wpseo_profile_updated
57 (object)
not nullable
|
meta_key__wpseo_title
58 (object)
not nullable
|
meta_key__wpseo_metadesc
59 (object)
not nullable
|
meta_key__wpseo_metakey
60 (object)
not nullable
|
meta_key__googleplus
61 (object)
not nullable
|
meta_key__twitter
62 (object)
not nullable
|
meta_key__facebook
63 (object)
not nullable
|
meta_key__meta-box-order_post
64 (object)
not nullable
|
meta_key__screen_layout_post
65 (object)
not nullable
|
meta_key__closedpostboxes_page
66 (object)
not nullable
|
meta_key__metaboxhidden_page
67 (object)
not nullable
|
meta_key__manageedit-postcolumnshidden
68 (object)
not nullable
|
meta_key__easycontactforms_stat_pointer
69 (object)
not nullable
|
meta_key__wp_s2member_capability_times
70 (object)
not nullable
|
meta_key__wp_s2member_subscr_baid
71 (object)
not nullable
|
meta_key__closedpostboxes_dashboard
72 (object)
not nullable
|
meta_key__metaboxhidden_dashboard
73 (object)
not nullable
|
meta_key__closedpostboxes_attachment
74 (object)
not nullable
|
meta_key__metaboxhidden_attachment
75 (object)
not nullable
|
meta_key__meta-box-order_attachment
76 (object)
not nullable
|
meta_key__screen_layout_attachment
77 (object)
not nullable
|
meta_key__session_tokens
78 (object)
not nullable
|
meta_key___edd_htaccess_missing_dismissed
79 (object)
not nullable
|
meta_key__wp_media_library_mode
80 (object)
not nullable
|
meta_key__closedpostboxes_download
81 (object)
not nullable
|
meta_key__metaboxhidden_download
82 (object)
not nullable
|
meta_key__billing_country
83 (object)
not nullable
|
meta_key__billing_first_name
84 (object)
not nullable
|
meta_key__billing_last_name
85 (object)
not nullable
|
meta_key__billing_company
86 (object)
not nullable
|
meta_key__billing_address_1
87 (object)
not nullable
|
meta_key__billing_address_2
88 (object)
not nullable
|
meta_key__billing_city
89 (object)
not nullable
|
meta_key__billing_state
90 (object)
not nullable
|
meta_key__billing_postcode
91 (object)
not nullable
|
meta_key__billing_email
92 (object)
not nullable
|
meta_key__billing_phone
93 (object)
not nullable
|
meta_key__shipping_country
94 (object)
not nullable
|
meta_key__shipping_first_name
95 (object)
not nullable
|
meta_key__shipping_last_name
96 (object)
not nullable
|
meta_key__shipping_company
97 (object)
not nullable
|
meta_key__shipping_address_1
98 (object)
not nullable
|
meta_key__shipping_address_2
99 (object)
not nullable
|
meta_key__shipping_city
100 (object)
not nullable
|
meta_key__shipping_state
101 (object)
not nullable
|
meta_key__shipping_postcode
102 (object)
not nullable
|
meta_key__user_title
103 (object)
not nullable
|
meta_key__linkedin
104 (object)
not nullable
|
meta_key__digg
105 (object)
not nullable
|
meta_key__flickr
106 (object)
not nullable
|
meta_key__stumbleupon
107 (object)
not nullable
|
meta_key__youtube
108 (object)
not nullable
|
meta_key__yelp
109 (object)
not nullable
|
meta_key__reddit
110 (object)
not nullable
|
meta_key__delicious
111 (object)
not nullable
|
meta_key__closedpostboxes_spucpt
112 (object)
not nullable
|
meta_key__metaboxhidden_spucpt
113 (object)
not nullable
|
meta_key__default_category_id_for_user
114 (object)
not nullable
|
meta_key___edd_nginx_redirect_dismissed
115 (object)
not nullable
|
meta_key__meta-box-order_download
116 (object)
not nullable
|
meta_key__screen_layout_download
117 (object)
not nullable
|
meta_key___edd_pending_verification
118 (object)
not nullable
|
meta_key__ecae_premium_ignore_count_notice
119 (object)
not nullable
|
meta_key__ecae_premium_ignore_notice
120 (object)
not nullable
|
meta_key__ecae_premium_ignore_count_notice_total
121 (object)
not nullable
|
meta_key__wpseo_ignore_tour
122 (object)
not nullable
|
meta_key__wpseo_noindex_author
123 (object)
not nullable
|
meta_key__wpseo_seen_about_version
124 (object)
not nullable
|
meta_key__meta-box-order_page
125 (object)
not nullable
|
meta_key__screen_layout_page
126 (object)
not nullable
|
meta_key__amt_twitter_author_username
127 (object)
not nullable
|
meta_key__amt_facebook_author_profile_url
128 (object)
not nullable
|
meta_key__amt_googleplus_author_profile_url
129 (object)
not nullable
|
meta_key__locale
130 (object)
not nullable
|
meta_key__wp_s2member_access_cap_times
131 (object)
not nullable
|
meta_key__wp_s2member_last_auto_eot_time
132 (object)
not nullable
|
meta_key__manageedit-shop_ordercolumnshidden
133 (object)
not nullable
|
meta_key__wp_s2member_subscr_cid
134 (object)
not nullable
|
meta_key___woocommerce_persistent_cart
135 (object)
not nullable
|
meta_key__last_update
136 (object)
not nullable
|
meta_key__wp_s2member_coupon_codes
137 (object)
not nullable
|
meta_key__wp_s2member_reminders_enable
138 (object)
not nullable
|
meta_key__as3cfpro-dismiss-licence-notice
139 (object)
not nullable
|
meta_key__edit_page_per_page
140 (object)
not nullable
|
meta_key__wpseo_content_analysis_disable
141 (object)
not nullable
|
meta_key__wpseo_keyword_analysis_disable
142 (object)
not nullable
|
meta_key__closedpostboxes_surl
143 (object)
not nullable
|
meta_key__metaboxhidden_surl
144 (object)
not nullable
|
meta_key__edit_download_per_page
145 (object)
not nullable
|
meta_key__edit_post_per_page
146 (object)
not nullable
|
meta_key__closedpostboxes_acf-field-group
147 (object)
not nullable
|
meta_key__metaboxhidden_acf-field-group
148 (object)
not nullable
|
meta_key__meta-box-order_surl
149 (object)
not nullable
|
meta_key__screen_layout_surl
150 (object)
not nullable
|
meta_key__closedpostboxes_popupbuilder
151 (object)
not nullable
|
meta_key__metaboxhidden_popupbuilder
152 (object)
not nullable
|
meta_key__meta-box-order_popupbuilder
153 (object)
not nullable
|
meta_key__screen_layout_popupbuilder
154 (object)
not nullable
|
meta_key__community-events-location
155 (object)
not nullable
|
meta_key__as3cf_dismissed_notices
156 (object)
not nullable
|
meta_key__syntax_highlighting
157 (object)
not nullable
|
meta_key__instagram
158 (object)
not nullable
|
meta_key__myspace
159 (object)
not nullable
|
meta_key__pinterest
160 (object)
not nullable
|
meta_key__soundcloud
161 (object)
not nullable
|
meta_key__tumblr
162 (object)
not nullable
|
meta_key__wikipedia
163 (object)
not nullable
|
meta_key___edd_user_address
164 (object)
not nullable
|
meta_key__closedpostboxes_wbcr-snippets
165 (object)
not nullable
|
meta_key__metaboxhidden_wbcr-snippets
166 (object)
not nullable
|
meta_key__q_eud_exports
167 (object)
not nullable
|
meta_key___wtlwp_user
168 (object)
not nullable
|
meta_key___wtlwp_created
169 (object)
not nullable
|
meta_key___wtlwp_expire
170 (object)
not nullable
|
meta_key___wtlwp_token
171 (object)
not nullable
|
meta_key__show_welcome_panel
172 (object)
not nullable
|
meta_key__tlwp_feedback_do_not_ask_again
173 (object)
not nullable
|
meta_key__tlwp_feedback_do_not_ask_again_time
174 (object)
not nullable
|
meta_key___wtlwp_last_login
175 (object)
not nullable
|
meta_key___wtlwp_login_count
176 (object)
not nullable
|
meta_key__wp_table_pixie_options
177 (object)
not nullable
|
meta_key__wp_yoast_notifications
178 (object)
not nullable
|
meta_key__tlwp_feedback_review_done
179 (object)
not nullable
|
meta_key__tlwp_feedback_review_done_time
180 (object)
not nullable
|
meta_key___aal_elementor_install_notice
181 (object)
not nullable
|
meta_key__jetpack_tracks_anon_id
182 (object)
not nullable
|
meta_key__jetpack_tracks_wpcom_id
183 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
31
|
altruisted
|
altruisted
|
altruisted@gmail.com
|
nan
|
2013-07-07⋅13:09:38
|
nan
|
0
|
Maria⋅Alejandra⋅Perez⋅Rincon
|
s2member_level4
|
nan
|
Maria⋅Alejandra
|
Perez⋅Rincon
|
altruisted
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Tue⋅Sep⋅15⋅2020⋅11:19:04⋅GMT-0400
|
207.244.173.176
|
1793
|
{"level":1373202578,"level4":1373202578}
|
nan
|
paypal
|
nan
|
nan
|
nan
|
nan
|
Wed⋅Dec⋅31⋅1969⋅20:00:00⋅GMT-0400
|
nan
|
nan
|
nan
|
nan
|
1389306397
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:35:{s:64:"92565f85df41bab23251b193d2b2099987e3d7217acbf25adfaab77138e1a65e";a:4:{s:10:"expiration";i:1626361816;s:2:"ip";s:...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
32
|
claudiacj
|
claudiacj
|
claudiajimenez@hotmail.com
|
nan
|
2013-07-07⋅14:28:03
|
nan
|
0
|
Claudia⋅Jimenez
|
s2member_level4
|
nan
|
Claudia
|
Jimenez
|
claudiacj
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Sat⋅Jul⋅25⋅2020⋅23:33:08⋅GMT-0400
|
75.61.89.151
|
221
|
{"level":1373207283,"level4":1373207283}
|
1
|
paypal
|
I-RXFPFJL337TH
|
miastral.com
|
{"txn_type":"subscr_signup","subscr_id":"I-RXFPFJL337TH","last_name":"Jimenez","option_selection1":"miastral.com","option_sel...
|
17E171324K3821620
|
Mon⋅Sep⋅07⋅2020⋅09:11:59⋅GMT-0400
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:2:{s:64:"24394ce4202c7b689169434459f6584f1c096393eaac1cdb866abcc0db7efaf2";a:4:{s:10:"expiration";i:1626966340;s:2:"ip";s:3...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
33
|
almarincon
|
almarincon
|
almarinconruiz@hotmail.com
|
nan
|
2013-07-07⋅14:33:45
|
nan
|
0
|
alma⋅rincon
|
subscriber
|
nan
|
alma
|
rincon
|
almarincon
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"subscriber":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Wed⋅Jul⋅22⋅2020⋅01:58:20⋅GMT-0400
|
187.176.169.108
|
1000
|
{"level":1373207625,"level4":1373207625}
|
1
|
nan
|
nan
|
nan
|
nan
|
nan
|
Wed⋅Dec⋅31⋅1969⋅20:00:00⋅GMT-0400
|
nan
|
nan
|
Demoted⋅by⋅s2Member:⋅Thu⋅Jan⋅28,⋅2016⋅11:44⋅am⋅UTC
Paid⋅Subscr....
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:4:{i:1453981458;s:16:"-s2member_level4";i:1456354781;s:15:"s2member_level4";i:1464957333;s:16:"-s2member_level4";i:14652407...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:3:{s:64:"cfc0a70bb5d1ca582c8afb1daafa12eac0f3c44e6f78cc4257c566a59a3f3b56";a:4:{s:10:"expiration";i:1595835084;s:2:"ip";s:1...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
{"1600951852.0021":"-level1","1600951852.0022":"-level2","1600951852.0023":"-level3","1600951852.0024":"-level4"}
|
1599302831
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
42
|
Nina
|
nina
|
unmailparanina@gmail.com
|
nan
|
2013-07-08⋅14:12:40
|
$P$BdT5dfJ/ya0i2YoprnjEf/JTkWfdsE0
|
0
|
Celi⋅N
|
s2member_level4
|
nan
|
Celi
|
N
|
Nina
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Mon⋅Sep⋅07⋅2020⋅09:42:22⋅GMT-0400
|
190.188.10.224
|
1403
|
{"level":1373292760,"level3":1373292760,"level4":1379108078}
|
nan
|
paypal
|
I-Y5XGWKYCNDSH
|
nan
|
nan
|
7JA57275B9868032P
|
Sun⋅Oct⋅12⋅2014⋅07:20:17⋅GMT-0430
|
nan
|
nan
|
nan
|
nan
|
1379108104
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:17:{s:64:"42c49864364e439a76186e4237acbc3e9ef4e01f961930ddcb7e1cb529168d4e";a:4:{s:10:"expiration";i:1626286710;s:2:"ip";s:...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
44
|
Rebe22222
|
rebe22222
|
rebeca.omana@yahoo.com.mx
|
nan
|
2013-07-08⋅14:57:53
|
nan
|
0
|
Rebeca⋅Omana⋅Bruguera
|
s2member_level4
|
nan
|
Rebeca
|
Omana⋅Bruguera
|
Rebe22222
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Sun⋅Jul⋅05⋅2020⋅11:50:35⋅GMT-0400
|
69.174.87.140
|
996
|
{"level":1373295473,"level4":1373295473}
|
1
|
paypal
|
I-GDDHAAPKNT4R
|
nan
|
nan
|
nan
|
Wed⋅Dec⋅31⋅1969⋅20:00:00⋅GMT-0400
|
nan
|
nan
|
nan
|
nan
|
1379337854
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:1:{s:64:"1ab370b34ee335ca3db5979209ac1a1e1ff3c4458d04c9077f30a21f9f5270e9";a:4:{s:10:"expiration";i:1595173835;s:2:"ip";s:1...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
45
|
Clara
|
clara
|
claraalfaro@me.com
|
nan
|
2013-07-08⋅16:46:56
|
$P$BIwpSjEYtbb0r2Yop4HvgbHSl6f0qZ/
|
0
|
Clara⋅Alfaro
|
s2member_level4
|
nan
|
Clara
|
Alfaro
|
Clara
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Mon⋅Sep⋅07⋅2020⋅06:21:49⋅GMT-0400
|
5.152.172.137
|
323
|
{"level":1373302016,"level4":1373302016}
|
1
|
paypal
|
I-L9A7GPEKGRRV
|
miastral.com
|
{"txn_type":"subscr_signup","subscr_id":"I-L9A7GPEKGRRV","last_name":"Alfaro⋅Brujo","option_selection1":"miastral.com",...
|
3AJ64428S53837158
|
Wed⋅Jul⋅08⋅2020⋅08:17:38⋅GMT-0400
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:3:{s:64:"8086733ad3764358d56675287c9ba17a4163d16b8fe04346f2f4d80a4f341eae";a:4:{s:10:"expiration";i:1627326617;s:2:"ip";s:1...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
48
|
wenvillarruel
|
wenvillarruel
|
awhernandez75@gmail.com
|
nan
|
2013-07-08⋅21:57:02
|
iE9NCjqVrAJtHWHtMAUG
|
0
|
Wendy⋅Hernandez
|
s2member_level4
|
nan
|
Wendy
|
Hernandez
|
wenvillarruel
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Sun⋅Mar⋅11⋅2018⋅23:39:46⋅GMT-0400
|
187.237.39.24
|
178
|
{"level":1373320622,"level4":1373320622}
|
nan
|
paypal
|
nan
|
nan
|
nan
|
nan
|
Wed⋅Dec⋅31⋅1969⋅20:00:00⋅GMT-0400
|
nan
|
nan
|
nan
|
nan
|
1378743081
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:1:{s:64:"b440f9a42f9d9d5ffbd74daf09f13abfe5dab564098eba3932eaf64839025feb";a:4:{s:10:"expiration";i:1522035586;s:2:"ip";s:1...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
61
|
franciscaroni
|
franciscaroni
|
franciscaroni@gmail.com
|
nan
|
2013-07-12⋅22:58:21
|
nan
|
0
|
francis⋅suarez
|
s2member_level4
|
nan
|
francis
|
suarez
|
franciscaroni
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Tue⋅Sep⋅15⋅2020⋅23:40:46⋅GMT-0400
|
71.196.55.231
|
1727
|
{"level":1373669901,"level4":1373669901}
|
nan
|
paypal
|
nan
|
nan
|
nan
|
nan
|
Wed⋅Dec⋅31⋅1969⋅20:00:00⋅GMT-0400
|
nan
|
nan
|
Demoted⋅by⋅s2Member:⋅Wed⋅Jun⋅8,⋅2016⋅12:14⋅pm⋅UTC
Paid⋅Subscr....
|
nan
|
1393386477
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:4:{i:1430669658;s:16:"-s2member_level4";i:1433951729;s:15:"s2member_level4";i:1465388059;s:16:"-s2member_level4";i:14684373...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:10:{s:64:"aeacffe39ae680d415194905c4468538103857b2e18b4032c1dae3ee95ee07a3";a:4:{s:10:"expiration";i:1626799639;s:2:"ip";s:...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
true
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
66
|
Suza
|
suza
|
susanavasquez17@gmail.com
|
nan
|
2013-07-13⋅23:33:40
|
nan
|
0
|
Susana⋅Vasquez
|
s2member_level4
|
nan
|
Susana
|
Vasquez
|
Suza
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Sun⋅Sep⋅06⋅2020⋅23:37:47⋅GMT-0400
|
190.154.10.88
|
1580
|
{"level":1373758420,"level4":1373758420}
|
nan
|
paypal
|
I-0MVM4DE1LXF4
|
miastral.com
|
{"mc_gross":"30.00","invoice":"58d328bac4cd9~186.70.226.72","protection_eligibility":"Ineligible","payer_id":"FFTBZKUQKN6PY",...
|
7JD512058N8491315
|
Sat⋅Aug⋅22⋅2020⋅07:14:17⋅GMT-0400
|
nan
|
nan
|
Demoted⋅by⋅s2Member:⋅Fri⋅Aug⋅23,⋅2013⋅11:44⋅am⋅UTC
Paid⋅Subscr....
|
nan
|
1381758200
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:4:{i:1432301976;s:16:"-s2member_level4";i:1438566917;s:15:"s2member_level4";i:1468328757;s:16:"-s2member_level4";i:14683388...
|
I-0MVM4DE1LXF4
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:11:{s:64:"892aec3ae7aa1ce3d0b87dabf0da559cde675f8fdf47950596cbf22be2aebf4a";a:4:{s:10:"expiration";i:1626664209;s:2:"ip";s:...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
{"1490189487.0001":"-level1","1490189487.0002":"-level2","1490189487.0003":"-level3","1490189487.0004":"-level4","1490233608....
|
1490189487
|
nan
|
I-0MVM4DE1LXF4
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
true
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
71
|
Satoriv
|
satoriv
|
satori.chi@gmail.com
|
nan
|
2013-07-15⋅19:06:34
|
nan
|
0
|
Raquel⋅Rojas
|
s2member_level4
|
nan
|
Raquel
|
Rojas
|
Satoriv
|
nan
|
true
|
false
|
fresh
|
0
|
true
|
{"s2member_level4":true}
|
0
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
Wed⋅Sep⋅09⋅2020⋅19:48:29⋅GMT-0400
|
76.109.182.254
|
1047
|
{"level":1373915194,"level3":1373915194,"level4":1389977194}
|
nan
|
paypal
|
I-A9TW8VSAN6A1
|
miastral.com
|
{"mc_gross":"150.00","invoice":"599a0349a3225~2606:6000:6193:a400:9846:fbd2:6cf7:97fd","protection_eligibility":"Ineligible",...
|
84L65821UH583163C
|
Thu⋅Aug⋅20⋅2020⋅07:00:59⋅GMT-0400
|
nan
|
nan
|
Demoted⋅by⋅s2Member:⋅Wed⋅Jan⋅4,⋅2017⋅1:34⋅pm⋅UTC
Paid⋅Subscr.Y...
|
nan
|
1595425513
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:1:{i:1483536857;s:16:"-s2member_level4";}
|
I-A9TW8VSAN6A1
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
a:13:{s:64:"dcad2b2f336d561eaa422fb198cff5c0ab30ca969d9ea998d3d50668ad3c1f2e";a:4:{s:10:"expiration";i:1627013868;s:2:"ip";s:...
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
{"1503265701.0001":"level1","1503265701.0002":"level2","1503265701.0003":"level3","1503265701.0004":"level4"}
|
nan
|
nan
|
I-A9TW8VSAN6A1
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
true
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
nan
|
None
|
None
|
_output = df.ext.profile(columns="*", output="json")
_output = df.ext.set_buffer("*")
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
df.ext.buffer_window("*").cols.date_format("year", "%d/%m/%Y %I:%M:%S %p", "%d-%m-%Y", output_cols="new year").ext.profile(["new year"])
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
{'columns': {'new year': {'stats': {'match': 45716, 'missing': 312, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%d-%m-%Y'}, 'frequency': [{'value': '01-01-2003', 'count': 3323}, {'value': '01-01-1979', 'count': 3046}, {'value': '01-01-1998', 'count': 2697}, {'value': '01-01-2006', 'count': 2456}, {'value': '01-01-1988', 'count': 2296}, {'value': '01-01-2002', 'count': 2078}, {'value': '01-01-2004', 'count': 1940}, {'value': '01-01-2000', 'count': 1792}, {'value': '01-01-1997', 'count': 1696}, {'value': '01-01-1999', 'count': 1691}, {'value': '01-01-2001', 'count': 1650}, {'value': '01-01-1990', 'count': 1518}, {'value': '01-01-2009', 'count': 1497}, {'value': '01-01-1986', 'count': 1375}, {'value': '01-01-2007', 'count': 1189}, {'value': '01-01-2010', 'count': 1005}, {'value': '01-01-1993', 'count': 979}, {'value': '01-01-2008', 'count': 957}, {'value': '01-01-1987', 'count': 916}, {'value': '01-01-1991', 'count': 877}, {'value': '01-01-2005', 'count': 875}, {'value': '01-01-1994', 'count': 719}, {'value': '01-01-2011', 'count': 713}, {'value': '01-01-1974', 'count': 691}, {'value': '01-01-1996', 'count': 583}, {'value': '01-01-1995', 'count': 487}, {'value': '01-01-1981', 'count': 463}, {'value': '01-01-1977', 'count': 421}, {'value': '01-01-1984', 'count': 402}, {'value': '01-01-1985', 'count': 378}, {'value': '01-01-1992', 'count': 372}, {'value': '01-01-1983', 'count': 360}, {'value': '01-01-1982', 'count': 344}], 'count_uniques': 245}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'date', 'format': '%d-%m-%Y'}}}, 'name': None, 'file_name': None, 'summary': {'cols_count': 11, 'rows_count': 45716, 'dtypes_list': ['int64', 'object', 'float64'], 'total_count_dtypes': 3, 'missing_count': 0, 'p_missing': 0.0}}
op.client.dashboard_link
'http://192.168.86.250:23223/status'
df = op.load.file("data/foo.csv").ext.cache()
df.meta.get()
data/foo.csv
{'file_name': 'data/foo.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'utf-8', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'line_terminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}]}
df= df.cols.sqrt("id")
df.meta.get()
{'file_name': 'data/foo.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'utf-8', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'line_terminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}], 'last_action_time': 1602444573, 'transformations': {'actions': [{'math': 'id'}]}}
df.ext.buffer_window()
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1.000000 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 1.414214 | André | Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
2 | 1.732051 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
3 | 2.000000 | PAUL | dirac$ | 521 | pizza | 8 | 1954/07/10 | you |
4 | 2.236068 | Albert | Einstein | 634 | pizza | 8 | 1990/07/11 | up |
5 | 2.449490 | Galileo | GALiLEI | 672 | arepa | 5 | 1930/08/12 | never |
6 | 2.645751 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna |
7 | 2.828427 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let |
8 | 3.000000 | Johannes | KEPLER | 735 | taco | 3 | 1920/04/22 | you |
9 | 3.162278 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down |
10 | 3.316625 | Isaac | Newton | 992 | pasta | 9 | 1999/02/15 | never |
11 | 3.464102 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993/12/08 | gonna |
12 | 3.605551 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994/01/04 | run |
13 | 3.741657 | Fred | Hoy&&&le | 553 | pizzza | 8 | 1997/06/27 | around |
14 | 3.872983 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956/11/30 | and |
15 | 4.000000 | William | Gilbert### | 886 | BEER | 2 | 1958/03/26 | desert |
16 | 4.123106 | Marie | CURIE | 912 | Rice | 1 | 2000/03/22 | you |
17 | 4.242641 | Arthur | COM%%%pton | 812 | 110790 | 5 | 1899/01/01 | # |
18 | 4.358899 | JAMES | Chadwick | 467 | NaN | 10 | 1921/05/03 | # |
df.ext.set_buffer()
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6167: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
df.meta.get()
{'file_name': 'data/foo.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'utf-8', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'line_terminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}], 'last_action_time': 1602444021, 'transformations': {'actions': [{'math': 'id'}]}, 'buffer_time': 1602444194}
df.meta.get("buffer_time") > df.meta.get("last_action_time")
True
df.cols.names()
['id', 'firstName', 'lastName', 'billingId', 'product', 'price', 'birth', 'dummyCol']
df.ext.profile(columns="*")
Dask DataFrame Structure: id firstName lastName billingId product price birth dummyCol npartitions=1 object object object object object object object object ... ... ... ... ... ... ... ... Dask Name: read-csv, 1 tasks *
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6167: UserWarning: Insufficient elements for `head`. 30 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
{'columns': {'id': {'stats': {'match': 19, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '9', 'count': 1}, {'value': '17', 'count': 1}, {'value': '10', 'count': 1}, {'value': '11', 'count': 1}, {'value': '12', 'count': 1}, {'value': '13', 'count': 1}, {'value': '14', 'count': 1}, {'value': '15', 'count': 1}, {'value': '16', 'count': 1}, {'value': '18', 'count': 1}, {'value': '8', 'count': 1}, {'value': '19', 'count': 1}, {'value': '2', 'count': 1}, {'value': '3', 'count': 1}, {'value': '4', 'count': 1}, {'value': '5', 'count': 1}, {'value': '6', 'count': 1}, {'value': '7', 'count': 1}, {'value': '1', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'firstName': {'stats': {'match': 19, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'William', 'count': 1}, {'value': 'Galileo', 'count': 1}, {'value': 'Albert', 'count': 1}, {'value': 'André', 'count': 1}, {'value': 'Arthur', 'count': 1}, {'value': 'CaRL', 'count': 1}, {'value': 'David', 'count': 1}, {'value': 'Emmy%%', 'count': 1}, {'value': 'Fred', 'count': 1}, {'value': 'Isaac', 'count': 1}, {'value': 'PAUL', 'count': 1}, {'value': 'JAMES', 'count': 1}, {'value': 'JaMES', 'count': 1}, {'value': 'Johannes', 'count': 1}, {'value': 'Luis', 'count': 1}, {'value': 'Marie', 'count': 1}, {'value': 'Max!!!', 'count': 1}, {'value': 'NiELS', 'count': 1}, {'value': '((( Heinrich )))))', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'lastName': {'stats': {'match': 19, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'dirac$', 'count': 1}, {'value': 'Ga%%%uss', 'count': 1}, {'value': 'Alvarez$$%!', 'count': 1}, {'value': 'Ampère', 'count': 1}, {'value': 'Böhr//((%%', 'count': 1}, {'value': 'COM%%%pton', 'count': 1}, {'value': 'CURIE', 'count': 1}, {'value': 'Chadwick', 'count': 1}, {'value': 'Einstein', 'count': 1}, {'value': 'Gilbert###', 'count': 1}, {'value': 'Planck!!!', 'count': 1}, {'value': 'H$$$ilbert', 'count': 1}, {'value': 'Hertz', 'count': 1}, {'value': 'Hoy&&&le', 'count': 1}, {'value': 'KEPLER', 'count': 1}, {'value': 'M$$ax%%well', 'count': 1}, {'value': 'Newton', 'count': 1}, {'value': 'Nöether$', 'count': 1}, {'value': ' GALiLEI', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'billingId': {'stats': {'match': 19, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '992', 'count': 1}, {'value': '551', 'count': 1}, {'value': '116', 'count': 1}, {'value': '123', 'count': 1}, {'value': '234', 'count': 1}, {'value': '323', 'count': 1}, {'value': '423', 'count': 1}, {'value': '467', 'count': 1}, {'value': '521', 'count': 1}, {'value': '553', 'count': 1}, {'value': '912', 'count': 1}, {'value': '624', 'count': 1}, {'value': '634', 'count': 1}, {'value': '672', 'count': 1}, {'value': '735', 'count': 1}, {'value': '812', 'count': 1}, {'value': '875', 'count': 1}, {'value': '886', 'count': 1}, {'value': '111', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'product': {'stats': {'match': 18, 'missing': 1, 'mismatch': 1, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'pizza', 'count': 4}, {'value': 'taco', 'count': 3}, {'value': 'pasta', 'count': 2}, {'value': 'taaaccoo', 'count': 1}, {'value': 'pizzza', 'count': 1}, {'value': 'piza', 'count': 1}, {'value': 'nan', 'count': 1}, {'value': 'hamburguer', 'count': 1}, {'value': 'arepa', 'count': 1}, {'value': 'Rice', 'count': 1}, {'value': 'Cake', 'count': 1}, {'value': 'BEER', 'count': 1}, {'value': '110790', 'count': 1}], 'count_uniques': 13}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}, 'price': {'stats': {'match': 19, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'int'}, 'frequency': [{'value': '8', 'count': 6}, {'value': '3', 'count': 4}, {'value': '9', 'count': 2}, {'value': '5', 'count': 2}, {'value': '10', 'count': 2}, {'value': '4', 'count': 1}, {'value': '2', 'count': 1}, {'value': '1', 'count': 1}], 'count_uniques': 8}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'int'}}, 'birth': {'stats': {'match': 19, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'date', 'format': '%Y/%m/%d'}, 'frequency': [{'value': '2000/03/22', 'count': 1}, {'value': '1956/11/30', 'count': 1}, {'value': '1920/04/22', 'count': 1}, {'value': '1921/05/03', 'count': 1}, {'value': '1923/03/12', 'count': 1}, {'value': '1930/08/12', 'count': 1}, {'value': '1950/07/08', 'count': 1}, {'value': '1950/07/14', 'count': 1}, {'value': '1954/07/10', 'count': 1}, {'value': '1958/03/26', 'count': 1}, {'value': '1999/02/15', 'count': 1}, {'value': '1970/07/13', 'count': 1}, {'value': '1980/07/07', 'count': 1}, {'value': '1990/07/09', 'count': 1}, {'value': '1990/07/11', 'count': 1}, {'value': '1993/12/08', 'count': 1}, {'value': '1994/01/04', 'count': 1}, {'value': '1997/06/27', 'count': 1}, {'value': '1899/01/01', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'date', 'format': '%Y/%m/%d'}}, 'dummyCol': {'stats': {'match': 19, 'missing': 0, 'mismatch': 0, 'profiler_dtype': {'dtype': 'string'}, 'frequency': [{'value': 'you', 'count': 3}, {'value': 'gonna', 'count': 3}, {'value': 'never', 'count': 2}, {'value': '#', 'count': 2}, {'value': 'up', 'count': 1}, {'value': 'run ', 'count': 1}, {'value': 'never ', 'count': 1}, {'value': 'let', 'count': 1}, {'value': 'give', 'count': 1}, {'value': 'down', 'count': 1}, {'value': 'desert', 'count': 1}, {'value': 'around', 'count': 1}, {'value': 'and', 'count': 1}], 'count_uniques': 13}, 'dtype': 'object', 'profiler_dtype': {'dtype': 'string'}}}, 'name': None, 'file_name': 'data/foo.csv', 'summary': {'cols_count': 8, 'rows_count': 19, 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 0, 'p_missing': 0.0}}
df1 = op.load.file("data/crime.csv").ext.cache()
df1.meta.get()
data/crime.csv
{'file_name': 'data/crime.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'iso-8859-1', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'line_terminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}]}
df1.meta.get()
{'file_name': 'data/foo.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'utf-8', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'line_terminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}]}
df.compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 2 | André | Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
2 | 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
3 | 4 | PAUL | dirac$ | 521 | pizza | 8 | 1954/07/10 | you |
4 | 5 | Albert | Einstein | 634 | pizza | 8 | 1990/07/11 | up |
5 | 6 | Galileo | GALiLEI | 672 | arepa | 5 | 1930/08/12 | never |
6 | 7 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna |
7 | 8 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let |
8 | 9 | Johannes | KEPLER | 735 | taco | 3 | 1920/04/22 | you |
9 | 10 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down |
10 | 11 | Isaac | Newton | 992 | pasta | 9 | 1999/02/15 | never |
11 | 12 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993/12/08 | gonna |
12 | 13 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994/01/04 | run |
13 | 14 | Fred | Hoy&&&le | 553 | pizzza | 8 | 1997/06/27 | around |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956/11/30 | and |
15 | 16 | William | Gilbert### | 886 | BEER | 2 | 1958/03/26 | desert |
16 | 17 | Marie | CURIE | 912 | Rice | 1 | 2000/03/22 | you |
17 | 18 | Arthur | COM%%%pton | 812 | 110790 | 5 | 1899/01/01 | # |
18 | 19 | JAMES | Chadwick | 467 | NaN | 10 | 1921/05/03 | # |
df.cols.sqrt('id').compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1.000000 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 1.414214 | André | Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
2 | 1.732051 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
3 | 2.000000 | PAUL | dirac$ | 521 | pizza | 8 | 1954/07/10 | you |
4 | 2.236068 | Albert | Einstein | 634 | pizza | 8 | 1990/07/11 | up |
5 | 2.449490 | Galileo | GALiLEI | 672 | arepa | 5 | 1930/08/12 | never |
6 | 2.645751 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna |
7 | 2.828427 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let |
8 | 3.000000 | Johannes | KEPLER | 735 | taco | 3 | 1920/04/22 | you |
9 | 3.162278 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down |
10 | 3.316625 | Isaac | Newton | 992 | pasta | 9 | 1999/02/15 | never |
11 | 3.464102 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993/12/08 | gonna |
12 | 3.605551 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994/01/04 | run |
13 | 3.741657 | Fred | Hoy&&&le | 553 | pizzza | 8 | 1997/06/27 | around |
14 | 3.872983 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956/11/30 | and |
15 | 4.000000 | William | Gilbert### | 886 | BEER | 2 | 1958/03/26 | desert |
16 | 4.123106 | Marie | CURIE | 912 | Rice | 1 | 2000/03/22 | you |
17 | 4.242641 | Arthur | COM%%%pton | 812 | 110790 | 5 | 1899/01/01 | # |
18 | 4.358899 | JAMES | Chadwick | 467 | NaN | 10 | 1921/05/03 | # |
import math
math.kurtosis('1')
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-89-d8d0e1b4abc0> in <module> 1 import math ----> 2 math.kurtosis('1') AttributeError: module 'math' has no attribute 'kurtosis'
from optimus.expressions import reserved_words, Parser
p = Parser()
df.cols.set(value=p.parse('ATANH(1.1)'), output_cols="ida").compute()
..\optimus\engines\functions.py:187: RuntimeWarning: invalid value encountered in arctanh
id | firstName | lastName | billingId | product | price | birth | dummyCol | ida | |
---|---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never | NaN |
1 | 2 | André | Ampère | 423 | piza | 8 | 1950/07/08 | gonna | NaN |
2 | 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give | NaN |
3 | 4 | PAUL | dirac$ | 521 | pizza | 8 | 1954/07/10 | you | NaN |
4 | 5 | Albert | Einstein | 634 | pizza | 8 | 1990/07/11 | up | NaN |
5 | 6 | Galileo | GALiLEI | 672 | arepa | 5 | 1930/08/12 | never | NaN |
6 | 7 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna | NaN |
7 | 8 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let | NaN |
8 | 9 | Johannes | KEPLER | 735 | taco | 3 | 1920/04/22 | you | NaN |
9 | 10 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down | NaN |
10 | 11 | Isaac | Newton | 992 | pasta | 9 | 1999/02/15 | never | NaN |
11 | 12 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993/12/08 | gonna | NaN |
12 | 13 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994/01/04 | run | NaN |
13 | 14 | Fred | Hoy&&&le | 553 | pizzza | 8 | 1997/06/27 | around | NaN |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956/11/30 | and | NaN |
15 | 16 | William | Gilbert### | 886 | BEER | 2 | 1958/03/26 | desert | NaN |
16 | 17 | Marie | CURIE | 912 | Rice | 1 | 2000/03/22 | you | NaN |
17 | 18 | Arthur | COM%%%pton | 812 | 110790 | 5 | 1899/01/01 | # | NaN |
18 | 19 | JAMES | Chadwick | 467 | NaN | 10 | 1921/05/03 | # | NaN |
df.ext.buffer_window("*")
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-9-69de813ccc9f> in <module> ----> 1 df.ext.buffer_window("*") ~\Documents\Optimus\optimus\engines\base\extension.py in buffer_window(self, columns, lower_bound, upper_bound) 131 def buffer_window(self, columns=None, lower_bound=None, upper_bound=None): 132 --> 133 if not self.df._buffer: 134 self.df.ext.set_buffer(columns) 135 ~\Anaconda3\lib\site-packages\dask\dataframe\core.py in __getattr__(self, key) 3597 return self[key] 3598 else: -> 3599 raise AttributeError("'DataFrame' object has no attribute %r" % key) 3600 3601 def __dir__(self): AttributeError: 'DataFrame' object has no attribute '_buffer'
_output = df.ext.profile(columns="*", output="json")
<optimus.engines.dask.extension.ext.<locals>.Ext object at 0x0000025054888A48> <optimus.engines.dask.extension.ext.<locals>.Ext object at 0x0000025054888388> <optimus.engines.dask.extension.ext.<locals>.Ext object at 0x000002505488AE48> <optimus.engines.pandas.extension.ext.<locals>.Ext object at 0x00000250548888C8>
C:\Users\argenisleon\Anaconda3\lib\site-packages\dask\dataframe\core.py:6167: UserWarning: Insufficient elements for `head`. 30 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
<optimus.engines.dask.extension.ext.<locals>.Ext object at 0x0000025054B42B48> <optimus.engines.dask.extension.ext.<locals>.Ext object at 0x0000025054A8F2C8> <optimus.engines.dask.extension.ext.<locals>.Ext object at 0x0000025054A5C788>
df[col_name].astype(str).str.match(func[dtype]).value_counts().ext.to_dict()
df["cedula"].astype(str).value_counts().ext.to_dict()
{'28479393': 1, '23760628': 1, '21857839': 1, '19748383': 1, '17484892': 1}
df.cols.names()
['cedula', 'edad', 'apellido']
print(_output)
{"columns": {"cedula": {"stats": {"match": 5, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "int"}, "frequency": [{"value": "28479393", "count": 1}, {"value": "23760628", "count": 1}, {"value": "21857839", "count": 1}, {"value": "19748383", "count": 1}, {"value": "17484892", "count": 1}], "count_uniques": 5}, "dtype": "object", "profiler_dtype": {"dtype": "int"}}, "edad": {"stats": {"match": 5, "missing": 0, "mismatch": 0, "profiler_dtype": {"dtype": "int"}, "frequency": [{"value": "28", "count": 1}, {"value": "27", "count": 1}, {"value": "26", "count": 1}, {"value": "24", "count": 1}, {"value": "20", "count": 1}], "count_uniques": 5}, "dtype": "object", "profiler_dtype": {"dtype": "int"}}, "apellido": {"stats": {"match": 2, "missing": 3, "mismatch": 3, "profiler_dtype": {"dtype": "string"}, "frequency": [{"value": "nan", "count": 3}, {"value": "martinez", "count": 1}, {"value": "contreras", "count": 1}], "count_uniques": 3}, "dtype": "object", "profiler_dtype": {"dtype": "string"}}}, "name": null, "file_name": "https://bumblebee.nyc3.digitaloceanspaces.com/luisaguirre/edad-a3b720f7-931e-49f7-bc82-c31e97c98768.csv", "summary": {"cols_count": 3, "rows_count": 5, "dtypes_list": ["object"], "total_count_dtypes": 1, "missing_count": 0, "p_missing": 0.0}}
df.ext.display()
A
1 (object)
not nullable
|
B
2 (object)
not nullable
|
---|---|
1
|
2
|
1.1
|
2.2
|
Optimus
|
Megatron
|
true
|
false
|
10/4/1980
|
29/11/2007
|
[1,2,3,4,5]
|
[10,20,30,40,50]
|
{"a":1,"b":2,"c":3}
|
{"d":1,"e":2,"f":3}
|
male
|
female
|
111.111.111.111
|
192.0.0.1
|
https://hi-bumblebee.com
|
https://google.com
|
df.cols.frequency("*")
{'frequency': {'A': {'values': [{'value': '{"a":1,"b":2,"c":3}', 'count': 1}, {'value': 'true', 'count': 1}, {'value': 'male', 'count': 1}, {'value': 'https://hi-bumblebee.com', 'count': 1}, {'value': 'argenis@hi-bumblebee.com', 'count': 1}, {'value': '[1,2,3,4,5]', 'count': 1}, {'value': 'Optimus', 'count': 1}, {'value': '5555555555554444', 'count': 1}, {'value': '30345', 'count': 1}, {'value': '111.111.111.111', 'count': 1}, {'value': '10/4/1980', 'count': 1}, {'value': '1.1', 'count': 1}, {'value': '1', 'count': 1}]}, 'B': {'values': [{'value': '{"d":1,"e":2,"f":3}', 'count': 1}, {'value': 'luis@hi-bumblebee.com', 'count': 1}, {'value': 'https://google.com', 'count': 1}, {'value': 'female', 'count': 1}, {'value': 'false', 'count': 1}, {'value': '[10,20,30,40,50]', 'count': 1}, {'value': 'Megatron', 'count': 1}, {'value': '4111111111111111', 'count': 1}, {'value': '29/11/2007', 'count': 1}, {'value': '2.2', 'count': 1}, {'value': '2', 'count': 1}, {'value': '192.0.0.1', 'count': 1}, {'value': '11529', 'count': 1}]}}}
import fastnumbers
value = "1.1"
print(fastnumbers.isfloat(value) is True and fastnumbers.isint(value) is False)
print("---")
print(fastnumbers.isint(value))
print(fastnumbers.isfloat(value))
True --- False True
print(df.cols.count_mismatch({"A":"int"}))
print(df.cols.count_mismatch({"A":"decimal"}))
print(df.cols.count_mismatch({"A":"boolean"}))
print(df.cols.count_mismatch({"B":"date"}))
print(df.cols.count_mismatch({"A":"array"}))
print(df.cols.count_mismatch({"A":"object"}))
print(df.cols.count_mismatch({"A":"gender"}))
print(df.cols.count_mismatch({"A":"ip"}))
print(df.cols.count_mismatch({"A":"url"}))
print(df.cols.count_mismatch({"A":"email"}))
print(df.cols.count_mismatch({"A":"credit_card_number"}))
print(df.cols.count_mismatch({"A":"zip_code"}))
<function profiler_dtype_func.<locals>._int at 0x000001EAE9428438> 1 <function profiler_dtype_func.<locals>._int at 0x000001EAE9428438> 5555555555554444 <function profiler_dtype_func.<locals>._int at 0x000001EAE9428438> 30345 {'A': {'mismatch': 10, 'missing': 0, 'match': 3, 'profiler_dtype': 'int'}} <function profiler_dtype_func.<locals>._float at 0x000001EAE88CC828> 1.1 {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'decimal'}} <function str_to_boolean at 0x000001EAE918ECA8> TRUE {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'boolean'}} <function str_to_date at 0x000001EAE92DBD38> 29/11/2007 {'B': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'date'}} <function str_to_array at 0x000001EAE91CF048> 1 <function str_to_array at 0x000001EAE91CF048> 1.1 <function str_to_array at 0x000001EAE91CF048> Optimus <function str_to_array at 0x000001EAE91CF048> TRUE <function str_to_array at 0x000001EAE91CF048> 10/4/1980 <function str_to_array at 0x000001EAE91CF048> [1,2,3,4,5] <function str_to_array at 0x000001EAE91CF048> {"a":1,"b":2,"c":3} <function str_to_array at 0x000001EAE91CF048> Male <function str_to_array at 0x000001EAE91CF048> 111.111.111.111 <function str_to_array at 0x000001EAE91CF048> https://hi-bumblebee.com <function str_to_array at 0x000001EAE91CF048> argenis@hi-bumblebee.com <function str_to_array at 0x000001EAE91CF048> 5555555555554444 <function str_to_array at 0x000001EAE91CF048> 30345 {'A': {'mismatch': 0, 'missing': 0, 'match': 13, 'profiler_dtype': 'array'}} <function str_to_object at 0x000001EAE91CF0D8> {"a":1,"b":2,"c":3} {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'object'}} <function str_to_gender at 0x000001EAE92DBEE8> Male {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'gender'}} <function str_to_ip at 0x000001EAE91CF1F8> 111.111.111.111 {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'ip'}} <function str_to_url at 0x000001EAE91CF168> https://hi-bumblebee.com {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'url'}} <function str_to_email at 0x000001EAE91CF288> argenis@hi-bumblebee.com {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'email'}} {'A': {'mismatch': 13, 'missing': 0, 'match': 0, 'profiler_dtype': 'credit_card_number'}} <function str_to_zip_code at 0x000001EAE91CF3A8> 30345 {'A': {'mismatch': 12, 'missing': 0, 'match': 1, 'profiler_dtype': 'zip_code'}}
for i in df["A"]:
print(type(i),i)
<class 'str'> 1 <class 'str'> 1.1 <class 'str'> Optimus <class 'str'> TRUE <class 'str'> 10/4/1980 <class 'str'> [1,2,3,4,5] <class 'str'> {"a":1,"b":2,"c":3} <class 'str'> Male <class 'str'> 111.111.111.111 <class 'str'> https://hi-bumblebee.com <class 'str'> argenis@hi-bumblebee.com <class 'str'> 5555555555554444 <class 'str'> 30345
!pip list
Package Version ----------------------------- ------------------- -aex-core 0.7.2 -illow 7.0.0 -umba 0.48.0 -umpy 1.15.4 -ymongo 3.8.0 absl-py 0.7.0 airtable-python-wrapper 0.11.3.post1 alabaster 0.7.12 alembic 1.4.2 altair 3.0.1 amqp 2.4.1 anaconda-client 1.7.2 anaconda-navigator 1.9.12 ansiwrap 0.8.4 apache-airflow 1.10.9 apiclient 1.0.4 apispec 1.3.3 aplus 0.11.0 argcomplete 1.11.1 argh 0.26.2 arrow 0.15.6 asn1crypto 1.3.0 astor 0.7.1 astroid 2.3.3 astropy 4.0.1.post1 atomicwrites 1.3.0 attrs 19.3.0 autopep8 1.4.4 autovizwidget 0.12.9 Babel 2.8.0 backcall 0.1.0 backoff 1.8.0 backports.functools-lru-cache 1.6.1 backports.tempfile 1.0 backports.weakref 1.0.post1 bcrypt 3.1.7 beautifulsoup4 4.8.2 bleach 3.1.4 bokeh 2.0.1 boltons 19.1.0 boto3 1.9.139 botocore 1.12.139 bqplot 0.12.3 branca 0.3.1 cached-property 1.5.1 cachetools 3.1.1 cattrs 0.9.0 certifi 2020.4.5.1 cffi 1.14.0 chardet 3.0.4 ciso8601 2.1.3 click 7.1.1 cloudpickle 1.3.0 clyent 1.2.2 colorama 0.4.3 colorcet 2.0.1 colorlog 4.0.2 conda 4.8.3 conda-build 3.17.8 conda-package-handling 1.6.0 conda-verify 3.4.2 configparser 3.5.3 croniter 0.3.31 cryptography 2.8 cycler 0.10.0 Cython 0.29.19 cytoolz 0.10.1 dask 2.14.0 dask-glm 0.2.0 dask-ml 1.2.0 dask-tensorflow 0.0.2 dask-xgboost 0.1.9 databricks-cli 0.8.6 datashader 0.8.0 decorator 4.4.2 deepdiff 4.0.6 defusedxml 0.6.0 Deprecated 1.2.5 diff-match-patch 20181111 dill 0.3.0 distributed 2.14.0 dlib 19.17.0 docker 3.7.2 docker-pycreds 0.4.0 docutils 0.15.2 dufte 0.2.3 entrypoints 0.3 face 19.1.2 face-recognition 1.2.3 face-recognition-models 0.3.0 fast-histogram 0.8 fastavro 0.22.7 fastnumbers 3.0.0 fastparquet 0.3.3 filelock 3.0.12 findspark 1.3.0 flake8 3.7.9 Flask-Admin 1.5.4 Flask-AppBuilder 2.3.0 Flask-Babel 1.0.0 Flask-Caching 1.3.3 Flask-JWT-Extended 3.24.1 Flask-Login 0.4.1 Flask-OpenID 1.2.5 Flask-SQLAlchemy 2.4.1 flask-swagger 0.2.13 Flask-WTF 0.14.3 fsspec 0.7.1 funcsigs 1.0.2 future 0.16.0 gast 0.2.2 genson 1.1.0 gitdb2 2.0.5 GitPython 2.1.11 glob2 0.7 glom 19.2.0 google-api-python-client 1.7.9 google-auth 1.6.3 google-auth-httplib2 0.0.3 google-auth-oauthlib 0.2.0 graphviz 0.13 grpcio 1.19.0 gspread 3.1.0 gunicorn 19.9.0 h2o-pysparkling-2.3 2.3.24 h2o-pysparkling-2.4 2.4.10 h5py 2.10.0 handyspark 0.2.1a1 hdijupyterutils 0.12.9 HeapDict 1.0.1 holoviews 1.12.6 httplib2 0.12.1 humanize 0.5.1 hyperloglog 0.0.12 hypothesis 5.8.3 idna 2.9 imagesize 1.2.0 imgkit 1.0.1 importlib-metadata 1.5.0 intervaltree 3.0.2 ipydatawidgets 4.0.1 ipykernel 5.1.4 ipyleaflet 0.12.2 ipympl 0.4.1 ipython 7.5.0 ipython-genutils 0.2.0 ipyvolume 0.5.2 ipyvue 1.1.0 ipyvuetify 1.1.1 ipywebrtc 0.5.0 ipywidgets 7.5.1 iso8601 0.1.12 isort 4.3.21 jedi 0.14.1 Jinja2 2.11.1 jmespath 0.9.4 joblib 0.14.1 json-merge-patch 0.2 json5 0.9.4 jsonpickle 1.1 jsonschema 3.2.0 jupyter-client 6.1.2 jupyter-core 4.6.3 jupyterlab 1.2.5 jupyterlab-server 1.1.0 jupytext 1.1.1 Keras 2.2.4 Keras-Applications 1.0.7 Keras-Preprocessing 1.0.9 keyring 21.1.1 kiwisolver 1.1.0 kombu 4.5.0 lazy-object-proxy 1.4.3 libarchive-c 2.9 livy 0.6.0 llvmlite 0.31.0 locket 0.2.0 lockfile 0.12.2 Mako 1.1.2 Markdown 2.6.11 MarkupSafe 1.1.1 marshmallow 3.5.1 marshmallow-enum 1.5.1 marshmallow-oneofschema 2.0.1 marshmallow-sqlalchemy 0.22.3 matplotlib 3.0.3 mccabe 0.6.1 menuinst 1.4.16 mistune 0.8.4 mkl-fft 1.0.15 mkl-random 1.1.0 mkl-service 2.3.0 mleap 0.8.1 mlflow 0.9.1 mock 4.0.1 modin 0.7.0 more-itertools 8.2.0 msgpack 1.0.0 multipledispatch 0.6.0 mypy-extensions 0.4.3 mysqlclient 1.4.6 navigator-updater 0.2.1 nbconvert 5.6.1 nbformat 5.0.4 nltk 3.4.5 nose-exclude 0.5.0 notebook 6.0.2 numba 0.49.0 numpy 1.18.2 numpydoc 0.9.2 oauthlib 3.0.1 olefile 0.46 ordered-set 3.1.1 ordered-set-stubs 0.1.3 orjson 2.6.1 packaging 20.3 paho-mqtt 1.4.0 pandas 1.0.3 pandocfilters 1.4.2 panel 0.6.4 papermill 1.0.1 param 1.9.0 paramiko 2.7.1 parso 0.5.2 partd 1.1.0 pathtools 0.1.2 patsy 0.5.1 pendulum 2.1.0 perfplot 0.8.1 pexpect 4.8.0 pickleshare 0.7.5 pika 0.12.0 Pillow 7.1.1 pip 20.0.2 pkginfo 1.5.0.1 plotly 4.1.0 pluggy 0.13.1 prefect 0.9.8 prison 0.1.2 progressbar2 3.37.1 prometheus-client 0.7.1 prompt-toolkit 3.0.4 protobuf 3.7.0 psutil 5.7.0 py 1.8.1 py4j 0.10.7 pyarrow 0.16.0 pyasn1 0.4.5 pyasn1-modules 0.2.4 pybind11 2.5.0 pycodestyle 2.5.0 pycosat 0.6.3 pycparser 2.20 pycryptodome 3.8.2 pycryptodomex 3.8.2 pyct 0.4.6 pydocstyle 4.0.1 pyflakes 2.1.1 Pygments 2.6.1 PyJWT 1.7.1 pylint 2.4.4 pylivy 0.0.3 pymongo 3.8.0 PyMySQL 0.9.3 PyNaCl 1.3.0 pyOpenSSL 19.1.0 pyparsing 2.4.6 PyPika 0.32.0 pypiwin32 223 pyreadline 2.1 pyrsistent 0.16.0 PySocks 1.7.1 pyspark 2.3.1 pytest 5.4.1 pytest-arraydiff 0.3 pytest-astropy 0.8.0 pytest-astropy-header 0.1.2 pytest-doctestplus 0.5.0 pytest-openfiles 0.4.0 pytest-remotedata 0.3.2 pytest-runner 5.2 python-box 4.2.2 python-daemon 2.1.2 python-dateutil 2.8.1 python-editor 1.0.4 python-jsonrpc-server 0.3.4 python-language-server 0.31.9 python-magic 0.4.15 python-magic-bin 0.4.14 python-slugify 4.0.0 python-utils 2.3.0 python3-openid 3.1.0 pythreejs 2.1.1 pytz 2019.3 pytzdata 2019.3 pyviz-comms 0.7.2 pywin32 227 pywin32-ctypes 0.2.0 pywinpty 0.5.7 PyYAML 5.3.1 pyzmq 18.1.1 QDarkStyle 2.8 QtAwesome 0.7.0 qtconsole 4.7.2 QtPy 1.9.0 querystring-parser 1.2.3 ratelimit 2.2.1 readme-renderer 24.0 requests 2.23.0 requests-kerberos 0.12.0 requests-oauthlib 1.2.0 requests-toolbelt 0.9.1 retrying 1.3.3 rope 0.16.0 rsa 4.0 Rtree 0.9.3 ruamel-yaml 0.15.87 ruamel.yaml 0.16.10 ruamel.yaml.clib 0.2.0 s3fs 0.2.2 s3transfer 0.2.0 scikit-learn 0.22.1 scipy 1.4.1 seaborn 0.10.0 Send2Trash 1.5.0 setproctitle 1.1.10 setuptools 46.1.3.post20200330 sightengine 1.4.0 simplejson 3.16.0 singleton-decorator 1.0.0 six 1.14.0 sklearn 0.0 smmap2 2.0.5 snowballstemmer 2.0.0 sortedcontainers 2.1.0 soupsieve 2.0 sparkmagic 0.12.9 sparse-dot-topn 0.2.9 Sphinx 2.4.4 sphinxcontrib-applehelp 1.0.2 sphinxcontrib-devhelp 1.0.2 sphinxcontrib-htmlhelp 1.0.3 sphinxcontrib-jsmath 1.0.1 sphinxcontrib-qthelp 1.0.3 sphinxcontrib-serializinghtml 1.1.4 spyder 4.0.1 spyder-kernels 1.8.1 SQLAlchemy 1.3.16 SQLAlchemy-JSONField 0.9.0 SQLAlchemy-Utils 0.36.3 sqlparse 0.3.0 statsmodels 0.11.0 streamz 0.5.3 string-grouper 0.1.0 supervisor 4.1.0 tabulate 0.8.7 tblib 1.6.0 tenacity 4.12.0 tensorboard 1.13.0 tensorflow 1.13.1 tensorflow-estimator 1.13.0 termcolor 1.1.0 terminado 0.8.3 termtables 0.1.2 testfixtures 6.10.3 testpath 0.4.4 text-unidecode 1.3 textwrap3 0.9.2 thrift 0.13.0 toml 0.10.0 toolz 0.10.0 tornado 6.0.4 tqdm 4.44.1 traitlets 4.3.3 traittypes 0.2.1 turbodbc 4.0.0 twine 2.0.0 typing 3.7.4.1 typing-extensions 3.7.4.1 tzlocal 1.5.1 ujson 1.35 uritemplate 3.0.0 urllib3 1.25.9 vaex 2.6.1 vaex-arrow 0.4.2 vaex-astro 0.6.1 vaex-core 1.5.0 vaex-hdf5 0.5.6 vaex-jupyter 0.4.1 vaex-ml 0.8.0 vaex-server 0.2.1 vaex-viz 0.3.8 vega 2.1.0 vega-datasets 0.7.0 vine 1.2.0 virtualenv 16.6.2 watchdog 0.10.2 wcwidth 0.1.9 webencodings 0.5.1 websocket-client 0.56.0 wheel 0.34.2 widgetsnbextension 3.5.1 win-inet-pton 1.1.0 wincertstore 0.2 winkerberos 0.7.0 wrapt 1.12.1 WTForms 2.2.1 xarray 0.12.1 xgboost 0.90 xlrd 1.2.0 xmltodict 0.12.0 xxhash 1.4.3 yapf 0.28.0 zict 2.0.0 zipp 2.2.0 zope.deprecation 4.4.0
import pendulum
pendulum.from_format("29/11/2007", 'DD/MM/YYYY')
# import ciso8601
# ciso8601.parse_datetime("11 11 1980", strict= False)
DateTime(2007, 11, 29, 0, 0, 0, tzinfo=Timezone('UTC'))
df = df.ext.optimize().persist()
df = df.ext.repartition(8).ext.cache()
%%time
cols_to_profile = {'INCIDENT_NUMBER': 'string', 'OFFENSE_CODE': 'int', 'OFFENSE_CODE_GROUP': 'string', 'OFFENSE_DESCRIPTION': 'string', 'DISTRICT': 'string', 'REPORTING_AREA': 'int', 'SHOOTING': 'object', 'OCCURRED_ON_DATE': 'date', 'YEAR': 'int', 'MONTH': 'int', 'DAY_OF_WEEK': 'date', 'HOUR': 'int', 'UCR_PART': 'string', 'STREET': 'string', 'Lat': 'decimal', 'Long': 'decimal', 'Location': 'array'}
cols_and_inferred_dtype = df.cols.infer_profiler_dtypes(cols_to_profile)
Wall time: 42 ms
df = df.cols.cast_to_profiler_dtypes(columns=cols_and_inferred_dtype).persist()
INCIDENT_NUMBER object OFFENSE_CODE int OFFENSE_CODE_GROUP object OFFENSE_DESCRIPTION object DISTRICT object REPORTING_AREA int SHOOTING object OCCURRED_ON_DATE date YEAR int MONTH int DAY_OF_WEEK object HOUR int UCR_PART object STREET object Lat float Long float Location object
df.compute()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | I182070945 | 619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | NaN | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.3578 | -71.1394 | (42.35779134, -71.13937053) |
1 | I182070943 | 1402 | Vandalism | VANDALISM | C11 | 347 | NaN | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.3068 | -71.0603 | (42.30682138, -71.06030035) |
2 | I182070941 | 3410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | NaN | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.3466 | -71.0724 | (42.34658879, -71.07242943) |
3 | I182070940 | 3114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | NaN | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.3342 | -71.0787 | (42.33418175, -71.07866441) |
4 | I182070938 | 3114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | NaN | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.2754 | -71.0904 | (42.27536542, -71.09036101) |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
319068 | I050310906-00 | 3125 | Warrant Arrests | WARRANT ARREST | D4 | 285 | NaN | 2016-06-05 17:25:00 | 2016 | 6 | Sunday | 17 | Part Three | COVENTRY ST | 42.337 | -71.0857 | (42.33695098, -71.08574813) |
319069 | I030217815-08 | 111 | Homicide | MURDER, NON-NEGLIGIENT MANSLAUGHTER | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part One | RIVER ST | 42.2559 | -71.1232 | (42.25592648, -71.12317207) |
319070 | I030217815-08 | 3125 | Warrant Arrests | WARRANT ARREST | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part Three | RIVER ST | 42.2559 | -71.1232 | (42.25592648, -71.12317207) |
319071 | I010370257-00 | 3125 | Warrant Arrests | WARRANT ARREST | E13 | 569 | NaN | 2016-05-31 19:35:00 | 2016 | 5 | Tuesday | 19 | Part Three | NEW WASHINGTON ST | 42.3023 | -71.1116 | (42.30233307, -71.11156487) |
319072 | 142052550 | 3125 | Warrant Arrests | WARRANT ARREST | D4 | 903 | NaN | 2015-06-22 00:12:00 | 2015 | 6 | Monday | 0 | Part Three | WASHINGTON ST | 42.3338 | -71.0803 | (42.33383935, -71.08029038) |
319073 rows × 17 columns
%time df.cols.count_mismatch(cols_to_profile, infer= True)
Wall time: 6.58 s
{'INCIDENT_NUMBER': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'string'}, 'OFFENSE_CODE': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'int'}, 'OFFENSE_CODE_GROUP': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'string'}, 'OFFENSE_DESCRIPTION': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'string'}, 'DISTRICT': {'mismatch': 0, 'missing': 112, 'match': 39773, 'profiler_dtype': 'string'}, 'REPORTING_AREA': {'mismatch': 0, 'missing': 1217, 'match': 38668, 'profiler_dtype': 'int'}, 'SHOOTING': {'mismatch': 133, 'missing': 39752, 'match': 0, 'profiler_dtype': 'object'}, 'OCCURRED_ON_DATE': {'mismatch': 39885, 'missing': 0, 'match': 0, 'profiler_dtype': 'date'}, 'YEAR': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'int'}, 'MONTH': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'int'}, 'DAY_OF_WEEK': {'mismatch': 39885, 'missing': 0, 'match': 0, 'profiler_dtype': 'date'}, 'HOUR': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'int'}, 'UCR_PART': {'mismatch': 0, 'missing': 3, 'match': 39882, 'profiler_dtype': 'string'}, 'STREET': {'mismatch': 0, 'missing': 925, 'match': 38960, 'profiler_dtype': 'string'}, 'Lat': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'decimal'}, 'Long': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'decimal'}, 'Location': {'mismatch': 0, 'missing': 0, 'match': 39885, 'profiler_dtype': 'array'}}
df.visualize()
%%time
# for col_name in df.cols.names():
df.ext.profile(columns=df.cols.names(), infer=True, output="json", flush=True)
Wall time: 6.39 s
'{"columns": {"INCIDENT_NUMBER": {"stats": {"mismatch": 0, "missing": 0, "match": 159537, "frequency": [{"value": "I162030584", "count": 13}, {"value": "I152080623", "count": 11}, {"value": "I172013170", "count": 10}, {"value": "I182065208", "count": 10}, {"value": "I172096394", "count": 10}, {"value": "I162071327", "count": 9}, {"value": "I162001871", "count": 9}, {"value": "I172056883", "count": 9}, {"value": "I172022524", "count": 9}, {"value": "I172054429", "count": 9}, {"value": "I162098170", "count": 9}, {"value": "I162078338", "count": 8}, {"value": "I162074826", "count": 8}, {"value": "I162090278", "count": 8}, {"value": "I172069723", "count": 8}, {"value": "I162022140", "count": 8}, {"value": "I130041200-00", "count": 8}, {"value": "I162087224", "count": 8}, {"value": "I152076465", "count": 8}, {"value": "I162056703", "count": 8}, {"value": "I162064331", "count": 8}, {"value": "I162082917", "count": 8}, {"value": "I152105431", "count": 8}, {"value": "I172053616", "count": 8}, 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{"lower": 11.3125, "upper": 11.65625, "count": 0}, {"lower": 11.65625, "upper": 12.0, "count": 15834}], "count_uniques": 12}, "dtype": "uint8", "profiler_dtype": "int"}, "DAY_OF_WEEK": {"stats": {"mismatch": 0, "missing": 0, "match": 159537, "frequency": [{"value": "Friday", "count": 48495}, {"value": "Wednesday", "count": 46729}, {"value": "Thursday", "count": 46656}, {"value": "Tuesday", "count": 46383}, {"value": "Monday", "count": 45679}, {"value": "Saturday", "count": 44818}, {"value": "Sunday", "count": 40313}], "count_uniques": 7}, "dtype": "object", "profiler_dtype": "string"}, "HOUR": {"stats": {"mismatch": 0, "missing": 0, "match": 159537, "hist": [{"lower": 0.0, "upper": 0.71875, "count": 7477}, {"lower": 0.71875, "upper": 1.4375, "count": 4778}, {"lower": 1.4375, "upper": 2.15625, "count": 3964}, {"lower": 2.15625, "upper": 2.875, "count": 0}, {"lower": 2.875, "upper": 3.59375, "count": 2337}, {"lower": 3.59375, "upper": 4.3125, "count": 1704}, {"lower": 4.3125, "upper": 5.03125, "count": 1648}, {"lower": 5.03125, "upper": 5.75, "count": 0}, {"lower": 5.75, "upper": 6.46875, "count": 2490}, {"lower": 6.46875, "upper": 7.1875, "count": 4413}, {"lower": 7.1875, "upper": 7.90625, "count": 0}, {"lower": 7.90625, "upper": 8.625, "count": 6491}, {"lower": 8.625, "upper": 9.34375, "count": 7367}, {"lower": 9.34375, "upper": 10.0625, "count": 8053}, {"lower": 10.0625, "upper": 10.78125, "count": 0}, {"lower": 10.78125, "upper": 11.5, "count": 8095}, {"lower": 11.5, "upper": 12.21875, "count": 9302}, {"lower": 12.21875, "upper": 12.9375, "count": 0}, {"lower": 12.9375, "upper": 13.65625, "count": 8285}, {"lower": 13.65625, "upper": 14.375, "count": 8614}, {"lower": 14.375, "upper": 15.09375, "count": 8188}, {"lower": 15.09375, "upper": 15.8125, "count": 0}, {"lower": 15.8125, "upper": 16.53125, "count": 9869}, {"lower": 16.53125, "upper": 17.25, "count": 10491}, {"lower": 17.25, "upper": 17.96875, "count": 0}, {"lower": 17.96875, "upper": 18.6875, "count": 10319}, {"lower": 18.6875, "upper": 19.40625, "count": 8878}, {"lower": 19.40625, "upper": 20.125, "count": 8011}, {"lower": 20.125, "upper": 20.84375, "count": 0}, {"lower": 20.84375, "upper": 21.5625, "count": 6925}, {"lower": 21.5625, "upper": 22.28125, "count": 6513}, {"lower": 22.28125, "upper": 23.0, "count": 5325}], "count_uniques": 24}, "dtype": "uint8", "profiler_dtype": "int"}, "UCR_PART": {"stats": {"mismatch": 0, "missing": 36, "match": 159501, "frequency": [{"value": "Part Three", "count": 158553}, {"value": "Part Two", "count": 97569}, {"value": "Part One", "count": 61629}, {"value": "Other", "count": 1232}], "count_uniques": 4}, "dtype": "object", "profiler_dtype": "string"}, "STREET": {"stats": {"mismatch": 0, "missing": 7419, "match": 152118, "frequency": [{"value": "WASHINGTON ST", "count": 14194}, {"value": "BLUE HILL AVE", "count": 7794}, {"value": "BOYLSTON ST", "count": 7221}, {"value": "DORCHESTER AVE", "count": 5149}, {"value": "TREMONT ST", "count": 4796}, {"value": "MASSACHUSETTS AVE", "count": 4708}, {"value": "HARRISON AVE", "count": 4609}, {"value": "CENTRE ST", "count": 4383}, {"value": "COMMONWEALTH AVE", "count": 4134}, {"value": "HYDE PARK AVE", "count": 3470}, {"value": "COLUMBIA RD", "count": 3195}, {"value": "HUNTINGTON AVE", "count": 2989}, {"value": "RIVER ST", "count": 2831}, {"value": "DUDLEY ST", "count": 2352}, {"value": "WARREN ST", "count": 2336}, {"value": "COLUMBUS AVE", "count": 2304}, {"value": "ADAMS ST", "count": 2042}, {"value": "BEACON ST", "count": 1966}, {"value": "NEWBURY ST", "count": 1889}, {"value": "CAMBRIDGE ST", "count": 1670}, {"value": "BOWDOIN ST", "count": 1656}, {"value": "AMERICAN LEGION HWY", "count": 1631}, {"value": "W BROADWAY", "count": 1630}, {"value": "GENEVA AVE", "count": 1628}, {"value": "ALBANY ST", "count": 1429}, {"value": "ALLSTATE RD", "count": 1296}, {"value": "SUMMER ST", "count": 1250}, {"value": "HANCOCK ST", "count": 1246}, {"value": "BORDER ST", "count": 1213}, {"value": "NEW SUDBURY ST", "count": 1213}, {"value": "CUMMINS HWY", "count": 1151}, {"value": "NORFOLK ST", "count": 1115}, {"value": "BENNINGTON ST", "count": 1084}], "count_uniques": 4657}, "dtype": "object", "profiler_dtype": "string"}, "Lat": {"stats": {"mismatch": 0, "missing": 0, "match": 159537, "frequency": [{"value": "42.34862382", "count": 1243}, {"value": "42.36183857", "count": 1208}, {"value": "42.28482577", "count": 1121}, {"value": "42.32866284", "count": 1042}, {"value": "42.25621592", "count": 898}, {"value": "42.29755533", "count": 783}, {"value": "42.34128751", "count": 773}, {"value": "-1.00000000", "count": 745}, {"value": "42.33152148", "count": 735}, {"value": "42.35231190", "count": 688}, {"value": "42.33954199", "count": 655}, {"value": "42.32696647", "count": 652}, {"value": "42.35512339", "count": 584}, {"value": "42.30971857", "count": 573}, {"value": "42.29848866", "count": 562}, {"value": "42.33401829", "count": 561}, {"value": "42.33367922", "count": 550}, {"value": "42.35095909", "count": 523}, {"value": "42.31043400", "count": 523}, {"value": "42.35241815", "count": 515}, {"value": "42.37081805", "count": 507}, {"value": "42.33428841", "count": 504}, {"value": "42.34980175", "count": 472}, {"value": "42.32696802", "count": 472}, {"value": "42.33511904", "count": 455}, {"value": "42.35037870", "count": 445}, {"value": "42.34653820", "count": 444}, {"value": "42.36643546", "count": 440}, {"value": "42.28709355", "count": 438}, {"value": "42.35602373", "count": 436}, {"value": "42.34840576", "count": 432}, {"value": "42.34905600", "count": 427}, {"value": "42.33555954", "count": 421}], "count_uniques": 18178}, "dtype": "object", "profiler_dtype": "decimal"}, "Long": {"stats": {"mismatch": 0, "missing": 0, "match": 159537, "frequency": [{"value": "-71.08277637", "count": 1243}, {"value": "-71.05976489", "count": 1208}, {"value": "-71.09137369", "count": 1121}, {"value": "-71.08563401", "count": 1042}, {"value": "-71.12401947", "count": 898}, {"value": "-71.05970910", "count": 783}, {"value": "-71.05467933", "count": 773}, {"value": "-1.00000000", "count": 745}, {"value": "-71.07085307", "count": 735}, {"value": "-71.06370510", "count": 688}, {"value": "-71.06940877", "count": 655}, {"value": "-71.06198607", "count": 652}, {"value": "-71.06087980", "count": 584}, {"value": "-71.10429432", "count": 573}, {"value": "-71.06313294", "count": 562}, {"value": "-71.07638124", "count": 561}, {"value": "-71.09187755", "count": 550}, {"value": "-71.07412780", "count": 523}, {"value": "-71.06134010", "count": 523}, {"value": "-71.06525499", "count": 515}, {"value": "-71.03929078", "count": 507}, {"value": "-71.07239518", "count": 504}, {"value": "-71.08051941", "count": 472}, {"value": "-71.07840978", "count": 472}, {"value": "-71.07491710", "count": 455}, {"value": "-71.07626098", "count": 445}, {"value": "-71.09880598", "count": 444}, {"value": "-71.06135413", "count": 440}, {"value": "-71.14822128", "count": 438}, {"value": "-71.06177615", "count": 436}, {"value": "-71.08688339", "count": 432}, {"value": "-71.15049850", "count": 427}, {"value": "-71.07436364", "count": 421}], "count_uniques": 18178}, "dtype": "object", "profiler_dtype": "decimal"}, "Location": {"stats": {"mismatch": 0, "missing": 0, "match": 159537, "frequency": [{"value": "(0.00000000, 0.00000000)", "count": 19999}, {"value": "(42.34862382, -71.08277637)", "count": 1243}, {"value": "(42.36183857, -71.05976489)", "count": 1208}, {"value": "(42.28482577, -71.09137369)", "count": 1121}, {"value": "(42.32866284, -71.08563401)", "count": 1042}, {"value": "(42.25621592, -71.12401947)", "count": 898}, {"value": "(42.29755533, -71.05970910)", "count": 783}, {"value": "(42.34128751, -71.05467933)", "count": 773}, {"value": "(-1.00000000, -1.00000000)", "count": 745}, {"value": "(42.33152148, -71.07085307)", "count": 735}, {"value": "(42.35231190, -71.06370510)", "count": 688}, {"value": "(42.33954199, -71.06940877)", "count": 655}, {"value": "(42.32696647, -71.06198607)", "count": 652}, {"value": "(42.35512339, -71.06087980)", "count": 584}, {"value": "(42.30971857, -71.10429432)", "count": 573}, {"value": "(42.29848866, -71.06313294)", "count": 562}, {"value": "(42.33401829, -71.07638124)", "count": 561}, {"value": "(42.33367922, -71.09187755)", "count": 550}, {"value": "(42.31043400, -71.06134010)", "count": 523}, {"value": "(42.35095909, -71.07412780)", "count": 523}, {"value": "(42.35241815, -71.06525499)", "count": 515}, {"value": "(42.37081805, -71.03929078)", "count": 507}, {"value": "(42.33428841, -71.07239518)", "count": 504}, {"value": "(42.34980175, -71.07840978)", "count": 472}, {"value": "(42.32696802, -71.08051941)", "count": 472}, {"value": "(42.33511904, -71.07491710)", "count": 455}, {"value": "(42.35037870, -71.07626098)", "count": 445}, {"value": "(42.34653820, -71.09880598)", "count": 444}, {"value": "(42.36643546, -71.06135413)", "count": 440}, {"value": "(42.28709355, -71.14822128)", "count": 438}, {"value": "(42.35602373, -71.06177615)", "count": 436}, {"value": "(42.34840576, -71.08688339)", "count": 432}, {"value": "(42.34905600, -71.15049850)", "count": 427}], "count_uniques": 18194}, "dtype": "object", "profiler_dtype": "string"}}, "name": null, "file_name": "crime.csv", "summary": {"cols_count": 17, "rows_count": 319073, "dtypes_list": ["uint8", "uint16", "object"], "total_count_dtypes": 3, "missing_count": 0, "p_missing": 0.0}}'
op.client
Client
|
Cluster
|
columns = df.cols.names()
cols_to_profile = df.ext.calculate_cols_to_profile(df, columns)
cols_and_inferred_dtype = df.cols.infer_profiler_dtypes(cols_to_profile)
df = df.cols.cast_to_profiler_dtypes(columns=cols_and_inferred_dtype).persist()
print(cols_and_inferred_dtype)
{'STREET': 'string', 'YEAR': 'int', 'Location': 'array', 'REPORTING_AREA': 'int', 'DISTRICT': 'string', 'DAY_OF_WEEK': 'int', 'OFFENSE_CODE': 'int', 'Lat': 'decimal', 'INCIDENT_NUMBER': 'string', 'Long': 'decimal', 'OFFENSE_CODE_GROUP': 'string', 'UCR_PART': 'string', 'OCCURRED_ON_DATE': 'int', 'HOUR': 'int', 'SHOOTING': 'object', 'OFFENSE_DESCRIPTION': 'string', 'MONTH': 'int'}
df.visualize()
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-14-f59d7c8cf36e> in <module> ----> 1 df.ext.partitions(8) TypeError: partitions() takes 0 positional arguments but 1 was given
%%time
df.ext.profile(columns="*", infer=True, output="json", flush=True)
df.visualize()
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-12-3856e416249c> in <module> ----> 1 df.visualize() ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\generic.py in __getattr__(self, name) 5272 if self._info_axis._can_hold_identifiers_and_holds_name(name): 5273 return self[name] -> 5274 return object.__getattribute__(self, name) 5275 5276 def __setattr__(self, name: str, value) -> None: AttributeError: 'DataFrame' object has no attribute 'visualize'
bins = 33
compute = False
if numeric_cols is not None:
hist = df.cols.hist(numeric_cols, buckets=bins, compute=compute)
freq_uniques = df.cols.count_uniques(numeric_cols, estimate=False, compute=compute)
freq = None
if string_cols is not None:
freq = df.cols.frequency(string_cols, n=bins, count_uniques=True, compute=compute)
hist.visualize()
freq.visualize()
df.visualize()
df.ext.display()
INCIDENT_NUMBER
1 (object)
not nullable
|
OFFENSE_CODE
2 (object)
not nullable
|
OFFENSE_CODE_GROUP
3 (object)
not nullable
|
OFFENSE_DESCRIPTION
4 (object)
not nullable
|
DISTRICT
5 (object)
not nullable
|
REPORTING_AREA
6 (object)
not nullable
|
SHOOTING
7 (object)
not nullable
|
OCCURRED_ON_DATE
8 (object)
not nullable
|
YEAR
9 (object)
not nullable
|
MONTH
10 (object)
not nullable
|
DAY_OF_WEEK
11 (object)
not nullable
|
HOUR
12 (object)
not nullable
|
UCR_PART
13 (object)
not nullable
|
STREET
14 (object)
not nullable
|
Lat
15 (object)
not nullable
|
Long
16 (object)
not nullable
|
Location
17 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
I182070945
|
619
|
Larceny
|
LARCENY⋅ALL⋅OTHERS
|
D14
|
808
|
nan
|
1
|
2018
|
9
|
1
|
13
|
Part⋅One
|
LINCOLN⋅ST
|
42.35779134
|
-71.13937053
|
(42.35779134,⋅-71.13937053)
|
I182070943
|
1402
|
Vandalism
|
VANDALISM
|
C11
|
347
|
nan
|
1
|
2018
|
8
|
1
|
0
|
Part⋅Two
|
HECLA⋅ST
|
42.30682138
|
-71.06030035
|
(42.30682138,⋅-71.06030035)
|
I182070941
|
3410
|
Towed
|
TOWED⋅MOTOR⋅VEHICLE
|
D4
|
151
|
nan
|
1
|
2018
|
9
|
1
|
19
|
Part⋅Three
|
CAZENOVE⋅ST
|
42.34658879
|
-71.07242943
|
(42.34658879,⋅-71.07242943)
|
I182070940
|
3114
|
Investigate⋅Property
|
INVESTIGATE⋅PROPERTY
|
D4
|
272
|
nan
|
1
|
2018
|
9
|
1
|
21
|
Part⋅Three
|
NEWCOMB⋅ST
|
42.33418175
|
-71.07866441
|
(42.33418175,⋅-71.07866441)
|
I182070938
|
3114
|
Investigate⋅Property
|
INVESTIGATE⋅PROPERTY
|
B3
|
421
|
nan
|
1
|
2018
|
9
|
1
|
21
|
Part⋅Three
|
DELHI⋅ST
|
42.27536542
|
-71.09036101
|
(42.27536542,⋅-71.09036101)
|
I182070936
|
3820
|
Motor⋅Vehicle⋅Accident⋅Response
|
M/V⋅ACCIDENT⋅INVOLVING⋅PEDESTRIAN⋅-⋅INJURY
|
C11
|
398
|
nan
|
1
|
2018
|
9
|
1
|
21
|
Part⋅Three
|
TALBOT⋅AVE
|
42.29019621
|
-71.07159012
|
(42.29019621,⋅-71.07159012)
|
I182070933
|
724
|
Auto⋅Theft
|
AUTO⋅THEFT
|
B2
|
330
|
nan
|
1
|
2018
|
9
|
1
|
21
|
Part⋅One
|
NORMANDY⋅ST
|
42.30607218
|
-71.0827326
|
(42.30607218,⋅-71.08273260)
|
I182070932
|
3301
|
Verbal⋅Disputes
|
VERBAL⋅DISPUTE
|
B2
|
584
|
nan
|
1
|
2018
|
9
|
1
|
20
|
Part⋅Three
|
LAWN⋅ST
|
42.32701648
|
-71.10555088
|
(42.32701648,⋅-71.10555088)
|
I182070931
|
301
|
Robbery
|
ROBBERY⋅-⋅STREET
|
C6
|
177
|
nan
|
1
|
2018
|
9
|
1
|
20
|
Part⋅One
|
MASSACHUSETTS⋅AVE
|
42.33152148
|
-71.07085307
|
(42.33152148,⋅-71.07085307)
|
I182070929
|
3301
|
Verbal⋅Disputes
|
VERBAL⋅DISPUTE
|
C11
|
364
|
nan
|
1
|
2018
|
9
|
1
|
20
|
Part⋅Three
|
LESLIE⋅ST
|
42.29514664
|
-71.05860832
|
(42.29514664,⋅-71.05860832)
|
from dateutil.parser import parse as dparse
dparse("Sunday")
datetime.datetime(2020, 5, 24, 0, 0)
df.cols.cast("")
df = df.cols.nest(["OFFENSE_CODE_GROUP", "OFFENSE_DESCRIPTION"], separator=", ", output_col="OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION").ext.cache()
_output = df.ext.profile(columns="*", infer=True, output="json")
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-4f668fd7a1e9> in <module> 1 df = df.cols.nest(["OFFENSE_CODE_GROUP", "OFFENSE_DESCRIPTION"], separator=", ", output_col="OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION").ext.cache() ----> 2 _output = df.ext.profile(columns="*", infer=True, output="json") ~\Documents\Optimus\optimus\engines\dask\extension.py in profile(columns, bins, output, infer, flush, size) 104 df = self 105 if flush is False: --> 106 cols_to_profile = df.ext.calculate_cols_to_profile(df, columns) 107 # print("cols to profile", cols_to_profile) 108 else: ~\Documents\Optimus\optimus\engines\base\extension.py in calculate_cols_to_profile(self, df, columns) 362 363 # Remove duplicated. --> 364 calculate_columns = list(set(calculate_columns)) 365 366 elif not are_actions: TypeError: unhashable type: 'list'
# for i in df.columns:
# df.cols.count_mismatch({i:"int"})
df.ext.profile(columns="*", infer=True, flush=True)["columns"]["birth"]
columns {'INCIDENT_NUMBER': 'string', 'OFFENSE_CODE': 'int', 'OFFENSE_CODE_GROUP': 'string', 'OFFENSE_DESCRIPTION': 'string', 'DISTRICT': 'string', 'REPORTING_AREA': 'int', 'SHOOTING': 'object', 'OCCURRED_ON_DATE': 'date', 'YEAR': 'int', 'MONTH': 'int', 'DAY_OF_WEEK': 'date', 'HOUR': 'int', 'UCR_PART': 'string', 'STREET': 'string', 'Lat': 'decimal', 'Long': 'decimal', 'Location': 'array'} dtype--- object dtype--- int dtype--- object dtype--- object dtype--- object dtype--- int dtype--- object dtype--- date dtype--- int dtype--- int dtype--- date dtype--- int dtype--- object dtype--- object dtype--- float dtype--- float dtype--- object
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-145-ced090af8def> in <module> ----> 1 df.ext.profile(columns="*", infer=True, flush=True)["columns"]["birth"] KeyError: 'birth'
df = df.cols.nest(["OFFENSE_CODE_GROUP", "OFFENSE_DESCRIPTION"], separator=", ", output_col="OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION").ext.cache()
_output = df.ext.profile(columns="*", infer=True, output="json")
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-142-a77503706f1d> in <module> 1 ----> 2 df = df.cols.nest(["OFFENSE_CODE_GROUP", "OFFENSE_DESCRIPTION"], separator=", ", output_col="OFFENSE_CODE_GROUP_OFFENSE_DESCRIPTION").ext.cache() 3 _output = df.ext.profile(columns="*", infer=True, output="json") ~\Documents\Optimus\optimus\engines\base\dask\columns.py in nest(self, input_cols, shape, separator, output_col) 1178 1179 df = self.df -> 1180 input_cols = parse_columns(df, input_cols) 1181 # output_col = val_to_list(output_col) 1182 check_column_numbers(input_cols, 2) ~\Documents\Optimus\optimus\helpers\columns.py in parse_columns(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert) 196 # Check for missing columns 197 if accepts_missing_cols is False: --> 198 check_for_missing_columns(df, cols) 199 200 # Filter by column data type ~\Documents\Optimus\optimus\helpers\columns.py in check_for_missing_columns(df, col_names) 346 347 if len(missing_columns) > 0: --> 348 RaiseIt.value_error(missing_columns, _col_names) 349 return False 350 ~\Documents\Optimus\optimus\helpers\raiseit.py in value_error(var, data_values, extra_text) 74 type=divisor.join(map( 75 lambda x: "'" + x + "'", ---> 76 data_values)), var_type=one_list_to_val(var), extra_text=extra_text)) 77 78 @staticmethod ValueError: 'missing_columns' must be 'id', 'firstName', 'lastName', 'billingId', 'product', 'price', 'birth', 'dummyCol', received '['OFFENSE_CODE_GROUP', 'OFFENSE_DESCRIPTION']'.
df.compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980-07-07 | never |
1 | 2 | André | Ampère | 423 | piza | 8 | 1950-07-08 | gonna |
2 | 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990-07-09 | give |
3 | 4 | PAUL | dirac$ | 521 | pizza | 8 | 1954-07-10 | you |
4 | 5 | Albert | Einstein | 634 | pizza | 8 | 1990-07-11 | up |
5 | 6 | Galileo | GALiLEI | 672 | arepa | 5 | 1930-08-12 | never |
6 | 7 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970-07-13 | gonna |
7 | 8 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950-07-14 | let |
8 | 9 | Johannes | KEPLER | 735 | taco | 3 | 1920-04-22 | you |
9 | 10 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923-03-12 | down |
10 | 11 | Isaac | Newton | 992 | pasta | 9 | 1999-02-15 | never |
11 | 12 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993-12-08 | gonna |
12 | 13 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994-01-04 | run |
13 | 14 | Fred | Hoy&&&le | 553 | pizzza | 8 | 1997-06-27 | around |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956-11-30 | and |
15 | 16 | William | Gilbert### | 886 | BEER | 2 | 1958-03-26 | desert |
16 | 17 | Marie | CURIE | 912 | Rice | 1 | 2000-03-22 | you |
17 | 18 | Arthur | COM%%%pton | 812 | 110790 | 5 | 1899-01-01 | # |
18 | 19 | JAMES | Chadwick | 467 | NaN | 10 | 1921-05-03 | # |
import pandas as pd
for i in df["birth"]:
print(i.year, i.month, i.day, i.hour, i.minute, i.second, i.weekday(), i.weekofyear)
dir(i)
1980 7 7 0 0 0 0 28 1950 7 8 0 0 0 5 27 1990 7 9 0 0 0 0 28 1954 7 10 0 0 0 5 27 1990 7 11 0 0 0 2 28 1930 8 12 0 0 0 1 33 1970 7 13 0 0 0 0 29 1950 7 14 0 0 0 4 28 1920 4 22 0 0 0 3 17 1923 3 12 0 0 0 0 11 1999 2 15 0 0 0 0 7 1993 12 8 0 0 0 2 49 1994 1 4 0 0 0 1 1 1997 6 27 0 0 0 4 26 1956 11 30 0 0 0 4 48 1958 3 26 0 0 0 2 13 2000 3 22 0 0 0 2 12 1899 1 1 0 0 0 6 52 1921 5 3 0 0 0 1 18
['__add__', '__array_priority__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__pyx_vtable__', '__radd__', '__reduce__', '__reduce_ex__', '__repr__', '__rsub__', '__setattr__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__weakref__', '_date_attributes', '_date_repr', '_get_date_name_field', '_get_start_end_field', '_has_time_component', '_repr_base', '_round', '_short_repr', '_time_repr', 'asm8', 'astimezone', 'ceil', 'combine', 'ctime', 'date', 'day', 'day_name', 'dayofweek', 'dayofyear', 'days_in_month', 'daysinmonth', 'dst', 'floor', 'fold', 'freq', 'freqstr', 'fromisoformat', 'fromordinal', 'fromtimestamp', 'hour', 'is_leap_year', 'is_month_end', 'is_month_start', 'is_quarter_end', 'is_quarter_start', 'is_year_end', 'is_year_start', 'isocalendar', 'isoformat', 'isoweekday', 'max', 'microsecond', 'min', 'minute', 'month', 'month_name', 'nanosecond', 'normalize', 'now', 'quarter', 'replace', 'resolution', 'round', 'second', 'strftime', 'strptime', 'time', 'timestamp', 'timetuple', 'timetz', 'to_datetime64', 'to_julian_date', 'to_numpy', 'to_period', 'to_pydatetime', 'today', 'toordinal', 'tz', 'tz_convert', 'tz_localize', 'tzinfo', 'tzname', 'utcfromtimestamp', 'utcnow', 'utcoffset', 'utctimetuple', 'value', 'week', 'weekday', 'weekofyear', 'year']
df.cols.year("birth", output_cols="new_birth").compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | new_birth | |
---|---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980-07-07 | never | 1980 |
1 | 2 | André | Ampère | 423 | piza | 8 | 1950-07-08 | gonna | 1950 |
2 | 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990-07-09 | give | 1990 |
3 | 4 | PAUL | dirac$ | 521 | pizza | 8 | 1954-07-10 | you | 1954 |
4 | 5 | Albert | Einstein | 634 | pizza | 8 | 1990-07-11 | up | 1990 |
5 | 6 | Galileo | GALiLEI | 672 | arepa | 5 | 1930-08-12 | never | 1930 |
6 | 7 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970-07-13 | gonna | 1970 |
7 | 8 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950-07-14 | let | 1950 |
8 | 9 | Johannes | KEPLER | 735 | taco | 3 | 1920-04-22 | you | 1920 |
9 | 10 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923-03-12 | down | 1923 |
10 | 11 | Isaac | Newton | 992 | pasta | 9 | 1999-02-15 | never | 1999 |
11 | 12 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993-12-08 | gonna | 1993 |
12 | 13 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994-01-04 | run | 1994 |
13 | 14 | Fred | Hoy&&&le | 553 | pizzza | 8 | 1997-06-27 | around | 1997 |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956-11-30 | and | 1956 |
15 | 16 | William | Gilbert### | 886 | BEER | 2 | 1958-03-26 | desert | 1958 |
16 | 17 | Marie | CURIE | 912 | Rice | 1 | 2000-03-22 | you | 2000 |
17 | 18 | Arthur | COM%%%pton | 812 | 110790 | 5 | 1899-01-01 | # | 1899 |
18 | 19 | JAMES | Chadwick | 467 | NaN | 10 | 1921-05-03 | # | 1921 |
df["birth"].dt
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-117-ad13294c6e50> in <module> ----> 1 df["birth"].dt pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__() ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in dt(self) 2616 def dt(self): 2617 """ Namespace of datetime methods """ -> 2618 return DatetimeAccessor(self) 2619 2620 @cache_readonly ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\accessor.py in __init__(self, series) 34 if hasattr(series_meta, "to_series"): # is index-like 35 series_meta = series_meta.to_series() ---> 36 meta = getattr(series_meta, self._accessor_name) 37 38 self._meta = meta ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\generic.py in __getattr__(self, name) 5268 or name in self._accessors 5269 ): -> 5270 return object.__getattribute__(self, name) 5271 else: 5272 if self._info_axis._can_hold_identifiers_and_holds_name(name): ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\accessor.py in __get__(self, obj, cls) 185 # we're accessing the attribute of the class, i.e., Dataset.geo 186 return self._accessor --> 187 accessor_obj = self._accessor(obj) 188 # Replace the property with the accessor object. Inspired by: 189 # http://www.pydanny.com/cached-property.html ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\indexes\accessors.py in __new__(cls, data) 336 return DatetimeProperties(data, orig) 337 --> 338 raise AttributeError("Can only use .dt accessor with datetimelike values") AttributeError: Can only use .dt accessor with datetimelike values
from dateutil.parser import parse as dparse
type(dparse("1"))
datetime.datetime
# for col_name in df.cols.names():
# print(col_name)
{'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '2000/03/22', 'count': 1}, {'value': '1999/02/15', 'count': 1}, {'value': '1997/06/27', 'count': 1}, {'value': '1994/01/04', 'count': 1}, {'value': '1993/12/08', 'count': 1}, {'value': '1990/07/11', 'count': 1}, {'value': '1990/07/09', 'count': 1}, {'value': '1980/07/07', 'count': 1}, {'value': '1970/07/13', 'count': 1}, {'value': '1958/03/26', 'count': 1}, {'value': '1956/11/30', 'count': 1}, {'value': '1954/07/10', 'count': 1}, {'value': '1950/07/14', 'count': 1}, {'value': '1950/07/08', 'count': 1}, {'value': '1930/08/12', 'count': 1}, {'value': '1923/03/12', 'count': 1}, {'value': '1921/05/03', 'count': 1}, {'value': '1920/04/22', 'count': 1}, {'value': '1899/01/01', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'date'}
df.compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980-07-07 | never |
1 | 2 | André | Ampère | 423 | piza | 8 | 1950-07-08 | gonna |
2 | 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990-07-09 | give |
3 | 4 | PAUL | dirac$ | 521 | pizza | 8 | 1954-07-10 | you |
4 | 5 | Albert | Einstein | 634 | pizza | 8 | 1990-07-11 | up |
5 | 6 | Galileo | GALiLEI | 672 | arepa | 5 | 1930-08-12 | never |
6 | 7 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970-07-13 | gonna |
7 | 8 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950-07-14 | let |
8 | 9 | Johannes | KEPLER | 735 | taco | 3 | 1920-04-22 | you |
9 | 10 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923-03-12 | down |
10 | 11 | Isaac | Newton | 992 | pasta | 9 | 1999-02-15 | never |
11 | 12 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993-12-08 | gonna |
12 | 13 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994-01-04 | run |
13 | 14 | Fred | Hoy&&&le | 553 | pizzza | 8 | 1997-06-27 | around |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956-11-30 | and |
15 | 16 | William | Gilbert### | 886 | BEER | 2 | 1958-03-26 | desert |
16 | 17 | Marie | CURIE | 912 | Rice | 1 | 2000-03-22 | you |
17 | 18 | Arthur | COM%%%pton | 812 | 110790 | 5 | 1899-01-01 | # |
18 | 19 | JAMES | Chadwick | 467 | NaN | 10 | 1921-05-03 | # |
import arrow
date = 1
arrow.get(date)
<Arrow [1970-01-01T00:00:01+00:00]>
from dateutil.parser import parse as dparse
dparse("10")
datetime.datetime(2020, 5, 10, 0, 0)
df.compute()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | I182070945 | 619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | NaN | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) |
1 | I182070943 | 1402 | Vandalism | VANDALISM | C11 | 347 | NaN | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) |
2 | I182070941 | 3410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | NaN | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) |
3 | I182070940 | 3114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | NaN | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) |
4 | I182070938 | 3114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | NaN | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
319068 | I050310906-00 | 3125 | Warrant Arrests | WARRANT ARREST | D4 | 285 | NaN | 2016-06-05 17:25:00 | 2016 | 6 | Sunday | 17 | Part Three | COVENTRY ST | 42.33695098 | -71.08574813 | (42.33695098, -71.08574813) |
319069 | I030217815-08 | 111 | Homicide | MURDER, NON-NEGLIGIENT MANSLAUGHTER | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part One | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) |
319070 | I030217815-08 | 3125 | Warrant Arrests | WARRANT ARREST | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part Three | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) |
319071 | I010370257-00 | 3125 | Warrant Arrests | WARRANT ARREST | E13 | 569 | NaN | 2016-05-31 19:35:00 | 2016 | 5 | Tuesday | 19 | Part Three | NEW WASHINGTON ST | 42.30233307 | -71.11156487 | (42.30233307, -71.11156487) |
319072 | 142052550 | 3125 | Warrant Arrests | WARRANT ARREST | D4 | 903 | NaN | 2015-06-22 00:12:00 | 2015 | 6 | Monday | 0 | Part Three | WASHINGTON ST | 42.33383935 | -71.08029038 | (42.33383935, -71.08029038) |
319073 rows × 17 columns
columns {'SHOOTING': 'object'} _dtype object cols_and_inferred_dtype {'SHOOTING': 'object'}
'{"columns": {"SHOOTING": {"stats": {"mismatch": 1019, "missing": 318054, "match": 0, "frequency": [{"value": "Y", "count": 1019}], "count_uniques": 1}, "dtype": "object", "profiler_dtype": "object"}}, "name": null, "file_name": "crime.csv", "summary": {"cols_count": 17, "rows_count": 319073, "size": "35.1 MB", "dtypes_list": ["uint16", "object", "uint8"], "total_count_dtypes": 3, "missing_count": 318054, "p_missing": 99.68}}'
_output = preview_df.ext.profile(columns="*", infer=True, output="json")
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-8-2050f2c614df> in <module> ----> 1 _output = preview_df.ext.profile(columns="*", infer=True, output="json") NameError: name 'preview_df' is not defined
# df = op.load.csv("http://159.65.217.17:5003/uploads/datasetFile-1589815911139.csv", sep=",", error_bad_lines=False, header=True, null_value="null", infer_schema="true", encoding="UTF-8", quoting=0, lineterminator=None, cache=True).ext.cache()
# df = op.load.csv("http://159.65.217.17:5003/uploads/datasetFile-1590006020188.csv", sep=",", error_bad_lines=False, header=True, null_value="null", infer_schema="true", encoding="UTF-8", quoting=0, lineterminator=None, cache=True).ext.cache()
# df = op.load.file("data/dataset - transactions.csv").ext.cache()
# df = op.load.file("data/crime.csv", n_rows=10).ext.cache()
df = op.load.file("data/foo.csv", n_rows=10).ext.cache()
df = df.ext.optimize()
df.compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 2 | André | Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
2 | 3 | NiELS | Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
3 | 4 | PAUL | dirac$ | 521 | pizza | 8 | 1954/07/10 | you |
4 | 5 | Albert | Einstein | 634 | pizza | 8 | 1990/07/11 | up |
5 | 6 | Galileo | GALiLEI | 672 | arepa | 5 | 1930/08/12 | never |
6 | 7 | CaRL | Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna |
7 | 8 | David | H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let |
8 | 9 | Johannes | KEPLER | 735 | taco | 3 | 1920/04/22 | you |
9 | 10 | JaMES | M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down |
df.ext.profile(columns="*", infer=True,)
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 19 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
columns {'id': 'int', 'firstName': 'string', 'lastName': 'string', 'billingId': 'int', 'product': 'string', 'price': 'int', 'birth': 'date', 'dummyCol': 'string'} dtype--- int dtype--- object dtype--- object dtype--- int dtype--- object dtype--- int dtype--- date dtype--- object
{'columns': {'id': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'hist': [{'lower': 1.0, 'upper': 1.5625, 'count': 1}, {'lower': 1.5625, 'upper': 2.125, 'count': 1}, {'lower': 2.125, 'upper': 2.6875, 'count': 0}, {'lower': 2.6875, 'upper': 3.25, 'count': 1}, {'lower': 3.25, 'upper': 3.8125, 'count': 0}, {'lower': 3.8125, 'upper': 4.375, 'count': 1}, {'lower': 4.375, 'upper': 4.9375, 'count': 0}, {'lower': 4.9375, 'upper': 5.5, 'count': 1}, {'lower': 5.5, 'upper': 6.0625, 'count': 1}, {'lower': 6.0625, 'upper': 6.625, 'count': 0}, {'lower': 6.625, 'upper': 7.1875, 'count': 1}, {'lower': 7.1875, 'upper': 7.75, 'count': 0}, {'lower': 7.75, 'upper': 8.3125, 'count': 1}, {'lower': 8.3125, 'upper': 8.875, 'count': 0}, {'lower': 8.875, 'upper': 9.4375, 'count': 1}, {'lower': 9.4375, 'upper': 10.0, 'count': 0}, {'lower': 10.0, 'upper': 10.5625, 'count': 1}, {'lower': 10.5625, 'upper': 11.125, 'count': 1}, {'lower': 11.125, 'upper': 11.6875, 'count': 0}, {'lower': 11.6875, 'upper': 12.25, 'count': 1}, {'lower': 12.25, 'upper': 12.8125, 'count': 0}, {'lower': 12.8125, 'upper': 13.375, 'count': 1}, {'lower': 13.375, 'upper': 13.9375, 'count': 0}, {'lower': 13.9375, 'upper': 14.5, 'count': 1}, {'lower': 14.5, 'upper': 15.0625, 'count': 1}, {'lower': 15.0625, 'upper': 15.625, 'count': 0}, {'lower': 15.625, 'upper': 16.1875, 'count': 1}, {'lower': 16.1875, 'upper': 16.75, 'count': 0}, {'lower': 16.75, 'upper': 17.3125, 'count': 1}, {'lower': 17.3125, 'upper': 17.875, 'count': 0}, {'lower': 17.875, 'upper': 18.4375, 'count': 1}, {'lower': 18.4375, 'upper': 19.0, 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'firstName': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'William', 'count': 1}, {'value': 'PAUL', 'count': 1}, {'value': 'NiELS', 'count': 1}, {'value': 'Max!!!', 'count': 1}, {'value': 'Marie', 'count': 1}, {'value': 'Luis', 'count': 1}, {'value': 'Johannes', 'count': 1}, {'value': 'JaMES', 'count': 1}, {'value': 'JAMES', 'count': 1}, {'value': 'Isaac', 'count': 1}, {'value': 'Galileo', 'count': 1}, {'value': 'Fred', 'count': 1}, {'value': 'Emmy%%', 'count': 1}, {'value': 'David', 'count': 1}, {'value': 'CaRL', 'count': 1}, {'value': 'Arthur', 'count': 1}, {'value': 'André', 'count': 1}, {'value': 'Albert', 'count': 1}, {'value': '((( Heinrich )))))', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'lastName': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'dirac$', 'count': 1}, {'value': 'Planck!!!', 'count': 1}, {'value': 'Nöether$', 'count': 1}, {'value': 'Newton', 'count': 1}, {'value': 'M$$ax%%well', 'count': 1}, {'value': 'KEPLER', 'count': 1}, {'value': 'Hoy&&&le', 'count': 1}, {'value': 'Hertz', 'count': 1}, {'value': 'H$$$ilbert', 'count': 1}, {'value': 'Gilbert###', 'count': 1}, {'value': 'Ga%%%uss', 'count': 1}, {'value': 'Einstein', 'count': 1}, {'value': 'Chadwick', 'count': 1}, {'value': 'CURIE', 'count': 1}, {'value': 'COM%%%pton', 'count': 1}, {'value': 'Böhr//((%%', 'count': 1}, {'value': 'Ampère', 'count': 1}, {'value': 'Alvarez$$%!', 'count': 1}, {'value': ' GALiLEI', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'billingId': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'hist': [{'lower': 111.0, 'upper': 138.53125, 'count': 3}, {'lower': 138.53125, 'upper': 166.0625, 'count': 0}, {'lower': 166.0625, 'upper': 193.59375, 'count': 0}, {'lower': 193.59375, 'upper': 221.125, 'count': 0}, {'lower': 221.125, 'upper': 248.65625, 'count': 1}, {'lower': 248.65625, 'upper': 276.1875, 'count': 0}, {'lower': 276.1875, 'upper': 303.71875, 'count': 0}, {'lower': 303.71875, 'upper': 331.25, 'count': 1}, {'lower': 331.25, 'upper': 358.78125, 'count': 0}, {'lower': 358.78125, 'upper': 386.3125, 'count': 0}, {'lower': 386.3125, 'upper': 413.84375, 'count': 0}, {'lower': 413.84375, 'upper': 441.375, 'count': 1}, {'lower': 441.375, 'upper': 468.90625, 'count': 1}, {'lower': 468.90625, 'upper': 496.4375, 'count': 0}, {'lower': 496.4375, 'upper': 523.96875, 'count': 1}, {'lower': 523.96875, 'upper': 551.5, 'count': 1}, {'lower': 551.5, 'upper': 579.03125, 'count': 1}, {'lower': 579.03125, 'upper': 606.5625, 'count': 0}, {'lower': 606.5625, 'upper': 634.09375, 'count': 2}, {'lower': 634.09375, 'upper': 661.625, 'count': 0}, {'lower': 661.625, 'upper': 689.15625, 'count': 1}, {'lower': 689.15625, 'upper': 716.6875, 'count': 0}, {'lower': 716.6875, 'upper': 744.21875, 'count': 1}, {'lower': 744.21875, 'upper': 771.75, 'count': 0}, {'lower': 771.75, 'upper': 799.28125, 'count': 0}, {'lower': 799.28125, 'upper': 826.8125, 'count': 1}, {'lower': 826.8125, 'upper': 854.34375, 'count': 0}, {'lower': 854.34375, 'upper': 881.875, 'count': 1}, {'lower': 881.875, 'upper': 909.40625, 'count': 1}, {'lower': 909.40625, 'upper': 936.9375, 'count': 1}, {'lower': 936.9375, 'upper': 964.46875, 'count': 0}, {'lower': 964.46875, 'upper': 992.0, 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'product': {'stats': {'mismatch': 0, 'missing': 1, 'match': 18, 'frequency': [{'value': 'pizza', 'count': 4}, {'value': 'taco', 'count': 3}, {'value': 'pasta', 'count': 2}, {'value': 'taaaccoo', 'count': 1}, {'value': 'pizzza', 'count': 1}, {'value': 'piza', 'count': 1}, {'value': 'hamburguer', 'count': 1}, {'value': 'arepa', 'count': 1}, {'value': 'Rice', 'count': 1}, {'value': 'Cake', 'count': 1}, {'value': 'BEER', 'count': 1}, {'value': '110790', 'count': 1}], 'count_uniques': 12}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'price': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'hist': [{'lower': 1.0, 'upper': 1.28125, 'count': 1}, {'lower': 1.28125, 'upper': 1.5625, 'count': 0}, {'lower': 1.5625, 'upper': 1.84375, 'count': 0}, {'lower': 1.84375, 'upper': 2.125, 'count': 1}, {'lower': 2.125, 'upper': 2.40625, 'count': 0}, {'lower': 2.40625, 'upper': 2.6875, 'count': 0}, {'lower': 2.6875, 'upper': 2.96875, 'count': 0}, {'lower': 2.96875, 'upper': 3.25, 'count': 4}, {'lower': 3.25, 'upper': 3.53125, 'count': 0}, {'lower': 3.53125, 'upper': 3.8125, 'count': 0}, {'lower': 3.8125, 'upper': 4.09375, 'count': 1}, {'lower': 4.09375, 'upper': 4.375, 'count': 0}, {'lower': 4.375, 'upper': 4.65625, 'count': 0}, {'lower': 4.65625, 'upper': 4.9375, 'count': 0}, {'lower': 4.9375, 'upper': 5.21875, 'count': 2}, {'lower': 5.21875, 'upper': 5.5, 'count': 0}, {'lower': 5.5, 'upper': 5.78125, 'count': 0}, {'lower': 5.78125, 'upper': 6.0625, 'count': 0}, {'lower': 6.0625, 'upper': 6.34375, 'count': 0}, {'lower': 6.34375, 'upper': 6.625, 'count': 0}, {'lower': 6.625, 'upper': 6.90625, 'count': 0}, {'lower': 6.90625, 'upper': 7.1875, 'count': 0}, {'lower': 7.1875, 'upper': 7.46875, 'count': 0}, {'lower': 7.46875, 'upper': 7.75, 'count': 0}, {'lower': 7.75, 'upper': 8.03125, 'count': 6}, {'lower': 8.03125, 'upper': 8.3125, 'count': 0}, {'lower': 8.3125, 'upper': 8.59375, 'count': 0}, {'lower': 8.59375, 'upper': 8.875, 'count': 0}, {'lower': 8.875, 'upper': 9.15625, 'count': 2}, {'lower': 9.15625, 'upper': 9.4375, 'count': 0}, {'lower': 9.4375, 'upper': 9.71875, 'count': 0}, {'lower': 9.71875, 'upper': 10.0, 'count': 2}], 'count_uniques': 8}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'birth': {'stats': {'mismatch': 19, 'missing': 0, 'match': 0, 'frequency': [{'value': '2000/03/22', 'count': 1}, {'value': '1999/02/15', 'count': 1}, {'value': '1997/06/27', 'count': 1}, {'value': '1994/01/04', 'count': 1}, {'value': '1993/12/08', 'count': 1}, {'value': '1990/07/11', 'count': 1}, {'value': '1990/07/09', 'count': 1}, {'value': '1980/07/07', 'count': 1}, {'value': '1970/07/13', 'count': 1}, {'value': '1958/03/26', 'count': 1}, {'value': '1956/11/30', 'count': 1}, {'value': '1954/07/10', 'count': 1}, {'value': '1950/07/14', 'count': 1}, {'value': '1950/07/08', 'count': 1}, {'value': '1930/08/12', 'count': 1}, {'value': '1923/03/12', 'count': 1}, {'value': '1921/05/03', 'count': 1}, {'value': '1920/04/22', 'count': 1}, {'value': '1899/01/01', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'date'}, 'dummyCol': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'you', 'count': 3}, {'value': 'gonna', 'count': 3}, {'value': 'never', 'count': 2}, {'value': '#', 'count': 2}, {'value': 'up', 'count': 1}, {'value': 'run ', 'count': 1}, {'value': 'never ', 'count': 1}, {'value': 'let', 'count': 1}, {'value': 'give', 'count': 1}, {'value': 'down', 'count': 1}, {'value': 'desert', 'count': 1}, {'value': 'around', 'count': 1}, {'value': 'and', 'count': 1}], 'count_uniques': 13}, 'dtype': 'object', 'profiler_dtype': 'string'}}, 'name': None, 'file_name': 'foo.csv', 'summary': {'cols_count': 8, 'rows_count': 19, 'size': '1.3 kB', 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 1, 'p_missing': 5.26}}
df = df.cols.nest(["firstName", "lastName"], separator=", ", output_col="firstName_lastName").ext.cache()
_output = df.ext.profile(columns="*", infer=True, output="json")
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 10 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
print(_output)
{"columns": {"id": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 1.0, "upper": 1.28125, "count": 1}, {"lower": 1.28125, "upper": 1.5625, "count": 0}, {"lower": 1.5625, "upper": 1.84375, "count": 0}, {"lower": 1.84375, "upper": 2.125, "count": 1}, {"lower": 2.125, "upper": 2.40625, "count": 0}, {"lower": 2.40625, "upper": 2.6875, "count": 0}, {"lower": 2.6875, "upper": 2.96875, "count": 0}, {"lower": 2.96875, "upper": 3.25, "count": 1}, {"lower": 3.25, "upper": 3.53125, "count": 0}, {"lower": 3.53125, "upper": 3.8125, "count": 0}, {"lower": 3.8125, "upper": 4.09375, "count": 1}, {"lower": 4.09375, "upper": 4.375, "count": 0}, {"lower": 4.375, "upper": 4.65625, "count": 0}, {"lower": 4.65625, "upper": 4.9375, "count": 0}, {"lower": 4.9375, "upper": 5.21875, "count": 1}, {"lower": 5.21875, "upper": 5.5, "count": 0}, {"lower": 5.5, "upper": 5.78125, "count": 0}, {"lower": 5.78125, "upper": 6.0625, "count": 1}, {"lower": 6.0625, "upper": 6.34375, "count": 0}, {"lower": 6.34375, "upper": 6.625, "count": 0}, {"lower": 6.625, "upper": 6.90625, "count": 0}, {"lower": 6.90625, "upper": 7.1875, "count": 1}, {"lower": 7.1875, "upper": 7.46875, "count": 0}, {"lower": 7.46875, "upper": 7.75, "count": 0}, {"lower": 7.75, "upper": 8.03125, "count": 1}, {"lower": 8.03125, "upper": 8.3125, "count": 0}, {"lower": 8.3125, "upper": 8.59375, "count": 0}, {"lower": 8.59375, "upper": 8.875, "count": 0}, {"lower": 8.875, "upper": 9.15625, "count": 1}, {"lower": 9.15625, "upper": 9.4375, "count": 0}, {"lower": 9.4375, "upper": 9.71875, "count": 0}, {"lower": 9.71875, "upper": 10.0, "count": 1}], "count_uniques": 10}, "dtype": "int32", "profiler_dtype": "int"}, "firstName": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "PAUL", "count": 1}, {"value": "NiELS", "count": 1}, {"value": "Luis", "count": 1}, {"value": "Johannes", "count": 1}, {"value": "JaMES", "count": 1}, {"value": "Galileo", "count": 1}, {"value": "David", "count": 1}, {"value": "CaRL", "count": 1}, {"value": "Andr\u00e9", "count": 1}, {"value": "Albert", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "lastName": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "dirac$", "count": 1}, {"value": "M$$ax%%well", "count": 1}, {"value": "KEPLER", "count": 1}, {"value": "H$$$ilbert", "count": 1}, {"value": "Ga%%%uss", "count": 1}, {"value": "Einstein", "count": 1}, {"value": "B\u00f6hr//((%%", "count": 1}, {"value": "Amp\u00e8re", "count": 1}, {"value": "Alvarez$$%!", "count": 1}, {"value": " GALiLEI", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "firstName_lastName": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "PAUL, dirac$", "count": 1}, {"value": "NiELS, B\u00f6hr//((%%", "count": 1}, {"value": "Luis, Alvarez$$%!", "count": 1}, {"value": "Johannes, KEPLER", "count": 1}, {"value": "JaMES, M$$ax%%well", "count": 1}, {"value": "Galileo, GALiLEI", "count": 1}, {"value": "David, H$$$ilbert", "count": 1}, {"value": "CaRL, Ga%%%uss", "count": 1}, {"value": "Andr\u00e9, Amp\u00e8re", "count": 1}, {"value": "Albert, Einstein", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "billingId": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 123.0, "upper": 146.5, "count": 1}, {"lower": 146.5, "upper": 170.0, "count": 0}, {"lower": 170.0, "upper": 193.5, "count": 0}, {"lower": 193.5, "upper": 217.0, "count": 0}, {"lower": 217.0, "upper": 240.5, "count": 0}, {"lower": 240.5, "upper": 264.0, "count": 0}, {"lower": 264.0, "upper": 287.5, "count": 0}, {"lower": 287.5, "upper": 311.0, "count": 0}, {"lower": 311.0, "upper": 334.5, "count": 1}, {"lower": 334.5, "upper": 358.0, "count": 0}, {"lower": 358.0, "upper": 381.5, "count": 0}, {"lower": 381.5, "upper": 405.0, "count": 0}, {"lower": 405.0, "upper": 428.5, "count": 1}, {"lower": 428.5, "upper": 452.0, "count": 0}, {"lower": 452.0, "upper": 475.5, "count": 0}, {"lower": 475.5, "upper": 499.0, "count": 0}, {"lower": 499.0, "upper": 522.5, "count": 1}, {"lower": 522.5, "upper": 546.0, "count": 0}, {"lower": 546.0, "upper": 569.5, "count": 1}, {"lower": 569.5, "upper": 593.0, "count": 0}, {"lower": 593.0, "upper": 616.5, "count": 0}, {"lower": 616.5, "upper": 640.0, "count": 2}, {"lower": 640.0, "upper": 663.5, "count": 0}, {"lower": 663.5, "upper": 687.0, "count": 1}, {"lower": 687.0, "upper": 710.5, "count": 0}, {"lower": 710.5, "upper": 734.0, "count": 0}, {"lower": 734.0, "upper": 757.5, "count": 1}, {"lower": 757.5, "upper": 781.0, "count": 0}, {"lower": 781.0, "upper": 804.5, "count": 0}, {"lower": 804.5, "upper": 828.0, "count": 0}, {"lower": 828.0, "upper": 851.5, "count": 0}, {"lower": 851.5, "upper": 875.0, "count": 1}], "count_uniques": 10}, "dtype": "int32", "profiler_dtype": "int"}, "product": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "taco", "count": 3}, {"value": "pizza", "count": 3}, {"value": "taaaccoo", "count": 1}, {"value": "piza", "count": 1}, {"value": "arepa", "count": 1}, {"value": "Cake", "count": 1}], "count_uniques": 6}, "dtype": "object", "profiler_dtype": "string"}, "price": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 3.0, "upper": 3.21875, "count": 4}, {"lower": 3.21875, "upper": 3.4375, "count": 0}, {"lower": 3.4375, "upper": 3.65625, "count": 0}, {"lower": 3.65625, "upper": 3.875, "count": 0}, {"lower": 3.875, "upper": 4.09375, "count": 0}, {"lower": 4.09375, "upper": 4.3125, "count": 0}, {"lower": 4.3125, "upper": 4.53125, "count": 0}, {"lower": 4.53125, "upper": 4.75, "count": 0}, {"lower": 4.75, "upper": 4.96875, "count": 0}, {"lower": 4.96875, "upper": 5.1875, "count": 1}, {"lower": 5.1875, "upper": 5.40625, "count": 0}, {"lower": 5.40625, "upper": 5.625, "count": 0}, {"lower": 5.625, "upper": 5.84375, "count": 0}, {"lower": 5.84375, "upper": 6.0625, "count": 0}, {"lower": 6.0625, "upper": 6.28125, "count": 0}, {"lower": 6.28125, "upper": 6.5, "count": 0}, {"lower": 6.5, "upper": 6.71875, "count": 0}, {"lower": 6.71875, "upper": 6.9375, "count": 0}, {"lower": 6.9375, "upper": 7.15625, "count": 0}, {"lower": 7.15625, "upper": 7.375, "count": 0}, {"lower": 7.375, "upper": 7.59375, "count": 0}, {"lower": 7.59375, "upper": 7.8125, "count": 0}, {"lower": 7.8125, "upper": 8.03125, "count": 4}, {"lower": 8.03125, "upper": 8.25, "count": 0}, {"lower": 8.25, "upper": 8.46875, "count": 0}, {"lower": 8.46875, "upper": 8.6875, "count": 0}, {"lower": 8.6875, "upper": 8.90625, "count": 0}, {"lower": 8.90625, "upper": 9.125, "count": 0}, {"lower": 9.125, "upper": 9.34375, "count": 0}, {"lower": 9.34375, "upper": 9.5625, "count": 0}, {"lower": 9.5625, "upper": 9.78125, "count": 0}, {"lower": 9.78125, "upper": 10.0, "count": 1}], "count_uniques": 4}, "dtype": "int32", "profiler_dtype": "int"}, "birth": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "1990/07/11", "count": 1}, {"value": "1990/07/09", "count": 1}, {"value": "1980/07/07", "count": 1}, {"value": "1970/07/13", "count": 1}, {"value": "1954/07/10", "count": 1}, {"value": "1950/07/14", "count": 1}, {"value": "1950/07/08", "count": 1}, {"value": "1930/08/12", "count": 1}, {"value": "1923/03/12", "count": 1}, {"value": "1920/04/22", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "date"}, "dummyCol": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "you", "count": 2}, {"value": "never", "count": 2}, {"value": "gonna", "count": 2}, {"value": "up", "count": 1}, {"value": "let", "count": 1}, {"value": "give", "count": 1}, {"value": "down", "count": 1}], "count_uniques": 7}, "dtype": "object", "profiler_dtype": "string"}}, "name": null, "file_name": "foo.csv", "summary": {"cols_count": 9, "rows_count": 10, "size": "728 Bytes", "dtypes_list": ["int32", "object"], "total_count_dtypes": 2, "missing_count": 0, "p_missing": 0.0}}
df.ext.display()
id
1 (int32)
not nullable
|
firstName
2 (object)
not nullable
|
lastName
3 (object)
not nullable
|
firstName_lastName
4 (object)
not nullable
|
billingId
5 (int32)
not nullable
|
product
6 (object)
not nullable
|
price
7 (int32)
not nullable
|
birth
8 (object)
not nullable
|
dummyCol
9 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|
1
|
Luis
|
Alvarez$$%!
|
Luis,⋅Alvarez$$%!
|
123
|
Cake
|
10
|
1980/07/07
|
never
|
2
|
André
|
Ampère
|
André,⋅Ampère
|
423
|
piza
|
8
|
1950/07/08
|
gonna
|
3
|
NiELS
|
Böhr//((%%
|
NiELS,⋅Böhr//((%%
|
551
|
pizza
|
8
|
1990/07/09
|
give
|
4
|
PAUL
|
dirac$
|
PAUL,⋅dirac$
|
521
|
pizza
|
8
|
1954/07/10
|
you
|
5
|
Albert
|
Einstein
|
Albert,⋅Einstein
|
634
|
pizza
|
8
|
1990/07/11
|
up
|
6
|
Galileo
|
⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
Galileo,⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
672
|
arepa
|
5
|
1930/08/12
|
never
|
7
|
CaRL
|
Ga%%%uss
|
CaRL,⋅Ga%%%uss
|
323
|
taco
|
3
|
1970/07/13
|
gonna
|
8
|
David
|
H$$$ilbert
|
David,⋅H$$$ilbert
|
624
|
taaaccoo
|
3
|
1950/07/14
|
let
|
9
|
Johannes
|
KEPLER
|
Johannes,⋅KEPLER
|
735
|
taco
|
3
|
1920/04/22
|
you
|
10
|
JaMES
|
M$$ax%%well
|
JaMES,⋅M$$ax%%well
|
875
|
taco
|
3
|
1923/03/12
|
down
|
df = df.cols.nest(["firstName","lastName"],output_col="lastName1", separator = ",")
df.ext.profile(columns="*", infer=True, output="json", flush=True)
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 10 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
'{"columns": {"id": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 1.0, "upper": 1.28125, "count": 1}, {"lower": 1.28125, "upper": 1.5625, "count": 0}, {"lower": 1.5625, "upper": 1.84375, "count": 0}, {"lower": 1.84375, "upper": 2.125, "count": 1}, {"lower": 2.125, "upper": 2.40625, "count": 0}, {"lower": 2.40625, "upper": 2.6875, "count": 0}, {"lower": 2.6875, "upper": 2.96875, "count": 0}, {"lower": 2.96875, "upper": 3.25, "count": 1}, {"lower": 3.25, "upper": 3.53125, "count": 0}, {"lower": 3.53125, "upper": 3.8125, "count": 0}, {"lower": 3.8125, "upper": 4.09375, "count": 1}, {"lower": 4.09375, "upper": 4.375, "count": 0}, {"lower": 4.375, "upper": 4.65625, "count": 0}, {"lower": 4.65625, "upper": 4.9375, "count": 0}, {"lower": 4.9375, "upper": 5.21875, "count": 1}, {"lower": 5.21875, "upper": 5.5, "count": 0}, {"lower": 5.5, "upper": 5.78125, "count": 0}, {"lower": 5.78125, "upper": 6.0625, "count": 1}, {"lower": 6.0625, "upper": 6.34375, "count": 0}, {"lower": 6.34375, "upper": 6.625, "count": 0}, {"lower": 6.625, "upper": 6.90625, "count": 0}, {"lower": 6.90625, "upper": 7.1875, "count": 1}, {"lower": 7.1875, "upper": 7.46875, "count": 0}, {"lower": 7.46875, "upper": 7.75, "count": 0}, {"lower": 7.75, "upper": 8.03125, "count": 1}, {"lower": 8.03125, "upper": 8.3125, "count": 0}, {"lower": 8.3125, "upper": 8.59375, "count": 0}, {"lower": 8.59375, "upper": 8.875, "count": 0}, {"lower": 8.875, "upper": 9.15625, "count": 1}, {"lower": 9.15625, "upper": 9.4375, "count": 0}, {"lower": 9.4375, "upper": 9.71875, "count": 0}, {"lower": 9.71875, "upper": 10.0, "count": 1}], "count_uniques": 10}, "dtype": "int32", "profiler_dtype": "int"}, "firstName": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "PAUL", "count": 1}, {"value": "NiELS", "count": 1}, {"value": "Luis", "count": 1}, {"value": "Johannes", "count": 1}, {"value": "JaMES", "count": 1}, {"value": "Galileo", "count": 1}, {"value": "David", "count": 1}, {"value": "CaRL", "count": 1}, {"value": "Andr\\u00e9", "count": 1}, {"value": "Albert", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "lastName": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "dirac$", "count": 1}, {"value": "M$$ax%%well", "count": 1}, {"value": "KEPLER", "count": 1}, {"value": "H$$$ilbert", "count": 1}, {"value": "Ga%%%uss", "count": 1}, {"value": "Einstein", "count": 1}, {"value": "B\\u00f6hr//((%%", "count": 1}, {"value": "Amp\\u00e8re", "count": 1}, {"value": "Alvarez$$%!", "count": 1}, {"value": " GALiLEI", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "lastName1": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "PAUL,dirac$", "count": 1}, {"value": "NiELS,B\\u00f6hr//((%%", "count": 1}, {"value": "Luis,Alvarez$$%!", "count": 1}, {"value": "Johannes,KEPLER", "count": 1}, {"value": "JaMES,M$$ax%%well", "count": 1}, {"value": "Galileo, GALiLEI", "count": 1}, {"value": "David,H$$$ilbert", "count": 1}, {"value": "CaRL,Ga%%%uss", "count": 1}, {"value": "Andr\\u00e9,Amp\\u00e8re", "count": 1}, {"value": "Albert,Einstein", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "billingId": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 123.0, "upper": 146.5, "count": 1}, {"lower": 146.5, "upper": 170.0, "count": 0}, {"lower": 170.0, "upper": 193.5, "count": 0}, {"lower": 193.5, "upper": 217.0, "count": 0}, {"lower": 217.0, "upper": 240.5, "count": 0}, {"lower": 240.5, "upper": 264.0, "count": 0}, {"lower": 264.0, "upper": 287.5, "count": 0}, {"lower": 287.5, "upper": 311.0, "count": 0}, {"lower": 311.0, "upper": 334.5, "count": 1}, {"lower": 334.5, "upper": 358.0, "count": 0}, {"lower": 358.0, "upper": 381.5, "count": 0}, {"lower": 381.5, "upper": 405.0, "count": 0}, {"lower": 405.0, "upper": 428.5, "count": 1}, {"lower": 428.5, "upper": 452.0, "count": 0}, {"lower": 452.0, "upper": 475.5, "count": 0}, {"lower": 475.5, "upper": 499.0, "count": 0}, {"lower": 499.0, "upper": 522.5, "count": 1}, {"lower": 522.5, "upper": 546.0, "count": 0}, {"lower": 546.0, "upper": 569.5, "count": 1}, {"lower": 569.5, "upper": 593.0, "count": 0}, {"lower": 593.0, "upper": 616.5, "count": 0}, {"lower": 616.5, "upper": 640.0, "count": 2}, {"lower": 640.0, "upper": 663.5, "count": 0}, {"lower": 663.5, "upper": 687.0, "count": 1}, {"lower": 687.0, "upper": 710.5, "count": 0}, {"lower": 710.5, "upper": 734.0, "count": 0}, {"lower": 734.0, "upper": 757.5, "count": 1}, {"lower": 757.5, "upper": 781.0, "count": 0}, {"lower": 781.0, "upper": 804.5, "count": 0}, {"lower": 804.5, "upper": 828.0, "count": 0}, {"lower": 828.0, "upper": 851.5, "count": 0}, {"lower": 851.5, "upper": 875.0, "count": 1}], "count_uniques": 10}, "dtype": "int32", "profiler_dtype": "int"}, "product": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "taco", "count": 3}, {"value": "pizza", "count": 3}, {"value": "taaaccoo", "count": 1}, {"value": "piza", "count": 1}, {"value": "arepa", "count": 1}, {"value": "Cake", "count": 1}], "count_uniques": 6}, "dtype": "object", "profiler_dtype": "string"}, "price": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 3.0, "upper": 3.21875, "count": 4}, {"lower": 3.21875, "upper": 3.4375, "count": 0}, {"lower": 3.4375, "upper": 3.65625, "count": 0}, {"lower": 3.65625, "upper": 3.875, "count": 0}, {"lower": 3.875, "upper": 4.09375, "count": 0}, {"lower": 4.09375, "upper": 4.3125, "count": 0}, {"lower": 4.3125, "upper": 4.53125, "count": 0}, {"lower": 4.53125, "upper": 4.75, "count": 0}, {"lower": 4.75, "upper": 4.96875, "count": 0}, {"lower": 4.96875, "upper": 5.1875, "count": 1}, {"lower": 5.1875, "upper": 5.40625, "count": 0}, {"lower": 5.40625, "upper": 5.625, "count": 0}, {"lower": 5.625, "upper": 5.84375, "count": 0}, {"lower": 5.84375, "upper": 6.0625, "count": 0}, {"lower": 6.0625, "upper": 6.28125, "count": 0}, {"lower": 6.28125, "upper": 6.5, "count": 0}, {"lower": 6.5, "upper": 6.71875, "count": 0}, {"lower": 6.71875, "upper": 6.9375, "count": 0}, {"lower": 6.9375, "upper": 7.15625, "count": 0}, {"lower": 7.15625, "upper": 7.375, "count": 0}, {"lower": 7.375, "upper": 7.59375, "count": 0}, {"lower": 7.59375, "upper": 7.8125, "count": 0}, {"lower": 7.8125, "upper": 8.03125, "count": 4}, {"lower": 8.03125, "upper": 8.25, "count": 0}, {"lower": 8.25, "upper": 8.46875, "count": 0}, {"lower": 8.46875, "upper": 8.6875, "count": 0}, {"lower": 8.6875, "upper": 8.90625, "count": 0}, {"lower": 8.90625, "upper": 9.125, "count": 0}, {"lower": 9.125, "upper": 9.34375, "count": 0}, {"lower": 9.34375, "upper": 9.5625, "count": 0}, {"lower": 9.5625, "upper": 9.78125, "count": 0}, {"lower": 9.78125, "upper": 10.0, "count": 1}], "count_uniques": 4}, "dtype": "int32", "profiler_dtype": "int"}, "birth": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "1990/07/11", "count": 1}, {"value": "1990/07/09", "count": 1}, {"value": "1980/07/07", "count": 1}, {"value": "1970/07/13", "count": 1}, {"value": "1954/07/10", "count": 1}, {"value": "1950/07/14", "count": 1}, {"value": "1950/07/08", "count": 1}, {"value": "1930/08/12", "count": 1}, {"value": "1923/03/12", "count": 1}, {"value": "1920/04/22", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "date"}, "dummyCol": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "you", "count": 2}, {"value": "never", "count": 2}, {"value": "gonna", "count": 2}, {"value": "up", "count": 1}, {"value": "let", "count": 1}, {"value": "give", "count": 1}, {"value": "down", "count": 1}], "count_uniques": 7}, "dtype": "object", "profiler_dtype": "string"}}, "name": null, "file_name": "foo.csv", "summary": {"cols_count": 9, "rows_count": 10, "size": "728 Bytes", "dtypes_list": ["int32", "object"], "total_count_dtypes": 2, "missing_count": 0, "p_missing": 0.0}}'
df.compute()
id | firstName | lastName | lastName1 | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | Luis,Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 2 | André | Ampère | André,Ampère | 423 | piza | 8 | 1950/07/08 | gonna |
2 | 3 | NiELS | Böhr//((%% | NiELS,Böhr//((%% | 551 | pizza | 8 | 1990/07/09 | give |
3 | 4 | PAUL | dirac$ | PAUL,dirac$ | 521 | pizza | 8 | 1954/07/10 | you |
4 | 5 | Albert | Einstein | Albert,Einstein | 634 | pizza | 8 | 1990/07/11 | up |
5 | 6 | Galileo | GALiLEI | Galileo, GALiLEI | 672 | arepa | 5 | 1930/08/12 | never |
6 | 7 | CaRL | Ga%%%uss | CaRL,Ga%%%uss | 323 | taco | 3 | 1970/07/13 | gonna |
7 | 8 | David | H$$$ilbert | David,H$$$ilbert | 624 | taaaccoo | 3 | 1950/07/14 | let |
8 | 9 | Johannes | KEPLER | Johannes,KEPLER | 735 | taco | 3 | 1920/04/22 | you |
9 | 10 | JaMES | M$$ax%%well | JaMES,M$$ax%%well | 875 | taco | 3 | 1923/03/12 | down |
df.ext.profile(columns="*", infer=True, output="json", flush=True)
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 10 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
'{"columns": {"id": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 1.0, "upper": 1.28125, "count": 1}, {"lower": 1.28125, "upper": 1.5625, "count": 0}, {"lower": 1.5625, "upper": 1.84375, "count": 0}, {"lower": 1.84375, "upper": 2.125, "count": 1}, {"lower": 2.125, "upper": 2.40625, "count": 0}, {"lower": 2.40625, "upper": 2.6875, "count": 0}, {"lower": 2.6875, "upper": 2.96875, "count": 0}, {"lower": 2.96875, "upper": 3.25, "count": 1}, {"lower": 3.25, "upper": 3.53125, "count": 0}, {"lower": 3.53125, "upper": 3.8125, "count": 0}, {"lower": 3.8125, "upper": 4.09375, "count": 1}, {"lower": 4.09375, "upper": 4.375, "count": 0}, {"lower": 4.375, "upper": 4.65625, "count": 0}, {"lower": 4.65625, "upper": 4.9375, "count": 0}, {"lower": 4.9375, "upper": 5.21875, "count": 1}, {"lower": 5.21875, "upper": 5.5, "count": 0}, {"lower": 5.5, "upper": 5.78125, "count": 0}, {"lower": 5.78125, "upper": 6.0625, "count": 1}, {"lower": 6.0625, "upper": 6.34375, "count": 0}, {"lower": 6.34375, "upper": 6.625, "count": 0}, {"lower": 6.625, "upper": 6.90625, "count": 0}, {"lower": 6.90625, "upper": 7.1875, "count": 1}, {"lower": 7.1875, "upper": 7.46875, "count": 0}, {"lower": 7.46875, "upper": 7.75, "count": 0}, {"lower": 7.75, "upper": 8.03125, "count": 1}, {"lower": 8.03125, "upper": 8.3125, "count": 0}, {"lower": 8.3125, "upper": 8.59375, "count": 0}, {"lower": 8.59375, "upper": 8.875, "count": 0}, {"lower": 8.875, "upper": 9.15625, "count": 1}, {"lower": 9.15625, "upper": 9.4375, "count": 0}, {"lower": 9.4375, "upper": 9.71875, "count": 0}, {"lower": 9.71875, "upper": 10.0, "count": 1}], "count_uniques": 10}, "dtype": "int32", "profiler_dtype": "int"}, "firstName": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "PAUL", "count": 1}, {"value": "NiELS", "count": 1}, {"value": "Luis", "count": 1}, {"value": "Johannes", "count": 1}, {"value": "JaMES", "count": 1}, {"value": "Galileo", "count": 1}, {"value": "David", "count": 1}, {"value": "CaRL", "count": 1}, {"value": "Andr\\u00e9", "count": 1}, {"value": "Albert", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "lastName": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "dirac$", "count": 1}, {"value": "M$$ax%%well", "count": 1}, {"value": "KEPLER", "count": 1}, {"value": "H$$$ilbert", "count": 1}, {"value": "Ga%%%uss", "count": 1}, {"value": "Einstein", "count": 1}, {"value": "B\\u00f6hr//((%%", "count": 1}, {"value": "Amp\\u00e8re", "count": 1}, {"value": "Alvarez$$%!", "count": 1}, {"value": " GALiLEI", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "lastName1": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "PAULdirac$", "count": 1}, {"value": "NiELSB\\u00f6hr//((%%", "count": 1}, {"value": "LuisAlvarez$$%!", "count": 1}, {"value": "JohannesKEPLER", "count": 1}, {"value": "JaMESM$$ax%%well", "count": 1}, {"value": "Galileo GALiLEI", "count": 1}, {"value": "DavidH$$$ilbert", "count": 1}, {"value": "CaRLGa%%%uss", "count": 1}, {"value": "Andr\\u00e9Amp\\u00e8re", "count": 1}, {"value": "AlbertEinstein", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "string"}, "billingId": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 123.0, "upper": 146.5, "count": 1}, {"lower": 146.5, "upper": 170.0, "count": 0}, {"lower": 170.0, "upper": 193.5, "count": 0}, {"lower": 193.5, "upper": 217.0, "count": 0}, {"lower": 217.0, "upper": 240.5, "count": 0}, {"lower": 240.5, "upper": 264.0, "count": 0}, {"lower": 264.0, "upper": 287.5, "count": 0}, {"lower": 287.5, "upper": 311.0, "count": 0}, {"lower": 311.0, "upper": 334.5, "count": 1}, {"lower": 334.5, "upper": 358.0, "count": 0}, {"lower": 358.0, "upper": 381.5, "count": 0}, {"lower": 381.5, "upper": 405.0, "count": 0}, {"lower": 405.0, "upper": 428.5, "count": 1}, {"lower": 428.5, "upper": 452.0, "count": 0}, {"lower": 452.0, "upper": 475.5, "count": 0}, {"lower": 475.5, "upper": 499.0, "count": 0}, {"lower": 499.0, "upper": 522.5, "count": 1}, {"lower": 522.5, "upper": 546.0, "count": 0}, {"lower": 546.0, "upper": 569.5, "count": 1}, {"lower": 569.5, "upper": 593.0, "count": 0}, {"lower": 593.0, "upper": 616.5, "count": 0}, {"lower": 616.5, "upper": 640.0, "count": 2}, {"lower": 640.0, "upper": 663.5, "count": 0}, {"lower": 663.5, "upper": 687.0, "count": 1}, {"lower": 687.0, "upper": 710.5, "count": 0}, {"lower": 710.5, "upper": 734.0, "count": 0}, {"lower": 734.0, "upper": 757.5, "count": 1}, {"lower": 757.5, "upper": 781.0, "count": 0}, {"lower": 781.0, "upper": 804.5, "count": 0}, {"lower": 804.5, "upper": 828.0, "count": 0}, {"lower": 828.0, "upper": 851.5, "count": 0}, {"lower": 851.5, "upper": 875.0, "count": 1}], "count_uniques": 10}, "dtype": "int32", "profiler_dtype": "int"}, "product": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "taco", "count": 3}, {"value": "pizza", "count": 3}, {"value": "taaaccoo", "count": 1}, {"value": "piza", "count": 1}, {"value": "arepa", "count": 1}, {"value": "Cake", "count": 1}], "count_uniques": 6}, "dtype": "object", "profiler_dtype": "string"}, "price": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "hist": [{"lower": 3.0, "upper": 3.21875, "count": 4}, {"lower": 3.21875, "upper": 3.4375, "count": 0}, {"lower": 3.4375, "upper": 3.65625, "count": 0}, {"lower": 3.65625, "upper": 3.875, "count": 0}, {"lower": 3.875, "upper": 4.09375, "count": 0}, {"lower": 4.09375, "upper": 4.3125, "count": 0}, {"lower": 4.3125, "upper": 4.53125, "count": 0}, {"lower": 4.53125, "upper": 4.75, "count": 0}, {"lower": 4.75, "upper": 4.96875, "count": 0}, {"lower": 4.96875, "upper": 5.1875, "count": 1}, {"lower": 5.1875, "upper": 5.40625, "count": 0}, {"lower": 5.40625, "upper": 5.625, "count": 0}, {"lower": 5.625, "upper": 5.84375, "count": 0}, {"lower": 5.84375, "upper": 6.0625, "count": 0}, {"lower": 6.0625, "upper": 6.28125, "count": 0}, {"lower": 6.28125, "upper": 6.5, "count": 0}, {"lower": 6.5, "upper": 6.71875, "count": 0}, {"lower": 6.71875, "upper": 6.9375, "count": 0}, {"lower": 6.9375, "upper": 7.15625, "count": 0}, {"lower": 7.15625, "upper": 7.375, "count": 0}, {"lower": 7.375, "upper": 7.59375, "count": 0}, {"lower": 7.59375, "upper": 7.8125, "count": 0}, {"lower": 7.8125, "upper": 8.03125, "count": 4}, {"lower": 8.03125, "upper": 8.25, "count": 0}, {"lower": 8.25, "upper": 8.46875, "count": 0}, {"lower": 8.46875, "upper": 8.6875, "count": 0}, {"lower": 8.6875, "upper": 8.90625, "count": 0}, {"lower": 8.90625, "upper": 9.125, "count": 0}, {"lower": 9.125, "upper": 9.34375, "count": 0}, {"lower": 9.34375, "upper": 9.5625, "count": 0}, {"lower": 9.5625, "upper": 9.78125, "count": 0}, {"lower": 9.78125, "upper": 10.0, "count": 1}], "count_uniques": 4}, "dtype": "int32", "profiler_dtype": "int"}, "birth": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "1990/07/11", "count": 1}, {"value": "1990/07/09", "count": 1}, {"value": "1980/07/07", "count": 1}, {"value": "1970/07/13", "count": 1}, {"value": "1954/07/10", "count": 1}, {"value": "1950/07/14", "count": 1}, {"value": "1950/07/08", "count": 1}, {"value": "1930/08/12", "count": 1}, {"value": "1923/03/12", "count": 1}, {"value": "1920/04/22", "count": 1}], "count_uniques": 10}, "dtype": "object", "profiler_dtype": "date"}, "dummyCol": {"stats": {"mismatch": 0, "missing": 0, "match": 10, "frequency": [{"value": "you", "count": 2}, {"value": "never", "count": 2}, {"value": "gonna", "count": 2}, {"value": "up", "count": 1}, {"value": "let", "count": 1}, {"value": "give", "count": 1}, {"value": "down", "count": 1}], "count_uniques": 7}, "dtype": "object", "profiler_dtype": "string"}}, "name": null, "file_name": "foo.csv", "summary": {"cols_count": 9, "rows_count": 10, "size": "728 Bytes", "dtypes_list": ["int32", "object"], "total_count_dtypes": 2, "missing_count": 0, "p_missing": 0.0}}'
df.ext.display()
id
1 (int32)
not nullable
|
firstName
2 (object)
not nullable
|
lastName
3 (object)
not nullable
|
lastName1
4 (object)
not nullable
|
billingId
5 (int32)
not nullable
|
product
6 (object)
not nullable
|
price
7 (int32)
not nullable
|
birth
8 (object)
not nullable
|
dummyCol
9 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|
1
|
Luis
|
Alvarez$$%!
|
LuisAlvarez$$%!
|
123
|
Cake
|
10
|
1980/07/07
|
never
|
2
|
André
|
Ampère
|
AndréAmpère
|
423
|
piza
|
8
|
1950/07/08
|
gonna
|
3
|
NiELS
|
Böhr//((%%
|
NiELSBöhr//((%%
|
551
|
pizza
|
8
|
1990/07/09
|
give
|
4
|
PAUL
|
dirac$
|
PAULdirac$
|
521
|
pizza
|
8
|
1954/07/10
|
you
|
5
|
Albert
|
Einstein
|
AlbertEinstein
|
634
|
pizza
|
8
|
1990/07/11
|
up
|
6
|
Galileo
|
⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
Galileo⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
672
|
arepa
|
5
|
1930/08/12
|
never
|
7
|
CaRL
|
Ga%%%uss
|
CaRLGa%%%uss
|
323
|
taco
|
3
|
1970/07/13
|
gonna
|
8
|
David
|
H$$$ilbert
|
DavidH$$$ilbert
|
624
|
taaaccoo
|
3
|
1950/07/14
|
let
|
9
|
Johannes
|
KEPLER
|
JohannesKEPLER
|
735
|
taco
|
3
|
1920/04/22
|
you
|
10
|
JaMES
|
M$$ax%%well
|
JaMESM$$ax%%well
|
875
|
taco
|
3
|
1923/03/12
|
down
|
# df[["customer_id", "product"]]
from dask import dataframe as dd
import pandas as pd
data = {"customer_id_x":["0f345kjh345oiuy345"], "transactoin_date":["hola"]}
pdf = pd.DataFrame(data)
print(pdf)
df_right = dd.from_pandas(pdf, npartitions=1)
customer_id_x transactoin_date 0 0f345kjh345oiuy345 hola
df.ext.display()
customer_id
1 (object)
not nullable
|
transactoin_date
2 (object)
not nullable
|
ticket_price
3 (object)
not nullable
|
discount
4 (object)
not nullable
|
product
5 (object)
not nullable
|
info
6 (uint8)
not nullable
|
---|---|---|---|---|---|
0f345kjh345oiuy345
|
2010/08/19
|
29.99
|
nan
|
platinum
|
1
|
0fju234978rfjkhsdf
|
2012/01/05
|
29.99
|
nan
|
platinum
|
2
|
0f34ruiy23e78y2r
|
2009/08/11
|
29.99
|
nan
|
platinum
|
3
|
0fue298y2r23r23r2
|
2010/08/19
|
29.99
|
nan
|
platinum
|
4
|
0f345kjh345oiuy346
|
2010/08/19
|
29.99
|
nan
|
platinum
|
5
|
0fju234978rfjkhsdf
|
2010/08/19
|
9.99
|
5%
|
basic
|
6
|
0f34ruiy23e78y2r
|
2010/08/19
|
9.99
|
nan
|
basic
|
7
|
0fue298y2r23r23r3
|
2010/08/19
|
9.99
|
nan
|
basic
|
8
|
0f345kjh345oiuy347
|
2010/08/19
|
9.99
|
nan
|
basic
|
9
|
0fju234978rfjkhsdf
|
2011/08/11
|
9.99
|
5%
|
basic
|
10
|
df.cols.join(df_right,how="left", left_on="customer_id", right_on="customer_id_x").ext.display(100)
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 100 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
transactoin_date_left
1 (object)
not nullable
|
ticket_price
2 (object)
not nullable
|
discount
3 (object)
not nullable
|
product
4 (object)
not nullable
|
customer_id
5 (object)
not nullable
|
info
6 (uint8)
not nullable
|
transactoin_date_right
7 (object)
not nullable
|
---|---|---|---|---|---|---|
2010/08/19
|
29.99
|
nan
|
platinum
|
0f345kjh345oiuy345
|
1
|
hola
|
2012/01/05
|
29.99
|
nan
|
platinum
|
0fju234978rfjkhsdf
|
2
|
nan
|
2009/08/11
|
29.99
|
nan
|
platinum
|
0f34ruiy23e78y2r
|
3
|
nan
|
2010/08/19
|
29.99
|
nan
|
platinum
|
0fue298y2r23r23r2
|
4
|
nan
|
2010/08/19
|
29.99
|
nan
|
platinum
|
0f345kjh345oiuy346
|
5
|
nan
|
2010/08/19
|
9.99
|
5%
|
basic
|
0fju234978rfjkhsdf
|
6
|
nan
|
2010/08/19
|
9.99
|
nan
|
basic
|
0f34ruiy23e78y2r
|
7
|
nan
|
2010/08/19
|
9.99
|
nan
|
basic
|
0fue298y2r23r23r3
|
8
|
nan
|
2010/08/19
|
9.99
|
nan
|
basic
|
0f345kjh345oiuy347
|
9
|
nan
|
2011/08/11
|
9.99
|
5%
|
basic
|
0fju234978rfjkhsdf
|
10
|
nan
|
2015/08/09
|
9.99
|
5%
|
basic
|
0f34ruiy23e78y2r
|
10
|
nan
|
nan
|
9.91
|
nan
|
basic
|
0fue298y2r23r23r4
|
10
|
nan
|
nan
|
9.99
|
nan
|
basic
|
0f345kjh345oiuy348
|
10
|
nan
|
nan
|
14.99
|
5%
|
deluxe
|
0fju234978rfjkhsdf
|
10
|
nan
|
nan
|
14.99
|
nan
|
deluxe
|
0f34ruiy23e78y2r
|
10
|
nan
|
nan
|
14.99
|
5%
|
deluxe
|
0fue298y2r23r23r5
|
10
|
nan
|
nan
|
14.99
|
nan
|
deluxe
|
0f345kjh345oiuy349
|
10
|
nan
|
data = {"customer_id":["0f345kjh345oiuy345"], "transactoin_date":["hola"]}
pdf = pd.DataFrame(data)
# print(pdf)
df_right = dd.from_pandas(pdf, npartitions=1)
df.cols.join(df_right,how="inner", left_on="customer_id", right_on="customer_id")
transactoin_date_left | ticket_price | discount | product | customer_id | info | transactoin_date_right | |
---|---|---|---|---|---|---|---|
npartitions=1 | |||||||
object | object | object | object | object | uint8 | object | |
... | ... | ... | ... | ... | ... | ... |
test_df.compute()
transactoin_date_left | ticket_price | discount | product | customer_id | info | transactoin_date_right | |
---|---|---|---|---|---|---|---|
0 | 2010/08/19 | 29.99 | NaN | platinum | 0f345kjh345oiuy345 | 1 | hola |
# z = test_df.cols.count()
# lz = test_df.cols.names()
lz = test_df.cols.count()
li = df.cols.count()
lj = df_right.cols.count()
print(lz-lj)
test_df.cols.names()[:li] + ["customer_id"]+test_df.cols.names()[lz-lj:lz]
6
['transactoin_date_left', 'ticket_price', 'discount', 'product', 'info', 'customer_id', 'customer_id', 'info', 'transactoin_date_right']
['transactoin_date_left', 'ticket_price', 'discount', 'product', 'info', 'info', 'transactoin_date_right']
'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', 'info', 'A', 'B'
df.ext.display()
customer_id
1 (object)
not nullable
|
transactoin_date
2 (object)
not nullable
|
ticket_price
3 (object)
not nullable
|
discount
4 (object)
not nullable
|
product
5 (object)
not nullable
|
info
6 (object)
not nullable
|
---|---|---|---|---|---|
0f345kjh345oiuy345
|
2010/08/19
|
29.99
|
None
|
platinum
|
1
|
0fju234978rfjkhsdf
|
2012/01/05
|
29.99
|
None
|
platinum
|
2
|
0f34ruiy23e78y2r
|
2009/08/11
|
29.99
|
None
|
platinum
|
3
|
0fue298y2r23r23r2
|
2010/08/19
|
29.99
|
None
|
platinum
|
4
|
0f345kjh345oiuy346
|
2010/08/19
|
29.99
|
None
|
platinum
|
5
|
0fju234978rfjkhsdf
|
2010/08/19
|
9.99
|
5%
|
basic
|
6
|
0f34ruiy23e78y2r
|
2010/08/19
|
9.99
|
None
|
basic
|
7
|
0fue298y2r23r23r3
|
2010/08/19
|
9.99
|
None
|
basic
|
8
|
0f345kjh345oiuy347
|
2010/08/19
|
9.99
|
None
|
basic
|
9
|
0fju234978rfjkhsdf
|
2011/08/11
|
9.99
|
5%
|
basic
|
10
|
df = df.cols.replace("discount", search=["%"], replace_by="", search_by="chars", ignore_case=True, output_cols="discount").ext.cache()
df.ext.display()
# pdf = df.compute()
# print("AAAAA",df.ext.profile(columns="*", infer=True)["columns"]["discount"])
customer_id
1 (object)
not nullable
|
transactoin_date
2 (object)
not nullable
|
ticket_price
3 (object)
not nullable
|
discount
4 (object)
not nullable
|
product
5 (object)
not nullable
|
info
6 (uint8)
not nullable
|
---|---|---|---|---|---|
0f345kjh345oiuy345
|
2010/08/19
|
29.99
|
nan
|
platinum
|
1
|
0fju234978rfjkhsdf
|
2012/01/05
|
29.99
|
nan
|
platinum
|
2
|
0f34ruiy23e78y2r
|
2009/08/11
|
29.99
|
nan
|
platinum
|
3
|
0fue298y2r23r23r2
|
2010/08/19
|
29.99
|
nan
|
platinum
|
4
|
0f345kjh345oiuy346
|
2010/08/19
|
29.99
|
nan
|
platinum
|
5
|
0fju234978rfjkhsdf
|
2010/08/19
|
9.99
|
5
|
basic
|
6
|
0f34ruiy23e78y2r
|
2010/08/19
|
9.99
|
nan
|
basic
|
7
|
0fue298y2r23r23r3
|
2010/08/19
|
9.99
|
nan
|
basic
|
8
|
0f345kjh345oiuy347
|
2010/08/19
|
9.99
|
nan
|
basic
|
9
|
0fju234978rfjkhsdf
|
2011/08/11
|
9.99
|
5
|
basic
|
10
|
for i in pdf["discount"]:
print(i, type(i))
nan <class 'float'> nan <class 'float'> nan <class 'float'> nan <class 'float'> nan <class 'float'> 5 <class 'str'> nan <class 'float'> nan <class 'float'> nan <class 'float'> 5 <class 'str'> 5 <class 'str'> nan <class 'float'> nan <class 'float'> 5 <class 'str'> nan <class 'float'> 5 <class 'str'> nan <class 'float'>
pdf.cols.profiler_dtypes("*")
{'customer_id': None, 'transactoin_date': None, 'ticket_price': None, 'discount': None, 'product': None, 'info': None}
df.ext.display()
customer_id
1 (object)
not nullable
|
transactoin_date
2 (object)
not nullable
|
ticket_price
3 (object)
not nullable
|
discount
4 (object)
not nullable
|
product
5 (object)
not nullable
|
info
6 (uint8)
not nullable
|
---|---|---|---|---|---|
0f345kjh345oiuy345
|
2010/08/19
|
29.99
|
nan
|
platinum
|
1
|
0fju234978rfjkhsdf
|
2012/01/05
|
29.99
|
nan
|
platinum
|
2
|
0f34ruiy23e78y2r
|
2009/08/11
|
29.99
|
nan
|
platinum
|
3
|
0fue298y2r23r23r2
|
2010/08/19
|
29.99
|
nan
|
platinum
|
4
|
0f345kjh345oiuy346
|
2010/08/19
|
29.99
|
nan
|
platinum
|
5
|
0fju234978rfjkhsdf
|
2010/08/19
|
9.99
|
5
|
basic
|
6
|
0f34ruiy23e78y2r
|
2010/08/19
|
9.99
|
nan
|
basic
|
7
|
0fue298y2r23r23r3
|
2010/08/19
|
9.99
|
nan
|
basic
|
8
|
0f345kjh345oiuy347
|
2010/08/19
|
9.99
|
nan
|
basic
|
9
|
0fju234978rfjkhsdf
|
2011/08/11
|
9.99
|
5
|
basic
|
10
|
df.cols.count_mismatch({"discount":"int"})
BBBB {'discount': 'int'} result {'discount': {'mismatch': 0, 'missing': 12, 'match': 5, 'profiler_dtype': 'int'}}
{'discount': {'mismatch': 0, 'missing': 12, 'match': 5, 'profiler_dtype': 'int'}}
df.cols.frequency("first_name", count_uniques = True)
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-95-345e5e4f3ed0> in <module> ----> 1 df.cols.frequency("first_name", count_uniques = True) ~\Documents\Optimus\optimus\engines\base\dask\columns.py in frequency(self, columns, n, percentage, total_rows, count_uniques, compute) 142 143 df = self.df --> 144 columns = parse_columns(df, columns) 145 146 @delayed ~\Documents\Optimus\optimus\helpers\columns.py in parse_columns(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert) 196 # Check for missing columns 197 if accepts_missing_cols is False: --> 198 check_for_missing_columns(df, cols) 199 200 # Filter by column data type ~\Documents\Optimus\optimus\helpers\columns.py in check_for_missing_columns(df, col_names) 346 347 if len(missing_columns) > 0: --> 348 RaiseIt.value_error(missing_columns, _col_names) 349 return False 350 ~\Documents\Optimus\optimus\helpers\raiseit.py in value_error(var, data_values, extra_text) 74 type=divisor.join(map( 75 lambda x: "'" + x + "'", ---> 76 data_values)), var_type=one_list_to_val(var), extra_text=extra_text)) 77 78 @staticmethod ValueError: 'missing_columns' must be 'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', 'info', received 'first_name'.
%timeit df.ext.profile("*")
3.57 ms ± 328 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
df.cols.count_uniques("customer_id")
{'customer_id': {'count_uniques': 1}}
df.cols.profiler_dtypes("*")
{'customer_id': None, 'first_name': None, 'last_name': None, 'current_credit_card': None, 'address_zip': None, 'email': None, 'address_street_number': None, 'address_city': None, 'address_state': None, 'adress_zip': None, 'phone_number': None, 'start_date': None, 'end_date': None, 'transaction_date': None, 'ticket_price': None, 'discount': None, 'discounted_ticket_price': None}
# for col_name in df.cols.names():
# print(col_name)
df.ext.profile("*")
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\numpy\lib\histograms.py:433: RuntimeWarning: invalid value encountered in greater if np.any(bin_edges[:-1] > bin_edges[1:]): C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 9 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
result {'customer_id': {'mismatch': 0, 'missing': 9, 'match': 0, 'profiler_dtype': 'object'}, 'first_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'last_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'current_credit_card': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'address_zip': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'email': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'email'}, 'address_street_number': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'address_city': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'address_state': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'adress_zip': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'phone_number': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'start_date': {'mismatch': 9, 'missing': 0, 'match': 0, 'profiler_dtype': 'date'}, 'end_date': {'mismatch': 3, 'missing': 6, 'match': 0, 'profiler_dtype': 'date'}, 'transaction_date': {'mismatch': 9, 'missing': 0, 'match': 0, 'profiler_dtype': 'date'}, 'ticket_price': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'decimal'}, 'discount': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'decimal'}, 'discounted_ticket_price': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'decimal'}}
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\numpy\lib\histograms.py:433: RuntimeWarning: invalid value encountered in greater if np.any(bin_edges[:-1] > bin_edges[1:]):
{'columns': {'customer_id': {'stats': {'mismatch': 0, 'missing': 9, 'match': 0, 'hist': [{'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 0}, {'lower': nan, 'upper': nan, 'count': 9}], 'count_uniques': 0}, 'dtype': 'float64', 'profiler_dtype': 'object'}, 'first_name': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'frequency': [{'value': 'LAUREY', 'count': 3}, {'value': 'GARRIE', 'count': 3}, {'value': 'ERIN', 'count': 2}, {'value': 'DEVINDER', 'count': 1}], 'count_uniques': 4}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'last_name': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'frequency': [{'value': 'MULANAX', 'count': 3}, {'value': 'DZVONIK', 'count': 3}, {'value': 'BIFFAR', 'count': 3}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'current_credit_card': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'hist': [{'lower': 4189789432146548.0, 'upper': 4189789432146548.0, 'count': 0}, {'lower': 4189789432146548.0, 'upper': 4189789432146548.5, 'count': 1}, {'lower': 4189789432146548.5, 'upper': 4189789432146549.0, 'count': 0}, {'lower': 4189789432146549.0, 'upper': 4189789432146549.0, 'count': 0}, {'lower': 4189789432146549.0, 'upper': 4189789432146549.0, 'count': 0}, {'lower': 4189789432146549.0, 'upper': 4189789432146549.5, 'count': 1}, {'lower': 4189789432146549.5, 'upper': 4189789432146550.0, 'count': 0}, {'lower': 4189789432146550.0, 'upper': 4189789432146550.0, 'count': 0}, {'lower': 4189789432146550.0, 'upper': 4189789432146550.0, 'count': 0}, {'lower': 4189789432146550.0, 'upper': 4189789432146550.5, 'count': 1}, {'lower': 4189789432146550.5, 'upper': 4189789432146551.0, 'count': 0}, {'lower': 4189789432146551.0, 'upper': 4189789432146551.0, 'count': 0}, {'lower': 4189789432146551.0, 'upper': 4189789432146551.0, 'count': 0}, {'lower': 4189789432146551.0, 'upper': 4189789432146551.5, 'count': 1}, {'lower': 4189789432146551.5, 'upper': 4189789432146552.0, 'count': 0}, {'lower': 4189789432146552.0, 'upper': 4189789432146552.0, 'count': 0}, {'lower': 4189789432146552.0, 'upper': 4189789432146552.0, 'count': 0}, {'lower': 4189789432146552.0, 'upper': 4189789432146552.5, 'count': 1}, {'lower': 4189789432146552.5, 'upper': 4189789432146553.0, 'count': 0}, {'lower': 4189789432146553.0, 'upper': 4189789432146553.0, 'count': 0}, {'lower': 4189789432146553.0, 'upper': 4189789432146553.0, 'count': 0}, {'lower': 4189789432146553.0, 'upper': 4189789432146553.5, 'count': 1}, {'lower': 4189789432146553.5, 'upper': 4189789432146554.0, 'count': 0}, {'lower': 4189789432146554.0, 'upper': 4189789432146554.0, 'count': 0}, {'lower': 4189789432146554.0, 'upper': 4189789432146554.0, 'count': 0}, {'lower': 4189789432146554.0, 'upper': 4189789432146554.5, 'count': 1}, {'lower': 4189789432146554.5, 'upper': 4189789432146555.0, 'count': 0}, {'lower': 4189789432146555.0, 'upper': 4189789432146555.0, 'count': 0}, {'lower': 4189789432146555.0, 'upper': 4189789432146555.0, 'count': 0}, {'lower': 4189789432146555.0, 'upper': 4189789432146555.5, 'count': 1}, {'lower': 4189789432146555.5, 'upper': 4189789432146556.0, 'count': 0}, {'lower': 4189789432146556.0, 'upper': 4189789432146556.0, 'count': 1}], 'count_uniques': 9}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'address_zip': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'hist': [{'lower': 7731.0, 'upper': 10544.28125, 'count': 3}, {'lower': 10544.28125, 'upper': 13357.5625, 'count': 0}, {'lower': 13357.5625, 'upper': 16170.84375, 'count': 0}, {'lower': 16170.84375, 'upper': 18984.125, 'count': 0}, {'lower': 18984.125, 'upper': 21797.40625, 'count': 0}, {'lower': 21797.40625, 'upper': 24610.6875, 'count': 0}, {'lower': 24610.6875, 'upper': 27423.96875, 'count': 0}, {'lower': 27423.96875, 'upper': 30237.25, 'count': 0}, {'lower': 30237.25, 'upper': 33050.53125, 'count': 0}, {'lower': 33050.53125, 'upper': 35863.8125, 'count': 0}, {'lower': 35863.8125, 'upper': 38677.09375, 'count': 0}, {'lower': 38677.09375, 'upper': 41490.375, 'count': 0}, {'lower': 41490.375, 'upper': 44303.65625, 'count': 0}, {'lower': 44303.65625, 'upper': 47116.9375, 'count': 0}, {'lower': 47116.9375, 'upper': 49930.21875, 'count': 0}, {'lower': 49930.21875, 'upper': 52743.5, 'count': 0}, {'lower': 52743.5, 'upper': 55556.78125, 'count': 0}, {'lower': 55556.78125, 'upper': 58370.0625, 'count': 0}, {'lower': 58370.0625, 'upper': 61183.34375, 'count': 0}, {'lower': 61183.34375, 'upper': 63996.625, 'count': 0}, {'lower': 63996.625, 'upper': 66809.90625, 'count': 0}, {'lower': 66809.90625, 'upper': 69623.1875, 'count': 0}, {'lower': 69623.1875, 'upper': 72436.46875, 'count': 0}, {'lower': 72436.46875, 'upper': 75249.75, 'count': 0}, {'lower': 75249.75, 'upper': 78063.03125, 'count': 0}, {'lower': 78063.03125, 'upper': 80876.3125, 'count': 0}, {'lower': 80876.3125, 'upper': 83689.59375, 'count': 0}, {'lower': 83689.59375, 'upper': 86502.875, 'count': 0}, {'lower': 86502.875, 'upper': 89316.15625, 'count': 0}, {'lower': 89316.15625, 'upper': 92129.4375, 'count': 0}, {'lower': 92129.4375, 'upper': 94942.71875, 'count': 3}, {'lower': 94942.71875, 'upper': 97756.0, 'count': 3}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'email': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'frequency': [{'value': 'mulanax60@yahoo.com', 'count': 3}, {'value': 'laurey.biffar@mac.com', 'count': 3}, {'value': 'dzvonik30@att.net', 'count': 3}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': 'email'}, 'address_street_number': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'frequency': [{'value': '9195 George Street', 'count': 3}, {'value': '444 Bank Street', 'count': 3}, {'value': '882 Park Place', 'count': 2}, {'value': '3278 7th AVE', 'count': 1}], 'count_uniques': 4}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'address_city': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'frequency': [{'value': 'Elk Frove', 'count': 3}, {'value': 'Cleveland', 'count': 3}, {'value': 'Santa Ana', 'count': 2}, {'value': 'Howell', 'count': 1}], 'count_uniques': 4}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'address_state': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'frequency': [{'value': 'CA', 'count': 5}, {'value': 'OH', 'count': 3}, {'value': 'NJ', 'count': 1}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'adress_zip': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'hist': [{'lower': 7731.0, 'upper': 10544.28125, 'count': 3}, {'lower': 10544.28125, 'upper': 13357.5625, 'count': 0}, {'lower': 13357.5625, 'upper': 16170.84375, 'count': 0}, {'lower': 16170.84375, 'upper': 18984.125, 'count': 0}, {'lower': 18984.125, 'upper': 21797.40625, 'count': 0}, {'lower': 21797.40625, 'upper': 24610.6875, 'count': 0}, {'lower': 24610.6875, 'upper': 27423.96875, 'count': 0}, {'lower': 27423.96875, 'upper': 30237.25, 'count': 0}, {'lower': 30237.25, 'upper': 33050.53125, 'count': 0}, {'lower': 33050.53125, 'upper': 35863.8125, 'count': 0}, {'lower': 35863.8125, 'upper': 38677.09375, 'count': 0}, {'lower': 38677.09375, 'upper': 41490.375, 'count': 0}, {'lower': 41490.375, 'upper': 44303.65625, 'count': 0}, {'lower': 44303.65625, 'upper': 47116.9375, 'count': 0}, {'lower': 47116.9375, 'upper': 49930.21875, 'count': 0}, {'lower': 49930.21875, 'upper': 52743.5, 'count': 0}, {'lower': 52743.5, 'upper': 55556.78125, 'count': 0}, {'lower': 55556.78125, 'upper': 58370.0625, 'count': 0}, {'lower': 58370.0625, 'upper': 61183.34375, 'count': 0}, {'lower': 61183.34375, 'upper': 63996.625, 'count': 0}, {'lower': 63996.625, 'upper': 66809.90625, 'count': 0}, {'lower': 66809.90625, 'upper': 69623.1875, 'count': 0}, {'lower': 69623.1875, 'upper': 72436.46875, 'count': 0}, {'lower': 72436.46875, 'upper': 75249.75, 'count': 0}, {'lower': 75249.75, 'upper': 78063.03125, 'count': 0}, {'lower': 78063.03125, 'upper': 80876.3125, 'count': 0}, {'lower': 80876.3125, 'upper': 83689.59375, 'count': 0}, {'lower': 83689.59375, 'upper': 86502.875, 'count': 0}, {'lower': 86502.875, 'upper': 89316.15625, 'count': 0}, {'lower': 89316.15625, 'upper': 92129.4375, 'count': 0}, {'lower': 92129.4375, 'upper': 94942.71875, 'count': 3}, {'lower': 94942.71875, 'upper': 97756.0, 'count': 3}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'phone_number': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'frequency': [{'value': '(732)353-4861', 'count': 3}, {'value': '(714) 633-4198', 'count': 3}, {'value': '(541) 727-8215', 'count': 3}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'start_date': {'stats': {'mismatch': 9, 'missing': 0, 'match': 0, 'frequency': [{'value': '2014/12/26', 'count': 3}, {'value': '2012/09/03', 'count': 3}, {'value': '2010/01/02', 'count': 3}], 'count_uniques': 3}, 'dtype': 'datetime64[ns]', 'profiler_dtype': 'date'}, 'end_date': {'stats': {'mismatch': 3, 'missing': 6, 'match': 0, 'frequency': [{'value': '2013/09/08', 'count': 3}], 'count_uniques': 1}, 'dtype': 'datetime64[ns]', 'profiler_dtype': 'date'}, 'transaction_date': {'stats': {'mismatch': 9, 'missing': 0, 'match': 0, 'frequency': [{'value': '2015/07/06', 'count': 2}, {'value': '2013/12/09', 'count': 2}, {'value': '2012/09/03', 'count': 2}, {'value': '2015/08/05', 'count': 1}, {'value': '2014/01/08', 'count': 1}, {'value': '2012/10/03', 'count': 1}], 'count_uniques': 6}, 'dtype': 'datetime64[ns]', 'profiler_dtype': 'date'}, 'ticket_price': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'hist': [{'lower': 0.01, 'upper': 0.415625, 'count': 3}, {'lower': 0.415625, 'upper': 0.82125, 'count': 0}, {'lower': 0.82125, 'upper': 1.226875, 'count': 0}, {'lower': 1.226875, 'upper': 1.6325, 'count': 0}, {'lower': 1.6325, 'upper': 2.038125, 'count': 0}, {'lower': 2.038125, 'upper': 2.4437499999999996, 'count': 0}, {'lower': 2.4437499999999996, 'upper': 2.8493749999999998, 'count': 0}, {'lower': 2.8493749999999998, 'upper': 3.255, 'count': 0}, {'lower': 3.255, 'upper': 3.660625, 'count': 0}, {'lower': 3.660625, 'upper': 4.06625, 'count': 0}, {'lower': 4.06625, 'upper': 4.471875, 'count': 0}, {'lower': 4.471875, 'upper': 4.8774999999999995, 'count': 0}, {'lower': 4.8774999999999995, 'upper': 5.283125, 'count': 0}, {'lower': 5.283125, 'upper': 5.68875, 'count': 0}, {'lower': 5.68875, 'upper': 6.094375, 'count': 0}, {'lower': 6.094375, 'upper': 6.5, 'count': 0}, {'lower': 6.5, 'upper': 6.905625, 'count': 0}, {'lower': 6.905625, 'upper': 7.31125, 'count': 0}, {'lower': 7.31125, 'upper': 7.716875, 'count': 0}, {'lower': 7.716875, 'upper': 8.1225, 'count': 0}, {'lower': 8.1225, 'upper': 8.528125, 'count': 0}, {'lower': 8.528125, 'upper': 8.93375, 'count': 0}, {'lower': 8.93375, 'upper': 9.339375, 'count': 0}, {'lower': 9.339375, 'upper': 9.745, 'count': 0}, {'lower': 9.745, 'upper': 10.150625, 'count': 2}, {'lower': 10.150625, 'upper': 10.55625, 'count': 0}, {'lower': 10.55625, 'upper': 10.961875000000001, 'count': 0}, {'lower': 10.961875000000001, 'upper': 11.3675, 'count': 0}, {'lower': 11.3675, 'upper': 11.773125, 'count': 0}, {'lower': 11.773125, 'upper': 12.17875, 'count': 0}, {'lower': 12.17875, 'upper': 12.584375, 'count': 0}, {'lower': 12.584375, 'upper': 12.99, 'count': 4}], 'count_uniques': 3}, 'dtype': 'float64', 'profiler_dtype': 'decimal'}, 'discount': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'hist': [{'lower': 0.0, 'upper': 0.003125, 'count': 8}, {'lower': 0.003125, 'upper': 0.00625, 'count': 0}, {'lower': 0.00625, 'upper': 0.009375000000000001, 'count': 0}, {'lower': 0.009375000000000001, 'upper': 0.0125, 'count': 0}, {'lower': 0.0125, 'upper': 0.015625, 'count': 0}, {'lower': 0.015625, 'upper': 0.018750000000000003, 'count': 0}, {'lower': 0.018750000000000003, 'upper': 0.021875000000000002, 'count': 0}, {'lower': 0.021875000000000002, 'upper': 0.025, 'count': 0}, {'lower': 0.025, 'upper': 0.028125, 'count': 0}, {'lower': 0.028125, 'upper': 0.03125, 'count': 0}, {'lower': 0.03125, 'upper': 0.034375, 'count': 0}, {'lower': 0.034375, 'upper': 0.037500000000000006, 'count': 0}, {'lower': 0.037500000000000006, 'upper': 0.040625, 'count': 0}, {'lower': 0.040625, 'upper': 0.043750000000000004, 'count': 0}, {'lower': 0.043750000000000004, 'upper': 0.046875, 'count': 0}, {'lower': 0.046875, 'upper': 0.05, 'count': 0}, {'lower': 0.05, 'upper': 0.053125000000000006, 'count': 0}, {'lower': 0.053125000000000006, 'upper': 0.05625, 'count': 0}, {'lower': 0.05625, 'upper': 0.059375000000000004, 'count': 0}, {'lower': 0.059375000000000004, 'upper': 0.0625, 'count': 0}, {'lower': 0.0625, 'upper': 0.065625, 'count': 0}, {'lower': 0.065625, 'upper': 0.06875, 'count': 0}, {'lower': 0.06875, 'upper': 0.07187500000000001, 'count': 0}, {'lower': 0.07187500000000001, 'upper': 0.07500000000000001, 'count': 0}, {'lower': 0.07500000000000001, 'upper': 0.078125, 'count': 0}, {'lower': 0.078125, 'upper': 0.08125, 'count': 0}, {'lower': 0.08125, 'upper': 0.084375, 'count': 0}, {'lower': 0.084375, 'upper': 0.08750000000000001, 'count': 0}, {'lower': 0.08750000000000001, 'upper': 0.09062500000000001, 'count': 0}, {'lower': 0.09062500000000001, 'upper': 0.09375, 'count': 0}, {'lower': 0.09375, 'upper': 0.096875, 'count': 0}, {'lower': 0.096875, 'upper': 0.1, 'count': 1}], 'count_uniques': 2}, 'dtype': 'float64', 'profiler_dtype': 'decimal'}, 'discounted_ticket_price': {'stats': {'mismatch': 0, 'missing': 0, 'match': 9, 'hist': [{'lower': 0.01, 'upper': 0.415625, 'count': 3}, {'lower': 0.415625, 'upper': 0.82125, 'count': 0}, {'lower': 0.82125, 'upper': 1.226875, 'count': 0}, {'lower': 1.226875, 'upper': 1.6325, 'count': 0}, {'lower': 1.6325, 'upper': 2.038125, 'count': 0}, {'lower': 2.038125, 'upper': 2.4437499999999996, 'count': 0}, {'lower': 2.4437499999999996, 'upper': 2.8493749999999998, 'count': 0}, {'lower': 2.8493749999999998, 'upper': 3.255, 'count': 0}, {'lower': 3.255, 'upper': 3.660625, 'count': 0}, {'lower': 3.660625, 'upper': 4.06625, 'count': 0}, {'lower': 4.06625, 'upper': 4.471875, 'count': 0}, {'lower': 4.471875, 'upper': 4.8774999999999995, 'count': 0}, {'lower': 4.8774999999999995, 'upper': 5.283125, 'count': 0}, {'lower': 5.283125, 'upper': 5.68875, 'count': 0}, {'lower': 5.68875, 'upper': 6.094375, 'count': 0}, {'lower': 6.094375, 'upper': 6.5, 'count': 0}, {'lower': 6.5, 'upper': 6.905625, 'count': 0}, {'lower': 6.905625, 'upper': 7.31125, 'count': 0}, {'lower': 7.31125, 'upper': 7.716875, 'count': 0}, {'lower': 7.716875, 'upper': 8.1225, 'count': 0}, {'lower': 8.1225, 'upper': 8.528125, 'count': 0}, {'lower': 8.528125, 'upper': 8.93375, 'count': 0}, {'lower': 8.93375, 'upper': 9.339375, 'count': 0}, {'lower': 9.339375, 'upper': 9.745, 'count': 0}, {'lower': 9.745, 'upper': 10.150625, 'count': 2}, {'lower': 10.150625, 'upper': 10.55625, 'count': 0}, {'lower': 10.55625, 'upper': 10.961875000000001, 'count': 0}, {'lower': 10.961875000000001, 'upper': 11.3675, 'count': 0}, {'lower': 11.3675, 'upper': 11.773125, 'count': 1}, {'lower': 11.773125, 'upper': 12.17875, 'count': 0}, {'lower': 12.17875, 'upper': 12.584375, 'count': 0}, {'lower': 12.584375, 'upper': 12.99, 'count': 3}], 'count_uniques': 4}, 'dtype': 'float64', 'profiler_dtype': 'decimal'}}, 'name': None, 'file_name': 'datasetFile-1590006020188.csv', 'summary': {'cols_count': 17, 'rows_count': 9, 'size': '1.4 kB', 'dtypes_list': ['datetime64[ns]', 'object', 'float64'], 'total_count_dtypes': 3, 'missing_count': 15, 'p_missing': 166.67}}
a = ({'customer_id': 'object', 'first_name': 'string', 'last_name': 'string', 'current_credit_card': 'credit_card_number',
})
mismatch = df.cols.count_mismatch(a, infer=True)
print(mismatch)
{'customer_id': {'mismatch': 0, 'missing': 9, 'match': 0, 'profiler_dtype': 'object'}, 'first_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'last_name': {'mismatch': 0, 'missing': 0, 'match': 9, 'profiler_dtype': 'string'}, 'current_credit_card': {'mismatch': 9, 'missing': 0, 'match': 0, 'profiler_dtype': 'credit_card_number'}}
df.cols.cast("email", object).compute()
df.cols.profiler_dtypes("*")
{'customer_id': None, 'transactoin_date': None, 'ticket_price': None, 'discount': None, 'product': None}
df.ext.profile(columns="*", infer=True)
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
{'columns': {'customer_id': {'stats': {'mismatch': 0, 'missing': 0, 'match': 17, 'frequency': [{'value': '0fju234978rfjkhsdf', 'count': 4}, {'value': '0f34ruiy23e78y2r', 'count': 4}, {'value': '0fue298y2r23r23r5', 'count': 1}, {'value': '0fue298y2r23r23r4', 'count': 1}, {'value': '0fue298y2r23r23r3', 'count': 1}, {'value': '0fue298y2r23r23r2', 'count': 1}, {'value': '0f345kjh345oiuy349', 'count': 1}, {'value': '0f345kjh345oiuy348', 'count': 1}, {'value': '0f345kjh345oiuy347', 'count': 1}, {'value': '0f345kjh345oiuy346', 'count': 1}, {'value': '0f345kjh345oiuy345', 'count': 1}], 'count_uniques': 11}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'transactoin_date': {'stats': {'mismatch': 0, 'missing': 6, 'match': 11, 'frequency': [{'value': '2010/08/19', 'count': 7}, {'value': '2015/08/09', 'count': 1}, {'value': '2012/01/05', 'count': 1}, {'value': '2011/08/11', 'count': 1}, {'value': '2009/08/11', 'count': 1}], 'count_uniques': 5}, 'dtype': 'object', 'profiler_dtype': 'date'}, 'ticket_price': {'stats': {'mismatch': 0, 'missing': 0, 'match': 17, 'frequency': [{'value': 9.99, 'count': 7}, {'value': 29.99, 'count': 5}, {'value': 14.99, 'count': 4}, {'value': 9.91, 'count': 1}], 'count_uniques': 4}, 'dtype': 'object', 'profiler_dtype': 'decimal'}, 'discount': {'stats': {'mismatch': 0, 'missing': 12, 'match': 5, 'frequency': [{'value': '5%', 'count': 5}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'product': {'stats': {'mismatch': 0, 'missing': 0, 'match': 17, 'frequency': [{'value': 'basic', 'count': 8}, {'value': 'platinum', 'count': 5}, {'value': 'deluxe', 'count': 4}], 'count_uniques': 3}, 'dtype': 'object', 'profiler_dtype': 'string'}}, 'name': None, 'file_name': 'datasetFile-1589815911139.csv', 'summary': {'cols_count': 5, 'rows_count': 17, 'size': '808 Bytes', 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 18, 'p_missing': 105.88}}
df.cols.profiler_dtypes("*")
{'customer_id': 'string', 'transactoin_date': 'date', 'ticket_price': 'decimal', 'discount': 'string', 'product': 'string'}
_output = df.ext.profile(columns="*", infer=True, output="json")
df.ext.set_buffer("*")
df = df.cols.replace("discount", search=["%"], replace_by="", search_by="chars", ignore_case=True, output_cols="discount").ext.cache()
_output = df.ext.profile(columns="*", output="json")
df.ext.display()
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r))) C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
customer_id
1 (object)
not nullable
|
transactoin_date
2 (object)
not nullable
|
ticket_price
3 (object)
not nullable
|
discount
4 (object)
not nullable
|
product
5 (object)
not nullable
|
---|---|---|---|---|
0f345kjh345oiuy345
|
2010/08/19
|
29.99
|
nan
|
platinum
|
0fju234978rfjkhsdf
|
2012/01/05
|
29.99
|
nan
|
platinum
|
0f34ruiy23e78y2r
|
2009/08/11
|
29.99
|
nan
|
platinum
|
0fue298y2r23r23r2
|
2010/08/19
|
29.99
|
nan
|
platinum
|
0f345kjh345oiuy346
|
2010/08/19
|
29.99
|
nan
|
platinum
|
0fju234978rfjkhsdf
|
2010/08/19
|
9.99
|
5
|
basic
|
0f34ruiy23e78y2r
|
2010/08/19
|
9.99
|
nan
|
basic
|
0fue298y2r23r23r3
|
2010/08/19
|
9.99
|
nan
|
basic
|
0f345kjh345oiuy347
|
2010/08/19
|
9.99
|
nan
|
basic
|
0fju234978rfjkhsdf
|
2011/08/11
|
9.99
|
5
|
basic
|
df.cols.profiler_dtypes("*")
{'customer_id': 'string', 'transactoin_date': 'date', 'ticket_price': 'decimal', 'discount': 'int', 'product': 'string'}
pdf= df.compute()
for i in pdf["discount"]:
print(type(i))
<class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'int'> <class 'float'> <class 'float'> <class 'float'> <class 'int'> <class 'int'> <class 'float'> <class 'float'> <class 'int'> <class 'float'> <class 'int'> <class 'float'>
# df.ext.set_buffer("*")
# df = df.cols.set( value="0", where='df["discount"].isnull()', output_cols=["discount"] ).ext.cache()
# _output = df.ext.profile(columns="*", infer=True, output="json")
# df.ext.set_buffer("*")
df = df.cols.set(value='0', where='df["discount"].isnull()', output_cols="discount").ext.cache()
df.ext.profile(columns="*", infer=True)["columns"]["discount"]
# df.ext.set_buffer("*") # error
# print(df.ext.buffer_window("*", 0, 17).ext.to_json("*")) # error
ASDASDF AAA {'discount': 'int'}
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
{'stats': {'mismatch': 0, 'missing': 0, 'match': 17, 'frequency': [{'value': 0, 'count': 12}, {'value': 5, 'count': 5}], 'count_uniques': 2}, 'dtype': 'object', 'profiler_dtype': 'int'}
df.ext.set_buffer("*")
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
profile_preview = df.ext.buffer_window("*", 0, 17).cols.set(value='mask["discount"]*mask["ticket_price"]', where='df["discount"]!=None', output_cols="new column")
profile_preview
customer_id | transactoin_date | ticket_price | discount | product | new column | |
---|---|---|---|---|---|---|
0 | 0f345kjh345oiuy345 | 2010/08/19 | 29.99 | 0 | platinum | |
1 | 0fju234978rfjkhsdf | 2012/01/05 | 29.99 | 0 | platinum | |
2 | 0f34ruiy23e78y2r | 2009/08/11 | 29.99 | 0 | platinum | |
3 | 0fue298y2r23r23r2 | 2010/08/19 | 29.99 | 0 | platinum | |
4 | 0f345kjh345oiuy346 | 2010/08/19 | 29.99 | 0 | platinum | |
5 | 0fju234978rfjkhsdf | 2010/08/19 | 9.99 | 5 | basic | 9.999.999.999.999.99 |
6 | 0f34ruiy23e78y2r | 2010/08/19 | 9.99 | 0 | basic | |
7 | 0fue298y2r23r23r3 | 2010/08/19 | 9.99 | 0 | basic | |
8 | 0f345kjh345oiuy347 | 2010/08/19 | 9.99 | 0 | basic | |
9 | 0fju234978rfjkhsdf | 2011/08/11 | 9.99 | 5 | basic | 9.999.999.999.999.99 |
10 | 0f34ruiy23e78y2r | 2015/08/09 | 9.99 | 5 | basic | 9.999.999.999.999.99 |
11 | 0fue298y2r23r23r4 | None | 9.91 | 0 | basic | |
12 | 0f345kjh345oiuy348 | None | 9.99 | 0 | basic | |
13 | 0fju234978rfjkhsdf | None | 14.99 | 5 | deluxe | 14.9914.9914.9914.9914.99 |
14 | 0f34ruiy23e78y2r | None | 14.99 | 0 | deluxe | |
15 | 0fue298y2r23r23r5 | None | 14.99 | 5 | deluxe | 14.9914.9914.9914.9914.99 |
16 | 0f345kjh345oiuy349 | None | 14.99 | 0 | deluxe |
profile_preview.ext.profile("*")["columns"]["discount"]
{'stats': {'match': 17, 'missing': 0, 'mismatch': 0, 'frequency': [{'value': 0, 'count': 12}, {'value': 5, 'count': 5}], 'count_uniques': 2}, 'dtype': 'object'}
df.cols.profiler_dtypes("new_discount")
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-58-d54f9991d335> in <module> ----> 1 df.cols.profiler_dtypes("new_discount") ~\Documents\Optimus\optimus\engines\base\dask\columns.py in profiler_dtypes(self, columns) 1040 """ 1041 df = self.df -> 1042 columns = parse_columns(df, columns) 1043 result = {} 1044 for col_name in columns: ~\Documents\Optimus\optimus\helpers\columns.py in parse_columns(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert) 196 # Check for missing columns 197 if accepts_missing_cols is False: --> 198 check_for_missing_columns(df, cols) 199 200 # Filter by column data type ~\Documents\Optimus\optimus\helpers\columns.py in check_for_missing_columns(df, col_names) 346 347 if len(missing_columns) > 0: --> 348 RaiseIt.value_error(missing_columns, _col_names) 349 return False 350 ~\Documents\Optimus\optimus\helpers\raiseit.py in value_error(var, data_values, extra_text) 74 type=divisor.join(map( 75 lambda x: "'" + x + "'", ---> 76 data_values)), var_type=one_list_to_val(var), extra_text=extra_text)) 77 78 @staticmethod ValueError: 'missing_columns' must be 'customer_id', 'transactoin_date', 'ticket_price', 'discount', 'product', received 'new_discount'.
customer_id | transactoin_date | ticket_price | discount | product | |
---|---|---|---|---|---|
0 | 0f345kjh345oiuy345 | 2010/08/19 | 29.99 | 0 | platinum |
1 | 0fju234978rfjkhsdf | 2012/01/05 | 29.99 | 0 | platinum |
2 | 0f34ruiy23e78y2r | 2009/08/11 | 29.99 | 0 | platinum |
3 | 0fue298y2r23r23r2 | 2010/08/19 | 29.99 | 0 | platinum |
4 | 0f345kjh345oiuy346 | 2010/08/19 | 29.99 | 0 | platinum |
5 | 0fju234978rfjkhsdf | 2010/08/19 | 9.99 | 5 | basic |
6 | 0f34ruiy23e78y2r | 2010/08/19 | 9.99 | 0 | basic |
7 | 0fue298y2r23r23r3 | 2010/08/19 | 9.99 | 0 | basic |
8 | 0f345kjh345oiuy347 | 2010/08/19 | 9.99 | 0 | basic |
9 | 0fju234978rfjkhsdf | 2011/08/11 | 9.99 | 5 | basic |
10 | 0f34ruiy23e78y2r | 2015/08/09 | 9.99 | 5 | basic |
11 | 0fue298y2r23r23r4 | None | 9.91 | 0 | basic |
12 | 0f345kjh345oiuy348 | None | 9.99 | 0 | basic |
13 | 0fju234978rfjkhsdf | None | 14.99 | 5 | deluxe |
14 | 0f34ruiy23e78y2r | None | 14.99 | 0 | deluxe |
15 | 0fue298y2r23r23r5 | None | 14.99 | 5 | deluxe |
16 | 0f345kjh345oiuy349 | None | 14.99 | 0 | deluxe |
df.cols.profiler_dtypes("*")
{'customer_id': 'string', 'transactoin_date': 'date', 'ticket_price': 'decimal', 'discount': 'int', 'product': 'string'}
# df.cols.count_mismatch({"discount":"int"}, infer=True)
df.ext.profile(columns="*", infer=True)["columns"]["discount"]
{'stats': {'mismatch': 0, 'missing': 12, 'match': 5, 'frequency': [{'value': '5', 'count': 5}], 'count_uniques': 1}, 'dtype': 'object', 'profiler_dtype': 'int'}
df = op.load.csv("http://159.65.217.17:5003/uploads/datasetFile-1589815911139.csv", sep=",", error_bad_lines=False, header=True, null_value="null", infer_schema="true", encoding="UTF-8", quoting=0, lineterminator=None, cache=True).ext.cache()
df = df.ext.optimize()
_output = df.ext.profile(columns="*", infer=True, output="json")
df = df.cols.profiler_dtype("ticket_price", "decimal").ext.cache()
_output = df.ext.set_buffer("*")
df.ext.get_buffer()
_output = df.ext.profile(columns="*", infer=True, output="json")
_output = df.ext.set_buffer("*")
df.ext.table()
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 17 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-91-6aec339f01fc> in <module> 6 7 ----> 8 df = df.cols.profiler_dtype("ticket_price", "decimal").ext.cache() 9 _output = df.ext.set_buffer("*") 10 ~\Documents\Optimus\optimus\engines\base\dask\columns.py in profiler_dtype(self, input_col, dtype, columns) 1068 1069 if not is_dict(columns): -> 1070 columns[input_col] = dtype 1071 1072 # Map from profiler dtype to python dtype TypeError: 'NoneType' object does not support item assignment
df.ext.get_buffer()
customer_id | transactoin_date | ticket_price | discount | product | |
---|---|---|---|---|---|
0 | 0f345kjh345oiuy345 | 2010/08/19 | 29.99 | NaN | platinum |
1 | 0fju234978rfjkhsdf | 2012/01/05 | 29.99 | NaN | platinum |
2 | 0f34ruiy23e78y2r | 2009/08/11 | 29.99 | NaN | platinum |
3 | 0fue298y2r23r23r2 | 2010/08/19 | 29.99 | NaN | platinum |
4 | 0f345kjh345oiuy346 | 2010/08/19 | 29.99 | NaN | platinum |
5 | 0fju234978rfjkhsdf | 2010/08/19 | 9.99 | 5% | basic |
6 | 0f34ruiy23e78y2r | 2010/08/19 | 9.99 | NaN | basic |
7 | 0fue298y2r23r23r3 | 2010/08/19 | 9.99 | NaN | basic |
8 | 0f345kjh345oiuy347 | 2010/08/19 | 9.99 | NaN | basic |
9 | 0fju234978rfjkhsdf | 2011/08/11 | 9.99 | 5% | basic |
10 | 0f34ruiy23e78y2r | 2015/08/09 | 9.99 | 5% | basic |
11 | 0fue298y2r23r23r4 | NaN | 9.91 | NaN | basic |
12 | 0f345kjh345oiuy348 | NaN | 9.99 | NaN | basic |
13 | 0fju234978rfjkhsdf | NaN | 14.99 | 5% | deluxe |
14 | 0f34ruiy23e78y2r | NaN | 14.99 | NaN | deluxe |
15 | 0fue298y2r23r23r5 | NaN | 14.99 | 5% | deluxe |
16 | 0f345kjh345oiuy349 | NaN | 14.99 | NaN | deluxe |
df.cols.dtypes()
{'customer_id': 'category', 'transactoin_date': 'category', 'ticket_price': 'object', 'discount': 'category', 'product': 'category'}
pdf = df.compute()
for i in pdf["ticket_price"]:
print(type(i))
<class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'> <class 'float'>
# df.ext.profile("*", infer=True)
df.cols.profiler_dtypes("*")
{'customer_id': 'string', 'transactoin_date': 'date', 'ticket_price': 'decimal', 'discount': 'string', 'product': 'string'}
df = df.cols.profiler_dtype("ticket_price","int")
df.ext.display()
customer_id
1 (category)
not nullable
|
transactoin_date
2 (category)
not nullable
|
ticket_price
3 (object)
not nullable
|
discount
4 (category)
not nullable
|
product
5 (category)
not nullable
|
---|---|---|---|---|
0f345kjh345oiuy345
|
2010/08/19
|
29
|
nan
|
platinum
|
0fju234978rfjkhsdf
|
2012/01/05
|
29
|
nan
|
platinum
|
0f34ruiy23e78y2r
|
2009/08/11
|
29
|
nan
|
platinum
|
0fue298y2r23r23r2
|
2010/08/19
|
29
|
nan
|
platinum
|
0f345kjh345oiuy346
|
2010/08/19
|
29
|
nan
|
platinum
|
0fju234978rfjkhsdf
|
2010/08/19
|
9
|
5%
|
basic
|
0f34ruiy23e78y2r
|
2010/08/19
|
9
|
nan
|
basic
|
0fue298y2r23r23r3
|
2010/08/19
|
9
|
nan
|
basic
|
0f345kjh345oiuy347
|
2010/08/19
|
9
|
nan
|
basic
|
0fju234978rfjkhsdf
|
2011/08/11
|
9
|
5%
|
basic
|
_output = df.ext.buffer_window("*", 0, 17).cols.set(value='mask["ticket_price"]+mask["ticket_price"]', where='df["ticket_price"]!=None', output_cols="new ticket_price").ext.to_json("*")
df = df.cols.set(value='mask["ticket_price"]*mask["ticket_price"]', where='df["ticket_price"]!=None', output_cols="ticket_price").ext.cache()
df.compute()
customer_id | transactoin_date | ticket_price | discount | product | |
---|---|---|---|---|---|
0 | 0f345kjh345oiuy345 | 2010/08/19 | 3364 | NaN | platinum |
1 | 0fju234978rfjkhsdf | 2012/01/05 | 3364 | NaN | platinum |
2 | 0f34ruiy23e78y2r | 2009/08/11 | 3364 | NaN | platinum |
3 | 0fue298y2r23r23r2 | 2010/08/19 | 3364 | NaN | platinum |
4 | 0f345kjh345oiuy346 | 2010/08/19 | 3364 | NaN | platinum |
5 | 0fju234978rfjkhsdf | 2010/08/19 | 324 | 5% | basic |
6 | 0f34ruiy23e78y2r | 2010/08/19 | 324 | NaN | basic |
7 | 0fue298y2r23r23r3 | 2010/08/19 | 324 | NaN | basic |
8 | 0f345kjh345oiuy347 | 2010/08/19 | 324 | NaN | basic |
9 | 0fju234978rfjkhsdf | 2011/08/11 | 324 | 5% | basic |
10 | 0f34ruiy23e78y2r | 2015/08/09 | 324 | 5% | basic |
11 | 0fue298y2r23r23r4 | NaN | 324 | NaN | basic |
12 | 0f345kjh345oiuy348 | NaN | 324 | NaN | basic |
13 | 0fju234978rfjkhsdf | NaN | 784 | 5% | deluxe |
14 | 0f34ruiy23e78y2r | NaN | 784 | NaN | deluxe |
15 | 0fue298y2r23r23r5 | NaN | 784 | 5% | deluxe |
16 | 0f345kjh345oiuy349 | NaN | 784 | NaN | deluxe |
df = op.load.file("data/Meteorite_Landings.csv").ext.cache()
# df1 = op.load.file("http://159.65.217.17:5003/uploads/datasetFile-1589317752438.csv").ext.cache()
# df.ext.profile("*", output="json")
# df = df.ext.optimize()
df1 = df.ext.optimize()
df1.ext.profile("*", output="json", infer=True)
# df.ext.set_buffer("*")
df1.ext.set_buffer("*")
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
import fastnumbers
fastnumbers.isfloat(1)
False
op.client
Client
|
Cluster
|
df.ext.display()
name
1 (object)
not nullable
|
id
2 (object)
not nullable
|
nametype
3 (object)
not nullable
|
recclass
4 (object)
not nullable
|
mass (g)
5 (object)
not nullable
|
fall
6 (object)
not nullable
|
year
7 (object)
not nullable
|
reclat
8 (object)
not nullable
|
reclong
9 (object)
not nullable
|
GeoLocation
10 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775000
|
6.083330
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.183330
|
10.233330
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.216670
|
-113.000000
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.883330
|
-99.900000
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.166670
|
-64.950000
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.100000
|
71.800000
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.833330
|
95.166670
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.216670
|
0.616670
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.600000
|
-65.233330
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.866670
|
-64.550000
|
(-30.866670,⋅-64.550000)
|
# Try https://stackoverflow.com/questions/42742810/speed-up-millions-of-regex-replacements-in-python-3/42789508#42789508
df["nametype"].str.match("^(\d{5})([- ])?(\d{4})?$").compute()
0 False 1 False 2 False 3 False 4 False ... 45711 False 45712 False 45713 False 45714 False 45715 False Name: nametype, Length: 45716, dtype: bool
df1.cols.profiler_dtypes("*")
{'name': 'string', 'id': 'int', 'nametype': 'string', 'recclass': 'string', 'mass (g)': 'int', 'fall': 'string', 'year': 'date', 'reclat': 'decimal', 'reclong': 'decimal', 'GeoLocation': 'array'}
df1.dtypes
name object id uint16 nametype category recclass object mass (g) object fall category year object reclat object reclong object GeoLocation object dtype: object
type(df1)
dask.dataframe.core.DataFrame
df.cols.count_mismatch({"name":"int"})
{'name': {'mismatch': 45716, 'missing': 0, 'match': 0, 'profiler_dtype': 'int'}}
df1 = df1.cols.profiler_dtype("name","string")
# df1.cols.replace("name","1","2")
# df1 =df1.cols.unnest("GeoLocation",",")
# df1.ext.profile("*",infer=True)
df1.ext.profile("*")
ACTION NAME profiler_dtype modified_columns ['name'] new_columns []
{'columns': {'name': {'stats': {'mismatch': 0, 'missing': 0, 'match': 45716, 'frequency': [{'value': 'Święcany', 'count': 1}, {'value': 'Hammadah al Hamra 007', 'count': 1}, {'value': 'Hammadah al Hamra 029', 'count': 1}, {'value': 'Hammadah al Hamra 028', 'count': 1}, {'value': 'Hammadah al Hamra 027', 'count': 1}, {'value': 'Hammadah al Hamra 026', 'count': 1}, {'value': 'Hammadah al Hamra 025', 'count': 1}, {'value': 'Hammadah al Hamra 024', 'count': 1}, {'value': 'Hammadah al Hamra 023', 'count': 1}, {'value': 'Hammadah al Hamra 022', 'count': 1}, {'value': 'Hammadah al Hamra 021', 'count': 1}, {'value': 'Hammadah al Hamra 020', 'count': 1}, {'value': 'Hammadah al Hamra 019', 'count': 1}, {'value': 'Hammadah al Hamra 018', 'count': 1}, {'value': 'Hammadah al Hamra 017', 'count': 1}, {'value': 'Hammadah al Hamra 014', 'count': 1}, {'value': 'Hammadah al Hamra 013', 'count': 1}, {'value': 'Hammadah al Hamra 012', 'count': 1}, {'value': 'Hammadah al Hamra 011', 'count': 1}, {'value': 'Hammadah al Hamra 010', 'count': 1}, {'value': 'Hammadah al Hamra 009', 'count': 1}, {'value': 'Hammadah al Hamra 030', 'count': 1}, {'value': 'Hammadah al Hamra 031', 'count': 1}, {'value': 'Hammadah al Hamra 032', 'count': 1}, {'value': 'Hammadah al Hamra 043', 'count': 1}, {'value': 'Hammadah al Hamra 051', 'count': 1}, {'value': 'Hammadah al Hamra 050', 'count': 1}, {'value': 'Hammadah al Hamra 049', 'count': 1}, {'value': 'Hammadah al Hamra 048', 'count': 1}, {'value': 'Hammadah al Hamra 047', 'count': 1}, {'value': 'Hammadah al Hamra 046', 'count': 1}, {'value': 'Hammadah al Hamra 045', 'count': 1}, {'value': 'Hammadah al Hamra 044', 'count': 1}], 'count_uniques': 45716}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'id': {'stats': {'mismatch': 0, 'missing': 0, 'match': 45716, 'hist': [{'lower': 1.0, 'upper': 1796.53125, 'count': 1712}, {'lower': 1796.53125, 'upper': 3592.0625, 'count': 1030}, {'lower': 3592.0625, 'upper': 5387.59375, 'count': 1498}, {'lower': 5387.59375, 'upper': 7183.125, 'count': 1765}, {'lower': 7183.125, 'upper': 8978.65625, 'count': 1787}, {'lower': 8978.65625, 'upper': 10774.1875, 'count': 1770}, {'lower': 10774.1875, 'upper': 12569.71875, 'count': 1748}, {'lower': 12569.71875, 'upper': 14365.25, 'count': 1791}, {'lower': 14365.25, 'upper': 16160.78125, 'count': 1774}, {'lower': 16160.78125, 'upper': 17956.3125, 'count': 1771}, {'lower': 17956.3125, 'upper': 19751.84375, 'count': 1768}, {'lower': 19751.84375, 'upper': 21547.375, 'count': 1796}, {'lower': 21547.375, 'upper': 23342.90625, 'count': 1784}, {'lower': 23342.90625, 'upper': 25138.4375, 'count': 1733}, {'lower': 25138.4375, 'upper': 26933.96875, 'count': 1597}, {'lower': 26933.96875, 'upper': 28729.5, 'count': 1250}, {'lower': 28729.5, 'upper': 30525.03125, 'count': 1654}, {'lower': 30525.03125, 'upper': 32320.5625, 'count': 872}, {'lower': 32320.5625, 'upper': 34116.09375, 'count': 1016}, {'lower': 34116.09375, 'upper': 35911.625, 'count': 1401}, {'lower': 35911.625, 'upper': 37707.15625, 'count': 1166}, {'lower': 37707.15625, 'upper': 39502.6875, 'count': 819}, {'lower': 39502.6875, 'upper': 41298.21875, 'count': 848}, {'lower': 41298.21875, 'upper': 43093.75, 'count': 0}, {'lower': 43093.75, 'upper': 44889.28125, 'count': 544}, {'lower': 44889.28125, 'upper': 46684.8125, 'count': 1660}, {'lower': 46684.8125, 'upper': 48480.34375, 'count': 1618}, {'lower': 48480.34375, 'upper': 50275.875, 'count': 1487}, {'lower': 50275.875, 'upper': 52071.40625, 'count': 1555}, {'lower': 52071.40625, 'upper': 53866.9375, 'count': 1293}, {'lower': 53866.9375, 'upper': 55662.46875, 'count': 1618}, {'lower': 55662.46875, 'upper': 57458.0, 'count': 1591}], 'count_uniques': 45716}, 'dtype': 'uint16', 'profiler_dtype': 'int'}, 'nametype': {'stats': {'mismatch': 0, 'missing': 0, 'match': 45716, 'frequency': [{'value': 'Valid', 'count': 45641}, {'value': 'Relict', 'count': 75}], 'count_uniques': 2}, 'dtype': 'category', 'profiler_dtype': 'string'}, 'recclass': {'stats': {'mismatch': 0, 'missing': 0, 'match': 45716, 'frequency': [{'value': 'L6', 'count': 8285}, {'value': 'H5', 'count': 7142}, {'value': 'L5', 'count': 4796}, {'value': 'H6', 'count': 4528}, {'value': 'H4', 'count': 4211}, {'value': 'LL5', 'count': 2766}, {'value': 'LL6', 'count': 2043}, {'value': 'L4', 'count': 1253}, {'value': 'H4/5', 'count': 428}, {'value': 'CM2', 'count': 416}, {'value': 'H3', 'count': 386}, {'value': 'L3', 'count': 365}, {'value': 'CO3', 'count': 335}, {'value': 'Ureilite', 'count': 300}, {'value': 'Iron, IIIAB', 'count': 285}, {'value': 'LL4', 'count': 268}, {'value': 'CV3', 'count': 256}, {'value': 'Diogenite', 'count': 241}, {'value': 'Howardite', 'count': 240}, {'value': 'LL', 'count': 225}, {'value': 'Eucrite', 'count': 221}, {'value': 'Eucrite-pmict', 'count': 207}, {'value': 'E3', 'count': 206}, {'value': 'H5/6', 'count': 193}, {'value': 'Mesosiderite', 'count': 137}, {'value': 'CR2', 'count': 135}, {'value': 'LL3', 'count': 128}, {'value': 'EH3', 'count': 120}, {'value': 'Iron, IIAB', 'count': 118}, {'value': 'Iron, ungrouped', 'count': 113}, {'value': 'H~5', 'count': 111}, {'value': 'L5/6', 'count': 109}, {'value': 'Eucrite-mmict', 'count': 108}], 'count_uniques': 466}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'mass (g)': {'stats': {'mismatch': 32526, 'missing': 131, 'match': 13059, 'frequency': [{'value': '1.3', 'count': 171}, {'value': '1.2', 'count': 140}, {'value': '1.4', 'count': 138}, {'value': '2.1', 'count': 130}, {'value': '2.4', 'count': 126}, {'value': '1.6', 'count': 120}, {'value': '0.5', 'count': 119}, {'value': '1.1', 'count': 116}, {'value': '3.8', 'count': 114}, {'value': '0.7', 'count': 111}, {'value': '1.5', 'count': 111}, {'value': '3.1', 'count': 109}, {'value': '3.2', 'count': 109}, {'value': '1.7', 'count': 109}, {'value': '3', 'count': 108}, {'value': '0.6', 'count': 108}, {'value': '0.9', 'count': 108}, {'value': '0.8', 'count': 107}, {'value': '1.8', 'count': 104}, {'value': '2.5', 'count': 103}, {'value': '2.7', 'count': 102}, {'value': '3.6', 'count': 96}, {'value': '1', 'count': 96}, {'value': '2', 'count': 96}, {'value': '4.2', 'count': 95}, {'value': '2.9', 'count': 93}, {'value': '2.8', 'count': 93}, {'value': '2.2', 'count': 92}, {'value': '2.6', 'count': 91}, {'value': '3.3', 'count': 88}, {'value': '4.1', 'count': 86}, {'value': '1.9', 'count': 86}, {'value': '4.6', 'count': 86}], 'count_uniques': 12576}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'fall': {'stats': {'mismatch': 0, 'missing': 0, 'match': 45716, 'frequency': [{'value': 'Found', 'count': 44609}, {'value': 'Fell', 'count': 1107}], 'count_uniques': 2}, 'dtype': 'category', 'profiler_dtype': 'string'}, 'year': {'stats': {'mismatch': 0, 'missing': 288, 'match': 45428, 'frequency': [{'value': '01/01/2003 12:00:00 AM', 'count': 3323}, {'value': '01/01/1979 12:00:00 AM', 'count': 3046}, {'value': '01/01/1998 12:00:00 AM', 'count': 2697}, {'value': '01/01/2006 12:00:00 AM', 'count': 2456}, {'value': '01/01/1988 12:00:00 AM', 'count': 2296}, {'value': '01/01/2002 12:00:00 AM', 'count': 2078}, {'value': '01/01/2004 12:00:00 AM', 'count': 1940}, {'value': '01/01/2000 12:00:00 AM', 'count': 1792}, {'value': '01/01/1997 12:00:00 AM', 'count': 1696}, {'value': '01/01/1999 12:00:00 AM', 'count': 1691}, {'value': '01/01/2001 12:00:00 AM', 'count': 1650}, {'value': '01/01/1990 12:00:00 AM', 'count': 1518}, {'value': '01/01/2009 12:00:00 AM', 'count': 1497}, {'value': '01/01/1986 12:00:00 AM', 'count': 1375}, {'value': '01/01/2007 12:00:00 AM', 'count': 1189}, {'value': '01/01/2010 12:00:00 AM', 'count': 1005}, {'value': '01/01/1993 12:00:00 AM', 'count': 979}, {'value': '01/01/2008 12:00:00 AM', 'count': 957}, {'value': '01/01/1987 12:00:00 AM', 'count': 916}, {'value': '01/01/1991 12:00:00 AM', 'count': 877}, {'value': '01/01/2005 12:00:00 AM', 'count': 875}, {'value': '01/01/1994 12:00:00 AM', 'count': 719}, {'value': '01/01/2011 12:00:00 AM', 'count': 713}, {'value': '01/01/1974 12:00:00 AM', 'count': 691}, {'value': '01/01/1996 12:00:00 AM', 'count': 583}, {'value': '01/01/1995 12:00:00 AM', 'count': 487}, {'value': '01/01/1981 12:00:00 AM', 'count': 463}, {'value': '01/01/1977 12:00:00 AM', 'count': 421}, {'value': '01/01/1984 12:00:00 AM', 'count': 402}, {'value': '01/01/1985 12:00:00 AM', 'count': 378}, {'value': '01/01/1992 12:00:00 AM', 'count': 372}, {'value': '01/01/1983 12:00:00 AM', 'count': 360}, {'value': '01/01/1982 12:00:00 AM', 'count': 344}], 'count_uniques': 269}, 'dtype': 'object', 'profiler_dtype': 'date'}, 'reclat': {'stats': {'mismatch': 0, 'missing': 0, 'match': 45716, 'frequency': [{'value': '0.000000', 'count': 6438}, {'value': '-71.500000', 'count': 4761}, {'value': '-84.000000', 'count': 3040}, {'value': '-72.000000', 'count': 1506}, {'value': '-79.683330', 'count': 1130}, {'value': '-76.716670', 'count': 680}, {'value': '-76.183330', 'count': 539}, {'value': '-84.216670', 'count': 263}, {'value': '-86.366670', 'count': 226}, {'value': '-86.716670', 'count': 217}, {'value': '-85.666670', 'count': 185}, {'value': '-24.850000', 'count': 178}, {'value': '-85.633330', 'count': 108}, {'value': '-72.954880', 'count': 74}, {'value': '-72.778890', 'count': 69}, {'value': '-72.983890', 'count': 67}, {'value': '58.583330', 'count': 64}, {'value': '-72.775000', 'count': 57}, {'value': '-72.778330', 'count': 52}, {'value': '-72.998890', 'count': 41}, {'value': '-72.779170', 'count': 40}, {'value': '34.083330', 'count': 40}, {'value': '-72.782500', 'count': 39}, {'value': '-72.983889', 'count': 37}, {'value': '-72.778610', 'count': 35}, {'value': '29.916670', 'count': 35}, {'value': '-83.250000', 'count': 35}, {'value': '-25.233330', 'count': 32}, {'value': '-82.500000', 'count': 32}, {'value': '-72.773610', 'count': 31}, {'value': '-72.989720', 'count': 31}, {'value': '-72.774720', 'count': 31}, {'value': '-72.773330', 'count': 30}], 'count_uniques': 12738}, 'dtype': 'object', 'profiler_dtype': 'decimal'}, 'reclong': {'stats': {'mismatch': 0, 'missing': 0, 'match': 45716, 'frequency': [{'value': '0.000000', 'count': 6214}, {'value': '35.666670', 'count': 4985}, {'value': '168.000000', 'count': 3040}, {'value': '26.000000', 'count': 1506}, {'value': '159.750000', 'count': 657}, {'value': '159.666670', 'count': 637}, {'value': '157.166670', 'count': 542}, {'value': '155.750000', 'count': 473}, {'value': '160.500000', 'count': 263}, {'value': '-70.000000', 'count': 228}, {'value': '-141.500000', 'count': 217}, {'value': '175.000000', 'count': 185}, {'value': '-70.533330', 'count': 178}, {'value': '-68.700000', 'count': 105}, {'value': '160.473280', 'count': 74}, {'value': '13.433330', 'count': 64}, {'value': '159.333330', 'count': 44}, {'value': '75.246390', 'count': 42}, {'value': '75.313610', 'count': 42}, {'value': '-103.500000', 'count': 35}, {'value': '157.000000', 'count': 34}, {'value': '75.187220', 'count': 33}, {'value': '-5.583330', 'count': 33}, {'value': '155.500000', 'count': 32}, {'value': '75.200000', 'count': 32}, {'value': '-69.716670', 'count': 32}, {'value': '75.340000', 'count': 31}, {'value': '75.246389', 'count': 30}, {'value': '161.500000', 'count': 30}, {'value': '161.083330', 'count': 29}, {'value': '156.383330', 'count': 27}, {'value': '-9.500000', 'count': 24}, {'value': '-69.766670', 'count': 24}], 'count_uniques': 14640}, 'dtype': 'object', 'profiler_dtype': 'decimal'}, 'GeoLocation': {'stats': {'mismatch': 0, 'missing': 7315, 'match': 38401, 'frequency': [{'value': '(0.000000, 0.000000)', 'count': 6214}, {'value': '(-71.500000, 35.666670)', 'count': 4761}, {'value': '(-84.000000, 168.000000)', 'count': 3040}, {'value': '(-72.000000, 26.000000)', 'count': 1505}, {'value': '(-79.683330, 159.750000)', 'count': 657}, {'value': '(-76.716670, 159.666670)', 'count': 637}, {'value': '(-76.183330, 157.166670)', 'count': 539}, {'value': '(-79.683330, 155.750000)', 'count': 473}, {'value': '(-84.216670, 160.500000)', 'count': 263}, {'value': '(-86.366670, -70.000000)', 'count': 226}, {'value': '(0.000000, 35.666670)', 'count': 223}, {'value': '(-86.716670, -141.500000)', 'count': 217}, {'value': '(-85.666670, 175.000000)', 'count': 185}, {'value': '(-24.850000, -70.533330)', 'count': 178}, {'value': '(-85.633330, -68.700000)', 'count': 105}, {'value': '(-72.954880, 160.473280)', 'count': 74}, {'value': '(58.583330, 13.433330)', 'count': 64}, {'value': '(-76.716670, 159.333330)', 'count': 42}, {'value': '(-72.778890, 75.313610)', 'count': 39}, {'value': '(-72.983890, 75.246390)', 'count': 38}, {'value': '(-83.250000, 157.000000)', 'count': 34}, {'value': '(29.916670, -5.583330)', 'count': 33}, {'value': '(-82.500000, 155.500000)', 'count': 32}, {'value': '(-72.998890, 75.187220)', 'count': 32}, {'value': '(-25.233330, -69.716670)', 'count': 32}, {'value': '(-84.266670, 161.500000)', 'count': 30}, {'value': '(-84.283330, 161.083330)', 'count': 29}, {'value': '(-73.083330, 75.200000)', 'count': 28}, {'value': '(-72.983889, 75.246389)', 'count': 27}, {'value': '(34.083330, -103.500000)', 'count': 27}, {'value': '(-80.066670, 156.383330)', 'count': 27}, {'value': '(-24.683330, -69.766670)', 'count': 24}, {'value': '(27.166670, -9.500000)', 'count': 24}], 'count_uniques': 17100}, 'dtype': 'object', 'profiler_dtype': 'array'}}, 'name': None, 'file_name': 'Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 45716, 'size': '2.7 MB', 'dtypes_list': ['object', 'category', 'uint16'], 'total_count_dtypes': 3, 'missing_count': 0, 'p_missing': 0.0}}
df.meta.get()["profile"]["columns"]["id"]["profiler_dtype"]
# print(df.meta.get()["transformations"]["actions"])
# print(df.meta.get()["transformations"]["columns"])
'int'
df1.meta.get()
df.cols.set("name", df.name!= None, df.name+"a").compute()
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-14-25e623f7a3cf> in <module> ----> 1 df.cols.set("name", df.name!= None, df.name+"a").compute() ~\Documents\Optimus\optimus\engines\base\dask\columns.py in set(self, where, value, output_cols) 647 """ 648 df = self.df --> 649 output_cols = parse_columns(df, output_cols, accepts_missing_cols=True) 650 651 for output_col in output_cols: ~\Documents\Optimus\optimus\helpers\columns.py in parse_columns(df, cols_args, get_args, is_regex, filter_by_column_dtypes, accepts_missing_cols, invert) 173 cols = list(filter(r.match, df_columns)) 174 --> 175 elif cols_args == "*" or cols_args is None: 176 cols = df_columns 177 ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in __bool__(self) 535 raise ValueError( 536 "The truth value of a {0} is ambiguous. " --> 537 "Use a.any() or a.all().".format(self.__class__.__name__) 538 ) 539 ValueError: The truth value of a Series is ambiguous. Use a.any() or a.all().
df.ext.set_buffer("*")
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 45716 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
import re
_value = "50"
regex = re.compile('|'.join("5"))
[[match.start(), match.end()] for match in regex.finditer(_value)]
# length = [[match.start(), match.end()] for match in
# regex.finditer(re.escape(_value), re.IGNORECASE)]
[[0, 1]]
df.cols.unnest("GeoLocation",",").compute()
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | GeoLocation_0 | GeoLocation_1 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21 | Fell | 01/01/1880 12:00:00 AM | 50.775000 | 6.083330 | (50.775000, 6.083330) | (50.775000 | 6.083330) |
1 | Aarhus | 2 | Valid | H6 | 720 | Fell | 01/01/1951 12:00:00 AM | 56.183330 | 10.233330 | (56.183330, 10.233330) | (56.183330 | 10.233330) |
2 | Abee | 6 | Valid | EH4 | 107000 | Fell | 01/01/1952 12:00:00 AM | 54.216670 | -113.000000 | (54.216670, -113.000000) | (54.216670 | -113.000000) |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 01/01/1976 12:00:00 AM | 16.883330 | -99.900000 | (16.883330, -99.900000) | (16.883330 | -99.900000) |
4 | Achiras | 370 | Valid | L6 | 780 | Fell | 01/01/1902 12:00:00 AM | -33.166670 | -64.950000 | (-33.166670, -64.950000) | (-33.166670 | -64.950000) |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
45711 | Zillah 002 | 31356 | Valid | Eucrite | 172 | Found | 01/01/1990 12:00:00 AM | 29.037000 | 17.018500 | (29.037000, 17.018500) | (29.037000 | 17.018500) |
45712 | Zinder | 30409 | Valid | Pallasite, ungrouped | 46 | Found | 01/01/1999 12:00:00 AM | 13.783330 | 8.966670 | (13.783330, 8.966670) | (13.783330 | 8.966670) |
45713 | Zlin | 30410 | Valid | H4 | 3.3 | Found | 01/01/1939 12:00:00 AM | 49.250000 | 17.666670 | (49.250000, 17.666670) | (49.250000 | 17.666670) |
45714 | Zubkovsky | 31357 | Valid | L6 | 2167 | Found | 01/01/2003 12:00:00 AM | 49.789170 | 41.504600 | (49.789170, 41.504600) | (49.789170 | 41.504600) |
45715 | Zulu Queen | 30414 | Valid | L3.7 | 200 | Found | 01/01/1976 12:00:00 AM | 33.983330 | -115.683330 | (33.983330, -115.683330) | (33.983330 | -115.683330) |
45716 rows × 12 columns
df.ext.display()
name
1 (object)
not nullable
|
id
2 (object)
not nullable
|
nametype
3 (object)
not nullable
|
recclass
4 (object)
not nullable
|
mass (g)
5 (object)
not nullable
|
fall
6 (object)
not nullable
|
year
7 (object)
not nullable
|
reclat
8 (object)
not nullable
|
reclong
9 (object)
not nullable
|
GeoLocation
10 (object)
not nullable
|
---|---|---|---|---|---|---|---|---|---|
Aachen
|
1
|
Valid
|
L5
|
21
|
Fell
|
01/01/1880⋅12:00:00⋅AM
|
50.775000
|
6.083330
|
(50.775000,⋅6.083330)
|
Aarhus
|
2
|
Valid
|
H6
|
720
|
Fell
|
01/01/1951⋅12:00:00⋅AM
|
56.183330
|
10.233330
|
(56.183330,⋅10.233330)
|
Abee
|
6
|
Valid
|
EH4
|
107000
|
Fell
|
01/01/1952⋅12:00:00⋅AM
|
54.216670
|
-113.000000
|
(54.216670,⋅-113.000000)
|
Acapulco
|
10
|
Valid
|
Acapulcoite
|
1914
|
Fell
|
01/01/1976⋅12:00:00⋅AM
|
16.883330
|
-99.900000
|
(16.883330,⋅-99.900000)
|
Achiras
|
370
|
Valid
|
L6
|
780
|
Fell
|
01/01/1902⋅12:00:00⋅AM
|
-33.166670
|
-64.950000
|
(-33.166670,⋅-64.950000)
|
Adhi⋅Kot
|
379
|
Valid
|
EH4
|
4239
|
Fell
|
01/01/1919⋅12:00:00⋅AM
|
32.100000
|
71.800000
|
(32.100000,⋅71.800000)
|
Adzhi-Bogdo⋅(stone)
|
390
|
Valid
|
LL3-6
|
910
|
Fell
|
01/01/1949⋅12:00:00⋅AM
|
44.833330
|
95.166670
|
(44.833330,⋅95.166670)
|
Agen
|
392
|
Valid
|
H5
|
30000
|
Fell
|
01/01/1814⋅12:00:00⋅AM
|
44.216670
|
0.616670
|
(44.216670,⋅0.616670)
|
Aguada
|
398
|
Valid
|
L6
|
1620
|
Fell
|
01/01/1930⋅12:00:00⋅AM
|
-31.600000
|
-65.233330
|
(-31.600000,⋅-65.233330)
|
Aguila⋅Blanca
|
417
|
Valid
|
L
|
1440
|
Fell
|
01/01/1920⋅12:00:00⋅AM
|
-30.866670
|
-64.550000
|
(-30.866670,⋅-64.550000)
|
length
[[2, 3]]
df.ext.buffer_window("*", 0, 34).cols.replace("GeoLocation", search=["5"], replace_by="", search_by="chars", ignore_case=True, output_cols="new GeoLocation").cols.find("GeoLocation", sub=["5"], ignore_case=True)
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | new GeoLocation | GeoLocation__match_positions__ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21 | Fell | 01/01/1880 12:00:00 AM | 50.775000 | 6.083330 | (50.775000, 6.083330) | (0.77000, 6.083330) | [[1, 2], [6, 7]] |
1 | Aarhus | 2 | Valid | H6 | 720 | Fell | 01/01/1951 12:00:00 AM | 56.183330 | 10.233330 | (56.183330, 10.233330) | (6.183330, 10.233330) | [[1, 2]] |
2 | Abee | 6 | Valid | EH4 | 107000 | Fell | 01/01/1952 12:00:00 AM | 54.216670 | -113.000000 | (54.216670, -113.000000) | (4.216670, -113.000000) | [[1, 2]] |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 01/01/1976 12:00:00 AM | 16.883330 | -99.900000 | (16.883330, -99.900000) | (16.883330, -99.900000) | None |
4 | Achiras | 370 | Valid | L6 | 780 | Fell | 01/01/1902 12:00:00 AM | -33.166670 | -64.950000 | (-33.166670, -64.950000) | (-33.166670, -64.90000) | [[18, 19]] |
5 | Adhi Kot | 379 | Valid | EH4 | 4239 | Fell | 01/01/1919 12:00:00 AM | 32.100000 | 71.800000 | (32.100000, 71.800000) | (32.100000, 71.800000) | None |
6 | Adzhi-Bogdo (stone) | 390 | Valid | LL3-6 | 910 | Fell | 01/01/1949 12:00:00 AM | 44.833330 | 95.166670 | (44.833330, 95.166670) | (44.833330, 9.166670) | [[13, 14]] |
7 | Agen | 392 | Valid | H5 | 30000 | Fell | 01/01/1814 12:00:00 AM | 44.216670 | 0.616670 | (44.216670, 0.616670) | (44.216670, 0.616670) | None |
8 | Aguada | 398 | Valid | L6 | 1620 | Fell | 01/01/1930 12:00:00 AM | -31.600000 | -65.233330 | (-31.600000, -65.233330) | (-31.600000, -6.233330) | [[15, 16]] |
9 | Aguila Blanca | 417 | Valid | L | 1440 | Fell | 01/01/1920 12:00:00 AM | -30.866670 | -64.550000 | (-30.866670, -64.550000) | (-30.866670, -64.0000) | [[17, 18], [18, 19]] |
10 | Aioun el Atrouss | 423 | Valid | Diogenite-pm | 1000 | Fell | 01/01/1974 12:00:00 AM | 16.398060 | -9.570280 | (16.398060, -9.570280) | (16.398060, -9.70280) | [[15, 16]] |
11 | Aïr | 424 | Valid | L6 | 24000 | Fell | 01/01/1925 12:00:00 AM | 19.083330 | 8.383330 | (19.083330, 8.383330) | (19.083330, 8.383330) | None |
12 | Aire-sur-la-Lys | 425 | Valid | Unknown | NaN | Fell | 01/01/1769 12:00:00 AM | 50.666670 | 2.333330 | (50.666670, 2.333330) | (0.666670, 2.333330) | [[1, 2]] |
13 | Akaba | 426 | Valid | L6 | 779 | Fell | 01/01/1949 12:00:00 AM | 29.516670 | 35.050000 | (29.516670, 35.050000) | (29.16670, 3.00000) | [[4, 5], [13, 14], [16, 17]] |
14 | Akbarpur | 427 | Valid | H4 | 1800 | Fell | 01/01/1838 12:00:00 AM | 29.716670 | 77.950000 | (29.716670, 77.950000) | (29.716670, 77.90000) | [[16, 17]] |
15 | Akwanga | 432 | Valid | H | 3000 | Fell | 01/01/1959 12:00:00 AM | 8.916670 | 8.433330 | (8.916670, 8.433330) | (8.916670, 8.433330) | None |
16 | Akyumak | 433 | Valid | Iron, IVA | 50000 | Fell | 01/01/1981 12:00:00 AM | 39.916670 | 42.816670 | (39.916670, 42.816670) | (39.916670, 42.816670) | None |
17 | Al Rais | 446 | Valid | CR2-an | 160 | Fell | 01/01/1957 12:00:00 AM | 24.416670 | 39.516670 | (24.416670, 39.516670) | (24.416670, 39.16670) | [[15, 16]] |
18 | Al Zarnkh | 447 | Valid | LL5 | 700 | Fell | 01/01/2001 12:00:00 AM | 13.660330 | 28.960000 | (13.660330, 28.960000) | (13.660330, 28.960000) | None |
19 | Alais | 448 | Valid | CI1 | 6000 | Fell | 01/01/1806 12:00:00 AM | 44.116670 | 4.083330 | (44.116670, 4.083330) | (44.116670, 4.083330) | None |
20 | Albareto | 453 | Valid | L/LL4 | 2000 | Fell | 01/01/1766 12:00:00 AM | 44.650000 | 11.016670 | (44.650000, 11.016670) | (44.60000, 11.016670) | [[5, 6]] |
21 | Alberta | 454 | Valid | L | 625 | Fell | 01/01/1949 12:00:00 AM | 2.000000 | 22.666670 | (2.000000, 22.666670) | (2.000000, 22.666670) | None |
22 | Alby sur Chéran | 458 | Valid | Eucrite-mmict | 252 | Fell | 01/01/2002 12:00:00 AM | 45.821330 | 6.015330 | (45.821330, 6.015330) | (4.821330, 6.01330) | [[2, 3], [16, 17]] |
23 | Aldsworth | 461 | Valid | LL5 | 700 | Fell | 01/01/1835 12:00:00 AM | 51.783330 | -1.783330 | (51.783330, -1.783330) | (1.783330, -1.783330) | [[1, 2]] |
24 | Aleppo | 462 | Valid | L6 | 3200 | Fell | 01/01/1873 12:00:00 AM | 36.233330 | 37.133330 | (36.233330, 37.133330) | (36.233330, 37.133330) | None |
25 | Alessandria | 463 | Valid | H5 | 908 | Fell | 01/01/1860 12:00:00 AM | 44.883330 | 8.750000 | (44.883330, 8.750000) | (44.883330, 8.70000) | [[15, 16]] |
26 | Alexandrovsky | 465 | Valid | H4 | 9251 | Fell | 01/01/1900 12:00:00 AM | 50.950000 | 31.816670 | (50.950000, 31.816670) | (0.90000, 31.816670) | [[1, 2], [5, 6]] |
27 | Alfianello | 466 | Valid | L6 | 228000 | Fell | 01/01/1883 12:00:00 AM | 45.266670 | 10.150000 | (45.266670, 10.150000) | (4.266670, 10.10000) | [[2, 3], [16, 17]] |
28 | Allegan | 2276 | Valid | H5 | 32000 | Fell | 01/01/1899 12:00:00 AM | 42.533330 | -85.883330 | (42.533330, -85.883330) | (42.33330, -8.883330) | [[4, 5], [14, 15]] |
29 | Allende | 2278 | Valid | CV3 | 2000000 | Fell | 01/01/1969 12:00:00 AM | 26.966670 | -105.316670 | (26.966670, -105.316670) | (26.966670, -10.316670) | [[15, 16]] |
30 | Almahata Sitta | 48915 | Valid | Ureilite-an | 3950 | Fell | 01/01/2008 12:00:00 AM | 20.745750 | 32.412750 | (20.745750, 32.412750) | (20.7470, 32.41270) | [[6, 7], [8, 9], [19, 20]] |
31 | Alta'ameem | 2284 | Valid | LL5 | 6000 | Fell | 01/01/1977 12:00:00 AM | 35.273330 | 44.215560 | (35.273330, 44.215560) | (3.273330, 44.2160) | [[2, 3], [17, 18], [18, 19]] |
32 | Ambapur Nagla | 2290 | Valid | H5 | 6400 | Fell | 01/01/1895 12:00:00 AM | 27.666670 | 78.250000 | (27.666670, 78.250000) | (27.666670, 78.20000) | [[16, 17]] |
33 | Andhara | 2294 | Valid | Stone-uncl | 2700 | Fell | 01/01/1880 12:00:00 AM | 26.583330 | 85.566670 | (26.583330, 85.566670) | (26.83330, 8.66670) | [[4, 5], [13, 14], [15, 16]] |
df = df.cols.replace("id","a","b")
df.meta.get()
{'file_name': 'tmpd9yzm44r.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'utf-8', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'lineterminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}], 'transformations': {'actions': {}}, 'profile': {'columns': {'id': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '9', 'count': 1}, {'value': '8', 'count': 1}, {'value': '7', 'count': 1}, {'value': '6', 'count': 1}, {'value': '5', 'count': 1}, {'value': '4', 'count': 1}, {'value': '3', 'count': 1}, {'value': '2', 'count': 1}, {'value': '19', 'count': 1}, {'value': '18', 'count': 1}, {'value': '17', 'count': 1}, {'value': '16', 'count': 1}, {'value': '15', 'count': 1}, {'value': '14', 'count': 1}, {'value': '13', 'count': 1}, {'value': '12', 'count': 1}, {'value': '11', 'count': 1}, {'value': '10', 'count': 1}, {'value': '1', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'firstName': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'William', 'count': 1}, {'value': 'PAUL', 'count': 1}, {'value': 'NiELS', 'count': 1}, {'value': 'Max!!!', 'count': 1}, {'value': 'Marie', 'count': 1}, {'value': 'Luis', 'count': 1}, {'value': 'Johannes', 'count': 1}, {'value': 'JaMES', 'count': 1}, {'value': 'JAMES', 'count': 1}, {'value': 'Isaac', 'count': 1}, {'value': 'Galileo', 'count': 1}, {'value': 'Fred', 'count': 1}, {'value': 'Emmy%%', 'count': 1}, {'value': 'David', 'count': 1}, {'value': 'CaRL', 'count': 1}, {'value': 'Arthur', 'count': 1}, {'value': 'André', 'count': 1}, {'value': 'Albert', 'count': 1}, {'value': '((( Heinrich )))))', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'lastName': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'dirac$', 'count': 1}, {'value': 'Planck!!!', 'count': 1}, {'value': 'Nöether$', 'count': 1}, {'value': 'Newton', 'count': 1}, {'value': 'M$$ax%%well', 'count': 1}, {'value': 'KEPLER', 'count': 1}, {'value': 'Hoy&&&le', 'count': 1}, {'value': 'Hertz', 'count': 1}, {'value': 'H$$$ilbert', 'count': 1}, {'value': 'Gilbert###', 'count': 1}, {'value': 'Ga%%%uss', 'count': 1}, {'value': 'Einstein', 'count': 1}, {'value': 'Chadwick', 'count': 1}, {'value': 'CURIE', 'count': 1}, {'value': 'COM%%%pton', 'count': 1}, {'value': 'Böhr//((%%', 'count': 1}, {'value': 'Ampère', 'count': 1}, {'value': 'Alvarez$$%!', 'count': 1}, {'value': ' GALiLEI', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'billingId': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '992', 'count': 1}, {'value': '912', 'count': 1}, {'value': '886', 'count': 1}, {'value': '875', 'count': 1}, {'value': '812', 'count': 1}, {'value': '735', 'count': 1}, {'value': '672', 'count': 1}, {'value': '634', 'count': 1}, {'value': '624', 'count': 1}, {'value': '553', 'count': 1}, {'value': '551', 'count': 1}, {'value': '521', 'count': 1}, {'value': '467', 'count': 1}, {'value': '423', 'count': 1}, {'value': '323', 'count': 1}, {'value': '234', 'count': 1}, {'value': '123', 'count': 1}, {'value': '116', 'count': 1}, {'value': '111', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'product': {'stats': {'mismatch': 0, 'missing': 1, 'match': 18, 'frequency': [{'value': 'pizza', 'count': 4}, {'value': 'taco', 'count': 3}, {'value': 'pasta', 'count': 2}, {'value': 'taaaccoo', 'count': 1}, {'value': 'pizzza', 'count': 1}, {'value': 'piza', 'count': 1}, {'value': 'hamburguer', 'count': 1}, {'value': 'arepa', 'count': 1}, {'value': 'Rice', 'count': 1}, {'value': 'Cake', 'count': 1}, {'value': 'BEER', 'count': 1}, {'value': '110790', 'count': 1}], 'count_uniques': 12}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'price': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '8', 'count': 6}, {'value': '3', 'count': 4}, {'value': '9', 'count': 2}, {'value': '5', 'count': 2}, {'value': '10', 'count': 2}, {'value': '4', 'count': 1}, {'value': '2', 'count': 1}, {'value': '1', 'count': 1}], 'count_uniques': 8}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'birth': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '2000/03/22', 'count': 1}, {'value': '1999/02/15', 'count': 1}, {'value': '1997/06/27', 'count': 1}, {'value': '1994/01/04', 'count': 1}, {'value': '1993/12/08', 'count': 1}, {'value': '1990/07/11', 'count': 1}, {'value': '1990/07/09', 'count': 1}, {'value': '1980/07/07', 'count': 1}, {'value': '1970/07/13', 'count': 1}, {'value': '1958/03/26', 'count': 1}, {'value': '1956/11/30', 'count': 1}, {'value': '1954/07/10', 'count': 1}, {'value': '1950/07/14', 'count': 1}, {'value': '1950/07/08', 'count': 1}, {'value': '1930/08/12', 'count': 1}, {'value': '1923/03/12', 'count': 1}, {'value': '1921/05/03', 'count': 1}, {'value': '1920/04/22', 'count': 1}, {'value': '1899/01/01', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'date'}, 'dummyCol': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'you', 'count': 3}, {'value': 'gonna', 'count': 3}, {'value': 'never', 'count': 2}, {'value': '#', 'count': 2}, {'value': 'up', 'count': 1}, {'value': 'run ', 'count': 1}, {'value': 'never ', 'count': 1}, {'value': 'let', 'count': 1}, {'value': 'give', 'count': 1}, {'value': 'down', 'count': 1}, {'value': 'desert', 'count': 1}, {'value': 'around', 'count': 1}, {'value': 'and', 'count': 1}], 'count_uniques': 13}, 'dtype': 'object', 'profiler_dtype': 'string'}}, 'name': None, 'file_name': 'tmpd9yzm44r.csv', 'summary': {'cols_count': 8, 'rows_count': 19, 'size': '1.3 kB', 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 1, 'p_missing': 5.26}, 'id': {'columns': {'profiler_dtype': 'string'}}}}
df.
import pandas as pd
ser = pd.Series(["1", "2"], dtype='object')
ser.astype(int)
# ser.dtypes
0 1 1 2 dtype: int32
df.meta.get()
{'file_name': 'tmpd9yzm44r.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'utf-8', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'lineterminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}], 'transformations': {'actions': {}}, 'profile': {'columns': {'id': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '9', 'count': 1}, {'value': '8', 'count': 1}, {'value': '7', 'count': 1}, {'value': '6', 'count': 1}, {'value': '5', 'count': 1}, {'value': '4', 'count': 1}, {'value': '3', 'count': 1}, {'value': '2', 'count': 1}, {'value': '19', 'count': 1}, {'value': '18', 'count': 1}, {'value': '17', 'count': 1}, {'value': '16', 'count': 1}, {'value': '15', 'count': 1}, {'value': '14', 'count': 1}, {'value': '13', 'count': 1}, {'value': '12', 'count': 1}, {'value': '11', 'count': 1}, {'value': '10', 'count': 1}, {'value': '1', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'firstName': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'William', 'count': 1}, {'value': 'PAUL', 'count': 1}, {'value': 'NiELS', 'count': 1}, {'value': 'Max!!!', 'count': 1}, {'value': 'Marie', 'count': 1}, {'value': 'Luis', 'count': 1}, {'value': 'Johannes', 'count': 1}, {'value': 'JaMES', 'count': 1}, {'value': 'JAMES', 'count': 1}, {'value': 'Isaac', 'count': 1}, {'value': 'Galileo', 'count': 1}, {'value': 'Fred', 'count': 1}, {'value': 'Emmy%%', 'count': 1}, {'value': 'David', 'count': 1}, {'value': 'CaRL', 'count': 1}, {'value': 'Arthur', 'count': 1}, {'value': 'André', 'count': 1}, {'value': 'Albert', 'count': 1}, {'value': '((( Heinrich )))))', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'lastName': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'dirac$', 'count': 1}, {'value': 'Planck!!!', 'count': 1}, {'value': 'Nöether$', 'count': 1}, {'value': 'Newton', 'count': 1}, {'value': 'M$$ax%%well', 'count': 1}, {'value': 'KEPLER', 'count': 1}, {'value': 'Hoy&&&le', 'count': 1}, {'value': 'Hertz', 'count': 1}, {'value': 'H$$$ilbert', 'count': 1}, {'value': 'Gilbert###', 'count': 1}, {'value': 'Ga%%%uss', 'count': 1}, {'value': 'Einstein', 'count': 1}, {'value': 'Chadwick', 'count': 1}, {'value': 'CURIE', 'count': 1}, {'value': 'COM%%%pton', 'count': 1}, {'value': 'Böhr//((%%', 'count': 1}, {'value': 'Ampère', 'count': 1}, {'value': 'Alvarez$$%!', 'count': 1}, {'value': ' GALiLEI', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'billingId': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '992', 'count': 1}, {'value': '912', 'count': 1}, {'value': '886', 'count': 1}, {'value': '875', 'count': 1}, {'value': '812', 'count': 1}, {'value': '735', 'count': 1}, {'value': '672', 'count': 1}, {'value': '634', 'count': 1}, {'value': '624', 'count': 1}, {'value': '553', 'count': 1}, {'value': '551', 'count': 1}, {'value': '521', 'count': 1}, {'value': '467', 'count': 1}, {'value': '423', 'count': 1}, {'value': '323', 'count': 1}, {'value': '234', 'count': 1}, {'value': '123', 'count': 1}, {'value': '116', 'count': 1}, {'value': '111', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'product': {'stats': {'mismatch': 0, 'missing': 1, 'match': 18, 'frequency': [{'value': 'pizza', 'count': 4}, {'value': 'taco', 'count': 3}, {'value': 'pasta', 'count': 2}, {'value': 'taaaccoo', 'count': 1}, {'value': 'pizzza', 'count': 1}, {'value': 'piza', 'count': 1}, {'value': 'hamburguer', 'count': 1}, {'value': 'arepa', 'count': 1}, {'value': 'Rice', 'count': 1}, {'value': 'Cake', 'count': 1}, {'value': 'BEER', 'count': 1}, {'value': '110790', 'count': 1}], 'count_uniques': 12}, 'dtype': 'object', 'profiler_dtype': 'string'}, 'price': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '8', 'count': 6}, {'value': '3', 'count': 4}, {'value': '9', 'count': 2}, {'value': '5', 'count': 2}, {'value': '10', 'count': 2}, {'value': '4', 'count': 1}, {'value': '2', 'count': 1}, {'value': '1', 'count': 1}], 'count_uniques': 8}, 'dtype': 'object', 'profiler_dtype': 'int'}, 'birth': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': '2000/03/22', 'count': 1}, {'value': '1999/02/15', 'count': 1}, {'value': '1997/06/27', 'count': 1}, {'value': '1994/01/04', 'count': 1}, {'value': '1993/12/08', 'count': 1}, {'value': '1990/07/11', 'count': 1}, {'value': '1990/07/09', 'count': 1}, {'value': '1980/07/07', 'count': 1}, {'value': '1970/07/13', 'count': 1}, {'value': '1958/03/26', 'count': 1}, {'value': '1956/11/30', 'count': 1}, {'value': '1954/07/10', 'count': 1}, {'value': '1950/07/14', 'count': 1}, {'value': '1950/07/08', 'count': 1}, {'value': '1930/08/12', 'count': 1}, {'value': '1923/03/12', 'count': 1}, {'value': '1921/05/03', 'count': 1}, {'value': '1920/04/22', 'count': 1}, {'value': '1899/01/01', 'count': 1}], 'count_uniques': 19}, 'dtype': 'object', 'profiler_dtype': 'date'}, 'dummyCol': {'stats': {'mismatch': 0, 'missing': 0, 'match': 19, 'frequency': [{'value': 'you', 'count': 3}, {'value': 'gonna', 'count': 3}, {'value': 'never', 'count': 2}, {'value': '#', 'count': 2}, {'value': 'up', 'count': 1}, {'value': 'run ', 'count': 1}, {'value': 'never ', 'count': 1}, {'value': 'let', 'count': 1}, {'value': 'give', 'count': 1}, {'value': 'down', 'count': 1}, {'value': 'desert', 'count': 1}, {'value': 'around', 'count': 1}, {'value': 'and', 'count': 1}], 'count_uniques': 13}, 'dtype': 'object', 'profiler_dtype': 'string'}}, 'name': None, 'file_name': 'tmpd9yzm44r.csv', 'summary': {'cols_count': 8, 'rows_count': 19, 'size': '1.3 kB', 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 1, 'p_missing': 5.26}}}
pdf = df1.ext.buffer_window("*", 0, 19).cols.unnest("birth", separator="/", splits=2, output_cols="birth")[:1]
pdf.cols.dtypes("birth")
'category'
pdf.dtypes
id uint8 firstName category lastName category billingId uint16 product category price uint8 birth category dummyCol category birth_0 object birth_1 object dtype: object
pdf.cols.find("birth", sub=["/"]).ext.display()
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-64-a6e879fb818f> in <module> ----> 1 pdf.cols.find("birth", sub=["/"]).ext.display() NameError: name 'pdf' is not defined
df = op.load.csv("https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/foo.csv", sep=",", error_bad_lines=False, header=True, null_value="null", infer_schema="true", encoding="UTF-8", quoting=0, lineterminator=None, cache=True).ext.cache()
df = df.rows.drop( df["lastName"].isin(["KEPLER","Hoy&&&le","Hertz","H$$$ilbert","Gilbert###"]) ).ext.cache()
# from dask.distributed import Client, LocalCluster
# cluster = LocalCluster()
# client = Client(cluster)
df = op.load.file("data/nvidia.xlsx", n_rows=10).ext.cache()
df.head()
# df= df.ext.optimize()
# df = op.load.file("data/Meteorite_Landings.csv", n_rows=100).ext.cache()
pclass | survived | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 1 | Allen, Miss. Elisabeth Walton | female | 29.0000 | 0 | 0 | 24160 | 211.3375 | B5 | S | 2 | NaN | St Louis, MO |
1 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.9167 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | 11 | NaN | Montreal, PQ / Chesterville, ON |
2 | 1 | 0 | Allison, Miss. Helen Loraine | female | 2.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NaN | NaN | Montreal, PQ / Chesterville, ON |
3 | 1 | 0 | Allison, Mr. Hudson Joshua Creighton | male | 30.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NaN | 135.0 | Montreal, PQ / Chesterville, ON |
4 | 1 | 0 | Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | female | 25.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NaN | NaN | Montreal, PQ / Chesterville, ON |
df.ext.display()
df.ext.set_buffer("*")
pdf = df.ext.get_buffer()
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 100 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
df.dtypes
name object id uint8 nametype category recclass category mass (g) object fall category year object reclat object reclong object GeoLocation object dtype: object
df["mass (g)"].compute()
0 21 1 720 2 107000 3 1914 4 780 ... 95 25000 96 19000 97 1770.5 98 3880 99 45000 Name: mass (g), Length: 100, dtype: object
fastnumbers.isreal(1, num_only=True)
True
import fastnumbers
pdf = df.compute()
pdf.rows.between('mass (g)',10,200)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-245-48656fdb86bb> in <module> ----> 1 pdf.rows.between('mass (g)',10,200) ~\Documents\Optimus\optimus\engines\pandas\rows.py in between(columns, lower_bound, upper_bound, invert, equal, bounds) 195 196 for col_name in columns: --> 197 df = df.rows.select(_between(col_name)) 198 # df = df.meta.preserve(self, Actions.DROP_ROW.value, df.cols.names()) 199 return df ~\Documents\Optimus\optimus\engines\pandas\rows.py in _between(_col_name) 189 for bound in bounds: 190 _lower_bound, _upper_bound = bound --> 191 sub_query.append(opb(op1(df[_col_name], _lower_bound), op2(df[_col_name], _upper_bound))) 192 query = functools.reduce(operator.__or__, sub_query) 193 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\common.py in new_method(self, other) 62 other = item_from_zerodim(other) 63 ---> 64 return method(self, other) 65 66 return new_method ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\__init__.py in wrapper(self, other) 527 rvalues = extract_array(other, extract_numpy=True) 528 --> 529 res_values = comparison_op(lvalues, rvalues, op) 530 531 return _construct_result(self, res_values, index=self.index, name=res_name) ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\array_ops.py in comparison_op(left, right, op) 245 246 elif is_object_dtype(lvalues.dtype): --> 247 res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues) 248 249 else: ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\array_ops.py in comp_method_OBJECT_ARRAY(op, x, y) 55 result = libops.vec_compare(x.ravel(), y, op) 56 else: ---> 57 result = libops.scalar_compare(x.ravel(), y, op) 58 return result.reshape(x.shape) 59 pandas\_libs\ops.pyx in pandas._libs.ops.scalar_compare() TypeError: '>' not supported between instances of 'str' and 'int'
def func(value):
# print(value, fastnumbers.isreal(value) and value==value)
if fastnumbers.isreal(value) is True and value==value:
return True
else:
return False
df['mass (g)'].apply(func, meta=bool).compute()
# pdf['mass (g)'].apply(func)
0 True 1 True 2 True 3 True 4 True ... 95 True 96 True 97 True 98 True 99 True Length: 100, dtype: bool
import pandas as pd
a = pd.to_numeric(df["mass (g)"])
df.query("'mass (g)'<=10000")
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\utils.py in raise_on_meta_error(funcname, udf) 171 try: --> 172 yield 173 except Exception as e: ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in _emulate(func, *args, **kwargs) 4940 with raise_on_meta_error(funcname(func), udf=kwargs.pop("udf", False)): -> 4941 return func(*_extract_meta(args, True), **_extract_meta(kwargs, True)) 4942 ~\AppData\Roaming\Python\Python37\site-packages\dask\utils.py in __call__(self, obj, *args, **kwargs) 879 def __call__(self, obj, *args, **kwargs): --> 880 return getattr(obj, self.method)(*args, **kwargs) 881 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\frame.py in query(self, expr, inplace, **kwargs) 3230 kwargs["target"] = None -> 3231 res = self.eval(expr, **kwargs) 3232 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\frame.py in eval(self, expr, inplace, **kwargs) 3345 -> 3346 return _eval(expr, inplace=inplace, **kwargs) 3347 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace) 336 eng_inst = eng(parsed_expr) --> 337 ret = eng_inst.evaluate() 338 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\engines.py in evaluate(self) 126 def evaluate(self): --> 127 return self.expr() 128 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\expr.py in __call__(self) 770 def __call__(self): --> 771 return self.terms(self.env) 772 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\ops.py in __call__(self, env) 395 --> 396 return self.func(left, right) 397 TypeError: '<=' not supported between instances of 'str' and 'int' During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) <ipython-input-148-885d83729ae5> in <module> ----> 1 df.query("'mass (g)'<=10000") ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in query(self, expr, **kwargs) 3707 pandas.DataFrame.query 3708 """ -> 3709 return self.map_partitions(M.query, expr, **kwargs) 3710 3711 @derived_from(pd.DataFrame) ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in map_partitions(self, func, *args, **kwargs) 636 >>> ddf.map_partitions(func).clear_divisions() # doctest: +SKIP 637 """ --> 638 return map_partitions(func, self, *args, **kwargs) 639 640 @insert_meta_param_description(pad=12) ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in map_partitions(func, meta, enforce_metadata, transform_divisions, *args, **kwargs) 4989 # Use non-normalized kwargs here, as we want the real values (not 4990 # delayed values) -> 4991 meta = _emulate(func, *args, udf=True, **kwargs) 4992 else: 4993 meta = make_meta(meta, index=meta_index) ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in _emulate(func, *args, **kwargs) 4939 """ 4940 with raise_on_meta_error(funcname(func), udf=kwargs.pop("udf", False)): -> 4941 return func(*_extract_meta(args, True), **_extract_meta(kwargs, True)) 4942 4943 ~\Anaconda3\lib\contextlib.py in __exit__(self, type, value, traceback) 128 value = type() 129 try: --> 130 self.gen.throw(type, value, traceback) 131 except StopIteration as exc: 132 # Suppress StopIteration *unless* it's the same exception that ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\utils.py in raise_on_meta_error(funcname, udf) 191 ) 192 msg = msg.format(" in `{0}`".format(funcname) if funcname else "", repr(e), tb) --> 193 raise ValueError(msg) 194 195 ValueError: Metadata inference failed in `query`. You have supplied a custom function and Dask is unable to determine the type of output that that function returns. To resolve this please provide a meta= keyword. The docstring of the Dask function you ran should have more information. Original error is below: ------------------------ TypeError("'<=' not supported between instances of 'str' and 'int'") Traceback: --------- File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\utils.py", line 172, in raise_on_meta_error yield File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py", line 4941, in _emulate return func(*_extract_meta(args, True), **_extract_meta(kwargs, True)) File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\utils.py", line 880, in __call__ return getattr(obj, self.method)(*args, **kwargs) File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\pandas\core\frame.py", line 3231, in query res = self.eval(expr, **kwargs) File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\pandas\core\frame.py", line 3346, in eval return _eval(expr, inplace=inplace, **kwargs) File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\eval.py", line 337, in eval ret = eng_inst.evaluate() File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\engines.py", line 127, in evaluate return self.expr() File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\expr.py", line 771, in __call__ return self.terms(self.env) File "C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\pandas\core\computation\ops.py", line 396, in __call__ return self.func(left, right)
_output = df.ext.buffer_window("*", 75, 113).rows.find( (df.ext.get_buffer()["mass (g)"]>=9000) & (df.ext.get_buffer()["mass (g)"]<=10000) ).ext.to_json("*")
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-137-45643f89e9fc> in <module> ----> 1 _output = df.ext.buffer_window("*", 75, 113).rows.find( (df.ext.get_buffer()["mass (g)"]>=9000) & (df.ext.get_buffer()["mass (g)"]<=10000) ).ext.to_json("*") ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\common.py in new_method(self, other) 62 other = item_from_zerodim(other) 63 ---> 64 return method(self, other) 65 66 return new_method ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\__init__.py in wrapper(self, other) 527 rvalues = extract_array(other, extract_numpy=True) 528 --> 529 res_values = comparison_op(lvalues, rvalues, op) 530 531 return _construct_result(self, res_values, index=self.index, name=res_name) ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\array_ops.py in comparison_op(left, right, op) 245 246 elif is_object_dtype(lvalues.dtype): --> 247 res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues) 248 249 else: ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\ops\array_ops.py in comp_method_OBJECT_ARRAY(op, x, y) 55 result = libops.vec_compare(x.ravel(), y, op) 56 else: ---> 57 result = libops.scalar_compare(x.ravel(), y, op) 58 return result.reshape(x.shape) 59 pandas\_libs\ops.pyx in pandas._libs.ops.scalar_compare() TypeError: '>=' not supported between instances of 'str' and 'int'
df.ext.buffer_window("*", 0, 34).cols.replace("GeoLocation", search=["6"], replace_by="", search_by="chars", ignore_case=True, output_cols="new GeoLocation").cols.find("GeoLocation", sub=["0"], ignore_case=True)
[[3, 4], [9, 10], [10, 11], [11, 12], [18, 19], [23, 24]] [[11, 12], [16, 17], [24, 25]] [[11, 12], [22, 23], [23, 24], [24, 25], [25, 26], [26, 27], [27, 28]] [[11, 12], [22, 23], [23, 24], [24, 25], [25, 26], [26, 27]] [[13, 14], [25, 26], [26, 27], [27, 28], [28, 29]] [[7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [20, 21], [21, 22], [22, 23], [23, 24], [24, 25]] [[11, 12], [24, 25]] [[11, 12], [15, 16], [23, 24]] [[9, 10], [10, 11], [11, 12], [12, 13], [13, 14], [28, 29]] [[5, 6], [13, 14], [25, 26], [26, 27], [27, 28], [28, 29]] [[9, 10], [11, 12], [22, 23], [25, 26]] [[6, 7], [11, 12], [23, 24]] [[3, 4], [11, 12], [23, 24]] [[11, 12], [19, 20], [21, 22], [22, 23], [23, 24], [24, 25]] [[11, 12], [21, 22], [22, 23], [23, 24], [24, 25]] [[10, 11], [22, 23]] [[11, 12], [24, 25]] [[11, 12], [24, 25]] [[8, 9], [11, 12], [21, 22], [22, 23], [23, 24], [24, 25]] [[11, 12], [18, 19], [23, 24]] [[8, 9], [9, 10], [10, 11], [11, 12], [19, 20], [24, 25]] [[5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [23, 24]] [[11, 12], [18, 19], [23, 24]] [[11, 12], [25, 26]] [[11, 12], [24, 25]] [[11, 12], [20, 21], [21, 22], [22, 23], [23, 24]] [[3, 4], [8, 9], [9, 10], [10, 11], [11, 12], [24, 25]] [[11, 12], [16, 17], [21, 22], [22, 23], [23, 24], [24, 25]] [[11, 12], [26, 27]] [[11, 12], [18, 19], [27, 28]] [[3, 4], [11, 12], [24, 25]] [[11, 12], [24, 25]] [[11, 12], [21, 22], [22, 23], [23, 24], [24, 25]] [[11, 12], [24, 25]]
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | new GeoLocation | GeoLocation__match_positions__ | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21 | Fell | 01/01/1880 12:00:00 AM | 50.775000 | 6.083330 | (50.775000, 6.083330) | (50.775000, .083330) | [[3, 4], [9, 10], [10, 11], [11, 12], [18, 19]... |
1 | Aarhus | 2 | Valid | H6 | 720 | Fell | 01/01/1951 12:00:00 AM | 56.183330 | 10.233330 | (56.183330, 10.233330) | (5.183330, 10.233330) | [[11, 12], [16, 17], [24, 25]] |
2 | Abee | 6 | Valid | EH4 | 107000 | Fell | 01/01/1952 12:00:00 AM | 54.216670 | -113.000000 | (54.216670, -113.000000) | (54.2170, -113.000000) | [[11, 12], [22, 23], [23, 24], [24, 25], [25, ... |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 01/01/1976 12:00:00 AM | 16.883330 | -99.900000 | (16.883330, -99.900000) | (1.883330, -99.900000) | [[11, 12], [22, 23], [23, 24], [24, 25], [25, ... |
4 | Achiras | 370 | Valid | L6 | 780 | Fell | 01/01/1902 12:00:00 AM | -33.166670 | -64.950000 | (-33.166670, -64.950000) | (-33.170, -4.950000) | [[13, 14], [25, 26], [26, 27], [27, 28], [28, ... |
5 | Adhi Kot | 379 | Valid | EH4 | 4239 | Fell | 01/01/1919 12:00:00 AM | 32.100000 | 71.800000 | (32.100000, 71.800000) | (32.100000, 71.800000) | [[7, 8], [8, 9], [9, 10], [10, 11], [11, 12], ... |
6 | Adzhi-Bogdo (stone) | 390 | Valid | LL3-6 | 910 | Fell | 01/01/1949 12:00:00 AM | 44.833330 | 95.166670 | (44.833330, 95.166670) | (44.833330, 95.170) | [[11, 12], [24, 25]] |
7 | Agen | 392 | Valid | H5 | 30000 | Fell | 01/01/1814 12:00:00 AM | 44.216670 | 0.616670 | (44.216670, 0.616670) | (44.2170, 0.170) | [[11, 12], [15, 16], [23, 24]] |
8 | Aguada | 398 | Valid | L6 | 1620 | Fell | 01/01/1930 12:00:00 AM | -31.600000 | -65.233330 | (-31.600000, -65.233330) | (-31.00000, -5.233330) | [[9, 10], [10, 11], [11, 12], [12, 13], [13, 1... |
9 | Aguila Blanca | 417 | Valid | L | 1440 | Fell | 01/01/1920 12:00:00 AM | -30.866670 | -64.550000 | (-30.866670, -64.550000) | (-30.870, -4.550000) | [[5, 6], [13, 14], [25, 26], [26, 27], [27, 28... |
10 | Aioun el Atrouss | 423 | Valid | Diogenite-pm | 1000 | Fell | 01/01/1974 12:00:00 AM | 16.398060 | -9.570280 | (16.398060, -9.570280) | (1.39800, -9.570280) | [[9, 10], [11, 12], [22, 23], [25, 26]] |
11 | Aïr | 424 | Valid | L6 | 24000 | Fell | 01/01/1925 12:00:00 AM | 19.083330 | 8.383330 | (19.083330, 8.383330) | (19.083330, 8.383330) | [[6, 7], [11, 12], [23, 24]] |
12 | Aire-sur-la-Lys | 425 | Valid | Unknown | NaN | Fell | 01/01/1769 12:00:00 AM | 50.666670 | 2.333330 | (50.666670, 2.333330) | (50.70, 2.333330) | [[3, 4], [11, 12], [23, 24]] |
13 | Akaba | 426 | Valid | L6 | 779 | Fell | 01/01/1949 12:00:00 AM | 29.516670 | 35.050000 | (29.516670, 35.050000) | (29.5170, 35.050000) | [[11, 12], [19, 20], [21, 22], [22, 23], [23, ... |
14 | Akbarpur | 427 | Valid | H4 | 1800 | Fell | 01/01/1838 12:00:00 AM | 29.716670 | 77.950000 | (29.716670, 77.950000) | (29.7170, 77.950000) | [[11, 12], [21, 22], [22, 23], [23, 24], [24, ... |
15 | Akwanga | 432 | Valid | H | 3000 | Fell | 01/01/1959 12:00:00 AM | 8.916670 | 8.433330 | (8.916670, 8.433330) | (8.9170, 8.433330) | [[10, 11], [22, 23]] |
16 | Akyumak | 433 | Valid | Iron, IVA | 50000 | Fell | 01/01/1981 12:00:00 AM | 39.916670 | 42.816670 | (39.916670, 42.816670) | (39.9170, 42.8170) | [[11, 12], [24, 25]] |
17 | Al Rais | 446 | Valid | CR2-an | 160 | Fell | 01/01/1957 12:00:00 AM | 24.416670 | 39.516670 | (24.416670, 39.516670) | (24.4170, 39.5170) | [[11, 12], [24, 25]] |
18 | Al Zarnkh | 447 | Valid | LL5 | 700 | Fell | 01/01/2001 12:00:00 AM | 13.660330 | 28.960000 | (13.660330, 28.960000) | (13.0330, 28.90000) | [[8, 9], [11, 12], [21, 22], [22, 23], [23, 24... |
19 | Alais | 448 | Valid | CI1 | 6000 | Fell | 01/01/1806 12:00:00 AM | 44.116670 | 4.083330 | (44.116670, 4.083330) | (44.1170, 4.083330) | [[11, 12], [18, 19], [23, 24]] |
20 | Albareto | 453 | Valid | L/LL4 | 2000 | Fell | 01/01/1766 12:00:00 AM | 44.650000 | 11.016670 | (44.650000, 11.016670) | (44.50000, 11.0170) | [[8, 9], [9, 10], [10, 11], [11, 12], [19, 20]... |
21 | Alberta | 454 | Valid | L | 625 | Fell | 01/01/1949 12:00:00 AM | 2.000000 | 22.666670 | (2.000000, 22.666670) | (2.000000, 22.70) | [[5, 6], [6, 7], [7, 8], [8, 9], [9, 10], [10,... |
22 | Alby sur Chéran | 458 | Valid | Eucrite-mmict | 252 | Fell | 01/01/2002 12:00:00 AM | 45.821330 | 6.015330 | (45.821330, 6.015330) | (45.821330, .015330) | [[11, 12], [18, 19], [23, 24]] |
23 | Aldsworth | 461 | Valid | LL5 | 700 | Fell | 01/01/1835 12:00:00 AM | 51.783330 | -1.783330 | (51.783330, -1.783330) | (51.783330, -1.783330) | [[11, 12], [25, 26]] |
24 | Aleppo | 462 | Valid | L6 | 3200 | Fell | 01/01/1873 12:00:00 AM | 36.233330 | 37.133330 | (36.233330, 37.133330) | (3.233330, 37.133330) | [[11, 12], [24, 25]] |
25 | Alessandria | 463 | Valid | H5 | 908 | Fell | 01/01/1860 12:00:00 AM | 44.883330 | 8.750000 | (44.883330, 8.750000) | (44.883330, 8.750000) | [[11, 12], [20, 21], [21, 22], [22, 23], [23, ... |
26 | Alexandrovsky | 465 | Valid | H4 | 9251 | Fell | 01/01/1900 12:00:00 AM | 50.950000 | 31.816670 | (50.950000, 31.816670) | (50.950000, 31.8170) | [[3, 4], [8, 9], [9, 10], [10, 11], [11, 12], ... |
27 | Alfianello | 466 | Valid | L6 | 228000 | Fell | 01/01/1883 12:00:00 AM | 45.266670 | 10.150000 | (45.266670, 10.150000) | (45.270, 10.150000) | [[11, 12], [16, 17], [21, 22], [22, 23], [23, ... |
28 | Allegan | 2276 | Valid | H5 | 32000 | Fell | 01/01/1899 12:00:00 AM | 42.533330 | -85.883330 | (42.533330, -85.883330) | (42.533330, -85.883330) | [[11, 12], [26, 27]] |
29 | Allende | 2278 | Valid | CV3 | 2000000 | Fell | 01/01/1969 12:00:00 AM | 26.966670 | -105.316670 | (26.966670, -105.316670) | (2.970, -105.3170) | [[11, 12], [18, 19], [27, 28]] |
30 | Almahata Sitta | 48915 | Valid | Ureilite-an | 3950 | Fell | 01/01/2008 12:00:00 AM | 20.745750 | 32.412750 | (20.745750, 32.412750) | (20.745750, 32.412750) | [[3, 4], [11, 12], [24, 25]] |
31 | Alta'ameem | 2284 | Valid | LL5 | 6000 | Fell | 01/01/1977 12:00:00 AM | 35.273330 | 44.215560 | (35.273330, 44.215560) | (35.273330, 44.21550) | [[11, 12], [24, 25]] |
32 | Ambapur Nagla | 2290 | Valid | H5 | 6400 | Fell | 01/01/1895 12:00:00 AM | 27.666670 | 78.250000 | (27.666670, 78.250000) | (27.70, 78.250000) | [[11, 12], [21, 22], [22, 23], [23, 24], [24, ... |
33 | Andhara | 2294 | Valid | Stone-uncl | 2700 | Fell | 01/01/1880 12:00:00 AM | 26.583330 | 85.566670 | (26.583330, 85.566670) | (2.583330, 85.570) | [[11, 12], [24, 25]] |
df.ext.buffer_window("*").cols.replace("GeoLocation", search=["6"], replace_by="", search_by="chars", ignore_case=True, output_cols="new GeoLocation")
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | new GeoLocation | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21 | Fell | 01/01/1880 12:00:00 AM | 50.775000 | 6.083330 | (50.775000, 6.083330) | (50.775000, .083330) |
1 | Aarhus | 2 | Valid | H6 | 720 | Fell | 01/01/1951 12:00:00 AM | 56.183330 | 10.233330 | (56.183330, 10.233330) | (5.183330, 10.233330) |
2 | Abee | 6 | Valid | EH4 | 107000 | Fell | 01/01/1952 12:00:00 AM | 54.216670 | -113.000000 | (54.216670, -113.000000) | (54.2170, -113.000000) |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 01/01/1976 12:00:00 AM | 16.883330 | -99.900000 | (16.883330, -99.900000) | (1.883330, -99.900000) |
4 | Achiras | 370 | Valid | L6 | 780 | Fell | 01/01/1902 12:00:00 AM | -33.166670 | -64.950000 | (-33.166670, -64.950000) | (-33.170, -4.950000) |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
95 | Benguerir | 30443 | Valid | LL6 | 25000 | Fell | 01/01/2004 12:00:00 AM | 32.250000 | -8.150000 | (32.250000, -8.150000) | (32.250000, -8.150000) |
96 | Beni M'hira | 5018 | Valid | L6 | 19000 | Fell | 01/01/2001 12:00:00 AM | 32.866670 | 10.800000 | (32.866670, 10.800000) | (32.870, 10.800000) |
97 | Benld | 5021 | Valid | H6 | 1770.5 | Fell | 01/01/1938 12:00:00 AM | 39.083330 | -89.150000 | (39.083330, -89.150000) | (39.083330, -89.150000) |
98 | Benoni | 5023 | Valid | H6 | 3880 | Fell | 01/01/1943 12:00:00 AM | -26.166670 | 28.416670 | (-26.166670, 28.416670) | (-2.170, 28.4170) |
99 | Bensour | 5024 | Valid | LL6 | 45000 | Fell | 01/01/2002 12:00:00 AM | 30.000000 | -7.000000 | (30.000000, -7.000000) | (30.000000, -7.000000) |
100 rows × 11 columns
df.ext.buffer_window("*", 0, 19).cols.unnest("birth", separator="/", splits=2, output_cols="birth").cols.find("birth", sub=["/"]).ext.to_json("*")
# df.ext.buffer_window("*", 0, 19).cols.unnest("birth", separator="/", splits=2, output_cols="birth").cols.find("birth", sub=["/"]).ext.to_json("*")
ASD ['/']
{'sample': {'columns': [{'title': 'id'}, {'title': 'firstName'}, {'title': 'lastName'}, {'title': 'billingId'}, {'title': 'product'}, {'title': 'price'}, {'title': 'birth'}, {'title': 'dummyCol'}, {'title': 'birth_0'}, {'title': 'birth_1'}, {'title': 'birth__match_positions__'}], 'value': [['1', 'Luis', 'Alvarez$$%!', '123', 'Cake', '10', '1980/07/07', 'never', '1980', '07', None], ['2', 'André', 'Ampère', '423', 'piza', '8', '1950/07/08', 'gonna', '1950', '07', None], ['3', 'NiELS', 'Böhr//((%%', '551', 'pizza', '8', '1990/07/09', 'give', '1990', '07', None], ['4', 'PAUL', 'dirac$', '521', 'pizza', '8', '1954/07/10', 'you', '1954', '07', None], ['5', 'Albert', 'Einstein', '634', 'pizza', '8', '1990/07/11', 'up', '1990', '07', None], ['6', 'Galileo', ' GALiLEI', '672', 'arepa', '5', '1930/08/12', 'never', '1930', '08', None], ['7', 'CaRL', 'Ga%%%uss', '323', 'taco', '3', '1970/07/13', 'gonna', '1970', '07', None], ['8', 'David', 'H$$$ilbert', '624', 'taaaccoo', '3', '1950/07/14', 'let', '1950', '07', None], ['9', 'Johannes', 'KEPLER', '735', 'taco', '3', '1920/04/22', 'you', '1920', '04', None], ['10', 'JaMES', 'M$$ax%%well', '875', 'taco', '3', '1923/03/12', 'down', '1923', '03', None], ['11', 'Isaac', 'Newton', '992', 'pasta', '9', '1999/02/15', 'never ', '1999', '02', None], ['12', 'Emmy%%', 'Nöether$', '234', 'pasta', '9', '1993/12/08', 'gonna', '1993', '12', None], ['13', 'Max!!!', 'Planck!!!', '111', 'hamburguer', '4', '1994/01/04', 'run ', '1994', '01', None], ['14', 'Fred', 'Hoy&&&le', '553', 'pizzza', '8', '1997/06/27', 'around', '1997', '06', None], ['15', '((( Heinrich )))))', 'Hertz', '116', 'pizza', '8', '1956/11/30', 'and', '1956', '11', None], ['16', 'William', 'Gilbert###', '886', 'BEER', '2', '1958/03/26', 'desert', '1958', '03', None], ['17', 'Marie', 'CURIE', '912', 'Rice', '1', '2000/03/22', 'you', '2000', '03', None], ['18', 'Arthur', 'COM%%%pton', '812', '110790', '5', '1899/01/01', '#', '1899', '01', None], ['19', 'JAMES', 'Chadwick', '467', nan, '10', '1921/05/03', '#', '1921', '05', None]]}}
_output
{'sample': {'columns': [{'title': 'id'}, {'title': 'firstName'}, {'title': 'lastName'}, {'title': 'billingId'}, {'title': 'product'}, {'title': 'price'}, {'title': 'birth'}, {'title': 'dummyCol'}, {'title': 'birth_0'}, {'title': 'birth_1'}, {'title': 'birth__match_positions__'}], 'value': [['1', 'Luis', 'Alvarez$$%!', '123', 'Cake', '10', '1980/07/07', 'never', '1980', '07', [[4, 5], [7, 8]]], ['2', 'André', 'Ampère', '423', 'piza', '8', '1950/07/08', 'gonna', '1950', '07', [[4, 5], [7, 8]]], ['3', 'NiELS', 'Böhr//((%%', '551', 'pizza', '8', '1990/07/09', 'give', '1990', '07', [[4, 5], [7, 8]]], ['4', 'PAUL', 'dirac$', '521', 'pizza', '8', '1954/07/10', 'you', '1954', '07', [[4, 5], [7, 8]]], ['5', 'Albert', 'Einstein', '634', 'pizza', '8', '1990/07/11', 'up', '1990', '07', [[4, 5], [7, 8]]], ['6', 'Galileo', ' GALiLEI', '672', 'arepa', '5', '1930/08/12', 'never', '1930', '08', [[4, 5], [7, 8]]], ['7', 'CaRL', 'Ga%%%uss', '323', 'taco', '3', '1970/07/13', 'gonna', '1970', '07', [[4, 5], [7, 8]]], ['8', 'David', 'H$$$ilbert', '624', 'taaaccoo', '3', '1950/07/14', 'let', '1950', '07', [[4, 5], [7, 8]]], ['9', 'Johannes', 'KEPLER', '735', 'taco', '3', '1920/04/22', 'you', '1920', '04', [[4, 5], [7, 8]]], ['10', 'JaMES', 'M$$ax%%well', '875', 'taco', '3', '1923/03/12', 'down', '1923', '03', [[4, 5], [7, 8]]], ['11', 'Isaac', 'Newton', '992', 'pasta', '9', '1999/02/15', 'never ', '1999', '02', [[4, 5], [7, 8]]], ['12', 'Emmy%%', 'Nöether$', '234', 'pasta', '9', '1993/12/08', 'gonna', '1993', '12', [[4, 5], [7, 8]]], ['13', 'Max!!!', 'Planck!!!', '111', 'hamburguer', '4', '1994/01/04', 'run ', '1994', '01', [[4, 5], [7, 8]]], ['14', 'Fred', 'Hoy&&&le', '553', 'pizzza', '8', '1997/06/27', 'around', '1997', '06', [[4, 5], [7, 8]]], ['15', '((( Heinrich )))))', 'Hertz', '116', 'pizza', '8', '1956/11/30', 'and', '1956', '11', [[4, 5], [7, 8]]], ['16', 'William', 'Gilbert###', '886', 'BEER', '2', '1958/03/26', 'desert', '1958', '03', [[4, 5], [7, 8]]], ['17', 'Marie', 'CURIE', '912', 'Rice', '1', '2000/03/22', 'you', '2000', '03', [[4, 5], [7, 8]]], ['18', 'Arthur', 'COM%%%pton', '812', '110790', '5', '1899/01/01', '#', '1899', '01', [[4, 5], [7, 8]]], ['19', 'JAMES', 'Chadwick', '467', nan, '10', '1921/05/03', '#', '1921', '05', [[4, 5], [7, 8]]]]}}
_output = df.ext.buffer_window("*", 0, 34).cols.lower("Id", output_cols="new id").ext.to_json("*")
SDFSF ['Id'] ['new id']
df1 = df.ext.optimize()
df1.ext.display(100)
Id
1 (uint16)
not nullable
|
PID
2 (uint32)
not nullable
|
MS SubClass
3 (uint8)
not nullable
|
MS Zoning
4 (category)
not nullable
|
Lot Frontage
5 (object)
not nullable
|
Lot Area
6 (uint16)
not nullable
|
Street
7 (category)
not nullable
|
Alley
8 (category)
not nullable
|
Lot Shape
9 (category)
not nullable
|
Land Contour
10 (category)
not nullable
|
Utilities
11 (category)
not nullable
|
Lot Config
12 (category)
not nullable
|
Land Slope
13 (category)
not nullable
|
Neighborhood
14 (category)
not nullable
|
Condition 1
15 (category)
not nullable
|
Condition 2
16 (category)
not nullable
|
Bldg Type
17 (category)
not nullable
|
House Style
18 (category)
not nullable
|
Overall Qual
19 (uint8)
not nullable
|
Overall Cond
20 (uint8)
not nullable
|
Year Built
21 (uint16)
not nullable
|
Year Remod/Add
22 (uint16)
not nullable
|
Roof Style
23 (category)
not nullable
|
Roof Matl
24 (category)
not nullable
|
Exterior 1st
25 (category)
not nullable
|
Exterior 2nd
26 (category)
not nullable
|
Mas Vnr Type
27 (category)
not nullable
|
Mas Vnr Area
28 (object)
not nullable
|
Exter Qual
29 (category)
not nullable
|
Exter Cond
30 (category)
not nullable
|
Foundation
31 (category)
not nullable
|
Bsmt Qual
32 (category)
not nullable
|
Bsmt Cond
33 (category)
not nullable
|
Bsmt Exposure
34 (category)
not nullable
|
BsmtFin Type 1
35 (category)
not nullable
|
BsmtFin SF 1
36 (uint16)
not nullable
|
BsmtFin Type 2
37 (category)
not nullable
|
BsmtFin SF 2
38 (uint16)
not nullable
|
Bsmt Unf SF
39 (uint8)
not nullable
|
Total Bsmt SF
40 (uint16)
not nullable
|
Heating
41 (category)
not nullable
|
Heating QC
42 (category)
not nullable
|
Central Air
43 (category)
not nullable
|
Electrical
44 (category)
not nullable
|
1st Flr SF
45 (uint16)
not nullable
|
2nd Flr SF
46 (uint16)
not nullable
|
Low Qual Fin SF
47 (uint8)
not nullable
|
Gr Liv Area
48 (uint16)
not nullable
|
Bsmt Full Bath
49 (uint8)
not nullable
|
Bsmt Half Bath
50 (uint8)
not nullable
|
Full Bath
51 (uint8)
not nullable
|
Half Bath
52 (uint8)
not nullable
|
Bedroom AbvGr
53 (uint8)
not nullable
|
Kitchen AbvGr
54 (uint8)
not nullable
|
Kitchen Qual
55 (category)
not nullable
|
TotRms AbvGrd
56 (uint8)
not nullable
|
Functional
57 (category)
not nullable
|
Fireplaces
58 (uint8)
not nullable
|
Fireplace Qu
59 (category)
not nullable
|
Garage Type
60 (category)
not nullable
|
Garage Yr Blt
61 (object)
not nullable
|
Garage Finish
62 (category)
not nullable
|
Garage Cars
63 (uint8)
not nullable
|
Garage Area
64 (uint16)
not nullable
|
Garage Qual
65 (category)
not nullable
|
Garage Cond
66 (category)
not nullable
|
Paved Drive
67 (category)
not nullable
|
Wood Deck SF
68 (uint16)
not nullable
|
Open Porch SF
69 (uint8)
not nullable
|
Enclosed Porch
70 (uint8)
not nullable
|
3Ssn Porch
71 (uint8)
not nullable
|
Screen Porch
72 (uint16)
not nullable
|
Pool Area
73 (uint16)
not nullable
|
Pool QC
74 (category)
not nullable
|
Fence
75 (category)
not nullable
|
Misc Feature
76 (category)
not nullable
|
Misc Val
77 (uint16)
not nullable
|
Mo Sold
78 (uint8)
not nullable
|
Yr Sold
79 (uint16)
not nullable
|
Sale Type
80 (category)
not nullable
|
SalePrice
81 (uint32)
not nullable
|
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
109
|
533352170
|
60
|
RL
|
nan
|
13517
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
CulDSac
|
Gtl
|
Sawyer
|
RRAe
|
Norm
|
1Fam
|
2Story
|
6
|
8
|
1976
|
2005
|
Gable
|
CompShg
|
HdBoard
|
Plywood
|
BrkFace
|
289
|
Gd
|
TA
|
CBlock
|
TA
|
TA
|
No
|
GLQ
|
533
|
Unf
|
0
|
192
|
725
|
GasA
|
Ex
|
Y
|
SBrkr
|
725
|
754
|
0
|
1479
|
0
|
0
|
2
|
1
|
3
|
1
|
Gd
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1976
|
RFn
|
2
|
475
|
TA
|
TA
|
Y
|
0
|
44
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
3
|
2010
|
WD⋅
|
130500
|
544
|
531379050
|
60
|
RL
|
43
|
11492
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
CulDSac
|
Gtl
|
SawyerW
|
Norm
|
Norm
|
1Fam
|
2Story
|
7
|
5
|
1996
|
1997
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
132
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
637
|
Unf
|
0
|
20
|
913
|
GasA
|
Ex
|
Y
|
SBrkr
|
913
|
1209
|
0
|
2122
|
1
|
0
|
2
|
1
|
4
|
1
|
Gd
|
8
|
Typ
|
1
|
TA
|
Attchd
|
1997
|
RFn
|
2
|
559
|
TA
|
TA
|
Y
|
0
|
74
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2009
|
WD⋅
|
220000
|
153
|
535304180
|
20
|
RL
|
68
|
7922
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
7
|
1953
|
2007
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
Gd
|
CBlock
|
TA
|
TA
|
No
|
GLQ
|
731
|
Unf
|
0
|
70
|
1057
|
GasA
|
TA
|
Y
|
SBrkr
|
1057
|
0
|
0
|
1057
|
1
|
0
|
1
|
0
|
3
|
1
|
Gd
|
5
|
Typ
|
0
|
nan
|
Detchd
|
1953
|
Unf
|
1
|
246
|
TA
|
TA
|
Y
|
0
|
52
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
1
|
2010
|
WD⋅
|
109000
|
318
|
916386060
|
60
|
RL
|
73
|
9802
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Timber
|
Norm
|
Norm
|
1Fam
|
2Story
|
5
|
5
|
2006
|
2007
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
TA
|
PConc
|
Gd
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
128
|
384
|
GasA
|
Gd
|
Y
|
SBrkr
|
744
|
700
|
0
|
1444
|
0
|
0
|
2
|
1
|
3
|
1
|
TA
|
7
|
Typ
|
0
|
nan
|
BuiltIn
|
2007
|
Fin
|
2
|
400
|
TA
|
TA
|
Y
|
100
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2010
|
WD⋅
|
174000
|
255
|
906425045
|
50
|
RL
|
82
|
14235
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
SawyerW
|
Norm
|
Norm
|
1Fam
|
1.5Fin
|
6
|
8
|
1900
|
1993
|
Gable
|
CompShg
|
Wd⋅Sdng
|
Plywood
|
None
|
0
|
TA
|
TA
|
PConc
|
Fa
|
Gd
|
No
|
Unf
|
0
|
Unf
|
0
|
164
|
676
|
GasA
|
TA
|
Y
|
SBrkr
|
831
|
614
|
0
|
1445
|
0
|
0
|
2
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Detchd
|
1957
|
Unf
|
2
|
484
|
TA
|
TA
|
N
|
0
|
59
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
3
|
2010
|
WD⋅
|
138500
|
138
|
535126040
|
20
|
RL
|
137
|
16492
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NAmes
|
PosA
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1966
|
2002
|
Gable
|
CompShg
|
BrkFace
|
Plywood
|
None
|
0
|
Gd
|
TA
|
CBlock
|
TA
|
TA
|
No
|
ALQ
|
247
|
Rec
|
713
|
45
|
1517
|
GasA
|
Ex
|
Y
|
SBrkr
|
1888
|
0
|
0
|
1888
|
0
|
0
|
2
|
1
|
2
|
1
|
Gd
|
6
|
Mod
|
1
|
Gd
|
Attchd
|
1966
|
Fin
|
2
|
578
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2010
|
WD⋅
|
190000
|
2827
|
908186070
|
180
|
RM
|
35
|
3675
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Edwards
|
Norm
|
Norm
|
TwnhsE
|
SFoyer
|
6
|
5
|
2005
|
2006
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
82
|
TA
|
TA
|
PConc
|
Gd
|
TA
|
Gd
|
GLQ
|
547
|
Unf
|
0
|
0
|
547
|
GasA
|
Gd
|
Y
|
SBrkr
|
1072
|
0
|
0
|
1072
|
1
|
0
|
2
|
0
|
2
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Basment
|
2005
|
Fin
|
2
|
525
|
TA
|
TA
|
Y
|
0
|
44
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2006
|
New
|
140000
|
145
|
535154050
|
20
|
RL
|
nan
|
12160
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1959
|
1959
|
Hip
|
CompShg
|
Plywood
|
Plywood
|
BrkFace
|
180
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
1000
|
Unf
|
0
|
188
|
1188
|
GasA
|
Fa
|
Y
|
SBrkr
|
1188
|
0
|
0
|
1188
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1959
|
RFn
|
2
|
531
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
5
|
2010
|
COD
|
142000
|
1942
|
535353130
|
20
|
RL
|
nan
|
15783
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
OldTown
|
Artery
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1952
|
1952
|
Gable
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
292
|
Unf
|
0
|
120
|
924
|
GasA
|
TA
|
Y
|
SBrkr
|
924
|
0
|
0
|
924
|
0
|
0
|
1
|
0
|
2
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Detchd
|
1952
|
Unf
|
1
|
420
|
TA
|
TA
|
Y
|
0
|
68
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
Shed
|
400
|
6
|
2007
|
WD⋅
|
112500
|
1956
|
535426130
|
60
|
RL
|
70
|
11606
|
Pave
|
nan
|
IR1
|
HLS
|
AllPub
|
Inside
|
Sev
|
NAmes
|
Norm
|
Norm
|
1Fam
|
2Story
|
5
|
5
|
1969
|
1969
|
Gable
|
CompShg
|
Plywood
|
Plywood
|
BrkFace
|
192
|
TA
|
TA
|
PConc
|
Gd
|
TA
|
Av
|
Rec
|
650
|
Unf
|
0
|
134
|
1040
|
GasA
|
TA
|
Y
|
SBrkr
|
1040
|
1040
|
0
|
2080
|
0
|
1
|
1
|
2
|
5
|
1
|
Fa
|
9
|
Typ
|
2
|
TA
|
Attchd
|
1969
|
Unf
|
2
|
504
|
TA
|
TA
|
Y
|
335
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
9
|
2007
|
WD⋅
|
135000
|
1044
|
527451290
|
160
|
RM
|
21
|
1680
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
BrDale
|
Norm
|
Norm
|
Twnhs
|
2Story
|
6
|
5
|
1971
|
1971
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
BrkFace
|
232
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
ALQ
|
387
|
Unf
|
0
|
96
|
483
|
GasA
|
TA
|
Y
|
SBrkr
|
483
|
504
|
0
|
987
|
0
|
0
|
1
|
1
|
2
|
1
|
TA
|
4
|
Typ
|
0
|
nan
|
Detchd
|
1971
|
Unf
|
1
|
264
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2008
|
COD
|
85400
|
2752
|
906380150
|
20
|
RL
|
64
|
7488
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
CollgCr
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
5
|
2005
|
2005
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Av
|
GLQ
|
393
|
Unf
|
0
|
47
|
1208
|
GasA
|
Ex
|
Y
|
SBrkr
|
1208
|
0
|
0
|
1208
|
0
|
0
|
2
|
0
|
2
|
1
|
Gd
|
6
|
Typ
|
0
|
nan
|
Attchd
|
2005
|
RFn
|
2
|
632
|
TA
|
TA
|
Y
|
105
|
58
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
2
|
2006
|
WD⋅
|
183600
|
807
|
906226060
|
70
|
RL
|
120
|
26400
|
Pave
|
nan
|
Reg
|
Bnk
|
AllPub
|
FR2
|
Gtl
|
SawyerW
|
Feedr
|
Norm
|
1Fam
|
2Story
|
5
|
7
|
1880
|
2007
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
None
|
0
|
Gd
|
TA
|
PConc
|
nan
|
nan
|
nan
|
nan
|
0
|
nan
|
0
|
0
|
0
|
GasA
|
Ex
|
Y
|
SBrkr
|
1288
|
728
|
0
|
2016
|
0
|
0
|
1
|
0
|
4
|
1
|
TA
|
7
|
Mod
|
1
|
TA
|
Attchd
|
1900
|
Unf
|
2
|
576
|
TA
|
TA
|
P
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2009
|
WD⋅
|
131000
|
1177
|
533236070
|
160
|
FV
|
24
|
2645
|
Pave
|
Pave
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
Twnhs
|
2Story
|
8
|
5
|
1999
|
2000
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
456
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
813
|
Unf
|
0
|
147
|
960
|
GasA
|
Ex
|
Y
|
SBrkr
|
962
|
645
|
0
|
1607
|
1
|
0
|
2
|
1
|
3
|
1
|
Gd
|
7
|
Typ
|
0
|
nan
|
Detchd
|
2000
|
Unf
|
2
|
480
|
TA
|
TA
|
Y
|
169
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
12
|
2008
|
ConLD
|
200000
|
1480
|
907418040
|
20
|
RL
|
74
|
9627
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
CollgCr
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
5
|
2007
|
2007
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Av
|
GLQ
|
24
|
Unf
|
0
|
47
|
1351
|
GasA
|
Ex
|
Y
|
SBrkr
|
1361
|
0
|
0
|
1361
|
0
|
0
|
2
|
0
|
3
|
1
|
Gd
|
6
|
Typ
|
0
|
nan
|
Attchd
|
2007
|
RFn
|
2
|
610
|
TA
|
TA
|
Y
|
0
|
50
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2008
|
WD⋅
|
193000
|
324
|
923202105
|
20
|
RL
|
93
|
10114
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Mitchel
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
2004
|
2005
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
TA
|
PConc
|
Ex
|
TA
|
Av
|
Unf
|
0
|
Unf
|
0
|
150
|
1430
|
GasA
|
Ex
|
Y
|
SBrkr
|
1430
|
0
|
0
|
1430
|
0
|
0
|
2
|
0
|
3
|
1
|
Gd
|
7
|
Typ
|
0
|
nan
|
Attchd
|
2004
|
RFn
|
2
|
624
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
3
|
2010
|
WD⋅
|
173500
|
1311
|
902301140
|
70
|
RM
|
34
|
4571
|
Pave
|
Grvl
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
OldTown
|
Norm
|
Norm
|
1Fam
|
2Story
|
5
|
5
|
1916
|
1950
|
Gable
|
CompShg
|
AsbShng
|
AsbShng
|
None
|
0
|
TA
|
TA
|
BrkTil
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
112
|
624
|
GasA
|
Fa
|
N
|
SBrkr
|
624
|
720
|
0
|
1344
|
0
|
0
|
1
|
0
|
4
|
1
|
TA
|
7
|
Typ
|
0
|
nan
|
Detchd
|
1916
|
Unf
|
3
|
513
|
Fa
|
Fa
|
Y
|
0
|
0
|
96
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
5
|
2008
|
COD
|
98000
|
770
|
905101490
|
20
|
RL
|
80
|
8816
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
Sawyer
|
Feedr
|
Norm
|
1Fam
|
1Story
|
5
|
6
|
1963
|
1963
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
TA
|
PConc
|
TA
|
TA
|
No
|
Rec
|
651
|
Unf
|
0
|
214
|
1121
|
GasA
|
TA
|
Y
|
SBrkr
|
1121
|
0
|
0
|
1121
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Detchd
|
1963
|
Unf
|
2
|
480
|
TA
|
TA
|
Y
|
0
|
80
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
6
|
2009
|
WD⋅
|
139000
|
2063
|
905107140
|
20
|
RL
|
71
|
8197
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Sawyer
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
5
|
1977
|
1977
|
Gable
|
CompShg
|
Plywood
|
Plywood
|
BrkFace
|
148
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
148
|
660
|
GasA
|
Ex
|
Y
|
SBrkr
|
1285
|
0
|
0
|
1285
|
0
|
0
|
1
|
1
|
3
|
1
|
TA
|
7
|
Typ
|
1
|
TA
|
Attchd
|
1977
|
RFn
|
2
|
528
|
TA
|
TA
|
Y
|
138
|
0
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
4
|
2007
|
WD⋅
|
143500
|
69
|
528458070
|
60
|
FV
|
72
|
8640
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
1Fam
|
2Story
|
7
|
5
|
2009
|
2009
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
TA
|
PConc
|
Gd
|
TA
|
Mn
|
GLQ
|
24
|
Unf
|
0
|
220
|
756
|
GasA
|
Ex
|
Y
|
SBrkr
|
764
|
783
|
0
|
1547
|
0
|
0
|
2
|
1
|
3
|
1
|
Gd
|
7
|
Typ
|
0
|
nan
|
Attchd
|
2009
|
Unf
|
2
|
614
|
TA
|
TA
|
Y
|
169
|
45
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2010
|
Con
|
215200
|
2062
|
905106150
|
20
|
RL
|
109
|
8724
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Sawyer
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1968
|
1968
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
TA
|
CBlock
|
Gd
|
TA
|
No
|
BLQ
|
492
|
Unf
|
0
|
146
|
894
|
GasA
|
Gd
|
Y
|
SBrkr
|
894
|
0
|
0
|
894
|
0
|
0
|
1
|
0
|
3
|
1
|
TA
|
5
|
Typ
|
1
|
Po
|
Attchd
|
1968
|
Fin
|
2
|
450
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
5
|
2007
|
WD⋅
|
129000
|
351
|
527131030
|
120
|
RL
|
40
|
5664
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
StoneBr
|
Norm
|
Norm
|
TwnhsE
|
1Story
|
8
|
5
|
2000
|
2000
|
Gable
|
CompShg
|
CemntBd
|
CmentBd
|
None
|
0
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
1158
|
Unf
|
0
|
87
|
1501
|
GasA
|
Ex
|
Y
|
SBrkr
|
1659
|
0
|
0
|
1659
|
1
|
0
|
2
|
0
|
2
|
1
|
Ex
|
5
|
Typ
|
1
|
Ex
|
Attchd
|
2000
|
Fin
|
2
|
499
|
TA
|
TA
|
Y
|
212
|
59
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
10
|
2009
|
WD⋅
|
278000
|
2393
|
528142010
|
60
|
RL
|
103
|
12867
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NridgHt
|
Norm
|
Norm
|
1Fam
|
2Story
|
8
|
5
|
2005
|
2006
|
Gable
|
CompShg
|
CemntBd
|
CmentBd
|
nan
|
nan
|
Gd
|
TA
|
PConc
|
Ex
|
TA
|
Av
|
Unf
|
0
|
Unf
|
0
|
185
|
1209
|
GasA
|
Ex
|
Y
|
SBrkr
|
1209
|
1044
|
0
|
2253
|
0
|
0
|
2
|
1
|
3
|
1
|
Ex
|
8
|
Typ
|
1
|
Gd
|
Attchd
|
2005
|
Fin
|
2
|
575
|
TA
|
TA
|
Y
|
243
|
142
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2006
|
New
|
344133
|
12
|
527165230
|
20
|
RL
|
nan
|
7980
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Gilbert
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
7
|
1992
|
2007
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
None
|
0
|
TA
|
Gd
|
PConc
|
Gd
|
TA
|
No
|
ALQ
|
935
|
Unf
|
0
|
233
|
1168
|
GasA
|
Ex
|
Y
|
SBrkr
|
1187
|
0
|
0
|
1187
|
1
|
0
|
2
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1992
|
Fin
|
2
|
420
|
TA
|
TA
|
Y
|
483
|
21
|
0
|
0
|
0
|
0
|
nan
|
GdPrv
|
Shed
|
500
|
3
|
2010
|
WD⋅
|
185000
|
2339
|
527250040
|
190
|
RL
|
100
|
34650
|
Pave
|
nan
|
Reg
|
Bnk
|
AllPub
|
Inside
|
Gtl
|
Gilbert
|
Norm
|
Norm
|
2fmCon
|
1Story
|
5
|
5
|
1955
|
1955
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
Mn
|
Rec
|
1056
|
Unf
|
0
|
0
|
1056
|
GasA
|
TA
|
N
|
SBrkr
|
1056
|
0
|
0
|
1056
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Attchd
|
1955
|
Fin
|
2
|
572
|
TA
|
TA
|
Y
|
264
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
1
|
2006
|
WD⋅
|
145000
|
624
|
535104120
|
20
|
RL
|
92
|
10573
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1961
|
1961
|
Hip
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
3
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
1312
|
Unf
|
0
|
141
|
1453
|
GasA
|
Ex
|
Y
|
SBrkr
|
1453
|
0
|
0
|
1453
|
1
|
0
|
2
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
TA
|
Attchd
|
1961
|
RFn
|
2
|
530
|
TA
|
TA
|
Y
|
0
|
49
|
0
|
0
|
288
|
0
|
nan
|
GdPrv
|
nan
|
0
|
4
|
2009
|
WD⋅
|
187500
|
86
|
532376160
|
20
|
RL
|
65
|
8450
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Sawyer
|
RRAe
|
Norm
|
1Fam
|
1Story
|
5
|
8
|
1965
|
2009
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
None
|
0
|
TA
|
Gd
|
CBlock
|
TA
|
TA
|
No
|
GLQ
|
553
|
BLQ
|
117
|
224
|
894
|
GasA
|
Ex
|
Y
|
SBrkr
|
894
|
0
|
0
|
894
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
5
|
Typ
|
1
|
Gd
|
Detchd
|
1973
|
Unf
|
1
|
336
|
TA
|
TA
|
Y
|
416
|
144
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
4
|
2010
|
WD⋅
|
138500
|
1534
|
909277040
|
50
|
RL
|
nan
|
11700
|
Pave
|
Grvl
|
IR1
|
HLS
|
AllPub
|
Inside
|
Mod
|
Crawfor
|
Norm
|
Norm
|
1Fam
|
1.5Fin
|
5
|
6
|
1937
|
1995
|
Gable
|
CompShg
|
WdShing
|
Wd⋅Shng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
BLQ
|
606
|
Unf
|
0
|
80
|
942
|
GasA
|
Ex
|
Y
|
SBrkr
|
1265
|
673
|
0
|
1938
|
0
|
0
|
2
|
0
|
4
|
1
|
Gd
|
7
|
Min2
|
1
|
Gd
|
Detchd
|
1937
|
Unf
|
1
|
240
|
TA
|
TA
|
Y
|
0
|
40
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2008
|
WD⋅
|
198000
|
2243
|
911103060
|
190
|
C⋅(all)
|
75
|
8250
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
IDOTRR
|
Feedr
|
Norm
|
2fmCon
|
2Story
|
5
|
6
|
1895
|
2006
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
Gd
|
CBlock
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
189
|
957
|
GasA
|
Fa
|
N
|
SBrkr
|
1034
|
957
|
0
|
1991
|
0
|
0
|
2
|
0
|
4
|
2
|
TA
|
9
|
Typ
|
0
|
nan
|
nan
|
nan
|
nan
|
0
|
0
|
nan
|
nan
|
N
|
0
|
0
|
133
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2007
|
WD⋅
|
119600
|
1957
|
535450190
|
50
|
RL
|
60
|
8064
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NAmes
|
Artery
|
Norm
|
1Fam
|
1.5Fin
|
5
|
7
|
1949
|
2006
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
None
|
0
|
TA
|
Gd
|
CBlock
|
TA
|
TA
|
Mn
|
Unf
|
0
|
Unf
|
0
|
160
|
672
|
GasA
|
Ex
|
Y
|
SBrkr
|
672
|
252
|
0
|
924
|
0
|
0
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
Po
|
Detchd
|
2003
|
Unf
|
2
|
576
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
Shed
|
2000
|
7
|
2007
|
WD⋅
|
122900
|
380
|
527355150
|
20
|
RL
|
80
|
9600
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NWAmes
|
PosN
|
Norm
|
1Fam
|
1Story
|
8
|
5
|
1981
|
1981
|
Hip
|
WdShngl
|
BrkFace
|
BrkFace
|
None
|
0
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
ALQ
|
1104
|
Unf
|
0
|
140
|
2524
|
GasA
|
TA
|
Y
|
SBrkr
|
2524
|
0
|
0
|
2524
|
1
|
0
|
2
|
1
|
4
|
1
|
Gd
|
9
|
Typ
|
1
|
Gd
|
Attchd
|
1981
|
Fin
|
2
|
542
|
TA
|
TA
|
Y
|
474
|
120
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
7
|
2009
|
WD⋅
|
278000
|
2860
|
909276010
|
70
|
RL
|
nan
|
11435
|
Pave
|
nan
|
IR1
|
HLS
|
AllPub
|
Corner
|
Mod
|
Crawfor
|
Norm
|
Norm
|
1Fam
|
2Story
|
8
|
7
|
1929
|
1950
|
Gable
|
CompShg
|
BrkFace
|
Stucco
|
None
|
0
|
TA
|
TA
|
PConc
|
Gd
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
24
|
792
|
GasA
|
Fa
|
Y
|
SBrkr
|
792
|
725
|
0
|
1517
|
0
|
0
|
1
|
0
|
3
|
1
|
Gd
|
7
|
Typ
|
2
|
Gd
|
Detchd
|
1931
|
Unf
|
2
|
400
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2006
|
WD⋅
|
230000
|
2517
|
533250130
|
120
|
RL
|
30
|
9549
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
CulDSac
|
Gtl
|
Veenker
|
Norm
|
Norm
|
TwnhsE
|
1Story
|
8
|
5
|
1995
|
1996
|
Hip
|
CompShg
|
BrkFace
|
BrkFace
|
None
|
0
|
Gd
|
Gd
|
PConc
|
Gd
|
Gd
|
Av
|
LwQ
|
437
|
GLQ
|
1057
|
0
|
1494
|
GasA
|
Ex
|
Y
|
SBrkr
|
1494
|
0
|
0
|
1494
|
1
|
0
|
1
|
1
|
2
|
1
|
Ex
|
6
|
Typ
|
1
|
Gd
|
Attchd
|
1995
|
Fin
|
2
|
481
|
TA
|
TA
|
Y
|
0
|
30
|
0
|
0
|
216
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2006
|
WD⋅
|
270000
|
1933
|
535302070
|
20
|
RL
|
60
|
10950
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1952
|
1952
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
ALQ
|
441
|
Unf
|
0
|
251
|
948
|
GasA
|
TA
|
Y
|
SBrkr
|
948
|
0
|
0
|
948
|
0
|
0
|
1
|
0
|
2
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Detchd
|
1952
|
Unf
|
2
|
410
|
TA
|
TA
|
N
|
0
|
48
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2007
|
WD⋅
|
125000
|
820
|
906340110
|
20
|
RL
|
79
|
10637
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
CollgCr
|
Norm
|
Norm
|
1Fam
|
1Story
|
8
|
5
|
2007
|
2008
|
Hip
|
CompShg
|
VinylSd
|
VinylSd
|
Stone
|
336
|
Gd
|
TA
|
PConc
|
Ex
|
TA
|
Gd
|
GLQ
|
1288
|
Unf
|
0
|
161
|
1705
|
GasA
|
Ex
|
Y
|
SBrkr
|
1718
|
0
|
0
|
1718
|
1
|
0
|
2
|
0
|
3
|
1
|
Gd
|
7
|
Typ
|
1
|
Gd
|
Attchd
|
2007
|
RFn
|
3
|
826
|
TA
|
TA
|
Y
|
208
|
44
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
9
|
2009
|
WD⋅
|
297000
|
675
|
535426150
|
20
|
RL
|
80
|
9000
|
Pave
|
nan
|
IR1
|
HLS
|
AllPub
|
Inside
|
Mod
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1958
|
1958
|
Flat
|
Tar&Grv
|
Wd⋅Sdng
|
Wd⋅Sdng
|
BrkFace
|
82
|
TA
|
TA
|
CBlock
|
Gd
|
TA
|
Gd
|
Unf
|
0
|
Unf
|
0
|
160
|
160
|
GasA
|
Fa
|
Y
|
SBrkr
|
1142
|
0
|
0
|
1142
|
0
|
0
|
1
|
0
|
2
|
1
|
TA
|
5
|
Typ
|
1
|
Gd
|
Basment
|
1958
|
RFn
|
1
|
384
|
TA
|
TA
|
Y
|
0
|
28
|
64
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2009
|
WD⋅
|
113500
|
2910
|
923225240
|
160
|
RM
|
41
|
2665
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
MeadowV
|
Norm
|
Norm
|
TwnhsE
|
2Story
|
5
|
6
|
1977
|
1977
|
Gable
|
CompShg
|
CemntBd
|
CmentBd
|
None
|
0
|
TA
|
TA
|
PConc
|
TA
|
TA
|
No
|
ALQ
|
548
|
Rec
|
173
|
36
|
757
|
GasA
|
Ex
|
Y
|
SBrkr
|
925
|
550
|
0
|
1475
|
0
|
0
|
2
|
0
|
4
|
1
|
TA
|
6
|
Typ
|
1
|
TA
|
Attchd
|
1977
|
RFn
|
1
|
336
|
TA
|
TA
|
Y
|
104
|
26
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2006
|
WD⋅
|
127000
|
21
|
527358140
|
20
|
RL
|
105
|
11751
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NWAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1977
|
1977
|
Hip
|
CompShg
|
Plywood
|
Plywood
|
BrkFace
|
480
|
TA
|
TA
|
CBlock
|
Gd
|
TA
|
No
|
BLQ
|
705
|
Unf
|
0
|
115
|
1844
|
GasA
|
Ex
|
Y
|
SBrkr
|
1844
|
0
|
0
|
1844
|
0
|
0
|
2
|
0
|
3
|
1
|
TA
|
7
|
Typ
|
1
|
TA
|
Attchd
|
1977
|
RFn
|
2
|
546
|
TA
|
TA
|
Y
|
0
|
122
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
1
|
2010
|
COD
|
190000
|
1873
|
534177210
|
20
|
RL
|
80
|
9600
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NWAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
6
|
1973
|
1973
|
Hip
|
CompShg
|
HdBoard
|
HdBoard
|
BrkFace
|
320
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
ALQ
|
916
|
Unf
|
0
|
70
|
1242
|
GasA
|
Fa
|
Y
|
SBrkr
|
1242
|
0
|
0
|
1242
|
0
|
0
|
1
|
1
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
TA
|
Attchd
|
1973
|
Unf
|
2
|
528
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
9
|
2007
|
WD⋅
|
175500
|
2125
|
907135050
|
20
|
RL
|
60
|
8428
|
Pave
|
nan
|
IR2
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
CollgCr
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
7
|
1994
|
1994
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
Gd
|
PConc
|
Gd
|
Gd
|
No
|
GLQ
|
420
|
Unf
|
0
|
58
|
990
|
GasA
|
Ex
|
Y
|
SBrkr
|
990
|
0
|
0
|
990
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Detchd
|
1995
|
Unf
|
1
|
384
|
TA
|
TA
|
Y
|
256
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2007
|
WD⋅
|
146000
|
1489
|
908102260
|
20
|
RL
|
75
|
9937
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Edwards
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
6
|
1965
|
1965
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
None
|
0
|
Gd
|
TA
|
CBlock
|
TA
|
TA
|
No
|
BLQ
|
830
|
LwQ
|
290
|
136
|
1256
|
GasA
|
Gd
|
Y
|
SBrkr
|
1256
|
0
|
0
|
1256
|
1
|
0
|
1
|
1
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1965
|
Fin
|
1
|
276
|
TA
|
TA
|
Y
|
736
|
68
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2008
|
WD⋅
|
147500
|
2383
|
528110050
|
20
|
RL
|
107
|
13891
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NridgHt
|
Norm
|
Norm
|
1Fam
|
1Story
|
10
|
5
|
2006
|
2006
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
nan
|
nan
|
Ex
|
TA
|
PConc
|
Ex
|
Gd
|
Gd
|
GLQ
|
1386
|
Unf
|
0
|
178
|
2076
|
GasA
|
Ex
|
Y
|
SBrkr
|
2076
|
0
|
0
|
2076
|
1
|
0
|
2
|
1
|
2
|
1
|
Ex
|
7
|
Typ
|
1
|
Gd
|
Attchd
|
2006
|
Fin
|
3
|
850
|
TA
|
TA
|
Y
|
216
|
229
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
9
|
2006
|
New
|
465000
|
983
|
923275040
|
85
|
RL
|
nan
|
9101
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
Mitchel
|
Norm
|
Norm
|
1Fam
|
SFoyer
|
5
|
6
|
1978
|
1978
|
Gable
|
CompShg
|
Plywood
|
Plywood
|
BrkFace
|
104
|
TA
|
Gd
|
PConc
|
Gd
|
TA
|
Av
|
GLQ
|
1097
|
Unf
|
0
|
0
|
1097
|
GasA
|
Ex
|
Y
|
SBrkr
|
1110
|
0
|
0
|
1110
|
1
|
0
|
1
|
0
|
1
|
1
|
Gd
|
4
|
Typ
|
1
|
TA
|
Attchd
|
1978
|
Fin
|
2
|
602
|
TA
|
TA
|
Y
|
303
|
30
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2009
|
WD⋅
|
165500
|
1325
|
902406090
|
50
|
RM
|
81
|
12150
|
Pave
|
Grvl
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
OldTown
|
Norm
|
Norm
|
1Fam
|
1.5Fin
|
5
|
5
|
1954
|
1954
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
335
|
TA
|
TA
|
BrkTil
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
26
|
1050
|
GasA
|
Ex
|
N
|
FuseF
|
1050
|
745
|
0
|
1795
|
0
|
0
|
2
|
0
|
4
|
1
|
TA
|
7
|
Typ
|
0
|
nan
|
Attchd
|
1954
|
Unf
|
1
|
352
|
Fa
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
11
|
2008
|
WD⋅
|
131500
|
1378
|
905102170
|
20
|
RL
|
70
|
11200
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Sawyer
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1965
|
1965
|
Hip
|
CompShg
|
HdBoard
|
HdBoard
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
906
|
Unf
|
0
|
134
|
1040
|
GasA
|
Ex
|
Y
|
SBrkr
|
1040
|
0
|
0
|
1040
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Detchd
|
1965
|
Unf
|
1
|
384
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
2
|
2008
|
WD⋅
|
129500
|
1537
|
909281110
|
70
|
RL
|
100
|
9670
|
Pave
|
nan
|
IR1
|
HLS
|
AllPub
|
Inside
|
Mod
|
Crawfor
|
Norm
|
Norm
|
1Fam
|
2Story
|
8
|
6
|
1935
|
1950
|
Gable
|
CompShg
|
BrkFace
|
Stucco
|
Stone
|
40
|
TA
|
TA
|
PConc
|
TA
|
Fa
|
No
|
LwQ
|
210
|
Unf
|
0
|
142
|
608
|
GasA
|
TA
|
Y
|
SBrkr
|
983
|
890
|
0
|
1873
|
0
|
0
|
1
|
1
|
4
|
1
|
TA
|
9
|
Typ
|
2
|
Gd
|
Detchd
|
1935
|
Fin
|
2
|
786
|
Fa
|
TA
|
Y
|
0
|
0
|
207
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2008
|
WD⋅
|
257076
|
905
|
909101140
|
50
|
RL
|
60
|
10998
|
Pave
|
Grvl
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Edwards
|
Norm
|
Norm
|
1Fam
|
1.5Fin
|
5
|
5
|
1941
|
1960
|
Gable
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
LwQ
|
408
|
BLQ
|
420
|
156
|
984
|
GasA
|
Ex
|
Y
|
SBrkr
|
984
|
620
|
0
|
1604
|
0
|
0
|
2
|
0
|
3
|
1
|
TA
|
6
|
Min2
|
0
|
nan
|
Detchd
|
1977
|
Unf
|
2
|
660
|
TA
|
TA
|
Y
|
0
|
68
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2009
|
WD⋅
|
117000
|
1532
|
909275050
|
70
|
RL
|
60
|
7200
|
Pave
|
nan
|
Reg
|
HLS
|
AllPub
|
Inside
|
Mod
|
Crawfor
|
Norm
|
Norm
|
1Fam
|
2Story
|
7
|
6
|
1931
|
2000
|
Gable
|
CompShg
|
Stucco
|
Wd⋅Shng
|
None
|
0
|
TA
|
Fa
|
BrkTil
|
Gd
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
8
|
776
|
GasA
|
TA
|
Y
|
SBrkr
|
851
|
651
|
0
|
1502
|
0
|
0
|
1
|
1
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
Gd
|
Attchd
|
1931
|
RFn
|
1
|
270
|
TA
|
TA
|
P
|
0
|
0
|
112
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
2
|
2008
|
WD⋅
|
149000
|
132
|
534475100
|
20
|
RL
|
80
|
9920
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1954
|
1954
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
Stone
|
110
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
354
|
LwQ
|
290
|
156
|
1056
|
GasA
|
TA
|
Y
|
SBrkr
|
1063
|
0
|
0
|
1063
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1954
|
RFn
|
1
|
280
|
TA
|
TA
|
Y
|
0
|
0
|
164
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
2
|
2010
|
WD⋅
|
128000
|
1078
|
528188090
|
160
|
RL
|
36
|
2448
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NridgHt
|
Norm
|
Norm
|
Twnhs
|
2Story
|
7
|
5
|
2003
|
2004
|
Gable
|
CompShg
|
VinylSd
|
Wd⋅Shng
|
Stone
|
106
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
252
|
764
|
GasA
|
Ex
|
Y
|
SBrkr
|
764
|
862
|
0
|
1626
|
0
|
0
|
2
|
1
|
2
|
1
|
Gd
|
6
|
Typ
|
0
|
nan
|
BuiltIn
|
2003
|
RFn
|
2
|
474
|
TA
|
TA
|
Y
|
0
|
27
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
10
|
2008
|
WD⋅
|
155000
|
2511
|
533221080
|
160
|
FV
|
nan
|
2998
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
TwnhsE
|
2Story
|
6
|
5
|
2000
|
2000
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
513
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
353
|
Unf
|
0
|
147
|
756
|
GasA
|
Ex
|
Y
|
SBrkr
|
768
|
756
|
0
|
1524
|
0
|
0
|
2
|
1
|
2
|
1
|
Gd
|
4
|
Typ
|
0
|
nan
|
Detchd
|
2000
|
Unf
|
2
|
440
|
TA
|
TA
|
Y
|
0
|
32
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2006
|
WD⋅
|
166000
|
912
|
909250220
|
50
|
RL
|
65
|
7804
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
SWISU
|
Norm
|
Norm
|
1Fam
|
1.5Fin
|
4
|
3
|
1928
|
1950
|
Gable
|
CompShg
|
WdShing
|
Plywood
|
None
|
0
|
TA
|
TA
|
BrkTil
|
TA
|
TA
|
No
|
BLQ
|
622
|
Unf
|
0
|
244
|
1122
|
GasA
|
TA
|
Y
|
SBrkr
|
1328
|
653
|
0
|
1981
|
1
|
0
|
2
|
0
|
4
|
1
|
Gd
|
7
|
Min2
|
2
|
TA
|
Detchd
|
1981
|
Unf
|
2
|
576
|
TA
|
TA
|
Y
|
431
|
44
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
12
|
2009
|
WD⋅
|
135000
|
2351
|
527356020
|
60
|
RL
|
80
|
16692
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NWAmes
|
RRAn
|
Norm
|
1Fam
|
2Story
|
7
|
5
|
1978
|
1978
|
Gable
|
CompShg
|
Plywood
|
Plywood
|
BrkFace
|
184
|
TA
|
TA
|
CBlock
|
Gd
|
TA
|
No
|
BLQ
|
790
|
LwQ
|
469
|
133
|
1392
|
GasA
|
TA
|
Y
|
SBrkr
|
1392
|
1392
|
0
|
2784
|
1
|
0
|
3
|
1
|
5
|
1
|
Gd
|
12
|
Typ
|
2
|
TA
|
Attchd
|
1978
|
RFn
|
2
|
564
|
TA
|
TA
|
Y
|
0
|
112
|
0
|
0
|
440
|
519
|
Fa
|
MnPrv
|
TenC
|
2000
|
7
|
2006
|
WD⋅
|
250000
|
330
|
923226250
|
160
|
RM
|
21
|
1476
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
MeadowV
|
Norm
|
Norm
|
Twnhs
|
2Story
|
4
|
7
|
1970
|
1970
|
Gable
|
CompShg
|
CemntBd
|
CmentBd
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
GLQ
|
176
|
Unf
|
0
|
114
|
546
|
GasA
|
Ex
|
Y
|
SBrkr
|
546
|
546
|
0
|
1092
|
0
|
0
|
1
|
1
|
3
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
nan
|
nan
|
nan
|
0
|
0
|
nan
|
nan
|
Y
|
200
|
26
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
3
|
2010
|
WD⋅
|
76000
|
2586
|
535305120
|
20
|
RL
|
75
|
10170
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1951
|
1951
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
BrkFace
|
522
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
216
|
216
|
GasA
|
TA
|
Y
|
SBrkr
|
1575
|
0
|
0
|
1575
|
0
|
0
|
1
|
1
|
2
|
1
|
Gd
|
5
|
Typ
|
1
|
Gd
|
Attchd
|
1951
|
Unf
|
2
|
400
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2006
|
WD⋅
|
155000
|
224
|
905106140
|
60
|
RL
|
80
|
9364
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
Sawyer
|
Norm
|
Norm
|
1Fam
|
2Story
|
6
|
7
|
1969
|
1969
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
Stone
|
143
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
ALQ
|
371
|
Unf
|
0
|
36
|
663
|
GasA
|
TA
|
Y
|
SBrkr
|
663
|
689
|
0
|
1352
|
0
|
0
|
1
|
1
|
4
|
1
|
TA
|
7
|
Typ
|
0
|
nan
|
Attchd
|
1969
|
Fin
|
1
|
299
|
TA
|
TA
|
Y
|
379
|
36
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
3
|
2010
|
WD⋅
|
158000
|
98
|
533212020
|
160
|
FV
|
24
|
2544
|
Pave
|
Pave
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
Twnhs
|
2Story
|
7
|
5
|
2004
|
2005
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
None
|
0
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
368
|
ALQ
|
42
|
190
|
600
|
GasA
|
Ex
|
Y
|
SBrkr
|
600
|
600
|
0
|
1200
|
1
|
0
|
2
|
1
|
2
|
1
|
Gd
|
4
|
Typ
|
0
|
nan
|
Detchd
|
2004
|
RFn
|
2
|
480
|
TA
|
TA
|
Y
|
0
|
172
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
2
|
2010
|
WD⋅
|
149500
|
2565
|
534479300
|
20
|
RL
|
82
|
9888
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
6
|
1954
|
1975
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
None
|
0
|
TA
|
Gd
|
CBlock
|
TA
|
TA
|
No
|
BLQ
|
486
|
Unf
|
0
|
194
|
936
|
GasA
|
TA
|
Y
|
FuseA
|
936
|
0
|
0
|
936
|
0
|
0
|
1
|
0
|
2
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Attchd
|
1954
|
Unf
|
1
|
240
|
TA
|
TA
|
Y
|
0
|
0
|
160
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
3
|
2006
|
WD⋅
|
121000
|
1934
|
535304170
|
20
|
RL
|
68
|
7942
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
7
|
1953
|
1953
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
85
|
ALQ
|
955
|
0
|
1040
|
GasA
|
TA
|
Y
|
FuseF
|
1040
|
0
|
0
|
1040
|
0
|
1
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1953
|
Fin
|
1
|
293
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
6
|
2007
|
WD⋅
|
136000
|
997
|
527107010
|
60
|
RL
|
nan
|
15038
|
Pave
|
nan
|
IR2
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
Gilbert
|
Norm
|
Norm
|
1Fam
|
2Story
|
6
|
5
|
1996
|
1996
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
None
|
0
|
TA
|
TA
|
PConc
|
Gd
|
TA
|
No
|
Rec
|
138
|
Unf
|
0
|
10
|
916
|
GasA
|
Gd
|
Y
|
SBrkr
|
916
|
720
|
0
|
1636
|
0
|
0
|
2
|
1
|
3
|
1
|
TA
|
7
|
Typ
|
1
|
TA
|
Attchd
|
1996
|
Fin
|
2
|
386
|
TA
|
TA
|
Y
|
168
|
84
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
11
|
2008
|
WD⋅
|
173000
|
64
|
528382020
|
60
|
RL
|
92
|
11764
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
CulDSac
|
Gtl
|
NoRidge
|
Norm
|
Norm
|
1Fam
|
2Story
|
8
|
7
|
1999
|
2007
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
348
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
524
|
Unf
|
0
|
116
|
1152
|
GasA
|
Ex
|
Y
|
SBrkr
|
1164
|
1106
|
0
|
2270
|
0
|
0
|
2
|
1
|
4
|
1
|
Gd
|
9
|
Typ
|
1
|
Gd
|
Attchd
|
1999
|
Fin
|
3
|
671
|
TA
|
TA
|
Y
|
132
|
57
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2010
|
WD⋅
|
290000
|
1799
|
528458130
|
60
|
FV
|
75
|
9000
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
1Fam
|
2Story
|
8
|
5
|
2007
|
2007
|
Gable
|
CompShg
|
CemntBd
|
CmentBd
|
None
|
0
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Av
|
GLQ
|
64
|
Unf
|
0
|
96
|
1184
|
GasA
|
Ex
|
Y
|
SBrkr
|
1184
|
1426
|
0
|
2610
|
0
|
0
|
2
|
1
|
4
|
1
|
Ex
|
11
|
Typ
|
1
|
Gd
|
BuiltIn
|
2007
|
Fin
|
2
|
550
|
TA
|
TA
|
Y
|
208
|
108
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
8
|
2007
|
New
|
303477
|
230
|
905109170
|
20
|
RL
|
nan
|
20062
|
Pave
|
nan
|
IR1
|
Low
|
AllPub
|
Inside
|
Mod
|
ClearCr
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
7
|
1977
|
2001
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
Gd
|
Gd
|
CBlock
|
Gd
|
TA
|
Gd
|
ALQ
|
1092
|
Unf
|
0
|
72
|
1420
|
GasA
|
Gd
|
Y
|
SBrkr
|
1483
|
0
|
0
|
1483
|
1
|
0
|
1
|
1
|
1
|
1
|
TA
|
4
|
Typ
|
2
|
TA
|
Attchd
|
1977
|
RFn
|
2
|
690
|
TA
|
TA
|
Y
|
496
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2010
|
WD⋅
|
270000
|
1295
|
902109160
|
50
|
RM
|
81
|
7308
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
OldTown
|
Artery
|
Norm
|
1Fam
|
1.5Fin
|
5
|
5
|
1920
|
1950
|
Gable
|
CompShg
|
WdShing
|
Wd⋅Shng
|
None
|
0
|
Fa
|
Fa
|
BrkTil
|
TA
|
TA
|
No
|
Rec
|
360
|
Unf
|
0
|
64
|
936
|
GasA
|
Gd
|
N
|
FuseA
|
960
|
780
|
0
|
1740
|
0
|
0
|
1
|
0
|
2
|
1
|
Ex
|
6
|
Typ
|
1
|
Gd
|
Detchd
|
1920
|
Unf
|
1
|
225
|
Fa
|
Fa
|
N
|
0
|
0
|
236
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2008
|
WD⋅
|
122250
|
638
|
535178120
|
20
|
RL
|
70
|
8750
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1955
|
1955
|
Gable
|
CompShg
|
AsbShng
|
AsbShng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
172
|
LwQ
|
483
|
215
|
1382
|
GasA
|
Gd
|
Y
|
FuseA
|
1382
|
0
|
0
|
1382
|
0
|
1
|
1
|
0
|
3
|
1
|
Gd
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1955
|
Unf
|
1
|
350
|
TA
|
TA
|
Y
|
280
|
0
|
0
|
0
|
0
|
0
|
nan
|
GdWo
|
nan
|
0
|
7
|
2009
|
WD⋅
|
153000
|
2278
|
923202134
|
20
|
RL
|
70
|
8402
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Mitchel
|
Feedr
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
2007
|
2007
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
TA
|
PConc
|
Gd
|
TA
|
No
|
ALQ
|
206
|
Unf
|
0
|
146
|
1120
|
GasA
|
Ex
|
Y
|
SBrkr
|
1120
|
0
|
0
|
1120
|
0
|
0
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
nan
|
nan
|
nan
|
0
|
0
|
nan
|
nan
|
Y
|
0
|
30
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
12
|
2007
|
New
|
147000
|
2202
|
909252150
|
70
|
RL
|
63
|
4000
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Crawfor
|
Norm
|
Norm
|
1Fam
|
2Story
|
7
|
8
|
1930
|
1995
|
Gable
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
BrkTil
|
Gd
|
TA
|
No
|
GLQ
|
246
|
Unf
|
0
|
29
|
531
|
GasA
|
TA
|
Y
|
SBrkr
|
567
|
531
|
0
|
1098
|
1
|
0
|
1
|
0
|
2
|
1
|
TA
|
5
|
Typ
|
1
|
Gd
|
Detchd
|
1930
|
Unf
|
1
|
216
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
MnPrv
|
nan
|
0
|
6
|
2007
|
WD⋅
|
148500
|
2833
|
908188150
|
160
|
RM
|
32
|
3363
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Edwards
|
Norm
|
Norm
|
TwnhsE
|
2Story
|
7
|
5
|
2004
|
2004
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
Stone
|
117
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
208
|
976
|
GasA
|
Ex
|
Y
|
SBrkr
|
976
|
732
|
0
|
1708
|
0
|
0
|
2
|
0
|
3
|
1
|
Gd
|
7
|
Maj1
|
0
|
nan
|
Detchd
|
2004
|
Unf
|
2
|
380
|
TA
|
TA
|
Y
|
0
|
40
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2006
|
WD⋅
|
130000
|
1707
|
528150040
|
20
|
RL
|
94
|
13615
|
Pave
|
nan
|
IR1
|
HLS
|
AllPub
|
Corner
|
Mod
|
NridgHt
|
Norm
|
Norm
|
1Fam
|
1Story
|
9
|
5
|
2006
|
2006
|
Hip
|
CompShg
|
MetalSd
|
MetalSd
|
Stone
|
510
|
Ex
|
TA
|
PConc
|
Ex
|
TA
|
Gd
|
Unf
|
0
|
Unf
|
0
|
10
|
1802
|
GasA
|
Ex
|
Y
|
SBrkr
|
1802
|
0
|
0
|
1802
|
0
|
0
|
2
|
1
|
3
|
1
|
Ex
|
7
|
Typ
|
1
|
Gd
|
Attchd
|
2006
|
Fin
|
3
|
843
|
TA
|
TA
|
Y
|
158
|
105
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2007
|
WD⋅
|
372000
|
2428
|
528228565
|
120
|
RL
|
43
|
3013
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Blmngtn
|
Norm
|
Norm
|
TwnhsE
|
1Story
|
7
|
5
|
2005
|
2005
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
145
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Gd
|
GLQ
|
16
|
Unf
|
0
|
66
|
1362
|
GasA
|
Ex
|
Y
|
SBrkr
|
1506
|
0
|
0
|
1506
|
0
|
0
|
2
|
0
|
2
|
1
|
Gd
|
6
|
Typ
|
1
|
Gd
|
Attchd
|
2005
|
Fin
|
2
|
440
|
TA
|
TA
|
Y
|
142
|
20
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2006
|
WD⋅
|
213490
|
242
|
905453080
|
20
|
RL
|
44
|
17485
|
Pave
|
nan
|
IR2
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Edwards
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
5
|
2009
|
2010
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
Stone
|
96
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Gd
|
GLQ
|
1346
|
Unf
|
0
|
162
|
1508
|
GasA
|
Ex
|
Y
|
SBrkr
|
1508
|
0
|
0
|
1508
|
1
|
0
|
1
|
0
|
1
|
1
|
Gd
|
5
|
Typ
|
2
|
TA
|
Attchd
|
2009
|
RFn
|
2
|
572
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
1
|
2010
|
Con
|
308030
|
1564
|
914453045
|
20
|
RL
|
nan
|
23730
|
Pave
|
nan
|
IR2
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Mitchel
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
5
|
1996
|
1997
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
668
|
Gd
|
TA
|
PConc
|
Ex
|
TA
|
Mn
|
GLQ
|
700
|
Unf
|
0
|
116
|
1840
|
GasA
|
Ex
|
Y
|
SBrkr
|
2032
|
0
|
0
|
2032
|
1
|
0
|
3
|
0
|
3
|
1
|
Gd
|
7
|
Typ
|
0
|
nan
|
Attchd
|
1996
|
Fin
|
3
|
786
|
TA
|
TA
|
Y
|
0
|
46
|
192
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
9
|
2008
|
WD⋅
|
300000
|
2613
|
535403150
|
20
|
RL
|
79
|
8910
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1959
|
1959
|
Hip
|
CompShg
|
BrkFace
|
BrkFace
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
Mn
|
ALQ
|
655
|
Unf
|
0
|
0
|
655
|
GasA
|
Ex
|
Y
|
SBrkr
|
1194
|
0
|
0
|
1194
|
0
|
1
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
Fa
|
BuiltIn
|
1954
|
Fin
|
2
|
539
|
TA
|
TA
|
Y
|
0
|
0
|
192
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2006
|
WD⋅
|
159500
|
2832
|
908188140
|
160
|
RM
|
24
|
2522
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Edwards
|
Norm
|
Norm
|
Twnhs
|
2Story
|
7
|
5
|
2004
|
2004
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
Stone
|
50
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
202
|
970
|
GasA
|
Ex
|
Y
|
SBrkr
|
970
|
739
|
0
|
1709
|
0
|
0
|
2
|
0
|
3
|
1
|
Gd
|
7
|
Maj1
|
0
|
nan
|
Detchd
|
2004
|
Unf
|
2
|
380
|
TA
|
TA
|
Y
|
0
|
40
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2006
|
WD⋅
|
137500
|
2459
|
528439030
|
20
|
RL
|
73
|
8688
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
5
|
2005
|
2005
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
228
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Av
|
Unf
|
0
|
Unf
|
0
|
80
|
1616
|
GasA
|
Ex
|
Y
|
SBrkr
|
1616
|
0
|
0
|
1616
|
0
|
0
|
2
|
0
|
3
|
1
|
Gd
|
7
|
Typ
|
0
|
nan
|
Attchd
|
2005
|
RFn
|
3
|
834
|
TA
|
TA
|
Y
|
208
|
59
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2006
|
WD⋅
|
232000
|
1360
|
903452025
|
30
|
RM
|
nan
|
6291
|
Grvl
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
IDOTRR
|
RRNe
|
Norm
|
1Fam
|
1Story
|
6
|
6
|
1930
|
1950
|
Gable
|
CompShg
|
Stucco
|
Wd⋅Shng
|
None
|
0
|
Gd
|
Gd
|
BrkTil
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
0
|
768
|
GasA
|
TA
|
Y
|
SBrkr
|
768
|
0
|
0
|
768
|
0
|
0
|
1
|
0
|
1
|
1
|
TA
|
4
|
Typ
|
0
|
nan
|
Detchd
|
1930
|
Unf
|
2
|
440
|
TA
|
TA
|
N
|
0
|
0
|
84
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2008
|
WD⋅
|
93850
|
2723
|
905200380
|
30
|
RL
|
nan
|
17529
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Sawyer
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1924
|
1950
|
Gable
|
CompShg
|
BrkFace
|
Wd⋅Sdng
|
Stone
|
65
|
TA
|
TA
|
BrkTil
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
104
|
872
|
GasA
|
Fa
|
N
|
FuseF
|
872
|
0
|
0
|
872
|
0
|
0
|
1
|
0
|
2
|
1
|
Fa
|
5
|
Mod
|
1
|
Gd
|
Detchd
|
1924
|
Unf
|
1
|
322
|
Fa
|
Fa
|
N
|
0
|
0
|
116
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2006
|
WD⋅
|
105000
|
1350
|
903235100
|
30
|
RM
|
60
|
6324
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
BrkSide
|
Feedr
|
RRNn
|
1Fam
|
1Story
|
4
|
6
|
1927
|
1950
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
None
|
0
|
TA
|
TA
|
BrkTil
|
TA
|
TA
|
No
|
Unf
|
0
|
Unf
|
0
|
8
|
520
|
GasA
|
Fa
|
N
|
SBrkr
|
520
|
0
|
0
|
520
|
0
|
0
|
1
|
0
|
1
|
1
|
Fa
|
4
|
Typ
|
0
|
nan
|
Detchd
|
1920
|
Unf
|
1
|
240
|
Fa
|
TA
|
Y
|
49
|
0
|
87
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
5
|
2008
|
WD⋅
|
68500
|
136
|
535106190
|
90
|
RL
|
80
|
11600
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NAmes
|
Feedr
|
Norm
|
Duplex
|
2Story
|
5
|
5
|
1960
|
1960
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
361
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
443
|
Unf
|
0
|
150
|
1105
|
GasA
|
TA
|
Y
|
FuseA
|
1105
|
1169
|
0
|
2274
|
0
|
0
|
2
|
0
|
5
|
2
|
TA
|
12
|
Typ
|
0
|
nan
|
Detchd
|
1960
|
Unf
|
2
|
480
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
1
|
2010
|
WD⋅
|
154300
|
2235
|
910201050
|
50
|
RM
|
50
|
7288
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
IDOTRR
|
Artery
|
Norm
|
1Fam
|
1.5Fin
|
5
|
7
|
1925
|
2003
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
Gd
|
Gd
|
BrkTil
|
TA
|
Po
|
No
|
Unf
|
0
|
Unf
|
0
|
168
|
936
|
GasA
|
Ex
|
Y
|
SBrkr
|
936
|
665
|
0
|
1601
|
0
|
0
|
2
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
nan
|
nan
|
nan
|
0
|
0
|
nan
|
nan
|
N
|
0
|
0
|
0
|
176
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
9
|
2007
|
WD⋅
|
129850
|
1993
|
902301130
|
70
|
RM
|
35
|
4571
|
Pave
|
Grvl
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
OldTown
|
Norm
|
Norm
|
1Fam
|
2Story
|
5
|
7
|
1910
|
1950
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
None
|
0
|
TA
|
TA
|
BrkTil
|
TA
|
TA
|
No
|
BLQ
|
168
|
Unf
|
0
|
192
|
616
|
GasA
|
Ex
|
Y
|
SBrkr
|
616
|
616
|
0
|
1232
|
0
|
0
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
0
|
nan
|
Detchd
|
1920
|
Unf
|
2
|
480
|
Fa
|
Fa
|
Y
|
280
|
0
|
143
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2007
|
WD⋅
|
114000
|
367
|
527214050
|
20
|
RL
|
63
|
17423
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
CulDSac
|
Gtl
|
StoneBr
|
Norm
|
Norm
|
1Fam
|
1Story
|
9
|
5
|
2008
|
2009
|
Hip
|
CompShg
|
VinylSd
|
VinylSd
|
Stone
|
748
|
Ex
|
TA
|
PConc
|
Ex
|
TA
|
No
|
GLQ
|
1904
|
Unf
|
0
|
56
|
2216
|
GasA
|
Ex
|
Y
|
SBrkr
|
2234
|
0
|
0
|
2234
|
1
|
0
|
2
|
0
|
1
|
1
|
Ex
|
9
|
Typ
|
1
|
Gd
|
Attchd
|
2009
|
Fin
|
3
|
1166
|
TA
|
TA
|
Y
|
0
|
60
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
7
|
2009
|
New
|
501837
|
1309
|
902300215
|
50
|
RM
|
100
|
12665
|
Pave
|
Grvl
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
OldTown
|
Artery
|
Norm
|
1Fam
|
1.5Fin
|
5
|
8
|
1915
|
1950
|
Gable
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
BrkTil
|
TA
|
TA
|
Mn
|
Unf
|
0
|
Unf
|
0
|
108
|
876
|
GasA
|
Gd
|
Y
|
SBrkr
|
876
|
540
|
0
|
1416
|
0
|
0
|
1
|
1
|
4
|
1
|
TA
|
7
|
Typ
|
1
|
Gd
|
Detchd
|
1949
|
Unf
|
3
|
720
|
TA
|
TA
|
Y
|
418
|
0
|
194
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2008
|
WD⋅
|
153900
|
402
|
527425110
|
20
|
RL
|
60
|
9000
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
FR2
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
4
|
7
|
1971
|
2006
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
TA
|
Gd
|
CBlock
|
TA
|
TA
|
No
|
ALQ
|
240
|
Unf
|
0
|
112
|
864
|
GasA
|
Gd
|
Y
|
SBrkr
|
864
|
0
|
0
|
864
|
0
|
0
|
1
|
0
|
3
|
1
|
TA
|
5
|
Typ
|
0
|
nan
|
Detchd
|
1986
|
Unf
|
2
|
576
|
TA
|
TA
|
Y
|
200
|
0
|
0
|
0
|
0
|
0
|
nan
|
GdWo
|
nan
|
0
|
7
|
2009
|
WD⋅
|
130000
|
1592
|
923203140
|
120
|
RM
|
32
|
4500
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
FR2
|
Gtl
|
Mitchel
|
Norm
|
Norm
|
TwnhsE
|
1Story
|
6
|
5
|
1997
|
1997
|
Hip
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
197
|
TA
|
TA
|
PConc
|
Ex
|
TA
|
No
|
GLQ
|
864
|
Unf
|
0
|
69
|
1189
|
GasA
|
Ex
|
Y
|
SBrkr
|
1189
|
0
|
0
|
1189
|
1
|
0
|
2
|
0
|
2
|
1
|
TA
|
4
|
Typ
|
0
|
nan
|
Attchd
|
1997
|
RFn
|
2
|
392
|
TA
|
TA
|
Y
|
0
|
122
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2008
|
WD⋅
|
160500
|
2409
|
528186110
|
120
|
RL
|
48
|
6373
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NridgHt
|
Norm
|
Norm
|
TwnhsE
|
1Story
|
9
|
5
|
2006
|
2006
|
Hip
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
572
|
Ex
|
TA
|
PConc
|
Ex
|
Gd
|
No
|
GLQ
|
415
|
Unf
|
0
|
227
|
1666
|
GasA
|
Ex
|
Y
|
SBrkr
|
1666
|
0
|
0
|
1666
|
1
|
0
|
2
|
0
|
2
|
1
|
Gd
|
6
|
Typ
|
1
|
Gd
|
Attchd
|
2006
|
RFn
|
2
|
575
|
TA
|
TA
|
Y
|
228
|
75
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2006
|
New
|
310090
|
539
|
531371050
|
20
|
RL
|
67
|
10083
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
SawyerW
|
Norm
|
Norm
|
1Fam
|
1Story
|
7
|
5
|
2003
|
2003
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
nan
|
nan
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
833
|
Unf
|
0
|
87
|
1176
|
GasA
|
Ex
|
Y
|
SBrkr
|
1200
|
0
|
0
|
1200
|
1
|
0
|
2
|
0
|
2
|
1
|
Gd
|
5
|
Typ
|
0
|
nan
|
Attchd
|
2003
|
RFn
|
2
|
555
|
TA
|
TA
|
Y
|
0
|
41
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
8
|
2009
|
WD⋅
|
184900
|
1940
|
535327200
|
20
|
RL
|
70
|
13300
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
7
|
1956
|
2000
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
CBlock
|
Gd
|
TA
|
No
|
Rec
|
377
|
Unf
|
0
|
39
|
928
|
GasA
|
TA
|
Y
|
SBrkr
|
928
|
0
|
0
|
928
|
0
|
0
|
1
|
0
|
2
|
1
|
TA
|
4
|
Typ
|
0
|
nan
|
Attchd
|
1956
|
Unf
|
1
|
252
|
TA
|
TA
|
Y
|
261
|
0
|
156
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2007
|
WD⋅
|
132000
|
588
|
534129370
|
20
|
RL
|
80
|
9600
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NWAmes
|
Feedr
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1969
|
1969
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
BrkFace
|
128
|
TA
|
TA
|
CBlock
|
Gd
|
TA
|
Mn
|
ALQ
|
553
|
Rec
|
147
|
76
|
1288
|
GasA
|
TA
|
Y
|
SBrkr
|
1336
|
0
|
0
|
1336
|
0
|
1
|
2
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
Fa
|
Attchd
|
1969
|
RFn
|
2
|
502
|
TA
|
TA
|
Y
|
312
|
11
|
0
|
0
|
0
|
0
|
nan
|
nan
|
Shed
|
650
|
8
|
2009
|
WD⋅
|
163000
|
96
|
533210060
|
160
|
FV
|
30
|
3215
|
Pave
|
Pave
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
TwnhsE
|
2Story
|
7
|
5
|
2004
|
2004
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
120
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Av
|
GLQ
|
280
|
Unf
|
0
|
64
|
600
|
GasA
|
Ex
|
Y
|
SBrkr
|
600
|
600
|
0
|
1200
|
0
|
0
|
2
|
1
|
2
|
1
|
Gd
|
4
|
Typ
|
0
|
nan
|
Detchd
|
2004
|
RFn
|
2
|
480
|
TA
|
TA
|
Y
|
0
|
172
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2010
|
ConLD
|
155000
|
1187
|
534127210
|
80
|
RL
|
nan
|
11104
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NWAmes
|
Norm
|
Norm
|
1Fam
|
SLvl
|
6
|
6
|
1969
|
1969
|
Gable
|
CompShg
|
MetalSd
|
MetalSd
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
Av
|
GLQ
|
828
|
Unf
|
0
|
87
|
1427
|
GasA
|
Gd
|
Y
|
SBrkr
|
1427
|
0
|
0
|
1427
|
0
|
1
|
2
|
0
|
4
|
1
|
TA
|
7
|
Typ
|
0
|
nan
|
Attchd
|
1969
|
RFn
|
2
|
516
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
216
|
0
|
nan
|
nan
|
nan
|
0
|
5
|
2008
|
WD⋅
|
183000
|
2154
|
907280090
|
60
|
RL
|
nan
|
15295
|
Pave
|
nan
|
IR3
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
CollgCr
|
Norm
|
Norm
|
1Fam
|
2Story
|
7
|
5
|
1996
|
2002
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
254
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
Mn
|
GLQ
|
762
|
Unf
|
0
|
98
|
860
|
GasA
|
Ex
|
Y
|
SBrkr
|
1212
|
780
|
0
|
1992
|
1
|
0
|
2
|
1
|
3
|
1
|
Gd
|
7
|
Min2
|
2
|
TA
|
Attchd
|
1996
|
RFn
|
2
|
608
|
TA
|
TA
|
Y
|
225
|
32
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2007
|
WD⋅
|
211000
|
550
|
531452260
|
60
|
RL
|
70
|
9109
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
SawyerW
|
RRAe
|
Norm
|
1Fam
|
2Story
|
7
|
5
|
1994
|
1994
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
None
|
0
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
LwQ
|
36
|
GLQ
|
596
|
122
|
754
|
GasA
|
Ex
|
Y
|
SBrkr
|
754
|
786
|
0
|
1540
|
1
|
0
|
2
|
1
|
3
|
1
|
Gd
|
6
|
Typ
|
0
|
nan
|
Attchd
|
1994
|
RFn
|
2
|
495
|
TA
|
TA
|
Y
|
140
|
32
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
10
|
2009
|
WD⋅
|
184000
|
811
|
906226100
|
90
|
RL
|
64
|
7018
|
Pave
|
nan
|
Reg
|
Bnk
|
AllPub
|
Inside
|
Gtl
|
SawyerW
|
Norm
|
Norm
|
Duplex
|
1Story
|
5
|
5
|
1979
|
1979
|
Gable
|
CompShg
|
HdBoard
|
HdBoard
|
None
|
0
|
TA
|
TA
|
Slab
|
nan
|
nan
|
nan
|
nan
|
0
|
nan
|
0
|
0
|
0
|
GasA
|
TA
|
Y
|
SBrkr
|
1535
|
0
|
0
|
1535
|
0
|
0
|
2
|
0
|
4
|
2
|
TA
|
8
|
Typ
|
0
|
nan
|
Attchd
|
1979
|
Unf
|
2
|
400
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
0
|
0
|
nan
|
GdPrv
|
nan
|
0
|
6
|
2009
|
WD⋅
|
118858
|
124
|
534403410
|
80
|
RL
|
nan
|
14112
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Corner
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
SLvl
|
5
|
7
|
1964
|
1964
|
Hip
|
CompShg
|
Wd⋅Sdng
|
HdBoard
|
BrkFace
|
86
|
TA
|
TA
|
PConc
|
TA
|
TA
|
Av
|
GLQ
|
1014
|
Unf
|
0
|
138
|
1152
|
GasA
|
TA
|
Y
|
SBrkr
|
1152
|
0
|
0
|
1152
|
1
|
0
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
Gd
|
Attchd
|
1964
|
RFn
|
2
|
484
|
TA
|
TA
|
Y
|
227
|
0
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
4
|
2010
|
WD⋅
|
180500
|
1836
|
533206060
|
120
|
FV
|
80
|
3523
|
Pave
|
Pave
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Somerst
|
Norm
|
Norm
|
TwnhsE
|
1Story
|
8
|
5
|
2006
|
2007
|
Gable
|
CompShg
|
VinylSd
|
VinylSd
|
Stone
|
30
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
GLQ
|
60
|
Unf
|
0
|
57
|
1141
|
GasA
|
Ex
|
Y
|
SBrkr
|
1151
|
0
|
0
|
1151
|
1
|
0
|
2
|
0
|
2
|
1
|
Gd
|
5
|
Typ
|
0
|
nan
|
Attchd
|
2006
|
Fin
|
2
|
484
|
TA
|
TA
|
Y
|
0
|
60
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
2
|
2007
|
New
|
166000
|
779
|
905200350
|
80
|
RL
|
88
|
15312
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
Sawyer
|
Norm
|
Norm
|
1Fam
|
SLvl
|
6
|
5
|
1960
|
1960
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
BrkFace
|
54
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
Av
|
BLQ
|
588
|
Unf
|
0
|
38
|
1138
|
GasA
|
Ex
|
Y
|
SBrkr
|
1138
|
0
|
0
|
1138
|
0
|
1
|
1
|
0
|
3
|
1
|
TA
|
6
|
Typ
|
1
|
TA
|
Attchd
|
1960
|
RFn
|
2
|
480
|
TA
|
TA
|
Y
|
0
|
0
|
0
|
0
|
140
|
0
|
nan
|
MnPrv
|
nan
|
0
|
3
|
2009
|
COD
|
148000
|
1886
|
534276180
|
20
|
RL
|
74
|
7450
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NAmes
|
Norm
|
Norm
|
1Fam
|
1Story
|
5
|
5
|
1956
|
1956
|
Hip
|
CompShg
|
Wd⋅Sdng
|
Wd⋅Sdng
|
None
|
0
|
TA
|
TA
|
CBlock
|
TA
|
TA
|
No
|
Rec
|
438
|
LwQ
|
206
|
250
|
894
|
GasA
|
Gd
|
Y
|
SBrkr
|
1074
|
0
|
0
|
1074
|
0
|
0
|
1
|
0
|
2
|
1
|
Gd
|
6
|
Min1
|
1
|
Gd
|
Detchd
|
1966
|
Unf
|
2
|
396
|
TA
|
TA
|
Y
|
0
|
72
|
0
|
0
|
0
|
0
|
nan
|
GdWo
|
nan
|
0
|
1
|
2007
|
WD⋅
|
124000
|
1724
|
528186090
|
120
|
RL
|
60
|
7313
|
Pave
|
nan
|
Reg
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NridgHt
|
Norm
|
Norm
|
TwnhsE
|
1Story
|
9
|
5
|
2005
|
2005
|
Hip
|
CompShg
|
MetalSd
|
MetalSd
|
BrkFace
|
246
|
Ex
|
TA
|
PConc
|
Ex
|
TA
|
No
|
GLQ
|
1153
|
Unf
|
0
|
152
|
1561
|
GasA
|
Ex
|
Y
|
SBrkr
|
1561
|
0
|
0
|
1561
|
1
|
0
|
2
|
0
|
2
|
1
|
Ex
|
6
|
Typ
|
1
|
Gd
|
Attchd
|
2005
|
Fin
|
2
|
556
|
TA
|
TA
|
Y
|
203
|
47
|
0
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
8
|
2007
|
WD⋅
|
277500
|
2450
|
528358030
|
60
|
RL
|
83
|
10019
|
Pave
|
nan
|
IR1
|
Lvl
|
AllPub
|
Inside
|
Gtl
|
NoRidge
|
Norm
|
Norm
|
1Fam
|
2Story
|
8
|
5
|
1995
|
1995
|
Hip
|
CompShg
|
VinylSd
|
VinylSd
|
BrkFace
|
397
|
Gd
|
TA
|
PConc
|
Gd
|
TA
|
No
|
ALQ
|
527
|
Unf
|
0
|
47
|
1342
|
GasA
|
Ex
|
Y
|
SBrkr
|
1358
|
1368
|
0
|
2726
|
0
|
0
|
2
|
1
|
4
|
1
|
Gd
|
9
|
Typ
|
1
|
Ex
|
Attchd
|
1995
|
RFn
|
3
|
725
|
TA
|
TA
|
Y
|
307
|
169
|
168
|
0
|
0
|
0
|
nan
|
nan
|
nan
|
0
|
6
|
2006
|
WD⋅
|
350000
|
df.meta.get()
{'file_name': 'jurgen.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'us-ascii', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'lineterminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}]}
df1.ext.profile(columns="*", infer=True, flush=True)
{'columns': {'Id': {'stats': {'mismatch': 0, 'missing': 0, 'match': 100, 'hist': [{'lower': 12.0, 'upper': 102.5625, 'count': 7}, {'lower': 102.5625, 'upper': 193.125, 'count': 7}, {'lower': 193.125, 'upper': 283.6875, 'count': 4}, {'lower': 283.6875, 'upper': 374.25, 'count': 5}, {'lower': 374.25, 'upper': 464.8125, 'count': 2}, {'lower': 464.8125, 'upper': 555.375, 'count': 3}, {'lower': 555.375, 'upper': 645.9375, 'count': 3}, {'lower': 645.9375, 'upper': 736.5, 'count': 1}, {'lower': 736.5, 'upper': 827.0625, 'count': 5}, {'lower': 827.0625, 'upper': 917.625, 'count': 2}, {'lower': 917.625, 'upper': 1008.1875, 'count': 2}, {'lower': 1008.1875, 'upper': 1098.75, 'count': 2}, {'lower': 1098.75, 'upper': 1189.3125, 'count': 2}, {'lower': 1189.3125, 'upper': 1279.875, 'count': 0}, {'lower': 1279.875, 'upper': 1370.4375, 'count': 6}, {'lower': 1370.4375, 'upper': 1461.0, 'count': 1}, {'lower': 1461.0, 'upper': 1551.5625, 'count': 5}, {'lower': 1551.5625, 'upper': 1642.125, 'count': 2}, 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875.0, 'upper': 937.5, 'count': 0}, {'lower': 937.5, 'upper': 1000.0, 'count': 0}, {'lower': 1000.0, 'upper': 1062.5, 'count': 0}, {'lower': 1062.5, 'upper': 1125.0, 'count': 0}, {'lower': 1125.0, 'upper': 1187.5, 'count': 0}, {'lower': 1187.5, 'upper': 1250.0, 'count': 0}, {'lower': 1250.0, 'upper': 1312.5, 'count': 0}, {'lower': 1312.5, 'upper': 1375.0, 'count': 0}, {'lower': 1375.0, 'upper': 1437.5, 'count': 0}, {'lower': 1437.5, 'upper': 1500.0, 'count': 0}, {'lower': 1500.0, 'upper': 1562.5, 'count': 0}, {'lower': 1562.5, 'upper': 1625.0, 'count': 0}, {'lower': 1625.0, 'upper': 1687.5, 'count': 0}, {'lower': 1687.5, 'upper': 1750.0, 'count': 0}, {'lower': 1750.0, 'upper': 1812.5, 'count': 0}, {'lower': 1812.5, 'upper': 1875.0, 'count': 0}, {'lower': 1875.0, 'upper': 1937.5, 'count': 0}, {'lower': 1937.5, 'upper': 2000.0, 'count': 2}], 'count_uniques': 5}, 'dtype': 'uint16', 'profiler_dtype': 'int'}, 'Mo Sold': {'stats': {'mismatch': 0, 'missing': 0, 'match': 100, 'hist': [{'lower': 1.0, 'upper': 1.34375, 'count': 6}, {'lower': 1.34375, 'upper': 1.6875, 'count': 0}, {'lower': 1.6875, 'upper': 2.03125, 'count': 6}, {'lower': 2.03125, 'upper': 2.375, 'count': 0}, {'lower': 2.375, 'upper': 2.71875, 'count': 0}, {'lower': 2.71875, 'upper': 3.0625, 'count': 8}, {'lower': 3.0625, 'upper': 3.40625, 'count': 0}, {'lower': 3.40625, 'upper': 3.75, 'count': 0}, {'lower': 3.75, 'upper': 4.09375, 'count': 18}, {'lower': 4.09375, 'upper': 4.4375, 'count': 0}, {'lower': 4.4375, 'upper': 4.78125, 'count': 0}, {'lower': 4.78125, 'upper': 5.125, 'count': 5}, {'lower': 5.125, 'upper': 5.46875, 'count': 0}, {'lower': 5.46875, 'upper': 5.8125, 'count': 0}, {'lower': 5.8125, 'upper': 6.15625, 'count': 24}, {'lower': 6.15625, 'upper': 6.5, 'count': 0}, {'lower': 6.5, 'upper': 6.84375, 'count': 0}, {'lower': 6.84375, 'upper': 7.1875, 'count': 15}, {'lower': 7.1875, 'upper': 7.53125, 'count': 0}, {'lower': 7.53125, 'upper': 7.875, 'count': 0}, {'lower': 7.875, 'upper': 8.21875, 'count': 4}, {'lower': 8.21875, 'upper': 8.5625, 'count': 0}, {'lower': 8.5625, 'upper': 8.90625, 'count': 0}, {'lower': 8.90625, 'upper': 9.25, 'count': 6}, {'lower': 9.25, 'upper': 9.59375, 'count': 0}, {'lower': 9.59375, 'upper': 9.9375, 'count': 0}, {'lower': 9.9375, 'upper': 10.28125, 'count': 3}, {'lower': 10.28125, 'upper': 10.625, 'count': 0}, {'lower': 10.625, 'upper': 10.96875, 'count': 0}, {'lower': 10.96875, 'upper': 11.3125, 'count': 2}, {'lower': 11.3125, 'upper': 11.65625, 'count': 0}, {'lower': 11.65625, 'upper': 12.0, 'count': 3}], 'count_uniques': 12}, 'dtype': 'uint8', 'profiler_dtype': 'int'}, 'Yr Sold': {'stats': {'mismatch': 0, 'missing': 0, 'match': 100, 'hist': [{'lower': 2006.0, 'upper': 2006.125, 'count': 20}, {'lower': 2006.125, 'upper': 2006.25, 'count': 0}, {'lower': 2006.25, 'upper': 2006.375, 'count': 0}, {'lower': 2006.375, 'upper': 2006.5, 'count': 0}, {'lower': 2006.5, 'upper': 2006.625, 'count': 0}, {'lower': 2006.625, 'upper': 2006.75, 'count': 0}, {'lower': 2006.75, 'upper': 2006.875, 'count': 0}, {'lower': 2006.875, 'upper': 2007.0, 'count': 0}, {'lower': 2007.0, 'upper': 2007.125, 'count': 21}, {'lower': 2007.125, 'upper': 2007.25, 'count': 0}, {'lower': 2007.25, 'upper': 2007.375, 'count': 0}, {'lower': 2007.375, 'upper': 2007.5, 'count': 0}, {'lower': 2007.5, 'upper': 2007.625, 'count': 0}, {'lower': 2007.625, 'upper': 2007.75, 'count': 0}, {'lower': 2007.75, 'upper': 2007.875, 'count': 0}, {'lower': 2007.875, 'upper': 2008.0, 'count': 0}, {'lower': 2008.0, 'upper': 2008.125, 'count': 19}, {'lower': 2008.125, 'upper': 2008.25, 'count': 0}, {'lower': 2008.25, 'upper': 2008.375, 'count': 0}, {'lower': 2008.375, 'upper': 2008.5, 'count': 0}, {'lower': 2008.5, 'upper': 2008.625, 'count': 0}, {'lower': 2008.625, 'upper': 2008.75, 'count': 0}, {'lower': 2008.75, 'upper': 2008.875, 'count': 0}, {'lower': 2008.875, 'upper': 2009.0, 'count': 0}, {'lower': 2009.0, 'upper': 2009.125, 'count': 19}, {'lower': 2009.125, 'upper': 2009.25, 'count': 0}, {'lower': 2009.25, 'upper': 2009.375, 'count': 0}, {'lower': 2009.375, 'upper': 2009.5, 'count': 0}, {'lower': 2009.5, 'upper': 2009.625, 'count': 0}, {'lower': 2009.625, 'upper': 2009.75, 'count': 0}, {'lower': 2009.75, 'upper': 2009.875, 'count': 0}, {'lower': 2009.875, 'upper': 2010.0, 'count': 21}], 'count_uniques': 5}, 'dtype': 'uint16', 'profiler_dtype': 'int'}, 'Sale Type': {'stats': {'mismatch': 0, 'missing': 0, 'match': 100, 'frequency': [{'value': 'WD ', 'count': 83}, {'value': 'New', 'count': 8}, {'value': 'COD', 'count': 5}, {'value': 'ConLD', 'count': 2}, {'value': 'Con', 'count': 2}], 'count_uniques': 5}, 'dtype': 'category', 'profiler_dtype': 'string'}, 'SalePrice': {'stats': {'mismatch': 0, 'missing': 0, 'match': 100, 'hist': [{'lower': 68500.0, 'upper': 82041.78125, 'count': 2}, {'lower': 82041.78125, 'upper': 95583.5625, 'count': 2}, {'lower': 95583.5625, 'upper': 109125.34375, 'count': 3}, {'lower': 109125.34375, 'upper': 122667.125, 'count': 8}, {'lower': 122667.125, 'upper': 136208.90625, 'count': 17}, {'lower': 136208.90625, 'upper': 149750.6875, 'count': 15}, {'lower': 149750.6875, 'upper': 163292.46875, 'count': 10}, {'lower': 163292.46875, 'upper': 176834.25, 'count': 7}, {'lower': 176834.25, 'upper': 190376.03125, 'count': 9}, {'lower': 190376.03125, 'upper': 203917.8125, 'count': 3}, {'lower': 203917.8125, 'upper': 217459.59375, 'count': 3}, {'lower': 217459.59375, 'upper': 231001.375, 'count': 2}, {'lower': 231001.375, 'upper': 244543.15625, 'count': 1}, {'lower': 244543.15625, 'upper': 258084.9375, 'count': 2}, {'lower': 258084.9375, 'upper': 271626.71875, 'count': 2}, {'lower': 271626.71875, 'upper': 285168.5, 'count': 3}, {'lower': 285168.5, 'upper': 298710.28125, 'count': 2}, {'lower': 298710.28125, 'upper': 312252.0625, 'count': 4}, {'lower': 312252.0625, 'upper': 325793.84375, 'count': 0}, {'lower': 325793.84375, 'upper': 339335.625, 'count': 0}, {'lower': 339335.625, 'upper': 352877.40625, 'count': 2}, {'lower': 352877.40625, 'upper': 366419.1875, 'count': 0}, {'lower': 366419.1875, 'upper': 379960.96875, 'count': 1}, {'lower': 379960.96875, 'upper': 393502.75, 'count': 0}, {'lower': 393502.75, 'upper': 407044.53125, 'count': 0}, {'lower': 407044.53125, 'upper': 420586.3125, 'count': 0}, {'lower': 420586.3125, 'upper': 434128.09375, 'count': 0}, {'lower': 434128.09375, 'upper': 447669.875, 'count': 0}, {'lower': 447669.875, 'upper': 461211.65625, 'count': 0}, {'lower': 461211.65625, 'upper': 474753.4375, 'count': 1}, {'lower': 474753.4375, 'upper': 488295.21875, 'count': 0}, {'lower': 488295.21875, 'upper': 501837.0, 'count': 1}], 'count_uniques': 91}, 'dtype': 'uint32', 'profiler_dtype': 'int'}}, 'name': None, 'file_name': 'jurgen.csv', 'summary': {'cols_count': 81, 'rows_count': 100, 'size': '20.1 kB', 'dtypes_list': ['uint16', 'object', 'category', 'uint8', 'uint32'], 'total_count_dtypes': 5, 'missing_count': 23, 'p_missing': 23.0}}
1==1
True
df.ext.set_buffer("*")
df.ext.buffer_window("*", 0, 34).rows.find( ~df.ext.get_buffer().cols.is_match("mass (g)", "int") )
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 100 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
name | id | nametype | recclass | mass (g) | fall | year | reclat | reclong | GeoLocation | __match__ | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | Aachen | 1 | Valid | L5 | 21 | Fell | 01/01/1880 12:00:00 AM | 50.775000 | 6.083330 | (50.775000, 6.083330) | False |
1 | Aarhus | 2 | Valid | H6 | 720 | Fell | 01/01/1951 12:00:00 AM | 56.183330 | 10.233330 | (56.183330, 10.233330) | False |
2 | Abee | 6 | Valid | EH4 | 107000 | Fell | 01/01/1952 12:00:00 AM | 54.216670 | -113.000000 | (54.216670, -113.000000) | False |
3 | Acapulco | 10 | Valid | Acapulcoite | 1914 | Fell | 01/01/1976 12:00:00 AM | 16.883330 | -99.900000 | (16.883330, -99.900000) | False |
4 | Achiras | 370 | Valid | L6 | 780 | Fell | 01/01/1902 12:00:00 AM | -33.166670 | -64.950000 | (-33.166670, -64.950000) | False |
5 | Adhi Kot | 379 | Valid | EH4 | 4239 | Fell | 01/01/1919 12:00:00 AM | 32.100000 | 71.800000 | (32.100000, 71.800000) | False |
6 | Adzhi-Bogdo (stone) | 390 | Valid | LL3-6 | 910 | Fell | 01/01/1949 12:00:00 AM | 44.833330 | 95.166670 | (44.833330, 95.166670) | False |
7 | Agen | 392 | Valid | H5 | 30000 | Fell | 01/01/1814 12:00:00 AM | 44.216670 | 0.616670 | (44.216670, 0.616670) | False |
8 | Aguada | 398 | Valid | L6 | 1620 | Fell | 01/01/1930 12:00:00 AM | -31.600000 | -65.233330 | (-31.600000, -65.233330) | False |
9 | Aguila Blanca | 417 | Valid | L | 1440 | Fell | 01/01/1920 12:00:00 AM | -30.866670 | -64.550000 | (-30.866670, -64.550000) | False |
10 | Aioun el Atrouss | 423 | Valid | Diogenite-pm | 1000 | Fell | 01/01/1974 12:00:00 AM | 16.398060 | -9.570280 | (16.398060, -9.570280) | False |
11 | Aïr | 424 | Valid | L6 | 24000 | Fell | 01/01/1925 12:00:00 AM | 19.083330 | 8.383330 | (19.083330, 8.383330) | False |
12 | Aire-sur-la-Lys | 425 | Valid | Unknown | NaN | Fell | 01/01/1769 12:00:00 AM | 50.666670 | 2.333330 | (50.666670, 2.333330) | False |
13 | Akaba | 426 | Valid | L6 | 779 | Fell | 01/01/1949 12:00:00 AM | 29.516670 | 35.050000 | (29.516670, 35.050000) | False |
14 | Akbarpur | 427 | Valid | H4 | 1800 | Fell | 01/01/1838 12:00:00 AM | 29.716670 | 77.950000 | (29.716670, 77.950000) | False |
15 | Akwanga | 432 | Valid | H | 3000 | Fell | 01/01/1959 12:00:00 AM | 8.916670 | 8.433330 | (8.916670, 8.433330) | False |
16 | Akyumak | 433 | Valid | Iron, IVA | 50000 | Fell | 01/01/1981 12:00:00 AM | 39.916670 | 42.816670 | (39.916670, 42.816670) | False |
17 | Al Rais | 446 | Valid | CR2-an | 160 | Fell | 01/01/1957 12:00:00 AM | 24.416670 | 39.516670 | (24.416670, 39.516670) | False |
18 | Al Zarnkh | 447 | Valid | LL5 | 700 | Fell | 01/01/2001 12:00:00 AM | 13.660330 | 28.960000 | (13.660330, 28.960000) | False |
19 | Alais | 448 | Valid | CI1 | 6000 | Fell | 01/01/1806 12:00:00 AM | 44.116670 | 4.083330 | (44.116670, 4.083330) | False |
20 | Albareto | 453 | Valid | L/LL4 | 2000 | Fell | 01/01/1766 12:00:00 AM | 44.650000 | 11.016670 | (44.650000, 11.016670) | False |
21 | Alberta | 454 | Valid | L | 625 | Fell | 01/01/1949 12:00:00 AM | 2.000000 | 22.666670 | (2.000000, 22.666670) | False |
22 | Alby sur Chéran | 458 | Valid | Eucrite-mmict | 252 | Fell | 01/01/2002 12:00:00 AM | 45.821330 | 6.015330 | (45.821330, 6.015330) | False |
23 | Aldsworth | 461 | Valid | LL5 | 700 | Fell | 01/01/1835 12:00:00 AM | 51.783330 | -1.783330 | (51.783330, -1.783330) | False |
24 | Aleppo | 462 | Valid | L6 | 3200 | Fell | 01/01/1873 12:00:00 AM | 36.233330 | 37.133330 | (36.233330, 37.133330) | False |
25 | Alessandria | 463 | Valid | H5 | 908 | Fell | 01/01/1860 12:00:00 AM | 44.883330 | 8.750000 | (44.883330, 8.750000) | False |
26 | Alexandrovsky | 465 | Valid | H4 | 9251 | Fell | 01/01/1900 12:00:00 AM | 50.950000 | 31.816670 | (50.950000, 31.816670) | False |
27 | Alfianello | 466 | Valid | L6 | 228000 | Fell | 01/01/1883 12:00:00 AM | 45.266670 | 10.150000 | (45.266670, 10.150000) | False |
28 | Allegan | 2276 | Valid | H5 | 32000 | Fell | 01/01/1899 12:00:00 AM | 42.533330 | -85.883330 | (42.533330, -85.883330) | False |
29 | Allende | 2278 | Valid | CV3 | 2000000 | Fell | 01/01/1969 12:00:00 AM | 26.966670 | -105.316670 | (26.966670, -105.316670) | False |
30 | Almahata Sitta | 48915 | Valid | Ureilite-an | 3950 | Fell | 01/01/2008 12:00:00 AM | 20.745750 | 32.412750 | (20.745750, 32.412750) | False |
31 | Alta'ameem | 2284 | Valid | LL5 | 6000 | Fell | 01/01/1977 12:00:00 AM | 35.273330 | 44.215560 | (35.273330, 44.215560) | False |
32 | Ambapur Nagla | 2290 | Valid | H5 | 6400 | Fell | 01/01/1895 12:00:00 AM | 27.666670 | 78.250000 | (27.666670, 78.250000) | False |
33 | Andhara | 2294 | Valid | Stone-uncl | 2700 | Fell | 01/01/1880 12:00:00 AM | 26.583330 | 85.566670 | (26.583330, 85.566670) | False |
df.ext.profile(columns="mass (g)", infer=True, flush=True)
{'columns': {'mass (g)': {'stats': {'mismatch': 5, 'missing': 4, 'match': 91, 'frequency': [{'value': '3200', 'count': 4}, {'value': '50000', 'count': 3}, {'value': '2000', 'count': 3}, {'value': '6000', 'count': 2}, {'value': '15000', 'count': 2}, {'value': '6400', 'count': 2}, {'value': '700', 'count': 2}, {'value': '1000', 'count': 2}, {'value': '3700', 'count': 2}, {'value': '2000000', 'count': 1}, {'value': '21', 'count': 1}, {'value': '21000', 'count': 1}, {'value': '1914', 'count': 1}, {'value': '16700', 'count': 1}, {'value': '19000', 'count': 1}, {'value': '228000', 'count': 1}, {'value': '18000', 'count': 1}, {'value': '23.2', 'count': 1}, {'value': '24000', 'count': 1}, {'value': '1800', 'count': 1}, {'value': '17900', 'count': 1}, {'value': '2500', 'count': 1}, {'value': '1770.5', 'count': 1}, {'value': '17', 'count': 1}, {'value': '16000', 'count': 1}, {'value': '1620', 'count': 1}, {'value': '1384.2', 'count': 1}, {'value': '10322', 'count': 1}, {'value': '107000', 'count': 1}, {'value': '11500', 'count': 1}, {'value': '1230', 'count': 1}, {'value': '1280', 'count': 1}, {'value': '1300', 'count': 1}], 'count_uniques': 83}, 'dtype': 'object', 'profiler_dtype': 'int'}}, 'name': None, 'file_name': 'Meteorite_Landings.csv', 'summary': {'cols_count': 10, 'rows_count': 100, 'size': '8.1 kB', 'dtypes_list': ['object'], 'total_count_dtypes': 1, 'missing_count': 4, 'p_missing': 4.0}}
# df = op.load.file("http://159.65.217.17:5003/uploads/datasetFile-1589317774772.csv").ext.cache()
df = op.load.file("http://159.65.217.17:5003/uploads/datasetFile-1589317774772.csv").ext.cache()
df1 = op.load.file("http://159.65.217.17:5003/uploads/datasetFile-1589317774772.csv").ext.cache()
# df.ext.profile("*", output="json")
df1.ext.profile("*", output="json")
# df = df.ext.optimize()
df1 = df1.ext.optimize()
# df.ext.set_buffer("*")
df1.ext.set_buffer("*")
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 500000 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
df.ext.display()
df1.ext.display()
id
1 (object)
not nullable
|
firstName
2 (object)
not nullable
|
lastName
3 (object)
not nullable
|
billingId
4 (object)
not nullable
|
product
5 (object)
not nullable
|
price
6 (object)
not nullable
|
birth
7 (object)
not nullable
|
dummyCol
8 (object)
not nullable
|
---|---|---|---|---|---|---|---|
1
|
Luis
|
Alvarez$$%!
|
123
|
Cake
|
10
|
1980/07/07
|
never
|
2
|
André
|
Ampère
|
423
|
piza
|
8
|
1950/07/08
|
gonna
|
3
|
NiELS
|
Böhr//((%%
|
551
|
pizza
|
8
|
1990/07/09
|
give
|
4
|
PAUL
|
dirac$
|
521
|
pizza
|
8
|
1954/07/10
|
you
|
5
|
Albert
|
Einstein
|
634
|
pizza
|
8
|
1990/07/11
|
up
|
6
|
Galileo
|
⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
672
|
arepa
|
5
|
1930/08/12
|
never
|
7
|
CaRL
|
Ga%%%uss
|
323
|
taco
|
3
|
1970/07/13
|
gonna
|
8
|
David
|
H$$$ilbert
|
624
|
taaaccoo
|
3
|
1950/07/14
|
let
|
9
|
Johannes
|
KEPLER
|
735
|
taco
|
3
|
1920/04/22
|
you
|
10
|
JaMES
|
M$$ax%%well
|
875
|
taco
|
3
|
1923/03/12
|
down
|
id
1 (uint8)
not nullable
|
firstName
2 (category)
not nullable
|
lastName
3 (category)
not nullable
|
billingId
4 (uint8)
not nullable
|
product
5 (category)
not nullable
|
price
6 (uint8)
not nullable
|
birth
7 (category)
not nullable
|
dummyCol
8 (category)
not nullable
|
---|---|---|---|---|---|---|---|
1
|
Luis
|
Alvarez$$%!
|
123
|
Cake
|
10
|
1980/07/07
|
never
|
2
|
André
|
Ampère
|
167
|
piza
|
8
|
1950/07/08
|
gonna
|
3
|
NiELS
|
Böhr//((%%
|
39
|
pizza
|
8
|
1990/07/09
|
give
|
4
|
PAUL
|
dirac$
|
9
|
pizza
|
8
|
1954/07/10
|
you
|
5
|
Albert
|
Einstein
|
122
|
pizza
|
8
|
1990/07/11
|
up
|
6
|
Galileo
|
⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
160
|
arepa
|
5
|
1930/08/12
|
never
|
7
|
CaRL
|
Ga%%%uss
|
67
|
taco
|
3
|
1970/07/13
|
gonna
|
8
|
David
|
H$$$ilbert
|
112
|
taaaccoo
|
3
|
1950/07/14
|
let
|
9
|
Johannes
|
KEPLER
|
223
|
taco
|
3
|
1920/04/22
|
you
|
10
|
JaMES
|
M$$ax%%well
|
107
|
taco
|
3
|
1923/03/12
|
down
|
df.index.astype("object")
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-225-bd78af032f0d> in <module> ----> 1 df.index.astype("object") ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in astype(self, dtype) 2296 meta = clear_known_categories(meta) 2297 return self.map_partitions( -> 2298 M.astype, dtype=dtype, meta=meta, enforce_metadata=False 2299 ) 2300 ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in map_partitions(self, func, *args, **kwargs) 636 >>> ddf.map_partitions(func).clear_divisions() # doctest: +SKIP 637 """ --> 638 return map_partitions(func, self, *args, **kwargs) 639 640 @insert_meta_param_description(pad=12) ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py in map_partitions(func, meta, enforce_metadata, transform_divisions, *args, **kwargs) 5062 pass 5063 else: -> 5064 if not valid_divisions(divisions): 5065 divisions = [None] * (dfs[0].npartitions + 1) 5066 ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\utils.py in valid_divisions(divisions) 952 return False 953 --> 954 if divisions[-2] > divisions[-1]: 955 return False 956 TypeError: '>' not supported between instances of 'NoneType' and 'NoneType'
df1.compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 2 | André | Ampère | 167 | piza | 8 | 1950/07/08 | gonna |
2 | 3 | NiELS | Böhr//((%% | 39 | pizza | 8 | 1990/07/09 | give |
3 | 4 | PAUL | dirac$ | 9 | pizza | 8 | 1954/07/10 | you |
4 | 5 | Albert | Einstein | 122 | pizza | 8 | 1990/07/11 | up |
5 | 6 | Galileo | GALiLEI | 160 | arepa | 5 | 1930/08/12 | never |
6 | 7 | CaRL | Ga%%%uss | 67 | taco | 3 | 1970/07/13 | gonna |
7 | 8 | David | H$$$ilbert | 112 | taaaccoo | 3 | 1950/07/14 | let |
8 | 9 | Johannes | KEPLER | 223 | taco | 3 | 1920/04/22 | you |
9 | 10 | JaMES | M$$ax%%well | 107 | taco | 3 | 1923/03/12 | down |
10 | 11 | Isaac | Newton | 224 | pasta | 9 | 1999/02/15 | never |
11 | 12 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993/12/08 | gonna |
12 | 13 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994/01/04 | run |
13 | 14 | Fred | Hoy&&&le | 41 | pizzza | 8 | 1997/06/27 | around |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956/11/30 | and |
15 | 16 | William | Gilbert### | 118 | BEER | 2 | 1958/03/26 | desert |
16 | 17 | Marie | CURIE | 144 | Rice | 1 | 2000/03/22 | you |
17 | 18 | Arthur | COM%%%pton | 44 | 110790 | 5 | 1899/01/01 | # |
18 | 19 | JAMES | Chadwick | 211 | NaN | 10 | 1921/05/03 | # |
df1.dtypes
id uint8 firstName category lastName category billingId uint8 product category price uint8 birth category dummyCol category dtype: object
df1.cols.join(df1,left_on= "id",right_on="product").compute()
firstName_x | lastName_x | billingId_x | product | price_x | birth_x | dummyCol_x | firstName_y | lastName_y | billingId_y | price_y | birth_y | dummyCol_y | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id |
a["id"][0]
'1'
df1.cols.cast("id","object").compute()
id | firstName | lastName | billingId | product | price | birth | dummyCol | |
---|---|---|---|---|---|---|---|---|
0 | 1 | Luis | Alvarez$$%! | 123 | Cake | 10 | 1980/07/07 | never |
1 | 2 | André | Ampère | 167 | piza | 8 | 1950/07/08 | gonna |
2 | 3 | NiELS | Böhr//((%% | 39 | pizza | 8 | 1990/07/09 | give |
3 | 4 | PAUL | dirac$ | 9 | pizza | 8 | 1954/07/10 | you |
4 | 5 | Albert | Einstein | 122 | pizza | 8 | 1990/07/11 | up |
5 | 6 | Galileo | GALiLEI | 160 | arepa | 5 | 1930/08/12 | never |
6 | 7 | CaRL | Ga%%%uss | 67 | taco | 3 | 1970/07/13 | gonna |
7 | 8 | David | H$$$ilbert | 112 | taaaccoo | 3 | 1950/07/14 | let |
8 | 9 | Johannes | KEPLER | 223 | taco | 3 | 1920/04/22 | you |
9 | 10 | JaMES | M$$ax%%well | 107 | taco | 3 | 1923/03/12 | down |
10 | 11 | Isaac | Newton | 224 | pasta | 9 | 1999/02/15 | never |
11 | 12 | Emmy%% | Nöether$ | 234 | pasta | 9 | 1993/12/08 | gonna |
12 | 13 | Max!!! | Planck!!! | 111 | hamburguer | 4 | 1994/01/04 | run |
13 | 14 | Fred | Hoy&&&le | 41 | pizzza | 8 | 1997/06/27 | around |
14 | 15 | ((( Heinrich ))))) | Hertz | 116 | pizza | 8 | 1956/11/30 | and |
15 | 16 | William | Gilbert### | 118 | BEER | 2 | 1958/03/26 | desert |
16 | 17 | Marie | CURIE | 144 | Rice | 1 | 2000/03/22 | you |
17 | 18 | Arthur | COM%%%pton | 44 | 110790 | 5 | 1899/01/01 | # |
18 | 19 | JAMES | Chadwick | 211 | NaN | 10 | 1921/05/03 | # |
df1.cols.set("nombre",df1["nombre"]=="luis",1,"new name").compute()
df1.dtypes
df1.ext.profile("*",infer=True, flush=True)
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 30 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
{'columns': {'cedula': {'stats': {'mismatch': 5, 'missing': 0, 'match': 0, 'frequency': [{'value': '24949394', 'count': 1}, {'value': '23760628', 'count': 1}, {'value': '21857839', 'count': 1}, {'value': '19748383', 'count': 1}, {'value': '17484892', 'count': 1}], 'count_uniques': 5, 'profiler_dtype': 'object', 'hist': [{'count': 1, 'lower': 1.0, 'upper': 0.0}, {'count': 0, 'lower': 0.0, 'upper': 1.0}, {'count': 1, 'lower': 1.0, 'upper': 0.0}, {'count': 0, 'lower': 0.0, 'upper': 0.0}, {'count': 0, 'lower': 0.0, 'upper': 1.0}, {'count': 1, 'lower': 1.0, 'upper': 0.0}, {'count': 0, 'lower': 0.0, 'upper': 1.0}, {'count': 1, 'lower': 1.0, 'upper': 1.0}]}, 'dtype': 'uint32'}, 'nombre': {'stats': {'mismatch': 5, 'missing': 0, 'match': 0, 'frequency': [{'value': 'pedro', 'count': 1}, {'value': 'luis', 'count': 1}, {'value': 'juan', 'count': 1}, {'value': 'jose', 'count': 1}, {'value': 'eddy', 'count': 1}], 'count_uniques': 5, 'profiler_dtype': 'object'}, 'dtype': 'category'}, 'apellido': {'stats': {'mismatch': 5, 'missing': 0, 'match': 0, 'frequency': [{'value': 'aguirre', 'count': 3}, {'value': 'martinez', 'count': 1}, {'value': 'gonzalez', 'count': 1}], 'count_uniques': 3, 'profiler_dtype': 'object'}, 'dtype': 'category'}}, 'name': None, 'file_name': 'tmp60lylob3.csv', 'summary': {'cols_count': 3, 'rows_count': 5, 'size': '462 Bytes', 'dtypes_list': ['uint32', 'category'], 'total_count_dtypes': 2, 'missing_count': 0, 'p_missing': 0.0}}
df1.ext.buffer_window("*", 0, 10).ext.to_json("*")
{'sample': {'columns': [{'title': 'cedula'}, {'title': 'nombre'}, {'title': 'apellido'}], 'value': [[23760628, 'luis', 'aguirre'], [21857839, 'jose', 'aguirre'], [19748383, 'eddy', 'aguirre'], [24949394, 'juan', 'gonzalez'], [17484892, 'pedro', 'martinez']]}}
df1.dtypes
cedula uint32 nombre category apellido category dtype: object
df1.ext.buffer_window("*", 0, 10).cols.upper("nombre", output_cols="new nombre").ext.to_json("*")
asdfasf nombre AAA ['nombre'] ['new nombre']
{'sample': {'columns': [{'title': 'cedula'}, {'title': 'nombre'}, {'title': 'apellido'}, {'title': 'new nombre'}], 'value': [[23760628, 'luis', 'aguirre', 'LUIS'], [21857839, 'jose', 'aguirre', 'JOSE'], [19748383, 'eddy', 'aguirre', 'EDDY'], [24949394, 'juan', 'gonzalez', 'JUAN'], [17484892, 'pedro', 'martinez', 'PEDRO']]}}
df1.ext.display()
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 10 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
cedula
1 (uint32)
not nullable
|
nombre
2 (category)
not nullable
|
apellido
3 (category)
not nullable
|
---|---|---|
23760628
|
luis
|
aguirre
|
21857839
|
jose
|
aguirre
|
19748383
|
eddy
|
aguirre
|
24949394
|
juan
|
gonzalez
|
17484892
|
pedro
|
martinez
|
pdf = df1.compute()
pdf.dtypes
cedula uint32 nombre category apellido category dtype: object
pdf["nombre"].cat.get_categories()
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-66-52365ed299c8> in <module> ----> 1 pdf["nombre"].cat.get_categories() AttributeError: 'CategoricalAccessor' object has no attribute 'get_categories'
pdf = df1.compute()
# print(pdf)
pdf.cols.set("nombre",pdf["nombre"]=="luis",1,"new name")
# pdf["nombre"].where(~(pdf["nombre"]=="luis"),1)
cedula | nombre | apellido | new name | |
---|---|---|---|---|
0 | 23760628 | luis | aguirre | 1 |
1 | 21857839 | jose | aguirre | jose |
2 | 19748383 | eddy | aguirre | eddy |
3 | 24949394 | juan | gonzalez | juan |
4 | 17484892 | pedro | martinez | pedro |
df1.cols.set("nombre",df1["nombre"]=="luis",1,"new name").compute()
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-65-1dc47e3f675c> in <module> ----> 1 df1.cols.set("nombre",df1["nombre"]=="luis",1,"new name").compute() ~\Documents\Optimus\optimus\engines\base\dask\columns.py in set(self, input_cols, where, value, output_cols) 663 if df.cols.dtypes(input_col) == "category": 664 --> 665 df[input_col] = df[input_col].cat.add_categories(val_to_list(value)) 666 667 df[output_col] = df[input_col].where(~(where), value) ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\accessor.py in _function_map(self, attr, *args, **kwargs) 61 else: 62 meta = self._delegate_method( ---> 63 self._series._meta_nonempty, self._accessor_name, attr, args, kwargs 64 ) 65 token = "%s-%s" % (self._accessor_name, attr) ~\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\accessor.py in _delegate_method(obj, accessor, attr, args, kwargs) 46 @staticmethod 47 def _delegate_method(obj, accessor, attr, args, kwargs): ---> 48 out = getattr(getattr(obj, accessor, obj), attr)(*args, **kwargs) 49 return maybe_wrap_pandas(obj, out) 50 ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\accessor.py in f(self, *args, **kwargs) 97 def _create_delegator_method(name): 98 def f(self, *args, **kwargs): ---> 99 return self._delegate_method(name, *args, **kwargs) 100 101 f.__name__ = name ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\arrays\categorical.py in _delegate_method(self, name, *args, **kwargs) 2558 2559 method = getattr(self._parent, name) -> 2560 res = method(*args, **kwargs) 2561 if res is not None: 2562 return Series(res, index=self._index, name=self._name) ~\AppData\Roaming\Python\Python37\site-packages\pandas\core\arrays\categorical.py in add_categories(self, new_categories, inplace) 1026 if len(already_included) != 0: 1027 raise ValueError( -> 1028 f"new categories must not include old categories: {already_included}" 1029 ) 1030 new_categories = list(self.dtype.categories) + list(new_categories) ValueError: new categories must not include old categories: {1}
df1.ext.display()
C:\Users\argenisleon\AppData\Roaming\Python\Python37\site-packages\dask\dataframe\core.py:5979: UserWarning: Insufficient elements for `head`. 10 elements requested, only 5 elements available. Try passing larger `npartitions` to `head`. warnings.warn(msg.format(n, len(r)))
cedula
1 (object)
not nullable
|
nombre
2 (object)
not nullable
|
apellido
3 (object)
not nullable
|
---|---|---|
23760628
|
luis
|
aguirre
|
21857839
|
jose
|
aguirre
|
19748383
|
eddy
|
aguirre
|
24949394
|
juan
|
gonzalez
|
17484892
|
pedro
|
martinez
|
df = op.load.csv("data/taa.csv", sep=",", error_bad_lines=False, header=True, null_value="null", infer_schema='true', encoding="latin1").ext.cache()
# Meteorite_Landings
distributed.worker - WARNING - Compute Failed Function: execute_task args: ((<function check_meta at 0x00000132CB9D5048>, (<function apply at 0x00000132CAE301F8>, <function pandas_read_text at 0x00000132CBA45B88>, [<function _make_parser_function.<locals>.parser_f at 0x00000132CAC063A8>, (<function read_block_from_file at 0x00000132CBAD94C8>, <OpenFile 'C:/Users/argenisleon/Documents/Optimus/examples/data/taa.csv'>, 0, 64000000, b'\n'), b'Company,City,State,District,County,Metro.Area,Product,Petitioner,Layoff.Date,Est..No..Workers,Cause,Country\r\n', (<class 'dict'>, [['sep', ','], ['header', 0], ['encoding', 'latin1'], ['quoting', 0], ['error_bad_lines', False], ['keep_default_na', True], ['na_values', None], ['engine', 'c']]), (<class 'dict'>, [['Company', dtype('O')], ['City', dtype('O')], ['State', dtype('O')], ['District', dtype('int64')], ['County', dtype('O')], ['Metro.Area', dtype('O')], ['Product', dtype('O')], ['Petitioner', dtype('O')], ['Layoff.Date', dtype('O')], ['Est..No..Workers', dtype('float64')], ['Cause', dtype('O')], ['Country', dtype('O' kwargs: {} Exception: ValueError("Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.\n\n+----------+---------+----------+\n| Column | Found | Expected |\n+----------+---------+----------+\n| District | float64 | int64 |\n+----------+---------+----------+\n\nUsually this is due to dask's dtype inference failing, and\n*may* be fixed by specifying dtypes manually by adding:\n\ndtype={'District': 'float64'}\n\nto the call to `read_csv`/`read_table`.\n\nAlternatively, provide `assume_missing=True` to interpret\nall unspecified integer columns as floats.")
df = op.load.file("data/crime.csv").ext.cache()
df =df.ext.optimize()
df.meta.get()
{'file_name': 'foo.csv', 'mime_info': [{'mime': 'text/plain', 'encoding': 'utf-8', 'file_ext': 'csv', 'file_type': 'csv', 'properties': {'delimiter': ',', 'doublequote': False, 'escapechar': None, 'lineterminator': '\r\n', 'quotechar': '"', 'quoting': 0, 'skipinitialspace': False}}]}
df.ext.display()
id
1 (uint8)
not nullable
|
firstName
2 (category)
not nullable
|
lastName
3 (category)
not nullable
|
billingId
4 (uint8)
not nullable
|
product
5 (category)
not nullable
|
price
6 (uint8)
not nullable
|
birth
7 (category)
not nullable
|
dummyCol
8 (category)
not nullable
|
---|---|---|---|---|---|---|---|
1
|
Luis
|
Alvarez$$%!
|
123
|
Cake
|
10
|
1980/07/07
|
never
|
2
|
André
|
Ampère
|
167
|
piza
|
8
|
1950/07/08
|
gonna
|
3
|
NiELS
|
Böhr//((%%
|
39
|
pizza
|
8
|
1990/07/09
|
give
|
4
|
PAUL
|
dirac$
|
9
|
pizza
|
8
|
1954/07/10
|
you
|
5
|
Albert
|
Einstein
|
122
|
pizza
|
8
|
1990/07/11
|
up
|
6
|
Galileo
|
⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅GALiLEI
|
160
|
arepa
|
5
|
1930/08/12
|
never
|
7
|
CaRL
|
Ga%%%uss
|
67
|
taco
|
3
|
1970/07/13
|
gonna
|
8
|
David
|
H$$$ilbert
|
112
|
taaaccoo
|
3
|
1950/07/14
|
let
|
9
|
Johannes
|
KEPLER
|
223
|
taco
|
3
|
1920/04/22
|
you
|
10
|
JaMES
|
M$$ax%%well
|
107
|
taco
|
3
|
1923/03/12
|
down
|
CHAR_DICT = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789 .!?:,\'%-\(\)/$|&;[]"'
def build_char_dictionary(char_dict=None, unknown_label='UNK'):
"""
Define possbile char set. Using "UNK" if character does not exist in this set
"""
if char_dict is None:
char_dict = CHAR_DICT
unknown_label = unknown_label
chars = []
for c in char_dict:
chars.append(c)
chars = list(set(chars))
chars.insert(0, unknown_label)
num_of_char = len(chars)
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
print('Totoal number of chars:', num_of_char)
print('First 3 char_indices sample:', {k: char_indices[k] for k in list(char_indices)[:3]})
print('First 3 indices_char sample:', {k: indices_char[k] for k in list(indices_char)[:3]})
return char_indices, indices_char, num_of_char
def preporcess(labels, char_dict=None, unknown_label='UNK'):
print('-----> Stage: preprocess')
build_char_dictionary(char_dict, unknown_label)
convert_labels(labels)
def convert_labels( labels):
"""
Convert label to numeric
"""
label2indexes = dict((l, i) for i, l in enumerate(labels))
index2labels = dict((i, l) for i, l in enumerate(labels))
print('Label to Index: ', label2indexes)
print('Index to Label: ', index2labels)
num_of_label = len(label2indexes)
return label2indexes, index2labels
preporcess(labels=df['firstName'].unique())
-----> Stage: preprocess Totoal number of chars: 83 First 3 char_indices sample: {'UNK': 0, '1': 1, ']': 2} First 3 indices_char sample: {0: 'UNK', 1: '1', 2: ']'} Label to Index: {'Luis': 0, 'André': 1, 'NiELS': 2, 'PAUL': 3, 'Albert': 4, 'Galileo': 5, 'CaRL': 6, 'David': 7, 'Johannes': 8, 'JaMES': 9, 'Isaac': 10, 'Emmy%%': 11, 'Max!!!': 12, 'Fred': 13, '((( Heinrich )))))': 14, 'William': 15, 'Marie': 16, 'Arthur': 17, 'JAMES': 18} Index to Label: {0: 'Luis', 1: 'André', 2: 'NiELS', 3: 'PAUL', 4: 'Albert', 5: 'Galileo', 6: 'CaRL', 7: 'David', 8: 'Johannes', 9: 'JaMES', 10: 'Isaac', 11: 'Emmy%%', 12: 'Max!!!', 13: 'Fred', 14: '((( Heinrich )))))', 15: 'William', 16: 'Marie', 17: 'Arthur', 18: 'JAMES'}
df.compute()
INCIDENT_NUMBER | OFFENSE_CODE | OFFENSE_CODE_GROUP | OFFENSE_DESCRIPTION | DISTRICT | REPORTING_AREA | SHOOTING | OCCURRED_ON_DATE | YEAR | MONTH | DAY_OF_WEEK | HOUR | UCR_PART | STREET | Lat | Long | Location | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | I182070945 | 00619 | Larceny | LARCENY ALL OTHERS | D14 | 808 | NaN | 2018-09-02 13:00:00 | 2018 | 9 | Sunday | 13 | Part One | LINCOLN ST | 42.35779134 | -71.13937053 | (42.35779134, -71.13937053) |
1 | I182070943 | 01402 | Vandalism | VANDALISM | C11 | 347 | NaN | 2018-08-21 00:00:00 | 2018 | 8 | Tuesday | 0 | Part Two | HECLA ST | 42.30682138 | -71.06030035 | (42.30682138, -71.06030035) |
2 | I182070941 | 03410 | Towed | TOWED MOTOR VEHICLE | D4 | 151 | NaN | 2018-09-03 19:27:00 | 2018 | 9 | Monday | 19 | Part Three | CAZENOVE ST | 42.34658879 | -71.07242943 | (42.34658879, -71.07242943) |
3 | I182070940 | 03114 | Investigate Property | INVESTIGATE PROPERTY | D4 | 272 | NaN | 2018-09-03 21:16:00 | 2018 | 9 | Monday | 21 | Part Three | NEWCOMB ST | 42.33418175 | -71.07866441 | (42.33418175, -71.07866441) |
4 | I182070938 | 03114 | Investigate Property | INVESTIGATE PROPERTY | B3 | 421 | NaN | 2018-09-03 21:05:00 | 2018 | 9 | Monday | 21 | Part Three | DELHI ST | 42.27536542 | -71.09036101 | (42.27536542, -71.09036101) |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
319068 | I050310906-00 | 03125 | Warrant Arrests | WARRANT ARREST | D4 | 285 | NaN | 2016-06-05 17:25:00 | 2016 | 6 | Sunday | 17 | Part Three | COVENTRY ST | 42.33695098 | -71.08574813 | (42.33695098, -71.08574813) |
319069 | I030217815-08 | 00111 | Homicide | MURDER, NON-NEGLIGIENT MANSLAUGHTER | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part One | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) |
319070 | I030217815-08 | 03125 | Warrant Arrests | WARRANT ARREST | E18 | 520 | NaN | 2015-07-09 13:38:00 | 2015 | 7 | Thursday | 13 | Part Three | RIVER ST | 42.25592648 | -71.12317207 | (42.25592648, -71.12317207) |
319071 | I010370257-00 | 03125 | Warrant Arrests | WARRANT ARREST | E13 | 569 | NaN | 2016-05-31 19:35:00 | 2016 | 5 | Tuesday | 19 | Part Three | NEW WASHINGTON ST | 42.30233307 | -71.11156487 | (42.30233307, -71.11156487) |
319072 | 142052550 | 03125 | Warrant Arrests | WARRANT ARREST | D4 | 903 | NaN | 2015-06-22 00:12:00 | 2015 | 6 | Monday | 0 | Part Three | WASHINGTON ST | 42.33383935 | -71.08029038 | (42.33383935, -71.08029038) |
319073 rows × 17 columns
from tensorflow.keras.preprocessing.text import Tokenizer
%%time
col_name = "INCIDENT_NUMBER"
tk = Tokenizer(num_words=None, char_level=True, oov_token='UNK')
X_train = df[col_name]
tk.fit_on_texts(X_train)
X_train = tk.texts_to_sequences(X_train)
Wall time: 2.33 s
print(sys.getsizeof(X_train))
arr = np.array(X_train, dtype=np.uint8)
print(sys.getsizeof(arr))
2857944
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) TypeError: int() argument must be a string, a bytes-like object or a number, not 'list' The above exception was the direct cause of the following exception: ValueError Traceback (most recent call last) <ipython-input-257-bfe19e01855c> in <module> 1 print(sys.getsizeof(X_train)) ----> 2 arr = np.array(X_train, dtype=np.uint8) 3 print(sys.getsizeof(arr)) ValueError: setting an array element with a sequence.
# %%time
import numpy as np
def func(value):
return [ord(c) for c in value]
a = np.ndarray(np.uint8)
type(a)
# pdf = df[col_name].map(func).astype(dtype=a).compute()
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-243-0570636c5e4f> in <module> 4 return [ord(c) for c in value] 5 ----> 6 a = np.ndarray(np.uint8) 7 type(a) 8 # pdf = df[col_name].map(func).astype(dtype=a).compute() TypeError: expected sequence object with len >= 0 or a single integer
# pdf = df[col_name].compute()
pdf[0]
[73, 49, 56, 50, 48, 55, 48, 57, 52, 53]
import sys
sys.getsizeof(pdf[0])
192
sys.getsizeof("I182070945")
59