#from __future__ import print_function
import numpy as np
import pandas as pd
from collections import OrderedDict #sorting participant df dict before pd.concat()
import matplotlib.pylab as plt
%matplotlib inline
pd.options.display.mpl_style = 'default'
import cPickle as pickle
pilot_data = ['010', '011', '012', '013', '014',]
non_english_fluent = ['023', '031', '045', '050', '070', '106',]
left_handed = ['042', '088',]
pro_inst_skill = ['026', '037']
excluded_all_tasks = pilot_data + non_english_fluent + left_handed + pro_inst_skill
def col_matches(df, regex):
'returns a list of columns in a df that match a regex string.'
import re
cols = list(enumerate(df.columns))
matches = [c for (i, c) in cols
if re.findall(regex, c)]
return matches
def compare_transformations(df, columns, functions, **kwargs):
print('raw')
df[columns].hist(**kwargs)
plt.show()
for name, func in functions.items():
print(name)
df[columns].apply(func).hist(**kwargs)
plt.show()
def quickcompare(r, df, size=(15,7)):
inverse = lambda x: 1.0/x
return compare_transformations(df, col_matches(df, r),
{'inverse': inverse,
'log1p': np.log1p,
'sqrt': np.sqrt, },
figsize=size)
# using this for inline documentation so that it's clear
# that the printing statement isn't part of the necessary
# transformation code.
def html_print(df):
from IPython.display import HTML
try:
out = df.to_html()
except AttributeError:
out = pd.DataFrame(df).to_html()
return HTML(out)
def htmljoin(df_list, delimiter=''):
from IPython.display import HTML
return HTML(delimiter.join([x.to_html() for x in df_list]))
def col_matches(df, regex):
import re
cols = list(enumerate(df.columns))
matches = [c for (i, c) in cols
if re.findall(regex, c)]
return matches
def concat_matches(df, *args):
assert all([len(r) for r in args])
import re
col_match_lists = [col_matches(df, regex) for regex in args]
col_set = [df[matches] for matches in col_match_lists]
if len(col_set) == 0:
return None
elif len(col_set) == 1:
return col_set[0]
else:
return pd.concat(col_set, axis=1)
def show_frames(frame_list, delimiter=''):
from IPython.display import HTML
if len(frame_list) == len(delimiter):
html_out = ""
item_template = '<p><strong>{}</strong></p>{}<br>'
for i, tup in enumerate(zip(frame_list, delimiter)):
frame = tup[0]
tag = tup[1]
html_out += item_template.format(tag, frame.to_html())
return HTML(html_out)
else:
html_out = [df.to_html() for df in frame_list]
return HTML(delimiter.join(html_out))
def hist_all(df, *args, **kwargs):
numcols = len(df.columns)
if numcols > 30:
yn = raw_input(str(numcols) + " columns. Proceed?")
if 'n' in yn: return None
for c in df:
print(c)
try:
plt.hist(df[c])
plt.show()
except:
print("\t(can't histogram this)\n")
def scatter_all(df, print_max=None, *args, **kwargs):
from itertools import combinations
numcols = len(df.columns)
if numcols > 6:
yn = raw_input(str(numcols) + " columns. Proceed?")
if 'n' in yn: return None
combos = combinations(df.columns, 2)
for c in combos:
print(c)
x = df[c[0]]
y = df[c[1]]
dfc = pd.concat([x, y], axis=1)
xsort = dfc.sort(columns=dfc.columns[0], inplace=False)
ysort = dfc.sort(columns=dfc.columns[1], inplace=False)
#print(dfc)
try:
dfc.plot(kind='scatter', x=0, y=1)
plt.show()
except:
print("can't plot")
if print_max:
print(xsort.head(print_max))
print(ysort.head(print_max))
pfilenames = "c:/db_pickles/pickle - dfo-{measure} - {updated}.pickle"
full_updated = '2014-10-13a'
#pfile = pfilenames.format(measure='full', updated=full_updated)
pfile = pfilenames.format(measure='flat', updated=full_updated)
print(pfile)
with open(pfile) as f:
dfo = pickle.load(f)
#for quick searches later
match = lambda x: concat_matches(dfo, x)
dfo = dfo.replace(77777, np.nan)
dfo = dfo.replace('77777', np.nan)
#task_pids = {k: sorted(set(v.index.get_level_values('pid')))
# for (k, v) in task_frames.items()}
to_drop = set(dfo.index).intersection(excluded_all_tasks)
dfo = dfo.drop(to_drop)
c:/db_pickles/pickle - dfo-flat - 2014-10-13a.pickle
dfo.count()
SCAL_session_day 97 SCAL_session_time 97 SCAL_session_isfemale 97 SCAL_exclusion_jitterlinearmissing 97 SCAL_exclusion_rhythmadminerror 97 SCAL_sex_femalezero 97 SCAL_participant_age 97 SCAL_calc_wasivocab_totalrawscore 97 SCAL_calc_wasimatrix_totalscore 96 SCAL_calc_wasivocab_tscore 97 SCAL_calc_wasimatrix_tscore 96 SCAL_calc_wasi_tscore_total 96 SCAL_calc_fsiq2 96 SCAL_calc_bfi_extraversion 97 SCAL_calc_bfi_agreeableness 97 ... I8P4_lagdev_mean 96 I8P4_lagdev_local_sq_abs 96 I8P4_lagdev_local 96 I8P4_lagdev_drift 96 I8L2_ints_count 96 I8L2_ints_mean 96 I8L2_ints_variance 96 I8L2_ints_stdev 96 I8L2_ints_lag2corr 96 I8L2_lag2devsq_sum 96 I8L2_lag2devsq_count 96 I8L2_lag2devsq_mean 96 I8L2_lag2devsq_local_sq_abs 96 I8L2_lag2devsq_local 96 I8L2_lag2devsq_drift 90 Length: 577, dtype: int64
match('order').T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SCAL_session_taskorder | 3. Lin, Iso, Jump | 1. Iso, Lin, Jump | 3. Lin, Iso, Jump | 5. Jump, Iso, Lin | 3. Lin, Iso, Jump | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 2. Iso, Jump, Lin | ... | 2. Iso, Jump, Lin | 5. Jump, Iso, Lin | 5. Jump, Iso, Lin | 2. Iso, Jump, Lin | 3. Lin, Iso, Jump | 6. Jump, Lin, Iso | 5. Jump, Iso, Lin | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 6. Jump, Lin, Iso |
SCAL_order_500ms_first | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
SCAL_order_rhythmfirst | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | ... | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
SCAL_notes_qbasic_neurodisorder | ADD & general anxiety | ADHD | ... | ||||||||||||||||||
SCAL_qbasic_neurodisorderyn | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
SCAL_orders_500 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | ... | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
SCAL_orders_800 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
SCAL_orders_iso | 1 | 0 | 1 | 1 | 1 | 2 | 0 | 2 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 0 | 2 |
SCAL_orders_phase | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 0 | 2 | 1 | ... | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 0 |
SCAL_orders_linear | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 1 | 1 | 2 | ... | 2 | 2 | 2 | 2 | 0 | 1 | 2 | 1 | 1 | 1 |
SCAL_order_iso5t1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | ... | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 1 |
SCAL_order_iso8t1 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | ... | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 | 2 |
SCAL_order_iso5t2 | 6 | 3 | 5 | 5 | 5 | 8 | 4 | 8 | 3 | 4 | ... | 4 | 6 | 6 | 4 | 5 | 7 | 5 | 7 | 4 | 7 |
SCAL_order_iso8t2 | 5 | 4 | 6 | 6 | 6 | 7 | 3 | 7 | 4 | 3 | ... | 3 | 5 | 5 | 3 | 6 | 8 | 6 | 8 | 3 | 8 |
SCAL_order_psh5t | 8 | 7 | 7 | 3 | 7 | 4 | 8 | 4 | 7 | 6 | ... | 6 | 4 | 4 | 6 | 7 | 3 | 3 | 3 | 8 | 3 |
SCAL_order_psh8t | 7 | 8 | 8 | 4 | 8 | 3 | 7 | 3 | 8 | 5 | ... | 5 | 3 | 3 | 5 | 8 | 4 | 4 | 4 | 7 | 4 |
SCAL_order_lin5t | 4 | 5 | 3 | 7 | 3 | 6 | 6 | 6 | 5 | 8 | ... | 8 | 8 | 8 | 8 | 3 | 5 | 7 | 5 | 6 | 5 |
SCAL_order_lin8t | 3 | 6 | 4 | 8 | 4 | 5 | 5 | 5 | 6 | 7 | ... | 7 | 7 | 7 | 7 | 4 | 6 | 8 | 6 | 5 | 6 |
SCAL_order_iso5j | 12 | 9 | 11 | 11 | 11 | 14 | 10 | 14 | 9 | 10 | ... | 10 | 12 | 12 | 10 | 11 | 13 | 11 | 13 | 10 | 13 |
SCAL_order_iso8j | 11 | 10 | 12 | 12 | 12 | 13 | 9 | 13 | 10 | 9 | ... | 9 | 11 | 11 | 9 | 12 | 14 | 12 | 14 | 9 | 14 |
SCAL_order_psh5j | 14 | 13 | 13 | 9 | 13 | 10 | 14 | 10 | 13 | 12 | ... | 12 | 10 | 10 | 12 | 13 | 9 | 9 | 9 | 14 | 9 |
SCAL_order_psh8j | 13 | 14 | 14 | 10 | 14 | 9 | 13 | 9 | 14 | 11 | ... | 11 | 9 | 9 | 11 | 14 | 10 | 10 | 10 | 13 | 10 |
SCAL_order_lin5j | 10 | 11 | 9 | 13 | 9 | 12 | 12 | 12 | 11 | 14 | ... | 14 | 14 | 14 | 14 | 9 | 11 | 13 | 11 | 12 | 11 |
SCAL_order_lin8j | 9 | 12 | 10 | 14 | 10 | 11 | 11 | 11 | 12 | 13 | ... | 13 | 13 | 13 | 13 | 10 | 12 | 14 | 12 | 11 | 12 |
SCAL_order_isip5 | 16 | 15 | 15 | 15 | 15 | 16 | 16 | 16 | 15 | 16 | ... | 16 | 16 | 16 | 16 | 15 | 15 | 15 | 15 | 16 | 15 |
SCAL_order_isip8 | 15 | 16 | 16 | 16 | 16 | 15 | 15 | 15 | 16 | 15 | ... | 15 | 15 | 15 | 15 | 16 | 16 | 16 | 16 | 15 | 16 |
26 rows × 97 columns
pasted_scales = '''
# the only 'order' variable needed when just looking at ISIP tasks
SCAL_order_500ms_first
SCAL_sex_femalezero
SCAL_orders_iso
SCAL_orders_phase
SCAL_orders_linear
SCAL_calc_wasivocab_tscore
SCAL_calc_wasimatrix_tscore
SCAL_calc_wasi_tscore_total
SCAL_calc_fsiq2
SCAL_calc_bfi_extraversion
SCAL_calc_bfi_agreeableness
SCAL_calc_bfi_conscientiousness
SCAL_calc_bfi_neuroticism
SCAL_calc_bfi_openness
# compare with usefulness of constructed index
SCAL_qmusic_dancelevel
SCAL_qmusic_instrumentlevel
SCAL_qmusic_drumlevel
SCAL_qmusic_behaviors_12_friendstaste # comment
SCAL_qmusic_behaviors_13_sharingint
SCAL_qmusic_behaviors_14_getinterest
'''
pasted_isip = '''
#from: list(match('local$|drift$').columns)
I5P4_lagdev_local
I8P4_lagdev_local
I8P4_lagdev_drift
I5P4_lagdev_drift
I8L2_lag2devsq_local
I5L2_lag2devsq_local
I8L2_lag2devsq_drift
I5L2_lag2devsq_drift
#needed for filtering out a P that didn't do many taps
I8P4_ints_count
I5P4_ints_count
I8L2_ints_count
I5L2_ints_count
'''
pasted_sms = '''
'''
def clean_pasted_vars(pstring):
pasted_vars = pstring.split('\n')
#keep line contents before comment
pasted_vars = [i.split('#')[0] for i in pasted_vars]
#remove hidden whitespace and blank lines
pasted_vars = [i.strip() for i in pasted_vars]
pasted_vars = filter(lambda i: i != "", pasted_vars)
return pasted_vars
df_scales = dfo[clean_pasted_vars(pasted_scales)]
df_isip = dfo[clean_pasted_vars(pasted_isip)]
df_isip = df_isip.rename(columns=lambda x: x.replace('lagdev_',""))
df_isip = df_isip.rename(columns=lambda x: x.replace('lag2devsq_',""))
for c in ['I8P4_drift', 'I8P4_local']:
ISI = 800
df_isip[c + 'perc'] = df_isip[c] * 100. / ISI
for c in ['I5P4_drift', 'I5P4_local']:
ISI = 500
df_isip[c + 'perc'] = df_isip[c] * 100. / ISI
df_isip.T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
I5P4_local | 2.918599 | 2.692996 | 2.764646 | 2.080451 | 1.397983 | 3.357435 | 3.454273 | 2.634599 | 2.834259 | 2.568266 | ... | 2.629085 | 1.898820 | 4.873864 | 2.944964 | 2.272517 | 2.884701 | 3.168178 | 1.649012 | 2.400801 | 2.143469 |
I8P4_local | 3.871535 | 2.975526 | 3.093386 | 3.284952 | 2.381819 | 3.785594 | 2.633204 | 1.550475 | 2.653945 | 2.340668 | ... | 3.286044 | 1.706451 | 4.606419 | 2.177921 | 2.076461 | 2.668836 | 3.357831 | 2.107230 | 2.504026 | 3.187984 |
I8P4_drift | 5.361354 | 2.148091 | 3.853557 | 2.249890 | 2.243927 | 4.145644 | 4.128266 | 2.268349 | 3.064198 | 4.878173 | ... | 4.578238 | 3.133905 | 3.512176 | 1.844496 | 1.585251 | 3.120648 | 2.771240 | 1.874337 | 2.308642 | 3.452782 |
I5P4_drift | 3.622814 | 2.134364 | 2.614372 | 1.649681 | 1.262769 | 3.145559 | 2.873748 | 2.029501 | 2.445649 | 3.253087 | ... | 4.305536 | 1.431661 | 3.896161 | 2.387312 | 1.767129 | 3.003352 | 3.208083 | 1.540343 | 2.400271 | 2.112349 |
I8L2_local | 4.633893 | 3.450640 | 3.821327 | 4.219624 | 3.002175 | 4.632072 | 3.191604 | 2.155060 | 3.318396 | 2.821277 | ... | 4.000088 | 2.019105 | 4.907391 | 2.770480 | 2.900587 | 3.620502 | 4.091578 | 2.564399 | 3.293310 | 4.138898 |
I5L2_local | 3.668177 | 3.682978 | 3.654972 | 2.455875 | 1.859199 | 4.017225 | 4.294289 | 3.458614 | 3.633339 | 3.150296 | ... | 3.028811 | 2.350576 | 6.159270 | 3.805219 | 3.103455 | 3.341830 | 3.927692 | 1.882107 | 3.212816 | 2.759899 |
I8L2_drift | 4.718045 | 1.249453 | 3.133114 | NaN | 1.302005 | 3.171907 | 3.713490 | 1.704434 | 2.328300 | 4.616893 | ... | 3.969590 | 2.942201 | 3.077659 | 0.685526 | NaN | 1.937290 | 1.487880 | 1.173633 | 0.868382 | 2.225864 |
I5L2_drift | 2.861377 | NaN | 1.058009 | 1.009157 | 0.303845 | 2.242502 | 1.322690 | NaN | 0.901708 | 2.693363 | ... | 4.034357 | 0.360505 | 0.999005 | NaN | NaN | 2.484712 | 2.214132 | 1.244817 | 1.096796 | 1.199764 |
I8P4_ints_count | 134.000000 | 112.000000 | 109.000000 | 114.000000 | 116.000000 | 111.000000 | 121.000000 | 109.000000 | 124.000000 | 105.000000 | ... | 119.000000 | 114.000000 | 117.000000 | 119.000000 | 113.000000 | 78.000000 | 109.000000 | 111.000000 | 121.000000 | 105.000000 |
I5P4_ints_count | 118.000000 | 107.000000 | 113.000000 | 117.000000 | 116.000000 | 108.000000 | 117.000000 | 118.000000 | 115.000000 | 106.000000 | ... | 113.000000 | 115.000000 | 117.000000 | 111.000000 | 113.000000 | 108.000000 | 113.000000 | 108.000000 | 120.000000 | 115.000000 |
I8L2_ints_count | 134.000000 | 112.000000 | 109.000000 | 114.000000 | 116.000000 | 111.000000 | 121.000000 | 109.000000 | 124.000000 | 105.000000 | ... | 119.000000 | 114.000000 | 117.000000 | 119.000000 | 113.000000 | 78.000000 | 109.000000 | 111.000000 | 121.000000 | 105.000000 |
I5L2_ints_count | 118.000000 | 107.000000 | 113.000000 | 117.000000 | 116.000000 | 108.000000 | 117.000000 | 118.000000 | 115.000000 | 106.000000 | ... | 113.000000 | 115.000000 | 117.000000 | 111.000000 | 113.000000 | 108.000000 | 113.000000 | 108.000000 | 120.000000 | 115.000000 |
I8P4_driftperc | 0.670169 | 0.268511 | 0.481695 | 0.281236 | 0.280491 | 0.518206 | 0.516033 | 0.283544 | 0.383025 | 0.609772 | ... | 0.572280 | 0.391738 | 0.439022 | 0.230562 | 0.198156 | 0.390081 | 0.346405 | 0.234292 | 0.288580 | 0.431598 |
I8P4_localperc | 0.483942 | 0.371941 | 0.386673 | 0.410619 | 0.297727 | 0.473199 | 0.329151 | 0.193809 | 0.331743 | 0.292584 | ... | 0.410755 | 0.213306 | 0.575802 | 0.272240 | 0.259558 | 0.333604 | 0.419729 | 0.263404 | 0.313003 | 0.398498 |
I5P4_driftperc | 0.724563 | 0.426873 | 0.522874 | 0.329936 | 0.252554 | 0.629112 | 0.574750 | 0.405900 | 0.489130 | 0.650617 | ... | 0.861107 | 0.286332 | 0.779232 | 0.477462 | 0.353426 | 0.600670 | 0.641617 | 0.308069 | 0.480054 | 0.422470 |
I5P4_localperc | 0.583720 | 0.538599 | 0.552929 | 0.416090 | 0.279597 | 0.671487 | 0.690855 | 0.526920 | 0.566852 | 0.513653 | ... | 0.525817 | 0.379764 | 0.974773 | 0.588993 | 0.454503 | 0.576940 | 0.633636 | 0.329802 | 0.480160 | 0.428694 |
16 rows × 97 columns
# (missing values propagate in pandas arithmetic operations)
total_hours = (dfo.SCAL_qmusic_drumhours +
dfo.SCAL_qmusic_instrumenthours +
dfo.SCAL_qmusic_dancehours)
any_hours = (total_hours > 0).astype(int)
#skipna = False: if any missing values, produce a missing-value result
max_skill_level = pd.concat([dfo.SCAL_qmusic_dancelevel,
dfo.SCAL_qmusic_instrumentlevel,
dfo.SCAL_qmusic_drumlevel], axis=1).T.max(skipna=False)
sum_skill_level = pd.concat([dfo.SCAL_qmusic_dancelevel,
dfo.SCAL_qmusic_instrumentlevel,
dfo.SCAL_qmusic_drumlevel], axis=1).T.sum(skipna=False)
social_importance = pd.concat([dfo.SCAL_qmusic_behaviors_12_friendstaste,
dfo.SCAL_qmusic_behaviors_13_sharingint,
dfo.SCAL_qmusic_behaviors_14_getinterest,], axis=1).T.sum(skipna=False)
# (there are no missing values for these three vars)
df_constructed = pd.concat(axis=1,
objs=[any_hours,
max_skill_level,
sum_skill_level,
social_importance],
keys=['qmusic_calc_anyhours',
'qmusic_calc_maxskill',
'qmusic_calc_sumskill',
'qmusic_calc_socialimp'])
df_constructed[df_constructed.qmusic_calc_maxskill.isnull()==True]
qmusic_calc_anyhours | qmusic_calc_maxskill | qmusic_calc_sumskill | qmusic_calc_socialimp | |
---|---|---|---|---|
064 | 0 | NaN | NaN | 8 |
def truncate(s):
z_limit = 2.97
maxval = s.mean() + z_limit * s.std()
minval = s.mean() - z_limit * s.std()
print "\n" + s.name
print "limits: {}, {}".format(maxval, minval)
assert minval < s.mean() < maxval
def truncval(val):
tstr = "truncated {} to {}."
if val > maxval:
print tstr.format(val, maxval)
return maxval
elif val < minval:
print tstr.format(val, minval)
if 'DPsd' in s.name:
print "WARNING: summary data should not have to be truncated in this direction."
return minval
else:
return val
out = s.apply(truncval)
if 'DPsd' in s.name:
#print('checking...')
assert out.min() >= 0
return out
def test_trunc(s):
print "Original"
s.hist()
plt.show()
print "Truncated"
truncate(s).hist()
plt.show()
test_trunc(df_isip.I5P4_drift)
Original
Truncated I5P4_drift limits: 5.52765569721, -0.222743472829 truncated 8.10995671656 to 5.52765569721.
drifts = concat_matches(df_isip, 'P4_drift$|local$').apply(truncate)
drifts.head(3)
I5P4_local limits: 4.90761157606, 0.755692091143 I5P4_local limits: 4.90761157606, 0.755692091143 I8P4_local limits: 5.29598227358, 0.470629963811 truncated 6.55088307793 to 5.29598227358. I8P4_drift limits: 6.87780682384, -0.545362091766 truncated 8.26350638952 to 6.87780682384. truncated 7.3133516044 to 6.87780682384. I5P4_drift limits: 5.52765569721, -0.222743472829 truncated 8.10995671656 to 5.52765569721. I8L2_local limits: 6.58549348051, 0.562338273468 truncated 9.0214050605 to 6.58549348051. I5L2_local limits: 6.29774676336, 0.891217901061 truncated 6.41196525963 to 6.29774676336.
I5P4_local | I8P4_local | I8P4_drift | I5P4_drift | I8L2_local | I5L2_local | |
---|---|---|---|---|---|---|
015 | 2.918599 | 3.871535 | 5.361354 | 3.622814 | 4.633893 | 3.668177 |
016 | 2.692996 | 2.975526 | 2.148091 | 2.134364 | 3.450640 | 3.682978 |
017 | 2.764646 | 3.093386 | 3.853557 | 2.614372 | 3.821327 | 3.654972 |
drifts.plot(kind='scatter', x=0,y=1)
#Interesting issue with p. 55 (the outlier on IP54_drift).
# It appears legitimate: in general the local variation is very
# small-- but there's a lot of variability, because the subject
# drifted way down to around 400ms, then jumped up to around 550
# immediately-- so there were only a couple of intervals where
# there was a big change from one interval to the next.
# especially if smoothing across four intervals.....
<matplotlib.axes.AxesSubplot at 0xd5f5940>
df_isip_out = pd.DataFrame(index = df_isip.index)
for c in df_isip.columns:
if 'ints_count' in c:
df_isip_out[c] = df_isip[c]
else:
df_isip_out[c + '_trunc'] = truncate(df_isip[c])
#del df_isip[c]
I5P4_local limits: 4.90761157606, 0.755692091143 I8P4_local limits: 5.29598227358, 0.470629963811 truncated 6.55088307793 to 5.29598227358. I8P4_drift limits: 6.87780682384, -0.545362091766 truncated 8.26350638952 to 6.87780682384. truncated 7.3133516044 to 6.87780682384. I5P4_drift limits: 5.52765569721, -0.222743472829 truncated 8.10995671656 to 5.52765569721. I8L2_local limits: 6.58549348051, 0.562338273468 truncated 9.0214050605 to 6.58549348051. I5L2_local limits: 6.29774676336, 0.891217901061 truncated 6.41196525963 to 6.29774676336. I8L2_drift limits: 6.38719154181, -1.72402557872 truncated 7.87241198138 to 6.38719154181. truncated 7.06373220524 to 6.38719154181. I5L2_drift limits: 5.13409149892, -1.67860799714 truncated 8.00023059382 to 5.13409149892. I8P4_driftperc limits: 0.85972585298, -0.0681702614708 truncated 1.03293829869 to 0.85972585298. truncated 0.914168950551 to 0.85972585298. I8P4_localperc limits: 0.661997784197, 0.0588287454764 truncated 0.818860384741 to 0.661997784197. I5P4_driftperc limits: 1.10553113944, -0.0445486945659 truncated 1.62199134331 to 1.10553113944. I5P4_localperc limits: 0.981522315213, 0.151138418229
df_isip_out.T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
I5P4_local_trunc | 2.918599 | 2.692996 | 2.764646 | 2.080451 | 1.397983 | 3.357435 | 3.454273 | 2.634599 | 2.834259 | 2.568266 | ... | 2.629085 | 1.898820 | 4.873864 | 2.944964 | 2.272517 | 2.884701 | 3.168178 | 1.649012 | 2.400801 | 2.143469 |
I8P4_local_trunc | 3.871535 | 2.975526 | 3.093386 | 3.284952 | 2.381819 | 3.785594 | 2.633204 | 1.550475 | 2.653945 | 2.340668 | ... | 3.286044 | 1.706451 | 4.606419 | 2.177921 | 2.076461 | 2.668836 | 3.357831 | 2.107230 | 2.504026 | 3.187984 |
I8P4_drift_trunc | 5.361354 | 2.148091 | 3.853557 | 2.249890 | 2.243927 | 4.145644 | 4.128266 | 2.268349 | 3.064198 | 4.878173 | ... | 4.578238 | 3.133905 | 3.512176 | 1.844496 | 1.585251 | 3.120648 | 2.771240 | 1.874337 | 2.308642 | 3.452782 |
I5P4_drift_trunc | 3.622814 | 2.134364 | 2.614372 | 1.649681 | 1.262769 | 3.145559 | 2.873748 | 2.029501 | 2.445649 | 3.253087 | ... | 4.305536 | 1.431661 | 3.896161 | 2.387312 | 1.767129 | 3.003352 | 3.208083 | 1.540343 | 2.400271 | 2.112349 |
I8L2_local_trunc | 4.633893 | 3.450640 | 3.821327 | 4.219624 | 3.002175 | 4.632072 | 3.191604 | 2.155060 | 3.318396 | 2.821277 | ... | 4.000088 | 2.019105 | 4.907391 | 2.770480 | 2.900587 | 3.620502 | 4.091578 | 2.564399 | 3.293310 | 4.138898 |
I5L2_local_trunc | 3.668177 | 3.682978 | 3.654972 | 2.455875 | 1.859199 | 4.017225 | 4.294289 | 3.458614 | 3.633339 | 3.150296 | ... | 3.028811 | 2.350576 | 6.159270 | 3.805219 | 3.103455 | 3.341830 | 3.927692 | 1.882107 | 3.212816 | 2.759899 |
I8L2_drift_trunc | 4.718045 | 1.249453 | 3.133114 | NaN | 1.302005 | 3.171907 | 3.713490 | 1.704434 | 2.328300 | 4.616893 | ... | 3.969590 | 2.942201 | 3.077659 | 0.685526 | NaN | 1.937290 | 1.487880 | 1.173633 | 0.868382 | 2.225864 |
I5L2_drift_trunc | 2.861377 | NaN | 1.058009 | 1.009157 | 0.303845 | 2.242502 | 1.322690 | NaN | 0.901708 | 2.693363 | ... | 4.034357 | 0.360505 | 0.999005 | NaN | NaN | 2.484712 | 2.214132 | 1.244817 | 1.096796 | 1.199764 |
I8P4_ints_count | 134.000000 | 112.000000 | 109.000000 | 114.000000 | 116.000000 | 111.000000 | 121.000000 | 109.000000 | 124.000000 | 105.000000 | ... | 119.000000 | 114.000000 | 117.000000 | 119.000000 | 113.000000 | 78.000000 | 109.000000 | 111.000000 | 121.000000 | 105.000000 |
I5P4_ints_count | 118.000000 | 107.000000 | 113.000000 | 117.000000 | 116.000000 | 108.000000 | 117.000000 | 118.000000 | 115.000000 | 106.000000 | ... | 113.000000 | 115.000000 | 117.000000 | 111.000000 | 113.000000 | 108.000000 | 113.000000 | 108.000000 | 120.000000 | 115.000000 |
I8L2_ints_count | 134.000000 | 112.000000 | 109.000000 | 114.000000 | 116.000000 | 111.000000 | 121.000000 | 109.000000 | 124.000000 | 105.000000 | ... | 119.000000 | 114.000000 | 117.000000 | 119.000000 | 113.000000 | 78.000000 | 109.000000 | 111.000000 | 121.000000 | 105.000000 |
I5L2_ints_count | 118.000000 | 107.000000 | 113.000000 | 117.000000 | 116.000000 | 108.000000 | 117.000000 | 118.000000 | 115.000000 | 106.000000 | ... | 113.000000 | 115.000000 | 117.000000 | 111.000000 | 113.000000 | 108.000000 | 113.000000 | 108.000000 | 120.000000 | 115.000000 |
I8P4_driftperc_trunc | 0.670169 | 0.268511 | 0.481695 | 0.281236 | 0.280491 | 0.518206 | 0.516033 | 0.283544 | 0.383025 | 0.609772 | ... | 0.572280 | 0.391738 | 0.439022 | 0.230562 | 0.198156 | 0.390081 | 0.346405 | 0.234292 | 0.288580 | 0.431598 |
I8P4_localperc_trunc | 0.483942 | 0.371941 | 0.386673 | 0.410619 | 0.297727 | 0.473199 | 0.329151 | 0.193809 | 0.331743 | 0.292584 | ... | 0.410755 | 0.213306 | 0.575802 | 0.272240 | 0.259558 | 0.333604 | 0.419729 | 0.263404 | 0.313003 | 0.398498 |
I5P4_driftperc_trunc | 0.724563 | 0.426873 | 0.522874 | 0.329936 | 0.252554 | 0.629112 | 0.574750 | 0.405900 | 0.489130 | 0.650617 | ... | 0.861107 | 0.286332 | 0.779232 | 0.477462 | 0.353426 | 0.600670 | 0.641617 | 0.308069 | 0.480054 | 0.422470 |
I5P4_localperc_trunc | 0.583720 | 0.538599 | 0.552929 | 0.416090 | 0.279597 | 0.671487 | 0.690855 | 0.526920 | 0.566852 | 0.513653 | ... | 0.525817 | 0.379764 | 0.974773 | 0.588993 | 0.454503 | 0.576940 | 0.633636 | 0.329802 | 0.480160 | 0.428694 |
16 rows × 97 columns
#list(match('DPm$|DPsd$'))
match('DP').T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SMSR_iso5t1_DPm | -3.202915 | -2.840413 | -2.290274 | -3.022502 | -0.588655 | -10.975295 | -3.609441 | -1.953836 | -5.568016 | -5.484477 | ... | -10.934370 | -0.918811 | -1.107983 | -0.594511 | -0.838101 | -7.211448 | -3.449031 | -0.357402 | -5.634339 | -4.897263 |
SMSR_iso5t1_DPsd | 8.582088 | 3.423481 | 3.607109 | 3.291210 | 2.583675 | 7.004012 | 4.608983 | 3.167766 | 3.170995 | 3.186261 | ... | 6.123362 | 2.799212 | 4.586392 | 4.075651 | 2.487950 | 5.184207 | 5.405130 | 2.827062 | 5.538719 | 4.245075 |
SMSR_iso5t1_DPct | 109.000000 | 116.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 | 116.000000 | 110.000000 | ... | 114.000000 | 115.000000 | 113.000000 | 112.000000 | 113.000000 | 114.000000 | 114.000000 | 114.000000 | 98.000000 | 114.000000 |
SMSR_iso8t1_DPm | -3.198754 | -2.827224 | -13.311147 | -0.553203 | -2.327200 | -3.092538 | -1.458121 | 0.968610 | -2.741571 | 0.354671 | ... | -5.737524 | 0.205687 | -0.826352 | -2.226755 | -1.372491 | -1.247007 | 1.162443 | -0.060845 | -9.056157 | -3.304496 |
SMSR_iso8t1_DPsd | 7.944039 | 2.964738 | 9.624027 | 5.005393 | 2.431597 | 6.505625 | 4.433459 | 2.268350 | 4.452189 | 4.964179 | ... | 8.144077 | 2.344456 | 6.235354 | 3.659721 | 2.334648 | 5.438937 | 3.613148 | 2.767637 | 4.298762 | 5.691151 |
SMSR_iso8t1_DPct | 84.000000 | 104.000000 | 103.000000 | 106.000000 | 107.000000 | 101.000000 | 106.000000 | 104.000000 | 107.000000 | 103.000000 | ... | 98.000000 | 104.000000 | 104.000000 | 106.000000 | 106.000000 | 103.000000 | 106.000000 | 104.000000 | 102.000000 | 105.000000 |
SMSR_iso5t2_DPm | -7.320309 | -0.609046 | -2.155283 | -2.628721 | -0.035660 | -3.025222 | -5.794925 | -1.195840 | -1.939757 | 0.793761 | ... | -10.848408 | 1.998485 | -2.249983 | -0.615855 | -2.109468 | -10.922451 | -1.735612 | 1.315051 | -11.177051 | -7.462378 |
SMSR_iso5t2_DPsd | 8.438450 | 3.522687 | 3.871627 | 3.373599 | 2.612512 | 7.021192 | 5.208270 | 3.488723 | 3.281671 | 5.276439 | ... | 5.511892 | 2.801852 | 6.319350 | 4.716004 | 3.143240 | 4.653159 | 4.076174 | 3.167036 | 5.855743 | 4.506521 |
SMSR_iso5t2_DPct | 102.000000 | 115.000000 | 113.000000 | 116.000000 | 117.000000 | 112.000000 | 116.000000 | 117.000000 | 116.000000 | 98.000000 | ... | 114.000000 | 113.000000 | 109.000000 | 116.000000 | 117.000000 | 114.000000 | 114.000000 | 116.000000 | 114.000000 | 110.000000 |
SMSR_iso8t2_DPm | NaN | -4.064043 | -4.258572 | -0.759809 | -1.704668 | -1.823981 | -1.913083 | 0.890989 | -2.384110 | 0.150065 | ... | -1.732022 | -0.082369 | -6.790021 | -1.854593 | -1.226942 | -3.161301 | 0.728819 | -1.877539 | -4.290588 | -5.760608 |
SMSR_iso8t2_DPsd | NaN | 3.101403 | 5.063099 | 5.568829 | 3.404020 | 6.388830 | 4.368410 | 2.339890 | 4.170302 | 4.422124 | ... | 5.838884 | 2.772427 | 6.077328 | 3.511975 | 2.276168 | 4.564913 | 2.928230 | 3.214585 | 3.713862 | 5.989694 |
SMSR_iso8t2_DPct | NaN | 105.000000 | 105.000000 | 107.000000 | 107.000000 | 102.000000 | 106.000000 | 106.000000 | 107.000000 | 101.000000 | ... | 96.000000 | 104.000000 | 102.000000 | 102.000000 | 105.000000 | 105.000000 | 104.000000 | 105.000000 | 106.000000 | 101.000000 |
SMSR_lin5t_DPm | NaN | -4.440936 | -8.097466 | -4.785265 | -2.822426 | -7.847768 | -8.081455 | -2.905709 | -2.321764 | -1.148714 | ... | -13.171428 | -1.070807 | -11.101964 | -4.147904 | -3.269580 | -6.060567 | -2.044255 | -4.613583 | -6.337286 | -7.769085 |
SMSR_lin5t_DPsd | NaN | 3.804005 | 4.959168 | 4.888448 | 4.628010 | 7.409937 | 5.736285 | 3.561546 | 4.532819 | 6.311407 | ... | 11.863541 | 3.458209 | 7.417736 | 4.439277 | 3.199540 | 6.582733 | 5.129986 | 4.204554 | 5.773415 | 7.357143 |
SMSR_lin5t_DPct | NaN | 155.000000 | 154.000000 | 154.000000 | 155.000000 | 149.000000 | 156.000000 | 155.000000 | 157.000000 | 149.000000 | ... | 121.000000 | 150.000000 | 148.000000 | 141.000000 | 154.000000 | 150.000000 | 154.000000 | 152.000000 | 157.000000 | 143.000000 |
SMSR_lin8t_DPm | -1.466780 | -2.207710 | -1.394524 | -0.767171 | -1.167629 | -2.313080 | -0.667972 | -0.196244 | -2.514913 | 2.739635 | ... | -2.061360 | 2.228288 | 1.177425 | -1.581256 | -2.067663 | -4.227632 | 2.762494 | 0.389404 | -2.219642 | -3.069498 |
SMSR_lin8t_DPsd | 11.049303 | 3.797363 | 5.997794 | 4.568257 | 4.510050 | 5.714329 | 4.928837 | 3.213383 | 5.215915 | 5.288537 | ... | 7.265519 | 2.892864 | 6.500740 | 3.968508 | 3.315846 | 6.912513 | 4.916196 | 3.477552 | 5.289070 | 5.277031 |
SMSR_lin8t_DPct | 114.000000 | 156.000000 | 151.000000 | 156.000000 | 156.000000 | 155.000000 | 156.000000 | 156.000000 | 157.000000 | 153.000000 | ... | 147.000000 | 154.000000 | 153.000000 | 146.000000 | 156.000000 | 134.000000 | 151.000000 | 155.000000 | 153.000000 | 150.000000 |
SMSR_phase5t_DPm | -8.176227 | -2.727189 | -4.037511 | -3.222491 | -0.056542 | -3.009335 | -4.691215 | -0.877772 | -1.028469 | 2.607912 | ... | -7.511362 | 2.751770 | -4.868650 | -0.907275 | -1.493659 | -7.582498 | 3.159855 | 2.719712 | -6.585747 | -2.178178 |
SMSR_phase5t_DPsd | 18.286210 | 4.636219 | 6.101449 | 6.015203 | 5.880533 | 8.832377 | 5.631446 | 5.549757 | 6.495490 | 8.974706 | ... | 8.618557 | 4.244219 | 7.097735 | 10.690847 | 4.345646 | 9.021404 | 8.359160 | 4.832540 | 6.673500 | 7.992546 |
SMSR_phase5t_DPct | 143.000000 | 155.000000 | 154.000000 | 155.000000 | 156.000000 | 144.000000 | 156.000000 | 154.000000 | 157.000000 | 147.000000 | ... | 154.000000 | 153.000000 | 152.000000 | 155.000000 | 152.000000 | 148.000000 | 153.000000 | 153.000000 | 153.000000 | 149.000000 |
SMSR_phase8t_DPm | -4.950975 | -3.443382 | -11.261584 | 2.320622 | -0.540736 | -2.155307 | -3.021885 | 0.223643 | -2.313902 | 1.552980 | ... | -5.626800 | 0.941978 | -7.163684 | -3.237063 | -1.668319 | -1.081923 | 2.320129 | -0.321672 | -5.452793 | -3.261694 |
SMSR_phase8t_DPsd | 30.292411 | 4.223427 | 6.801106 | 7.011985 | 3.834770 | 7.823846 | 5.464373 | 5.537968 | 5.319286 | 6.812584 | ... | 6.988509 | 5.164350 | 6.926644 | 5.851449 | 4.129328 | 7.146782 | 6.167211 | 4.298461 | 5.503316 | 11.802603 |
SMSR_phase8t_DPct | 132.000000 | 156.000000 | 155.000000 | 156.000000 | 155.000000 | 154.000000 | 156.000000 | 154.000000 | 156.000000 | 120.000000 | ... | 154.000000 | 154.000000 | 147.000000 | 152.000000 | 156.000000 | 153.000000 | 152.000000 | 153.000000 | 154.000000 | 143.000000 |
SMSR_iso5j_DPm | -7.884204 | -6.820084 | -5.270395 | -0.552320 | -3.343108 | -6.488198 | -3.588275 | -2.625491 | -5.292310 | -7.260642 | ... | -11.322089 | 0.992438 | -15.303739 | -1.575724 | -2.164280 | -14.291407 | 2.168237 | 1.390152 | -8.451801 | -1.549586 |
SMSR_iso5j_DPsd | 4.344289 | 5.180662 | 5.260285 | 6.896868 | 6.576153 | 6.369871 | 5.687223 | 5.210024 | 6.350138 | 5.514704 | ... | 6.121927 | 5.109652 | 8.639169 | 6.185684 | 4.871451 | 5.396906 | 8.370299 | 4.820479 | 5.017404 | 5.578518 |
SMSR_iso5j_DPct | 103.000000 | 106.000000 | 106.000000 | 106.000000 | 107.000000 | 102.000000 | 106.000000 | 107.000000 | 107.000000 | 100.000000 | ... | 105.000000 | 104.000000 | 96.000000 | 104.000000 | 105.000000 | 101.000000 | 104.000000 | 104.000000 | 104.000000 | 105.000000 |
SMSR_iso8j_DPm | NaN | -6.218841 | -10.064092 | -4.987855 | -9.660025 | -7.975747 | -8.929854 | 1.649018 | -6.925825 | -2.223467 | ... | -12.004863 | -2.133730 | -11.930682 | -5.926679 | -5.893919 | -4.632284 | 0.058726 | -5.148439 | -7.448876 | -10.669810 |
SMSR_iso8j_DPsd | NaN | 5.435017 | 8.475535 | 5.883866 | 5.660678 | 6.168150 | 5.515570 | 5.788754 | 6.075278 | 6.741509 | ... | 8.035791 | 3.907462 | 6.181960 | 5.100964 | 5.519451 | 4.905857 | 6.811418 | 4.125815 | 4.164116 | 5.553144 |
SMSR_iso8j_DPct | NaN | 106.000000 | 107.000000 | 105.000000 | 107.000000 | 106.000000 | 107.000000 | 106.000000 | 107.000000 | 100.000000 | ... | 103.000000 | 104.000000 | 103.000000 | 104.000000 | 106.000000 | 106.000000 | 106.000000 | 106.000000 | 104.000000 | 107.000000 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
SMSR_phase5t_nrm_DPm | NaN | -3.494445 | -6.023428 | -4.044406 | -0.096803 | -4.921805 | -6.586367 | -1.663917 | -2.646197 | NaN | ... | -5.554789 | 1.637512 | -2.384650 | -2.340948 | -1.170212 | NaN | 8.454378 | 2.841347 | -6.206281 | NaN |
SMSR_phase5t_nrm_DPsd | NaN | 3.447075 | 5.069596 | 4.472898 | 3.686194 | 6.961424 | 5.077150 | 3.764374 | 4.054006 | NaN | ... | 8.672406 | 2.702044 | 7.063415 | 6.217980 | 3.661628 | NaN | 4.156459 | 3.642057 | 6.085368 | NaN |
SMSR_phase5t_nrm_DPct | NaN | 58.000000 | 50.000000 | 58.000000 | 58.000000 | 51.000000 | 58.000000 | 50.000000 | 62.000000 | NaN | ... | 50.000000 | 49.000000 | 55.000000 | 54.000000 | 49.000000 | NaN | 49.000000 | 49.000000 | 50.000000 | NaN |
SMSR_phase8t_nrm_DPm | NaN | -2.738324 | -12.613996 | 2.200945 | -0.550671 | -2.789755 | -1.841289 | -1.104148 | -2.114991 | NaN | ... | -8.864571 | 1.167334 | -6.006915 | -3.760204 | -2.231690 | NaN | 0.507251 | -0.716042 | -5.191294 | NaN |
SMSR_phase8t_nrm_DPsd | NaN | 2.685532 | 7.588504 | 4.675063 | 2.654889 | 5.970898 | 4.366131 | 2.256640 | 4.987632 | NaN | ... | 6.390464 | 2.777576 | 5.458047 | 3.717879 | 3.070182 | NaN | 4.423234 | 2.651028 | 4.713386 | NaN |
SMSR_phase8t_nrm_DPct | NaN | 65.000000 | 57.000000 | 61.000000 | 57.000000 | 60.000000 | 61.000000 | 50.000000 | 61.000000 | NaN | ... | 50.000000 | 57.000000 | 56.000000 | 56.000000 | 62.000000 | NaN | 48.000000 | 56.000000 | 50.000000 | NaN |
SMSR_phase5j_nrm_DPm | -11.148969 | -3.325956 | -2.762491 | -7.626405 | -7.175838 | -8.508095 | -5.800228 | -10.196515 | -3.477511 | -4.929744 | ... | -11.247372 | 1.597960 | -10.049117 | -3.918322 | -5.244204 | NaN | -6.636588 | -4.443068 | NaN | NaN |
SMSR_phase5j_nrm_DPsd | 9.831048 | 4.675775 | 8.093669 | 6.432369 | 8.611338 | 7.374434 | 6.468034 | 7.577475 | 5.779530 | 9.186420 | ... | 7.306804 | 6.047968 | 8.028764 | 6.527422 | 5.395921 | NaN | 5.343027 | 5.422876 | NaN | NaN |
SMSR_phase5j_nrm_DPct | 46.000000 | 58.000000 | 52.000000 | 56.000000 | 58.000000 | 59.000000 | 58.000000 | 50.000000 | 62.000000 | 49.000000 | ... | 49.000000 | 44.000000 | 45.000000 | 49.000000 | 62.000000 | NaN | 50.000000 | 50.000000 | NaN | NaN |
SMSR_phase8j_nrm_DPm | -9.921800 | -5.485820 | -11.594407 | 0.012137 | -1.695137 | -13.082447 | -9.177448 | -1.539006 | -5.807808 | -1.357039 | ... | -11.380748 | -4.138816 | -11.430767 | -8.163726 | -8.763223 | -2.627231 | -4.346571 | -4.187103 | NaN | -12.947842 |
SMSR_phase8j_nrm_DPsd | 6.769322 | 3.882251 | 7.146704 | 5.035754 | 9.043339 | 9.028088 | 5.704594 | 5.154211 | 6.207823 | 6.458522 | ... | 6.210700 | 4.500170 | 7.667751 | 4.890995 | 5.952829 | 7.561797 | 6.687784 | 4.882306 | NaN | 5.635160 |
SMSR_phase8j_nrm_DPct | 49.000000 | 62.000000 | 57.000000 | 58.000000 | 62.000000 | 50.000000 | 62.000000 | 58.000000 | 62.000000 | 50.000000 | ... | 58.000000 | 50.000000 | 61.000000 | 57.000000 | 55.000000 | 56.000000 | 58.000000 | 58.000000 | NaN | 58.000000 |
SMSR_lint_610690_DPm | -5.904932 | -3.629545 | -5.018314 | -1.965972 | -1.046649 | -6.135196 | -4.141515 | -1.082157 | -2.521295 | 1.547984 | ... | -13.675623 | 0.477503 | -5.995810 | -2.384010 | -1.992656 | -3.592472 | 1.334023 | -2.638309 | -3.920254 | -2.610475 |
SMSR_lint_610690_DPsd | 14.214295 | 3.647491 | 6.254805 | 4.769801 | 5.044258 | 7.952984 | 6.309685 | 3.805393 | 4.389249 | 5.555110 | ... | 12.938677 | 3.283291 | 9.406283 | 4.755764 | 3.414272 | 5.783784 | 5.413948 | 4.346650 | 5.520486 | 5.307701 |
SMSR_lint_610690_DPct | 70.000000 | 89.000000 | 90.000000 | 89.000000 | 90.000000 | 87.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 | ... | 85.000000 | 90.000000 | 88.000000 | 85.000000 | 90.000000 | 90.000000 | 90.000000 | 89.000000 | 90.000000 | 87.000000 |
SMSR_linj_610690_DPm | -11.469696 | -4.611525 | -6.104771 | NaN | -2.766766 | -5.984495 | -1.798480 | -2.341115 | -6.378774 | -2.082932 | ... | NaN | 1.346563 | -17.484409 | -4.747037 | -3.671017 | -10.843955 | -1.537152 | -1.352451 | -3.832061 | -5.911024 |
SMSR_linj_610690_DPsd | 10.788868 | 6.692989 | 14.833960 | NaN | 12.881389 | 7.355279 | 10.090107 | 7.873377 | 7.713142 | 7.518116 | ... | NaN | 6.278382 | 9.227213 | 6.783321 | 5.841060 | 7.550096 | 7.191732 | 4.922011 | 7.405898 | 11.879577 |
SMSR_linj_610690_DPct | 88.000000 | 89.000000 | 86.000000 | NaN | 84.000000 | 88.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 | ... | NaN | 90.000000 | 88.000000 | 90.000000 | 89.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 |
SMSR_lint_700800_DPm | NaN | -2.836999 | -5.324347 | -2.588808 | -0.534889 | -3.920463 | -2.249709 | -1.423945 | -3.538202 | 2.053367 | ... | NaN | -0.203763 | -2.421917 | -3.037278 | -3.049772 | -3.185119 | 0.806381 | -1.259571 | -4.405488 | -6.258277 |
SMSR_lint_700800_DPsd | NaN | 4.188447 | 6.278797 | 5.991372 | 4.800754 | 6.452870 | 5.338793 | 3.525517 | 5.302861 | 5.957211 | ... | NaN | 3.474329 | 9.170531 | 4.447257 | 3.353788 | 8.064835 | 3.622448 | 3.628472 | 5.876414 | 9.321889 |
SMSR_lint_700800_DPct | NaN | 100.000000 | 99.000000 | 99.000000 | 100.000000 | 97.000000 | 101.000000 | 100.000000 | 102.000000 | 94.000000 | ... | NaN | 95.000000 | 97.000000 | 90.000000 | 99.000000 | 81.000000 | 99.000000 | 98.000000 | 102.000000 | 87.000000 |
SMSR_lint_500600_DPm | NaN | -2.935850 | -3.679656 | -3.081273 | -4.239167 | -4.696701 | -6.162629 | -2.058697 | -1.059976 | -0.138641 | ... | -1.890201 | 1.884941 | -6.121897 | -3.185709 | -2.553906 | -8.668644 | -0.270897 | -2.702535 | -3.943322 | -6.960489 |
SMSR_lint_500600_DPsd | NaN | 3.763389 | 7.094297 | 4.338958 | 3.428162 | 6.109977 | 7.413205 | 3.699728 | 4.834872 | 6.247794 | ... | 9.205697 | 3.589876 | 9.466895 | 3.924627 | 2.970704 | 5.239500 | 6.837897 | 5.603176 | 5.913442 | 4.700504 |
SMSR_lint_500600_DPct | NaN | 102.000000 | 96.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 | 102.000000 | 98.000000 | ... | 96.000000 | 99.000000 | 97.000000 | 95.000000 | 101.000000 | 94.000000 | 96.000000 | 100.000000 | 98.000000 | 99.000000 |
SMSR_linj_700800_DPm | -4.543913 | -5.536776 | -6.491154 | NaN | NaN | -9.080940 | -5.285475 | -1.128587 | -6.143924 | 1.101778 | ... | NaN | -0.760195 | NaN | -5.754093 | -2.759417 | -4.056123 | -2.236343 | -2.155906 | -6.996401 | -8.532782 |
SMSR_linj_700800_DPsd | 8.344733 | 5.658566 | 8.351930 | NaN | NaN | 7.484739 | 7.572755 | 5.879357 | 6.601137 | 6.673417 | ... | NaN | 5.307915 | NaN | 5.832539 | 5.775269 | 5.086778 | 5.948107 | 5.018592 | 4.189604 | 10.555065 |
SMSR_linj_700800_DPct | 82.000000 | 102.000000 | 94.000000 | NaN | NaN | 95.000000 | 101.000000 | 99.000000 | 100.000000 | 93.000000 | ... | NaN | 98.000000 | NaN | 101.000000 | 98.000000 | 99.000000 | 97.000000 | 97.000000 | 91.000000 | 98.000000 |
SMSR_linj_500600_DPm | -9.168428 | -4.356788 | -6.390442 | NaN | -6.003091 | -9.124480 | -4.398286 | -5.446709 | -3.476588 | -3.543392 | ... | -16.546086 | -0.700534 | -14.919772 | -5.130468 | -1.959767 | -12.389941 | -2.258212 | -1.509382 | -4.102402 | -2.914103 |
SMSR_linj_500600_DPsd | 9.660953 | 6.813903 | 14.502102 | NaN | 9.550193 | 11.361092 | 10.270239 | 11.104630 | 8.447056 | 6.763741 | ... | 11.726403 | 7.186386 | 13.197983 | 8.916590 | 6.994755 | 8.385819 | 9.242493 | 10.248535 | 5.844371 | 8.385738 |
SMSR_linj_500600_DPct | 88.000000 | 102.000000 | 83.000000 | NaN | 80.000000 | 91.000000 | 83.000000 | 99.000000 | 99.000000 | 91.000000 | ... | 79.000000 | 99.000000 | 96.000000 | 100.000000 | 101.000000 | 98.000000 | 94.000000 | 101.000000 | 99.000000 | 98.000000 |
96 rows × 97 columns
df_sms = match('DP')
df_sms_out = pd.DataFrame(index = df_sms.index)
for c in df_sms.columns:
trimname = 's_' + c[5:]
if ("DPct" in c) or ("DPm" in c):
df_sms_out[trimname] = df_sms[c]
else:
df_sms_out[trimname + '_trunc'] = truncate(df_sms[c])
df_sms_out.T
SMSR_iso5t1_DPsd limits: 8.60087044757, 0.203275967148 truncated 10.6900950028 to 8.60087044757. truncated 9.63481950073 to 8.60087044757. SMSR_iso8t1_DPsd limits: 9.87651971902, -0.763594860008 SMSR_iso5t2_DPsd limits: 10.3558807623, -0.608126417552 truncated 11.8612907326 to 10.3558807623. truncated 13.5791256854 to 10.3558807623. SMSR_iso8t2_DPsd limits: 9.59878522787, -0.712988554483 truncated 11.8655977545 to 9.59878522787. SMSR_lin5t_DPsd limits: 10.8070076853, 0.158185111673 truncated 11.8635409538 to 10.8070076853. SMSR_lin8t_DPsd limits: 10.0280231673, 0.294474921876 truncated 11.0493026542 to 10.0280231673. truncated 11.0265619805 to 10.0280231673. SMSR_phase5t_DPsd limits: 16.7535407837, -2.54631807882 truncated 18.2862100575 to 16.7535407837. truncated 27.5098384048 to 16.7535407837. truncated 17.0772452267 to 16.7535407837. SMSR_phase8t_DPsd limits: 25.0649109419, -9.78174786142 truncated 30.2924106841 to 25.0649109419. truncated 29.3655735002 to 25.0649109419. truncated 31.3696714705 to 25.0649109419. truncated 35.3537566366 to 25.0649109419. SMSR_iso5j_DPsd limits: 9.76005728626, 1.95406060147 truncated 10.4387748387 to 9.76005728626. truncated 10.4313044455 to 9.76005728626. truncated 11.36798779 to 9.76005728626. SMSR_iso8j_DPsd limits: 10.4665189452, 1.68514052117 SMSR_lin5j_DPsd limits: 11.2443131701, 1.72243300701 truncated 11.4630163932 to 11.2443131701. SMSR_lin8j_DPsd limits: 12.8418099592, 3.35718360955 truncated 13.7019946774 to 12.8418099592. SMSR_phase5j_DPsd limits: 19.4590993849, -2.59075231868 truncated 29.2275328231 to 19.4590993849. truncated 19.5795283111 to 19.4590993849. truncated 22.6466134823 to 19.4590993849. SMSR_phase8j_DPsd limits: 25.8521354004, -8.65582694262 truncated 27.0387786759 to 25.8521354004. truncated 32.4262272662 to 25.8521354004. truncated 33.7193492796 to 25.8521354004. truncated 39.7947850535 to 25.8521354004. SMSR_phase8j_psk_DPsd limits: 14.1453144241, 1.0792810039 truncated 14.9105059267 to 14.1453144241. truncated 17.3588478049 to 14.1453144241. SMSR_phase8j_psr_DPsd limits: 14.1638879692, 1.07619626791 truncated 14.9105059267 to 14.1638879692. truncated 16.8504597429 to 14.1638879692. truncated 14.7323511959 to 14.1638879692. SMSR_phase8t_psk_DPsd limits: 17.2730425126, -1.16562527323 truncated 19.5324307943 to 17.2730425126. truncated 19.6005924732 to 17.2730425126. SMSR_phase8t_psr_DPsd limits: 17.1087270275, -0.937097835085 truncated 19.5324307943 to 17.1087270275. truncated 19.1031420415 to 17.1087270275. SMSR_phase5j_psk_DPsd limits: 15.3704159134, 3.24397748433 SMSR_phase5j_psr_DPsd limits: 15.4729098117, 3.17698380026 SMSR_phase5t_psk_DPsd limits: 15.6260466573, 0.594047665708 truncated 16.5909759069 to 15.6260466573. SMSR_phase5t_psr_DPsd limits: 15.4723438306, 0.772750861551 truncated 15.9840524432 to 15.4723438306. truncated 15.5549046358 to 15.4723438306. SMSR_phase5t_nrm_DPsd limits: 9.57906060484, 0.0863548982969 truncated 11.167463781 to 9.57906060484. SMSR_phase8t_nrm_DPsd limits: 9.70501390299, -0.527050525708 truncated 10.2613675719 to 9.70501390299. SMSR_phase5j_nrm_DPsd limits: 10.7605241168, 1.75917296527 SMSR_phase8j_nrm_DPsd limits: 11.2631174044, 1.25496949494 SMSR_lint_610690_DPsd limits: 13.7149460015, -1.33088075612 truncated 14.2142951 to 13.7149460015. truncated 13.7196181966 to 13.7149460015. SMSR_linj_610690_DPsd limits: 14.9057616429, 1.91966356957 SMSR_lint_700800_DPsd limits: 12.9349960801, -1.2967639024 SMSR_lint_500600_DPsd limits: 11.9556365861, -0.349007960833 truncated 15.0164273031 to 11.9556365861. SMSR_linj_700800_DPsd limits: 11.94468936, 1.82632330467 truncated 13.0389206706 to 11.94468936. SMSR_linj_500600_DPsd limits: 14.3891779854, 3.45206607345 truncated 14.5021015221 to 14.3891779854.
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s_iso5t1_DPm | -3.202915 | -2.840413 | -2.290274 | -3.022502 | -0.588655 | -10.975295 | -3.609441 | -1.953836 | -5.568016 | -5.484477 | ... | -10.934370 | -0.918811 | -1.107983 | -0.594511 | -0.838101 | -7.211448 | -3.449031 | -0.357402 | -5.634339 | -4.897263 |
s_iso5t1_DPsd_trunc | 8.582088 | 3.423481 | 3.607109 | 3.291210 | 2.583675 | 7.004012 | 4.608983 | 3.167766 | 3.170995 | 3.186261 | ... | 6.123362 | 2.799212 | 4.586392 | 4.075651 | 2.487950 | 5.184207 | 5.405130 | 2.827062 | 5.538719 | 4.245075 |
s_iso5t1_DPct | 109.000000 | 116.000000 | 117.000000 | 116.000000 | 116.000000 | 117.000000 | 116.000000 | 114.000000 | 116.000000 | 110.000000 | ... | 114.000000 | 115.000000 | 113.000000 | 112.000000 | 113.000000 | 114.000000 | 114.000000 | 114.000000 | 98.000000 | 114.000000 |
s_iso8t1_DPm | -3.198754 | -2.827224 | -13.311147 | -0.553203 | -2.327200 | -3.092538 | -1.458121 | 0.968610 | -2.741571 | 0.354671 | ... | -5.737524 | 0.205687 | -0.826352 | -2.226755 | -1.372491 | -1.247007 | 1.162443 | -0.060845 | -9.056157 | -3.304496 |
s_iso8t1_DPsd_trunc | 7.944039 | 2.964738 | 9.624027 | 5.005393 | 2.431597 | 6.505625 | 4.433459 | 2.268350 | 4.452189 | 4.964179 | ... | 8.144077 | 2.344456 | 6.235354 | 3.659721 | 2.334648 | 5.438937 | 3.613148 | 2.767637 | 4.298762 | 5.691151 |
s_iso8t1_DPct | 84.000000 | 104.000000 | 103.000000 | 106.000000 | 107.000000 | 101.000000 | 106.000000 | 104.000000 | 107.000000 | 103.000000 | ... | 98.000000 | 104.000000 | 104.000000 | 106.000000 | 106.000000 | 103.000000 | 106.000000 | 104.000000 | 102.000000 | 105.000000 |
s_iso5t2_DPm | -7.320309 | -0.609046 | -2.155283 | -2.628721 | -0.035660 | -3.025222 | -5.794925 | -1.195840 | -1.939757 | 0.793761 | ... | -10.848408 | 1.998485 | -2.249983 | -0.615855 | -2.109468 | -10.922451 | -1.735612 | 1.315051 | -11.177051 | -7.462378 |
s_iso5t2_DPsd_trunc | 8.438450 | 3.522687 | 3.871627 | 3.373599 | 2.612512 | 7.021192 | 5.208270 | 3.488723 | 3.281671 | 5.276439 | ... | 5.511892 | 2.801852 | 6.319350 | 4.716004 | 3.143240 | 4.653159 | 4.076174 | 3.167036 | 5.855743 | 4.506521 |
s_iso5t2_DPct | 102.000000 | 115.000000 | 113.000000 | 116.000000 | 117.000000 | 112.000000 | 116.000000 | 117.000000 | 116.000000 | 98.000000 | ... | 114.000000 | 113.000000 | 109.000000 | 116.000000 | 117.000000 | 114.000000 | 114.000000 | 116.000000 | 114.000000 | 110.000000 |
s_iso8t2_DPm | NaN | -4.064043 | -4.258572 | -0.759809 | -1.704668 | -1.823981 | -1.913083 | 0.890989 | -2.384110 | 0.150065 | ... | -1.732022 | -0.082369 | -6.790021 | -1.854593 | -1.226942 | -3.161301 | 0.728819 | -1.877539 | -4.290588 | -5.760608 |
s_iso8t2_DPsd_trunc | NaN | 3.101403 | 5.063099 | 5.568829 | 3.404020 | 6.388830 | 4.368410 | 2.339890 | 4.170302 | 4.422124 | ... | 5.838884 | 2.772427 | 6.077328 | 3.511975 | 2.276168 | 4.564913 | 2.928230 | 3.214585 | 3.713862 | 5.989694 |
s_iso8t2_DPct | NaN | 105.000000 | 105.000000 | 107.000000 | 107.000000 | 102.000000 | 106.000000 | 106.000000 | 107.000000 | 101.000000 | ... | 96.000000 | 104.000000 | 102.000000 | 102.000000 | 105.000000 | 105.000000 | 104.000000 | 105.000000 | 106.000000 | 101.000000 |
s_lin5t_DPm | NaN | -4.440936 | -8.097466 | -4.785265 | -2.822426 | -7.847768 | -8.081455 | -2.905709 | -2.321764 | -1.148714 | ... | -13.171428 | -1.070807 | -11.101964 | -4.147904 | -3.269580 | -6.060567 | -2.044255 | -4.613583 | -6.337286 | -7.769085 |
s_lin5t_DPsd_trunc | NaN | 3.804005 | 4.959168 | 4.888448 | 4.628010 | 7.409937 | 5.736285 | 3.561546 | 4.532819 | 6.311407 | ... | 10.807008 | 3.458209 | 7.417736 | 4.439277 | 3.199540 | 6.582733 | 5.129986 | 4.204554 | 5.773415 | 7.357143 |
s_lin5t_DPct | NaN | 155.000000 | 154.000000 | 154.000000 | 155.000000 | 149.000000 | 156.000000 | 155.000000 | 157.000000 | 149.000000 | ... | 121.000000 | 150.000000 | 148.000000 | 141.000000 | 154.000000 | 150.000000 | 154.000000 | 152.000000 | 157.000000 | 143.000000 |
s_lin8t_DPm | -1.466780 | -2.207710 | -1.394524 | -0.767171 | -1.167629 | -2.313080 | -0.667972 | -0.196244 | -2.514913 | 2.739635 | ... | -2.061360 | 2.228288 | 1.177425 | -1.581256 | -2.067663 | -4.227632 | 2.762494 | 0.389404 | -2.219642 | -3.069498 |
s_lin8t_DPsd_trunc | 10.028023 | 3.797363 | 5.997794 | 4.568257 | 4.510050 | 5.714329 | 4.928837 | 3.213383 | 5.215915 | 5.288537 | ... | 7.265519 | 2.892864 | 6.500740 | 3.968508 | 3.315846 | 6.912513 | 4.916196 | 3.477552 | 5.289070 | 5.277031 |
s_lin8t_DPct | 114.000000 | 156.000000 | 151.000000 | 156.000000 | 156.000000 | 155.000000 | 156.000000 | 156.000000 | 157.000000 | 153.000000 | ... | 147.000000 | 154.000000 | 153.000000 | 146.000000 | 156.000000 | 134.000000 | 151.000000 | 155.000000 | 153.000000 | 150.000000 |
s_phase5t_DPm | -8.176227 | -2.727189 | -4.037511 | -3.222491 | -0.056542 | -3.009335 | -4.691215 | -0.877772 | -1.028469 | 2.607912 | ... | -7.511362 | 2.751770 | -4.868650 | -0.907275 | -1.493659 | -7.582498 | 3.159855 | 2.719712 | -6.585747 | -2.178178 |
s_phase5t_DPsd_trunc | 16.753541 | 4.636219 | 6.101449 | 6.015203 | 5.880533 | 8.832377 | 5.631446 | 5.549757 | 6.495490 | 8.974706 | ... | 8.618557 | 4.244219 | 7.097735 | 10.690847 | 4.345646 | 9.021404 | 8.359160 | 4.832540 | 6.673500 | 7.992546 |
s_phase5t_DPct | 143.000000 | 155.000000 | 154.000000 | 155.000000 | 156.000000 | 144.000000 | 156.000000 | 154.000000 | 157.000000 | 147.000000 | ... | 154.000000 | 153.000000 | 152.000000 | 155.000000 | 152.000000 | 148.000000 | 153.000000 | 153.000000 | 153.000000 | 149.000000 |
s_phase8t_DPm | -4.950975 | -3.443382 | -11.261584 | 2.320622 | -0.540736 | -2.155307 | -3.021885 | 0.223643 | -2.313902 | 1.552980 | ... | -5.626800 | 0.941978 | -7.163684 | -3.237063 | -1.668319 | -1.081923 | 2.320129 | -0.321672 | -5.452793 | -3.261694 |
s_phase8t_DPsd_trunc | 25.064911 | 4.223427 | 6.801106 | 7.011985 | 3.834770 | 7.823846 | 5.464373 | 5.537968 | 5.319286 | 6.812584 | ... | 6.988509 | 5.164350 | 6.926644 | 5.851449 | 4.129328 | 7.146782 | 6.167211 | 4.298461 | 5.503316 | 11.802603 |
s_phase8t_DPct | 132.000000 | 156.000000 | 155.000000 | 156.000000 | 155.000000 | 154.000000 | 156.000000 | 154.000000 | 156.000000 | 120.000000 | ... | 154.000000 | 154.000000 | 147.000000 | 152.000000 | 156.000000 | 153.000000 | 152.000000 | 153.000000 | 154.000000 | 143.000000 |
s_iso5j_DPm | -7.884204 | -6.820084 | -5.270395 | -0.552320 | -3.343108 | -6.488198 | -3.588275 | -2.625491 | -5.292310 | -7.260642 | ... | -11.322089 | 0.992438 | -15.303739 | -1.575724 | -2.164280 | -14.291407 | 2.168237 | 1.390152 | -8.451801 | -1.549586 |
s_iso5j_DPsd_trunc | 4.344289 | 5.180662 | 5.260285 | 6.896868 | 6.576153 | 6.369871 | 5.687223 | 5.210024 | 6.350138 | 5.514704 | ... | 6.121927 | 5.109652 | 8.639169 | 6.185684 | 4.871451 | 5.396906 | 8.370299 | 4.820479 | 5.017404 | 5.578518 |
s_iso5j_DPct | 103.000000 | 106.000000 | 106.000000 | 106.000000 | 107.000000 | 102.000000 | 106.000000 | 107.000000 | 107.000000 | 100.000000 | ... | 105.000000 | 104.000000 | 96.000000 | 104.000000 | 105.000000 | 101.000000 | 104.000000 | 104.000000 | 104.000000 | 105.000000 |
s_iso8j_DPm | NaN | -6.218841 | -10.064092 | -4.987855 | -9.660025 | -7.975747 | -8.929854 | 1.649018 | -6.925825 | -2.223467 | ... | -12.004863 | -2.133730 | -11.930682 | -5.926679 | -5.893919 | -4.632284 | 0.058726 | -5.148439 | -7.448876 | -10.669810 |
s_iso8j_DPsd_trunc | NaN | 5.435017 | 8.475535 | 5.883866 | 5.660678 | 6.168150 | 5.515570 | 5.788754 | 6.075278 | 6.741509 | ... | 8.035791 | 3.907462 | 6.181960 | 5.100964 | 5.519451 | 4.905857 | 6.811418 | 4.125815 | 4.164116 | 5.553144 |
s_iso8j_DPct | NaN | 106.000000 | 107.000000 | 105.000000 | 107.000000 | 106.000000 | 107.000000 | 106.000000 | 107.000000 | 100.000000 | ... | 103.000000 | 104.000000 | 103.000000 | 104.000000 | 106.000000 | 106.000000 | 106.000000 | 106.000000 | 104.000000 | 107.000000 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
s_phase5t_nrm_DPm | NaN | -3.494445 | -6.023428 | -4.044406 | -0.096803 | -4.921805 | -6.586367 | -1.663917 | -2.646197 | NaN | ... | -5.554789 | 1.637512 | -2.384650 | -2.340948 | -1.170212 | NaN | 8.454378 | 2.841347 | -6.206281 | NaN |
s_phase5t_nrm_DPsd_trunc | NaN | 3.447075 | 5.069596 | 4.472898 | 3.686194 | 6.961424 | 5.077150 | 3.764374 | 4.054006 | NaN | ... | 8.672406 | 2.702044 | 7.063415 | 6.217980 | 3.661628 | NaN | 4.156459 | 3.642057 | 6.085368 | NaN |
s_phase5t_nrm_DPct | NaN | 58.000000 | 50.000000 | 58.000000 | 58.000000 | 51.000000 | 58.000000 | 50.000000 | 62.000000 | NaN | ... | 50.000000 | 49.000000 | 55.000000 | 54.000000 | 49.000000 | NaN | 49.000000 | 49.000000 | 50.000000 | NaN |
s_phase8t_nrm_DPm | NaN | -2.738324 | -12.613996 | 2.200945 | -0.550671 | -2.789755 | -1.841289 | -1.104148 | -2.114991 | NaN | ... | -8.864571 | 1.167334 | -6.006915 | -3.760204 | -2.231690 | NaN | 0.507251 | -0.716042 | -5.191294 | NaN |
s_phase8t_nrm_DPsd_trunc | NaN | 2.685532 | 7.588504 | 4.675063 | 2.654889 | 5.970898 | 4.366131 | 2.256640 | 4.987632 | NaN | ... | 6.390464 | 2.777576 | 5.458047 | 3.717879 | 3.070182 | NaN | 4.423234 | 2.651028 | 4.713386 | NaN |
s_phase8t_nrm_DPct | NaN | 65.000000 | 57.000000 | 61.000000 | 57.000000 | 60.000000 | 61.000000 | 50.000000 | 61.000000 | NaN | ... | 50.000000 | 57.000000 | 56.000000 | 56.000000 | 62.000000 | NaN | 48.000000 | 56.000000 | 50.000000 | NaN |
s_phase5j_nrm_DPm | -11.148969 | -3.325956 | -2.762491 | -7.626405 | -7.175838 | -8.508095 | -5.800228 | -10.196515 | -3.477511 | -4.929744 | ... | -11.247372 | 1.597960 | -10.049117 | -3.918322 | -5.244204 | NaN | -6.636588 | -4.443068 | NaN | NaN |
s_phase5j_nrm_DPsd_trunc | 9.831048 | 4.675775 | 8.093669 | 6.432369 | 8.611338 | 7.374434 | 6.468034 | 7.577475 | 5.779530 | 9.186420 | ... | 7.306804 | 6.047968 | 8.028764 | 6.527422 | 5.395921 | NaN | 5.343027 | 5.422876 | NaN | NaN |
s_phase5j_nrm_DPct | 46.000000 | 58.000000 | 52.000000 | 56.000000 | 58.000000 | 59.000000 | 58.000000 | 50.000000 | 62.000000 | 49.000000 | ... | 49.000000 | 44.000000 | 45.000000 | 49.000000 | 62.000000 | NaN | 50.000000 | 50.000000 | NaN | NaN |
s_phase8j_nrm_DPm | -9.921800 | -5.485820 | -11.594407 | 0.012137 | -1.695137 | -13.082447 | -9.177448 | -1.539006 | -5.807808 | -1.357039 | ... | -11.380748 | -4.138816 | -11.430767 | -8.163726 | -8.763223 | -2.627231 | -4.346571 | -4.187103 | NaN | -12.947842 |
s_phase8j_nrm_DPsd_trunc | 6.769322 | 3.882251 | 7.146704 | 5.035754 | 9.043339 | 9.028088 | 5.704594 | 5.154211 | 6.207823 | 6.458522 | ... | 6.210700 | 4.500170 | 7.667751 | 4.890995 | 5.952829 | 7.561797 | 6.687784 | 4.882306 | NaN | 5.635160 |
s_phase8j_nrm_DPct | 49.000000 | 62.000000 | 57.000000 | 58.000000 | 62.000000 | 50.000000 | 62.000000 | 58.000000 | 62.000000 | 50.000000 | ... | 58.000000 | 50.000000 | 61.000000 | 57.000000 | 55.000000 | 56.000000 | 58.000000 | 58.000000 | NaN | 58.000000 |
s_lint_610690_DPm | -5.904932 | -3.629545 | -5.018314 | -1.965972 | -1.046649 | -6.135196 | -4.141515 | -1.082157 | -2.521295 | 1.547984 | ... | -13.675623 | 0.477503 | -5.995810 | -2.384010 | -1.992656 | -3.592472 | 1.334023 | -2.638309 | -3.920254 | -2.610475 |
s_lint_610690_DPsd_trunc | 13.714946 | 3.647491 | 6.254805 | 4.769801 | 5.044258 | 7.952984 | 6.309685 | 3.805393 | 4.389249 | 5.555110 | ... | 12.938677 | 3.283291 | 9.406283 | 4.755764 | 3.414272 | 5.783784 | 5.413948 | 4.346650 | 5.520486 | 5.307701 |
s_lint_610690_DPct | 70.000000 | 89.000000 | 90.000000 | 89.000000 | 90.000000 | 87.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 | ... | 85.000000 | 90.000000 | 88.000000 | 85.000000 | 90.000000 | 90.000000 | 90.000000 | 89.000000 | 90.000000 | 87.000000 |
s_linj_610690_DPm | -11.469696 | -4.611525 | -6.104771 | NaN | -2.766766 | -5.984495 | -1.798480 | -2.341115 | -6.378774 | -2.082932 | ... | NaN | 1.346563 | -17.484409 | -4.747037 | -3.671017 | -10.843955 | -1.537152 | -1.352451 | -3.832061 | -5.911024 |
s_linj_610690_DPsd_trunc | 10.788868 | 6.692989 | 14.833960 | NaN | 12.881389 | 7.355279 | 10.090107 | 7.873377 | 7.713142 | 7.518116 | ... | NaN | 6.278382 | 9.227213 | 6.783321 | 5.841060 | 7.550096 | 7.191732 | 4.922011 | 7.405898 | 11.879577 |
s_linj_610690_DPct | 88.000000 | 89.000000 | 86.000000 | NaN | 84.000000 | 88.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 | ... | NaN | 90.000000 | 88.000000 | 90.000000 | 89.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 | 90.000000 |
s_lint_700800_DPm | NaN | -2.836999 | -5.324347 | -2.588808 | -0.534889 | -3.920463 | -2.249709 | -1.423945 | -3.538202 | 2.053367 | ... | NaN | -0.203763 | -2.421917 | -3.037278 | -3.049772 | -3.185119 | 0.806381 | -1.259571 | -4.405488 | -6.258277 |
s_lint_700800_DPsd_trunc | NaN | 4.188447 | 6.278797 | 5.991372 | 4.800754 | 6.452870 | 5.338793 | 3.525517 | 5.302861 | 5.957211 | ... | NaN | 3.474329 | 9.170531 | 4.447257 | 3.353788 | 8.064835 | 3.622448 | 3.628472 | 5.876414 | 9.321889 |
s_lint_700800_DPct | NaN | 100.000000 | 99.000000 | 99.000000 | 100.000000 | 97.000000 | 101.000000 | 100.000000 | 102.000000 | 94.000000 | ... | NaN | 95.000000 | 97.000000 | 90.000000 | 99.000000 | 81.000000 | 99.000000 | 98.000000 | 102.000000 | 87.000000 |
s_lint_500600_DPm | NaN | -2.935850 | -3.679656 | -3.081273 | -4.239167 | -4.696701 | -6.162629 | -2.058697 | -1.059976 | -0.138641 | ... | -1.890201 | 1.884941 | -6.121897 | -3.185709 | -2.553906 | -8.668644 | -0.270897 | -2.702535 | -3.943322 | -6.960489 |
s_lint_500600_DPsd_trunc | NaN | 3.763389 | 7.094297 | 4.338958 | 3.428162 | 6.109977 | 7.413205 | 3.699728 | 4.834872 | 6.247794 | ... | 9.205697 | 3.589876 | 9.466895 | 3.924627 | 2.970704 | 5.239500 | 6.837897 | 5.603176 | 5.913442 | 4.700504 |
s_lint_500600_DPct | NaN | 102.000000 | 96.000000 | 102.000000 | 101.000000 | 101.000000 | 101.000000 | 101.000000 | 102.000000 | 98.000000 | ... | 96.000000 | 99.000000 | 97.000000 | 95.000000 | 101.000000 | 94.000000 | 96.000000 | 100.000000 | 98.000000 | 99.000000 |
s_linj_700800_DPm | -4.543913 | -5.536776 | -6.491154 | NaN | NaN | -9.080940 | -5.285475 | -1.128587 | -6.143924 | 1.101778 | ... | NaN | -0.760195 | NaN | -5.754093 | -2.759417 | -4.056123 | -2.236343 | -2.155906 | -6.996401 | -8.532782 |
s_linj_700800_DPsd_trunc | 8.344733 | 5.658566 | 8.351930 | NaN | NaN | 7.484739 | 7.572755 | 5.879357 | 6.601137 | 6.673417 | ... | NaN | 5.307915 | NaN | 5.832539 | 5.775269 | 5.086778 | 5.948107 | 5.018592 | 4.189604 | 10.555065 |
s_linj_700800_DPct | 82.000000 | 102.000000 | 94.000000 | NaN | NaN | 95.000000 | 101.000000 | 99.000000 | 100.000000 | 93.000000 | ... | NaN | 98.000000 | NaN | 101.000000 | 98.000000 | 99.000000 | 97.000000 | 97.000000 | 91.000000 | 98.000000 |
s_linj_500600_DPm | -9.168428 | -4.356788 | -6.390442 | NaN | -6.003091 | -9.124480 | -4.398286 | -5.446709 | -3.476588 | -3.543392 | ... | -16.546086 | -0.700534 | -14.919772 | -5.130468 | -1.959767 | -12.389941 | -2.258212 | -1.509382 | -4.102402 | -2.914103 |
s_linj_500600_DPsd_trunc | 9.660953 | 6.813903 | 14.389178 | NaN | 9.550193 | 11.361092 | 10.270239 | 11.104630 | 8.447056 | 6.763741 | ... | 11.726403 | 7.186386 | 13.197983 | 8.916590 | 6.994755 | 8.385819 | 9.242493 | 10.248535 | 5.844371 | 8.385738 |
s_linj_500600_DPct | 88.000000 | 102.000000 | 83.000000 | NaN | 80.000000 | 91.000000 | 83.000000 | 99.000000 | 99.000000 | 91.000000 | ... | 79.000000 | 99.000000 | 96.000000 | 100.000000 | 101.000000 | 98.000000 | 94.000000 | 101.000000 | 99.000000 | 98.000000 |
96 rows × 97 columns
df_nonzero_transformed = match('nonzero')
df_log_transformed = match('ln1p')
isip_using = ['I5P4_local_trunc',
'I8P4_local_trunc',
'I5P4_drift_trunc',
'I8P4_drift_trunc',]
df_log_isips = np.log(df_isip_out[isip_using])
df_log_isips.columns = [c + "_log" for c in df_log_isips.columns]
to_log = [c for c in df_sms_out if "DPsd" in c]
df_log_sms = np.log(df_sms_out[to_log])
df_log_sms.columns = [c + "_log" for c in df_log_sms.columns]
df_log_sms
s_iso5t1_DPsd_trunc_log | s_iso8t1_DPsd_trunc_log | s_iso5t2_DPsd_trunc_log | s_iso8t2_DPsd_trunc_log | s_lin5t_DPsd_trunc_log | s_lin8t_DPsd_trunc_log | s_phase5t_DPsd_trunc_log | s_phase8t_DPsd_trunc_log | s_iso5j_DPsd_trunc_log | s_iso8j_DPsd_trunc_log | ... | s_phase5t_nrm_DPsd_trunc_log | s_phase8t_nrm_DPsd_trunc_log | s_phase5j_nrm_DPsd_trunc_log | s_phase8j_nrm_DPsd_trunc_log | s_lint_610690_DPsd_trunc_log | s_linj_610690_DPsd_trunc_log | s_lint_700800_DPsd_trunc_log | s_lint_500600_DPsd_trunc_log | s_linj_700800_DPsd_trunc_log | s_linj_500600_DPsd_trunc_log | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
015 | 2.149677 | 2.072422 | 2.132799 | NaN | NaN | 2.305383 | 2.818610 | 3.221469 | 1.468862 | NaN | ... | NaN | NaN | 2.285546 | 1.912401 | 2.618486 | 2.378515 | NaN | NaN | 2.121631 | 2.268092 |
016 | 1.230658 | 1.086789 | 1.259224 | 1.131855 | 1.336055 | 1.334307 | 1.533899 | 1.440647 | 1.644933 | 1.692863 | ... | 1.237526 | 0.987879 | 1.542395 | 1.356415 | 1.294040 | 1.901060 | 1.432330 | 1.325320 | 1.733171 | 1.918965 |
017 | 1.282907 | 2.264263 | 1.353675 | 1.621979 | 1.601238 | 1.791392 | 1.808526 | 1.917085 | 1.660185 | 2.137184 | ... | 1.623261 | 2.026634 | 2.091082 | 1.966651 | 1.833350 | 2.696919 | 1.837178 | 1.959291 | 2.122493 | 2.666476 |
018 | 1.191255 | 1.610516 | 1.215980 | 1.717185 | 1.586875 | 1.519132 | 1.794290 | 1.947621 | 1.931067 | 1.772214 | ... | 1.498037 | 1.542243 | 1.861343 | 1.616563 | 1.562305 | NaN | 1.790320 | 1.467634 | NaN | NaN |
019 | 0.949213 | 0.888548 | 0.960312 | 1.224957 | 1.532127 | 1.506308 | 1.771647 | 1.344109 | 1.883450 | 1.733544 | ... | 1.304594 | 0.976403 | 2.153080 | 2.202028 | 1.618251 | 2.555784 | 1.568773 | 1.232024 | NaN | 2.256561 |
020 | 1.946483 | 1.872667 | 1.948933 | 1.854551 | 2.002822 | 1.742977 | 2.178424 | 2.057176 | 1.851579 | 1.819399 | ... | 1.940384 | 1.786897 | 1.998019 | 2.200341 | 2.073547 | 1.995418 | 1.864525 | 1.809923 | 2.012866 | 2.430195 |
021 | 1.528007 | 1.489180 | 1.650248 | 1.474399 | 1.746812 | 1.595103 | 1.728366 | 1.698249 | 1.738222 | 1.707575 | ... | 1.624750 | 1.473877 | 1.866872 | 1.741272 | 1.842086 | 2.311555 | 1.675000 | 2.003263 | 2.024557 | 2.329250 |
022 | 1.153027 | 0.819053 | 1.249536 | 0.850104 | 1.270195 | 1.167324 | 1.713754 | 1.711628 | 1.650584 | 1.755917 | ... | 1.325582 | 0.813877 | 2.025180 | 1.639814 | 1.336419 | 2.063487 | 1.260027 | 1.308259 | 1.771447 | 2.407362 |
024 | 1.154046 | 1.493396 | 1.188353 | 1.427988 | 1.511344 | 1.651714 | 1.871108 | 1.671339 | 1.848477 | 1.804228 | ... | 1.399705 | 1.606961 | 1.754322 | 1.825810 | 1.479158 | 2.042926 | 1.668247 | 1.575855 | 1.887242 | 2.133818 |
025 | 1.158848 | 1.602248 | 1.663251 | 1.486620 | 1.842359 | 1.665542 | 2.194410 | 1.918771 | 1.707418 | 1.908284 | ... | NaN | NaN | 2.217726 | 1.865401 | 1.714718 | 2.017316 | 1.784602 | 1.832228 | 1.898132 | 1.911576 |
027 | 1.214988 | 0.828005 | 1.164947 | 0.866506 | 1.186399 | 1.280419 | 1.769993 | 1.497272 | 1.665816 | 1.558870 | ... | 1.022401 | 1.141060 | NaN | 1.406696 | 1.267500 | 2.307323 | 1.217068 | 1.283543 | 2.480287 | NaN |
028 | 1.366933 | 1.220317 | 1.463579 | 1.125788 | 1.408837 | 1.402979 | 1.706406 | 1.600644 | 1.632847 | 1.634404 | ... | 1.732058 | 1.030859 | 1.826140 | 1.704556 | 1.613654 | 1.895121 | 1.317712 | 1.434900 | 1.676633 | 1.955105 |
029 | 1.586056 | 2.132944 | 1.685272 | 2.148802 | 2.265091 | NaN | 1.969318 | 2.183792 | 1.712491 | 1.953943 | ... | 1.398667 | 2.122931 | 1.593972 | 1.801566 | 2.588409 | 2.008597 | 2.496306 | NaN | 2.133729 | 2.217507 |
030 | 1.215353 | 1.032932 | 1.198445 | 1.286700 | 1.361396 | 1.611186 | 2.042024 | 1.660030 | 1.780308 | 1.653158 | ... | NaN | 1.359501 | 1.771705 | 1.732649 | 1.645449 | 1.736958 | 1.524256 | 1.541147 | 1.597709 | 2.159650 |
032 | 0.730980 | 0.953815 | 0.931665 | 0.957351 | 0.962286 | 1.067413 | 1.307490 | 1.427185 | 1.375821 | 1.585096 | ... | 0.994635 | 0.891385 | 1.650457 | 1.317304 | 0.989258 | 2.119965 | 0.864621 | 1.329376 | 2.015059 | 2.187460 |
033 | 1.294672 | 1.289049 | 1.381905 | 1.184637 | 1.381818 | 1.264568 | 1.775199 | 1.551936 | 1.539494 | 1.517684 | ... | NaN | 1.060631 | 2.111017 | 1.719209 | 1.456811 | 1.891067 | 1.317890 | 1.326461 | 1.542612 | 1.903832 |
034 | 0.950003 | 0.880918 | 1.032546 | 0.927776 | 1.277905 | 1.136625 | 1.275862 | 1.369869 | 1.417779 | 1.459033 | ... | 1.086269 | 1.016607 | 1.212294 | 1.629381 | 1.325006 | 1.831234 | 1.102818 | 1.561855 | 1.811249 | 2.092147 |
035 | 1.772024 | 1.604723 | 1.813727 | 1.506809 | 1.742526 | 1.511419 | 1.629492 | 1.688118 | 1.754228 | 2.106827 | ... | 1.537437 | 1.526862 | 1.633169 | 2.083243 | 1.873733 | 2.237716 | 1.956526 | 2.083396 | NaN | 2.365780 |
036 | 1.988666 | 2.270262 | 2.058694 | 2.255787 | 2.143534 | 1.993123 | 2.197500 | 2.439745 | 2.000589 | 2.120761 | ... | 2.053092 | 1.975793 | 2.296213 | 2.389359 | 2.119564 | 2.178587 | 2.357026 | 2.117718 | 2.305887 | 2.283641 |
038 | 1.349000 | 1.186314 | 1.200030 | 1.327411 | 1.935074 | 1.529049 | 1.610191 | 1.689371 | 1.606894 | 2.047864 | ... | 1.136915 | 1.425693 | 1.881336 | NaN | 2.175734 | 2.258559 | 1.555299 | 1.539148 | 1.885687 | NaN |
039 | 1.190395 | 1.659190 | 1.420872 | 1.269212 | 1.571259 | 1.533313 | 1.913795 | 1.979875 | 1.778573 | 1.474032 | ... | 1.729659 | 1.417454 | 1.952375 | 2.201466 | 1.540354 | 2.270023 | 1.713960 | 1.721283 | 2.113891 | 2.105389 |
040 | 1.507903 | 1.388848 | 1.335028 | 1.330698 | 1.602610 | 1.286600 | 1.975731 | 1.560555 | 1.633493 | 1.717268 | ... | 1.642887 | 1.135644 | 1.771934 | 1.778303 | 1.605856 | 2.250770 | 1.563731 | 1.299501 | 2.097774 | 2.608676 |
041 | 1.268629 | 1.156100 | 1.231555 | 1.221466 | 1.476011 | 1.342788 | 1.660153 | 1.653653 | 1.642166 | 1.665112 | ... | 1.056194 | 1.028205 | NaN | 1.796749 | 1.638167 | 2.196335 | 1.407219 | 1.563882 | 1.993542 | NaN |
043 | 1.770687 | 2.020416 | 1.887461 | 1.393694 | 2.027668 | 2.004970 | 2.065970 | 1.779030 | 1.948347 | 1.954462 | ... | 1.736332 | 1.753461 | 1.700811 | 2.048392 | 2.187593 | 2.030588 | 2.366285 | 2.023421 | 2.260785 | 2.052124 |
044 | 1.237567 | 1.075156 | 1.281449 | 1.319819 | 1.817071 | 1.671288 | 1.755334 | 1.633415 | 1.607361 | 1.897884 | ... | NaN | 1.481575 | 1.889121 | 1.628732 | 1.751771 | 2.436343 | 2.054416 | 1.517867 | 1.882276 | 2.350594 |
046 | 1.626021 | 1.682230 | 1.643886 | 1.272820 | 1.666841 | 1.768587 | 1.989147 | 1.736674 | 1.737062 | 1.860388 | ... | 1.877527 | 1.274797 | 2.111023 | 1.642103 | 1.838421 | 1.999868 | 1.642178 | 1.813503 | 1.671312 | 2.184300 |
047 | 1.380242 | 1.398949 | 1.650766 | 1.582803 | 1.722535 | 1.598411 | 1.792947 | 2.037111 | 1.736514 | 1.849484 | ... | 1.338539 | 1.514411 | 1.848916 | 1.932931 | 1.743906 | 1.997943 | 1.709317 | 1.714107 | 2.056160 | 2.176172 |
048 | 1.610828 | 1.472176 | 1.656246 | 1.803049 | 2.078463 | 1.711675 | 1.863482 | 2.087143 | 1.538067 | 1.518730 | ... | 1.410348 | 2.133366 | 1.622487 | 1.472279 | 1.994289 | 2.021399 | 2.135832 | 1.854509 | 1.928630 | 2.161561 |
049 | 2.151863 | 1.945161 | NaN | NaN | NaN | 2.305383 | 2.515234 | 3.221469 | 2.278298 | NaN | ... | 2.259580 | NaN | NaN | NaN | NaN | 2.579684 | NaN | NaN | NaN | NaN |
051 | 1.390749 | 1.182839 | 1.382496 | 1.271641 | 1.423009 | 1.359170 | 1.739229 | 1.898883 | 1.676281 | 1.539143 | ... | 1.715900 | 1.543755 | 2.140252 | 1.421400 | 1.626791 | 2.082926 | 1.548873 | 1.403939 | 1.931787 | 1.984162 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
091 | 1.449116 | 1.211588 | 1.333326 | 1.168292 | 1.566006 | 1.257787 | 1.597946 | 1.549170 | 1.499167 | 1.487188 | ... | 1.312618 | 1.201153 | 1.522880 | 1.496527 | 1.446165 | 1.612523 | 1.643412 | 1.396902 | 1.671687 | 2.032135 |
092 | 1.345000 | 1.487471 | 1.275930 | 1.485463 | 1.854254 | 1.759965 | 1.798088 | 1.784696 | 1.695897 | 1.930878 | ... | 1.593130 | 1.598847 | 2.037424 | 1.668468 | 1.788295 | 2.010605 | 2.163077 | 1.642071 | 1.604979 | 2.064925 |
093 | 1.585773 | 1.546694 | 1.637662 | 1.430066 | 1.811371 | 1.605881 | 1.860800 | 1.731256 | 1.739643 | 1.994637 | ... | 1.786350 | 1.494587 | 1.604424 | 1.937038 | 1.623223 | 2.210662 | 1.841742 | 1.887073 | 1.920084 | 2.071302 |
094 | 1.657986 | 1.631690 | 1.760155 | 1.702428 | 1.819522 | 1.731488 | 1.982937 | 2.056478 | 1.873470 | 2.043344 | ... | 1.623816 | 2.033164 | 1.822206 | 2.118901 | 1.959386 | 2.100451 | 2.046481 | 1.737708 | 1.894117 | 2.346369 |
095 | 1.350039 | 1.402022 | 1.648476 | 1.212946 | 1.625273 | 1.426034 | 1.679399 | 1.596319 | 1.818386 | 1.422117 | ... | 1.285899 | 1.673913 | 1.732342 | 1.435371 | 1.706072 | 2.514380 | 1.645232 | 1.619249 | 2.432453 | 2.160559 |
096 | 1.183688 | 1.257725 | 1.506640 | 1.262937 | 1.537900 | 1.572777 | 1.588762 | 1.686659 | 1.821079 | 1.710031 | ... | 1.223411 | 1.358376 | 1.693641 | 1.816137 | 1.590050 | 1.837736 | 1.514546 | 1.615584 | 1.791156 | 2.050975 |
097 | 1.553118 | 1.441133 | 1.734186 | 1.340447 | 1.491838 | 1.498280 | 1.793375 | 1.706463 | 1.681646 | 1.762506 | ... | 1.643316 | 1.475694 | 1.937992 | 1.937221 | 1.609334 | 2.240401 | 1.476850 | 1.596564 | 1.803030 | 2.166801 |
098 | 1.394417 | 1.117052 | 1.130257 | 1.176846 | 1.352986 | 1.430074 | 1.464891 | 1.556825 | 1.552316 | 1.439605 | ... | 1.235928 | NaN | 1.843422 | 1.353870 | 1.610200 | 1.781598 | 1.290688 | 1.401033 | 1.582123 | 1.988594 |
099 | 1.447692 | 1.463197 | 1.539633 | 1.345424 | 1.514859 | 1.873326 | 2.132830 | 2.150460 | 1.987616 | 1.823680 | ... | 1.521154 | 1.685878 | 1.807076 | 1.931085 | 1.817515 | 2.103678 | 1.696299 | 1.735805 | 1.902442 | 2.210110 |
100 | 1.499580 | 1.267989 | 1.156310 | 1.214675 | 1.667821 | 1.396713 | 1.642567 | 1.625895 | 1.685254 | 1.548109 | ... | 1.394467 | 1.579856 | 2.033780 | 1.962556 | 1.610190 | 2.184703 | 1.583766 | 1.712850 | 1.852059 | 2.106062 |
101 | 1.704965 | 1.360519 | 1.659895 | 1.557076 | 1.841867 | 1.731204 | 1.996825 | 1.913525 | 1.830431 | 1.909514 | ... | 1.593418 | 1.842436 | 1.767468 | 1.950009 | 1.987316 | 2.057702 | 1.736324 | 1.606754 | 1.904446 | 2.066410 |
102 | 1.392795 | 1.183798 | 1.490049 | 1.149026 | 1.563346 | 1.734824 | 1.683533 | 1.647868 | 1.773656 | 1.655395 | ... | 1.104012 | 1.233142 | 1.900904 | 1.670638 | 1.966298 | 1.993827 | 1.593197 | 1.761838 | 1.810530 | 2.153517 |
103 | 1.375605 | 1.484798 | 1.526401 | 1.354565 | 1.499534 | 1.789324 | 1.735533 | 1.555921 | 1.849073 | 1.791545 | ... | 1.196766 | 1.245895 | 1.907844 | 1.988744 | 1.822989 | 1.998910 | 1.575008 | 1.866607 | 1.864822 | 2.111631 |
104 | 1.545028 | 1.747669 | 1.566731 | NaN | 2.231160 | 1.791717 | 2.622076 | 2.938816 | NaN | 1.596170 | ... | NaN | NaN | NaN | 2.238897 | 2.198332 | NaN | 2.041397 | 2.050829 | NaN | 2.473611 |
105 | 1.801153 | 1.739995 | 1.901948 | 2.243605 | 2.276922 | 2.141057 | 1.944161 | 2.155787 | 1.862689 | 2.063905 | ... | 1.783304 | 1.549508 | 2.134887 | NaN | 2.275106 | 2.419920 | 2.286061 | 2.179113 | NaN | 2.522852 |
107 | 1.192646 | 1.036598 | 1.323714 | 1.213214 | 1.377351 | 1.461565 | 1.456936 | 1.391359 | 1.649415 | 1.594488 | ... | NaN | 1.219295 | NaN | NaN | 1.461152 | 1.954683 | 1.554497 | 1.276529 | 1.820042 | 2.091235 |
108 | 1.524372 | 1.342621 | 1.494930 | 1.296824 | 1.550446 | 1.647128 | 1.853779 | 1.669186 | 1.553136 | 1.678444 | ... | 1.763007 | 1.432985 | 1.862758 | 1.727473 | 1.806187 | 2.094723 | 1.748951 | 1.554200 | 1.690032 | 2.452959 |
109 | 1.405253 | 1.173891 | 1.531845 | 1.431531 | 1.680366 | 1.786235 | 2.200408 | 2.016352 | 1.675157 | 1.709617 | ... | NaN | NaN | NaN | 1.611400 | 1.989616 | 1.932359 | 1.704973 | 1.999620 | 1.626547 | 1.956897 |
110 | 1.345261 | 1.356246 | 1.296935 | 1.043001 | 1.357591 | 1.223697 | 1.538119 | 1.445808 | 1.534092 | 1.467484 | ... | 1.218798 | 0.844649 | 1.516383 | 1.374264 | 1.385988 | 2.029942 | 1.366578 | 1.406781 | 1.841802 | 2.002637 |
111 | 1.402827 | 1.438419 | 1.544511 | 1.533494 | 1.394360 | 1.456176 | 1.612266 | 1.943491 | 1.767450 | 1.599098 | ... | 1.442153 | 1.315708 | 1.677284 | 1.753042 | 1.284367 | 2.027371 | 1.518075 | 1.502315 | 1.830891 | 2.010234 |
112 | 1.812111 | 2.097291 | 1.706908 | 1.764540 | 2.380195 | 1.983140 | 2.153918 | 1.944267 | 1.811877 | 2.083905 | ... | 2.160146 | 1.854807 | 1.988806 | 1.826274 | 2.560221 | NaN | NaN | 2.219823 | NaN | 2.461843 |
113 | 1.029338 | 0.852053 | 1.030281 | 1.019723 | 1.240751 | 1.062247 | 1.445558 | 1.641779 | 1.631131 | 1.362888 | ... | 0.994008 | 1.021579 | 1.799722 | 1.504115 | 1.188846 | 1.837112 | 1.245401 | 1.278118 | 1.669199 | 1.972188 |
114 | 1.523094 | 1.830235 | 1.843616 | 1.804565 | 2.003874 | 1.871916 | 1.959776 | 1.935375 | 2.156306 | 1.821635 | ... | 1.954929 | 1.697091 | 2.083031 | 2.037023 | 2.241378 | 2.222157 | 2.215995 | 2.247801 | NaN | 2.580064 |
115 | 1.405030 | 1.297387 | 1.550962 | 1.256179 | 1.490492 | 1.378390 | 2.369388 | 1.766689 | 1.822238 | 1.629429 | ... | 1.827445 | 1.313153 | 1.876012 | 1.587396 | 1.559357 | 1.914467 | 1.492288 | 1.367271 | 1.763452 | 2.187914 |
116 | 0.911459 | 0.847861 | 1.145254 | 0.822493 | 1.163007 | 1.198713 | 1.469174 | 1.418115 | 1.583392 | 1.708278 | ... | 1.297908 | 1.121737 | 1.685643 | 1.783867 | 1.227964 | 1.764912 | 1.210090 | 1.088799 | 1.753585 | 1.945161 |
117 | 1.645617 | 1.693584 | 1.537546 | 1.518399 | 1.884450 | 1.933333 | 2.199600 | 1.966662 | 1.685826 | 1.590430 | ... | NaN | NaN | NaN | 2.023109 | 1.755058 | 2.021560 | 2.087513 | 1.656226 | 1.626645 | 2.126542 |
118 | 1.687348 | 1.284579 | 1.405159 | 1.074398 | 1.635103 | 1.592535 | 2.123358 | 1.819247 | 2.124690 | 1.918600 | ... | 1.424663 | 1.486871 | 1.675792 | 1.900283 | 1.688979 | 1.972932 | 1.287150 | 1.922480 | 1.783073 | 2.223812 |
119 | 1.039238 | 1.017994 | 1.152796 | 1.167698 | 1.436168 | 1.246329 | 1.575372 | 1.458257 | 1.572873 | 1.417264 | ... | 1.292549 | 0.974948 | 1.690626 | 1.585618 | 1.469405 | 1.593717 | 1.288812 | 1.723334 | 1.613149 | 2.327135 |
120 | 1.711763 | 1.458327 | 1.767423 | 1.312072 | 1.753264 | 1.665642 | 1.898144 | 1.705351 | 1.612913 | 1.426504 | ... | 1.805887 | 1.550407 | NaN | NaN | 1.708466 | 2.002277 | 1.770947 | 1.777228 | 1.432606 | 1.765479 |
121 | 1.445759 | 1.738912 | 1.505525 | 1.790040 | 1.995672 | 1.663364 | 2.078509 | 2.468320 | 1.718923 | 1.714364 | ... | NaN | NaN | NaN | 1.729026 | 1.669159 | 2.474821 | 2.232365 | 1.547670 | 2.356606 | 2.126532 |
97 rows × 32 columns
df_to_analyze = pd.concat(axis=1,
objs=[df_scales,
df_constructed,
df_log_transformed,
df_nonzero_transformed,
df_isip_out,
df_log_isips,
df_sms_out,
df_log_sms,
])
#concat_matches(df_to_analyze, 'I?P4_local_trunc|I?P4_drift_trunc').T
concat_matches(df_to_analyze, 'log$').T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
I5P4_local_trunc_log | 1.071104 | 0.990654 | 1.016912 | 0.732585 | 0.335030 | 1.211177 | 1.239612 | 0.968731 | 1.041781 | 0.943231 | ... | 0.966636 | 0.641233 | 1.583887 | 1.080096 | 0.820888 | 1.059421 | 1.153157 | 0.500176 | 0.875803 | 0.762425 |
I8P4_local_trunc_log | 1.353651 | 1.090421 | 1.129266 | 1.189352 | 0.867865 | 1.331203 | 0.968201 | 0.438561 | 0.976047 | 0.850436 | ... | 1.189684 | 0.534416 | 1.527451 | 0.778371 | 0.730665 | 0.981642 | 1.211295 | 0.745374 | 0.917900 | 1.159389 |
I5P4_drift_trunc_log | 1.287251 | 0.758169 | 0.961024 | 0.500582 | 0.233307 | 1.145992 | 1.055617 | 0.707790 | 0.894311 | 1.179604 | ... | 1.459902 | 0.358835 | 1.359992 | 0.870168 | 0.569356 | 1.099729 | 1.165674 | 0.432005 | 0.875582 | 0.747801 |
I8P4_drift_trunc_log | 1.679217 | 0.764580 | 1.348997 | 0.810881 | 0.808227 | 1.422058 | 1.417857 | 0.819052 | 1.119786 | 1.584771 | ... | 1.521314 | 1.142280 | 1.256236 | 0.612206 | 0.460743 | 1.138041 | 1.019295 | 0.628255 | 0.836660 | 1.239180 |
s_iso5t1_DPsd_trunc_log | 2.149677 | 1.230658 | 1.282907 | 1.191255 | 0.949213 | 1.946483 | 1.528007 | 1.153027 | 1.154046 | 1.158848 | ... | 1.812111 | 1.029338 | 1.523094 | 1.405030 | 0.911459 | 1.645617 | 1.687348 | 1.039238 | 1.711763 | 1.445759 |
s_iso8t1_DPsd_trunc_log | 2.072422 | 1.086789 | 2.264263 | 1.610516 | 0.888548 | 1.872667 | 1.489180 | 0.819053 | 1.493396 | 1.602248 | ... | 2.097291 | 0.852053 | 1.830235 | 1.297387 | 0.847861 | 1.693584 | 1.284579 | 1.017994 | 1.458327 | 1.738912 |
s_iso5t2_DPsd_trunc_log | 2.132799 | 1.259224 | 1.353675 | 1.215980 | 0.960312 | 1.948933 | 1.650248 | 1.249536 | 1.188353 | 1.663251 | ... | 1.706908 | 1.030281 | 1.843616 | 1.550962 | 1.145254 | 1.537546 | 1.405159 | 1.152796 | 1.767423 | 1.505525 |
s_iso8t2_DPsd_trunc_log | NaN | 1.131855 | 1.621979 | 1.717185 | 1.224957 | 1.854551 | 1.474399 | 0.850104 | 1.427988 | 1.486620 | ... | 1.764540 | 1.019723 | 1.804565 | 1.256179 | 0.822493 | 1.518399 | 1.074398 | 1.167698 | 1.312072 | 1.790040 |
s_lin5t_DPsd_trunc_log | NaN | 1.336055 | 1.601238 | 1.586875 | 1.532127 | 2.002822 | 1.746812 | 1.270195 | 1.511344 | 1.842359 | ... | 2.380195 | 1.240751 | 2.003874 | 1.490492 | 1.163007 | 1.884450 | 1.635103 | 1.436168 | 1.753264 | 1.995672 |
s_lin8t_DPsd_trunc_log | 2.305383 | 1.334307 | 1.791392 | 1.519132 | 1.506308 | 1.742977 | 1.595103 | 1.167324 | 1.651714 | 1.665542 | ... | 1.983140 | 1.062247 | 1.871916 | 1.378390 | 1.198713 | 1.933333 | 1.592535 | 1.246329 | 1.665642 | 1.663364 |
s_phase5t_DPsd_trunc_log | 2.818610 | 1.533899 | 1.808526 | 1.794290 | 1.771647 | 2.178424 | 1.728366 | 1.713754 | 1.871108 | 2.194410 | ... | 2.153918 | 1.445558 | 1.959776 | 2.369388 | 1.469174 | 2.199600 | 2.123358 | 1.575372 | 1.898144 | 2.078509 |
s_phase8t_DPsd_trunc_log | 3.221469 | 1.440647 | 1.917085 | 1.947621 | 1.344109 | 2.057176 | 1.698249 | 1.711628 | 1.671339 | 1.918771 | ... | 1.944267 | 1.641779 | 1.935375 | 1.766689 | 1.418115 | 1.966662 | 1.819247 | 1.458257 | 1.705351 | 2.468320 |
s_iso5j_DPsd_trunc_log | 1.468862 | 1.644933 | 1.660185 | 1.931067 | 1.883450 | 1.851579 | 1.738222 | 1.650584 | 1.848477 | 1.707418 | ... | 1.811877 | 1.631131 | 2.156306 | 1.822238 | 1.583392 | 1.685826 | 2.124690 | 1.572873 | 1.612913 | 1.718923 |
s_iso8j_DPsd_trunc_log | NaN | 1.692863 | 2.137184 | 1.772214 | 1.733544 | 1.819399 | 1.707575 | 1.755917 | 1.804228 | 1.908284 | ... | 2.083905 | 1.362888 | 1.821635 | 1.629429 | 1.708278 | 1.590430 | 1.918600 | 1.417264 | 1.426504 | 1.714364 |
s_lin5j_DPsd_trunc_log | 2.015058 | 1.649390 | 1.871980 | NaN | NaN | 1.917798 | 1.620410 | 2.070804 | 1.787552 | 1.761812 | ... | NaN | 1.395519 | 2.419863 | 1.719042 | 1.585274 | 2.041828 | 1.756458 | 1.810486 | 1.675215 | 2.132917 |
s_lin8j_DPsd_trunc_log | 2.116002 | 1.890228 | 2.313866 | 2.099799 | 2.200428 | 2.308440 | 2.141411 | 1.997351 | 2.083193 | 2.043251 | ... | 2.183058 | 1.850603 | 2.326427 | 1.971129 | 1.811144 | 2.128409 | 2.040155 | 1.914217 | 1.817584 | 2.400156 |
s_phase5j_DPsd_trunc_log | 2.422405 | 1.774558 | 2.262908 | 2.144661 | 2.739569 | 2.087969 | 1.802783 | 2.051451 | 2.067474 | 2.104918 | ... | 2.155658 | 2.254326 | 2.238179 | 1.951401 | 1.814308 | 2.232164 | 1.880433 | 1.736957 | 2.057894 | 2.333196 |
s_phase8j_DPsd_trunc_log | 2.540202 | 1.624128 | 2.170845 | 1.740630 | 2.359743 | 2.445744 | 1.774009 | 1.614812 | 1.849079 | 1.919424 | ... | 1.981102 | 1.731812 | 2.011558 | 1.692822 | 1.864767 | 2.026249 | 1.911932 | 1.699957 | 1.899737 | 2.077448 |
s_phase8j_psk_DPsd_trunc_log | 1.661158 | 1.612924 | 2.027585 | 1.761192 | 2.566462 | 2.115852 | 1.885312 | 1.602012 | 1.797048 | 2.073824 | ... | 2.199807 | 2.058860 | 1.900451 | 1.992052 | 1.584186 | 2.110640 | 1.740867 | 1.964700 | 1.941108 | 2.165882 |
s_phase8j_psr_DPsd_trunc_log | 1.822314 | 1.612924 | 2.027585 | 1.761192 | 2.566462 | 2.115852 | 1.885312 | 1.602012 | 1.797048 | 2.073824 | ... | 2.199807 | 2.058860 | 1.900451 | 1.992052 | 1.584186 | 2.080504 | 1.740867 | 1.964700 | 1.941108 | 2.165882 |
s_phase8t_psk_DPsd_trunc_log | 2.448769 | 1.412633 | 2.169406 | 1.752274 | 1.517247 | 2.419211 | 1.976305 | 2.309860 | 1.974328 | NaN | ... | 1.951454 | 2.154065 | 1.922902 | 2.187971 | 1.518313 | 1.894646 | 1.690675 | 1.524311 | 2.218610 | 2.187834 |
s_phase8t_psr_DPsd_trunc_log | 2.527728 | 1.616072 | 2.169406 | 1.752274 | 1.517247 | 2.416043 | 1.976305 | 2.309860 | 1.974328 | 1.670599 | ... | 1.951454 | 2.163158 | 1.922902 | 2.187971 | 1.626977 | 1.977654 | 1.690675 | 1.627795 | 2.218610 | 2.246768 |
s_phase5j_psk_DPsd_trunc_log | 2.293633 | 2.047200 | 2.600479 | 2.094738 | 2.409779 | 2.460101 | 1.940695 | 2.372425 | 2.388867 | 2.356191 | ... | 2.361161 | 2.327977 | 2.337696 | 2.297506 | 1.949023 | 2.458198 | 2.037533 | 1.767952 | 2.101445 | 2.328501 |
s_phase5j_psr_DPsd_trunc_log | 2.346532 | 2.047200 | 2.553754 | 2.094738 | 2.409779 | 2.493829 | 1.940695 | 2.372425 | 2.388867 | 2.356191 | ... | 2.361161 | 2.327977 | 2.474297 | 2.297506 | 1.949023 | 2.458198 | 2.037533 | 1.767952 | 2.101445 | 2.328501 |
s_phase5t_psk_DPsd_trunc_log | 2.748939 | 1.801437 | 2.241850 | 1.851662 | 1.960636 | 2.496431 | 1.970924 | 2.282671 | 2.420283 | 2.235988 | ... | 2.235615 | 1.836924 | 2.023312 | 2.012784 | 0.754183 | 2.469274 | 1.925059 | 2.033728 | 1.849398 | 2.562076 |
s_phase5t_psr_DPsd_trunc_log | 2.739054 | 1.801437 | 2.241850 | 1.851662 | 1.960636 | 2.428850 | 1.970924 | 2.282671 | 2.420283 | 2.202548 | ... | 2.235615 | 1.836924 | 2.047796 | 2.035536 | 0.754183 | 2.477226 | 1.925059 | 2.033728 | 1.949939 | 2.524329 |
s_phase5t_nrm_DPsd_trunc_log | NaN | 1.237526 | 1.623261 | 1.498037 | 1.304594 | 1.940384 | 1.624750 | 1.325582 | 1.399705 | NaN | ... | 2.160146 | 0.994008 | 1.954929 | 1.827445 | 1.297908 | NaN | 1.424663 | 1.292549 | 1.805887 | NaN |
s_phase8t_nrm_DPsd_trunc_log | NaN | 0.987879 | 2.026634 | 1.542243 | 0.976403 | 1.786897 | 1.473877 | 0.813877 | 1.606961 | NaN | ... | 1.854807 | 1.021579 | 1.697091 | 1.313153 | 1.121737 | NaN | 1.486871 | 0.974948 | 1.550407 | NaN |
s_phase5j_nrm_DPsd_trunc_log | 2.285546 | 1.542395 | 2.091082 | 1.861343 | 2.153080 | 1.998019 | 1.866872 | 2.025180 | 1.754322 | 2.217726 | ... | 1.988806 | 1.799722 | 2.083031 | 1.876012 | 1.685643 | NaN | 1.675792 | 1.690626 | NaN | NaN |
s_phase8j_nrm_DPsd_trunc_log | 1.912401 | 1.356415 | 1.966651 | 1.616563 | 2.202028 | 2.200341 | 1.741272 | 1.639814 | 1.825810 | 1.865401 | ... | 1.826274 | 1.504115 | 2.037023 | 1.587396 | 1.783867 | 2.023109 | 1.900283 | 1.585618 | NaN | 1.729026 |
s_lint_610690_DPsd_trunc_log | 2.618486 | 1.294040 | 1.833350 | 1.562305 | 1.618251 | 2.073547 | 1.842086 | 1.336419 | 1.479158 | 1.714718 | ... | 2.560221 | 1.188846 | 2.241378 | 1.559357 | 1.227964 | 1.755058 | 1.688979 | 1.469405 | 1.708466 | 1.669159 |
s_linj_610690_DPsd_trunc_log | 2.378515 | 1.901060 | 2.696919 | NaN | 2.555784 | 1.995418 | 2.311555 | 2.063487 | 2.042926 | 2.017316 | ... | NaN | 1.837112 | 2.222157 | 1.914467 | 1.764912 | 2.021560 | 1.972932 | 1.593717 | 2.002277 | 2.474821 |
s_lint_700800_DPsd_trunc_log | NaN | 1.432330 | 1.837178 | 1.790320 | 1.568773 | 1.864525 | 1.675000 | 1.260027 | 1.668247 | 1.784602 | ... | NaN | 1.245401 | 2.215995 | 1.492288 | 1.210090 | 2.087513 | 1.287150 | 1.288812 | 1.770947 | 2.232365 |
s_lint_500600_DPsd_trunc_log | NaN | 1.325320 | 1.959291 | 1.467634 | 1.232024 | 1.809923 | 2.003263 | 1.308259 | 1.575855 | 1.832228 | ... | 2.219823 | 1.278118 | 2.247801 | 1.367271 | 1.088799 | 1.656226 | 1.922480 | 1.723334 | 1.777228 | 1.547670 |
s_linj_700800_DPsd_trunc_log | 2.121631 | 1.733171 | 2.122493 | NaN | NaN | 2.012866 | 2.024557 | 1.771447 | 1.887242 | 1.898132 | ... | NaN | 1.669199 | NaN | 1.763452 | 1.753585 | 1.626645 | 1.783073 | 1.613149 | 1.432606 | 2.356606 |
s_linj_500600_DPsd_trunc_log | 2.268092 | 1.918965 | 2.666476 | NaN | 2.256561 | 2.430195 | 2.329250 | 2.407362 | 2.133818 | 1.911576 | ... | 2.461843 | 1.972188 | 2.580064 | 2.187914 | 1.945161 | 2.126542 | 2.223812 | 2.327135 | 1.765479 | 2.126532 |
36 rows × 97 columns
# TO DO:
# Calculate z scores for each DPsd
# Calculate the mean of the two z scores for each 500/800 pairing
# See if the value of this still correlates with the 500-first/800-first order variable
remove_unused = [c for c in df_to_analyze.columns
if ( '_psk_' in c
or 's_lint_' in c
or 's_linj_' in c)]
for c in remove_unused:
del df_to_analyze[c]
to_combine = concat_matches(df_to_analyze, 'DPsd_trunc')
#for p in list(to_combine.columns):
#print(p)
for c in ['I5P4_local_trunc',
'I8P4_local_trunc',
'I5P4_drift_trunc',
'I8P4_drift_trunc',]:
to_combine[c] = df_to_analyze[c]
z_to_combine = (to_combine.mean() - to_combine) / to_combine.std()
#proper column-wise z score output was confirmed
df_to_analyze['IP4_local_trunc_mz58'] = ( z_to_combine['I5P4_local_trunc']
+ z_to_combine['I8P4_local_trunc']) / 2
df_to_analyze['IP4_drift_trunc_mz58'] = ( z_to_combine['I5P4_drift_trunc']
+ z_to_combine['I8P4_drift_trunc']) / 2
df_to_analyze['iso_j_DPsd_trunc_mz58'] = ( z_to_combine['s_iso5j_DPsd_trunc']
+ z_to_combine['s_iso8j_DPsd_trunc']) / 2
df_to_analyze['iso_t1_DPsd_trunc_mz58'] = ( z_to_combine['s_iso5t1_DPsd_trunc']
+ z_to_combine['s_iso8t1_DPsd_trunc']) / 2
df_to_analyze['iso_t2_DPsd_trunc_mz58'] = ( z_to_combine['s_iso5t2_DPsd_trunc']
+ z_to_combine['s_iso8t2_DPsd_trunc']) / 2
df_to_analyze['lin_j_DPsd_trunc_mz58'] = ( z_to_combine['s_lin5j_DPsd_trunc']
+ z_to_combine['s_lin8j_DPsd_trunc']) / 2
df_to_analyze['lin_t_DPsd_trunc_mz58'] = ( z_to_combine['s_lin5t_DPsd_trunc']
+ z_to_combine['s_lin8t_DPsd_trunc']) / 2
df_to_analyze['phase_j_nrm_DPsd_trunc_mz58'] = ( z_to_combine['s_phase5j_nrm_DPsd_trunc']
+ z_to_combine['s_phase8j_nrm_DPsd_trunc']) / 2
df_to_analyze['phase_j_psr_DPsd_trunc_mz58'] = ( z_to_combine['s_phase5j_psr_DPsd_trunc']
+ z_to_combine['s_phase8j_psr_DPsd_trunc']) / 2
df_to_analyze['phase_t_nrm_DPsd_trunc_mz58'] = ( z_to_combine['s_phase5t_nrm_DPsd_trunc']
+ z_to_combine['s_phase8t_nrm_DPsd_trunc']) / 2
df_to_analyze['phase_t_psr_DPsd_trunc_mz58'] = ( z_to_combine['s_phase5t_psr_DPsd_trunc']
+ z_to_combine['s_phase8t_psr_DPsd_trunc']) / 2
#null values propagate to new measure (confirmed)
#df_to_analyze['IP4_drift_trunc_mz58'][20:]
update = {'measure': 'subset_to_spss',
'updated': '2014-10-15c'}
pfilenames = "c:/db_pickles/pickle - dfo-{measure} - {updated}.{ext}"
output_file_csv = pfilenames.format(measure=update['measure'],
updated=update['updated'],
ext="csv")
output_file_pickle = pfilenames.format(measure=update['measure'],
updated=update['updated'],
ext="pickle")
df_to_analyze.to_pickle(output_file_pickle)
dfo_to_analyze_missing_coded = df_to_analyze.replace(np.nan, '77777')
dfo_to_analyze_missing_coded.to_csv(output_file_csv)
print("\nSAVED: {}\n".format(output_file_csv))
df_to_analyze.T
SAVED: c:/db_pickles/pickle - dfo-subset_to_spss - 2014-10-15c.csv
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SCAL_order_500ms_first | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 |
SCAL_sex_femalezero | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | ... | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 |
SCAL_orders_iso | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 2.000000 | 1.000000 | 2.000000 | 0.000000 | 2.000000 |
SCAL_orders_phase | 2.000000 | 2.000000 | 2.000000 | 0.000000 | 2.000000 | 0.000000 | 2.000000 | 0.000000 | 2.000000 | 1.000000 | ... | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 |
SCAL_orders_linear | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | ... | 2.000000 | 2.000000 | 2.000000 | 2.000000 | 0.000000 | 1.000000 | 2.000000 | 1.000000 | 1.000000 | 1.000000 |
SCAL_calc_wasivocab_tscore | 49.000000 | 78.000000 | 55.000000 | 50.000000 | 55.000000 | 57.000000 | 53.000000 | 57.000000 | 44.000000 | 47.000000 | ... | 39.000000 | 44.000000 | 63.000000 | 74.000000 | 52.000000 | 46.000000 | 57.000000 | 51.000000 | 43.000000 | 48.000000 |
SCAL_calc_wasimatrix_tscore | 38.000000 | 53.000000 | 54.000000 | 53.000000 | 55.000000 | 49.000000 | 42.000000 | 46.000000 | 55.000000 | 48.000000 | ... | 46.000000 | 49.000000 | 62.000000 | 53.000000 | 71.000000 | 49.000000 | 52.000000 | 55.000000 | 57.000000 | 49.000000 |
SCAL_calc_wasi_tscore_total | 87.000000 | 131.000000 | 109.000000 | 103.000000 | 110.000000 | 106.000000 | 95.000000 | 103.000000 | 99.000000 | 95.000000 | ... | 85.000000 | 93.000000 | 125.000000 | 127.000000 | 123.000000 | 95.000000 | 109.000000 | 106.000000 | 100.000000 | 97.000000 |
SCAL_calc_fsiq2 | 89.000000 | 127.000000 | 108.000000 | 102.000000 | 109.000000 | 105.000000 | 95.000000 | 102.000000 | 99.000000 | 95.000000 | ... | 87.000000 | 94.000000 | 122.000000 | 123.000000 | 120.000000 | 95.000000 | 108.000000 | 105.000000 | 100.000000 | 97.000000 |
SCAL_calc_bfi_extraversion | 2.125000 | 4.000000 | 2.750000 | 3.000000 | 4.500000 | 1.750000 | 2.125000 | 3.250000 | 2.500000 | 2.625000 | ... | 3.875000 | 4.500000 | 3.000000 | 3.125000 | 4.875000 | 4.375000 | 4.375000 | 4.250000 | 3.000000 | 3.250000 |
SCAL_calc_bfi_agreeableness | 3.666667 | 4.111111 | 2.888889 | 4.111111 | 4.444444 | 4.222222 | 3.888889 | 3.555556 | 5.000000 | 2.222222 | ... | 5.000000 | 4.444444 | 4.444444 | 2.888889 | 3.666667 | 4.555556 | 4.111111 | 3.666667 | 3.111111 | 3.333333 |
SCAL_calc_bfi_conscientiousness | 3.777778 | 2.777778 | 3.555556 | 4.555556 | 4.000000 | 3.111111 | 3.888889 | 4.444444 | 3.444444 | 2.555556 | ... | 4.555556 | 4.000000 | 2.888889 | 2.888889 | 3.444444 | 3.555556 | 2.888889 | 3.444444 | 3.222222 | 3.555556 |
SCAL_calc_bfi_neuroticism | 3.125000 | 3.250000 | 3.000000 | 3.000000 | 1.750000 | 2.625000 | 3.875000 | 3.250000 | 2.125000 | 3.500000 | ... | 2.250000 | 1.375000 | 4.375000 | 2.500000 | 2.125000 | 3.875000 | 2.625000 | 2.875000 | 3.250000 | 3.142857 |
SCAL_calc_bfi_openness | 2.800000 | 3.700000 | 3.700000 | 3.600000 | 3.300000 | 2.500000 | 2.700000 | 4.700000 | 4.200000 | 3.200000 | ... | 4.300000 | 3.600000 | 3.600000 | 4.300000 | 4.400000 | 3.000000 | 3.900000 | 4.300000 | 4.900000 | 4.200000 |
SCAL_qmusic_dancelevel | 2.000000 | 2.000000 | 1.000000 | 3.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | ... | 5.000000 | 0.000000 | 3.000000 | 2.000000 | 3.000000 | 4.000000 | 3.000000 | 3.000000 | 0.000000 | 0.000000 |
SCAL_qmusic_instrumentlevel | 0.000000 | 3.000000 | 3.000000 | 0.000000 | 2.000000 | 0.000000 | 1.000000 | 4.000000 | 2.000000 | 0.000000 | ... | 0.000000 | 4.000000 | 4.000000 | 4.000000 | 3.000000 | 0.000000 | 3.000000 | 3.000000 | 0.000000 | 2.000000 |
SCAL_qmusic_drumlevel | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 0.000000 |
SCAL_qmusic_behaviors_12_friendstaste | 3.000000 | 3.000000 | 1.000000 | 3.000000 | 4.000000 | 3.000000 | 2.000000 | 2.000000 | 4.000000 | 4.000000 | ... | 7.000000 | 4.000000 | 2.000000 | 3.000000 | 5.000000 | 5.000000 | 4.000000 | 6.000000 | 5.000000 | 5.000000 |
SCAL_qmusic_behaviors_13_sharingint | 4.000000 | 5.000000 | 1.000000 | 4.000000 | 2.000000 | 4.000000 | 3.000000 | 5.000000 | 1.000000 | 3.000000 | ... | 5.000000 | 4.000000 | 2.000000 | 4.000000 | 7.000000 | 6.000000 | 5.000000 | 7.000000 | 5.000000 | 5.000000 |
SCAL_qmusic_behaviors_14_getinterest | 5.000000 | 5.000000 | 4.000000 | 4.000000 | 2.000000 | 4.000000 | 2.000000 | 3.000000 | 7.000000 | 4.000000 | ... | 5.000000 | 5.000000 | 3.000000 | 1.000000 | 7.000000 | 5.000000 | 5.000000 | 7.000000 | 5.000000 | 6.000000 |
qmusic_calc_anyhours | 1.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | ... | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 |
qmusic_calc_maxskill | 2.000000 | 3.000000 | 3.000000 | 3.000000 | 4.000000 | 0.000000 | 1.000000 | 4.000000 | 2.000000 | 3.000000 | ... | 5.000000 | 4.000000 | 4.000000 | 4.000000 | 3.000000 | 4.000000 | 3.000000 | 4.000000 | 0.000000 | 2.000000 |
qmusic_calc_sumskill | 2.000000 | 5.000000 | 4.000000 | 3.000000 | 6.000000 | 0.000000 | 1.000000 | 4.000000 | 3.000000 | 3.000000 | ... | 5.000000 | 8.000000 | 7.000000 | 6.000000 | 6.000000 | 4.000000 | 6.000000 | 10.000000 | 0.000000 | 2.000000 |
qmusic_calc_socialimp | 12.000000 | 13.000000 | 6.000000 | 11.000000 | 8.000000 | 11.000000 | 7.000000 | 10.000000 | 12.000000 | 11.000000 | ... | 17.000000 | 13.000000 | 7.000000 | 8.000000 | 19.000000 | 16.000000 | 14.000000 | 20.000000 | 15.000000 | 16.000000 |
SCAL_qmusic_behaviors_07_yourself_ln1p | 2.708050 | 2.944439 | 2.602690 | 3.258097 | 2.397895 | 3.931826 | 1.791759 | 3.931826 | 3.931826 | 1.945910 | ... | 1.609438 | 1.945910 | 2.397895 | 2.397895 | 3.433987 | 1.791759 | 3.044522 | 4.290459 | 1.791759 | 2.772589 |
SCAL_qmusic_behaviors_08_otherprs_ln1p | 2.397895 | 2.302585 | 1.791759 | 1.791759 | 1.791759 | 3.433987 | 2.708050 | 3.433987 | 2.397895 | 0.693147 | ... | 1.386294 | 1.098612 | 1.098612 | 2.708050 | 1.098612 | 3.433987 | 3.044522 | 4.394449 | 0.000000 | 1.791759 |
SCAL_qmusic_behaviors_09_danceprv_ln1p | 1.791759 | 0.530628 | 0.000000 | 1.098612 | 3.433987 | 2.397895 | 0.000000 | 3.931826 | 2.772589 | 0.000000 | ... | 0.000000 | 0.000000 | 0.693147 | 1.098612 | 0.916291 | 1.098612 | 0.000000 | 0.000000 | 0.693147 | 0.693147 |
SCAL_qmusic_dancelevel_ln1p | 1.098612 | 1.098612 | 0.693147 | 1.386294 | 1.609438 | 0.000000 | 0.000000 | 0.000000 | 0.693147 | 1.386294 | ... | 1.791759 | 0.000000 | 1.386294 | 1.098612 | 1.386294 | 1.609438 | 1.386294 | 1.386294 | 0.000000 | 0.000000 |
SCAL_qmusic_singinghours_nonzero | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
SCAL_qmusic_singingtimes_nonzero | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
s_iso8t2_DPsd_trunc_log | NaN | 1.131855 | 1.621979 | 1.717185 | 1.224957 | 1.854551 | 1.474399 | 0.850104 | 1.427988 | 1.486620 | ... | 1.764540 | 1.019723 | 1.804565 | 1.256179 | 0.822493 | 1.518399 | 1.074398 | 1.167698 | 1.312072 | 1.790040 |
s_lin5t_DPsd_trunc_log | NaN | 1.336055 | 1.601238 | 1.586875 | 1.532127 | 2.002822 | 1.746812 | 1.270195 | 1.511344 | 1.842359 | ... | 2.380195 | 1.240751 | 2.003874 | 1.490492 | 1.163007 | 1.884450 | 1.635103 | 1.436168 | 1.753264 | 1.995672 |
s_lin8t_DPsd_trunc_log | 2.305383 | 1.334307 | 1.791392 | 1.519132 | 1.506308 | 1.742977 | 1.595103 | 1.167324 | 1.651714 | 1.665542 | ... | 1.983140 | 1.062247 | 1.871916 | 1.378390 | 1.198713 | 1.933333 | 1.592535 | 1.246329 | 1.665642 | 1.663364 |
s_phase5t_DPsd_trunc_log | 2.818610 | 1.533899 | 1.808526 | 1.794290 | 1.771647 | 2.178424 | 1.728366 | 1.713754 | 1.871108 | 2.194410 | ... | 2.153918 | 1.445558 | 1.959776 | 2.369388 | 1.469174 | 2.199600 | 2.123358 | 1.575372 | 1.898144 | 2.078509 |
s_phase8t_DPsd_trunc_log | 3.221469 | 1.440647 | 1.917085 | 1.947621 | 1.344109 | 2.057176 | 1.698249 | 1.711628 | 1.671339 | 1.918771 | ... | 1.944267 | 1.641779 | 1.935375 | 1.766689 | 1.418115 | 1.966662 | 1.819247 | 1.458257 | 1.705351 | 2.468320 |
s_iso5j_DPsd_trunc_log | 1.468862 | 1.644933 | 1.660185 | 1.931067 | 1.883450 | 1.851579 | 1.738222 | 1.650584 | 1.848477 | 1.707418 | ... | 1.811877 | 1.631131 | 2.156306 | 1.822238 | 1.583392 | 1.685826 | 2.124690 | 1.572873 | 1.612913 | 1.718923 |
s_iso8j_DPsd_trunc_log | NaN | 1.692863 | 2.137184 | 1.772214 | 1.733544 | 1.819399 | 1.707575 | 1.755917 | 1.804228 | 1.908284 | ... | 2.083905 | 1.362888 | 1.821635 | 1.629429 | 1.708278 | 1.590430 | 1.918600 | 1.417264 | 1.426504 | 1.714364 |
s_lin5j_DPsd_trunc_log | 2.015058 | 1.649390 | 1.871980 | NaN | NaN | 1.917798 | 1.620410 | 2.070804 | 1.787552 | 1.761812 | ... | NaN | 1.395519 | 2.419863 | 1.719042 | 1.585274 | 2.041828 | 1.756458 | 1.810486 | 1.675215 | 2.132917 |
s_lin8j_DPsd_trunc_log | 2.116002 | 1.890228 | 2.313866 | 2.099799 | 2.200428 | 2.308440 | 2.141411 | 1.997351 | 2.083193 | 2.043251 | ... | 2.183058 | 1.850603 | 2.326427 | 1.971129 | 1.811144 | 2.128409 | 2.040155 | 1.914217 | 1.817584 | 2.400156 |
s_phase5j_DPsd_trunc_log | 2.422405 | 1.774558 | 2.262908 | 2.144661 | 2.739569 | 2.087969 | 1.802783 | 2.051451 | 2.067474 | 2.104918 | ... | 2.155658 | 2.254326 | 2.238179 | 1.951401 | 1.814308 | 2.232164 | 1.880433 | 1.736957 | 2.057894 | 2.333196 |
s_phase8j_DPsd_trunc_log | 2.540202 | 1.624128 | 2.170845 | 1.740630 | 2.359743 | 2.445744 | 1.774009 | 1.614812 | 1.849079 | 1.919424 | ... | 1.981102 | 1.731812 | 2.011558 | 1.692822 | 1.864767 | 2.026249 | 1.911932 | 1.699957 | 1.899737 | 2.077448 |
s_phase8j_psr_DPsd_trunc_log | 1.822314 | 1.612924 | 2.027585 | 1.761192 | 2.566462 | 2.115852 | 1.885312 | 1.602012 | 1.797048 | 2.073824 | ... | 2.199807 | 2.058860 | 1.900451 | 1.992052 | 1.584186 | 2.080504 | 1.740867 | 1.964700 | 1.941108 | 2.165882 |
s_phase8t_psr_DPsd_trunc_log | 2.527728 | 1.616072 | 2.169406 | 1.752274 | 1.517247 | 2.416043 | 1.976305 | 2.309860 | 1.974328 | 1.670599 | ... | 1.951454 | 2.163158 | 1.922902 | 2.187971 | 1.626977 | 1.977654 | 1.690675 | 1.627795 | 2.218610 | 2.246768 |
s_phase5j_psr_DPsd_trunc_log | 2.346532 | 2.047200 | 2.553754 | 2.094738 | 2.409779 | 2.493829 | 1.940695 | 2.372425 | 2.388867 | 2.356191 | ... | 2.361161 | 2.327977 | 2.474297 | 2.297506 | 1.949023 | 2.458198 | 2.037533 | 1.767952 | 2.101445 | 2.328501 |
s_phase5t_psr_DPsd_trunc_log | 2.739054 | 1.801437 | 2.241850 | 1.851662 | 1.960636 | 2.428850 | 1.970924 | 2.282671 | 2.420283 | 2.202548 | ... | 2.235615 | 1.836924 | 2.047796 | 2.035536 | 0.754183 | 2.477226 | 1.925059 | 2.033728 | 1.949939 | 2.524329 |
s_phase5t_nrm_DPsd_trunc_log | NaN | 1.237526 | 1.623261 | 1.498037 | 1.304594 | 1.940384 | 1.624750 | 1.325582 | 1.399705 | NaN | ... | 2.160146 | 0.994008 | 1.954929 | 1.827445 | 1.297908 | NaN | 1.424663 | 1.292549 | 1.805887 | NaN |
s_phase8t_nrm_DPsd_trunc_log | NaN | 0.987879 | 2.026634 | 1.542243 | 0.976403 | 1.786897 | 1.473877 | 0.813877 | 1.606961 | NaN | ... | 1.854807 | 1.021579 | 1.697091 | 1.313153 | 1.121737 | NaN | 1.486871 | 0.974948 | 1.550407 | NaN |
s_phase5j_nrm_DPsd_trunc_log | 2.285546 | 1.542395 | 2.091082 | 1.861343 | 2.153080 | 1.998019 | 1.866872 | 2.025180 | 1.754322 | 2.217726 | ... | 1.988806 | 1.799722 | 2.083031 | 1.876012 | 1.685643 | NaN | 1.675792 | 1.690626 | NaN | NaN |
s_phase8j_nrm_DPsd_trunc_log | 1.912401 | 1.356415 | 1.966651 | 1.616563 | 2.202028 | 2.200341 | 1.741272 | 1.639814 | 1.825810 | 1.865401 | ... | 1.826274 | 1.504115 | 2.037023 | 1.587396 | 1.783867 | 2.023109 | 1.900283 | 1.585618 | NaN | 1.729026 |
IP4_local_trunc_mz58 | -0.719911 | 0.030023 | -0.098648 | 0.264946 | 1.346369 | -0.977373 | -0.289685 | 1.007855 | 0.140207 | 0.536259 | ... | -0.128227 | 1.431727 | -2.601290 | 0.373697 | 0.921365 | 0.094343 | -0.561011 | 1.347167 | 0.548749 | 0.283562 |
IP4_drift_trunc_mz58 | -1.528469 | 0.714178 | -0.292168 | 0.958729 | 1.190850 | -0.730949 | -0.562301 | 0.725530 | 0.141896 | -1.104657 | ... | -1.602305 | 0.714119 | -0.908375 | 0.692513 | 1.170212 | -0.212934 | -0.186605 | 1.182491 | 0.488464 | 0.175288 |
iso_j_DPsd_trunc_mz58 | NaN | 0.485154 | -0.576302 | -0.380216 | -0.171384 | -0.257252 | 0.247292 | 0.353306 | -0.217636 | -0.095610 | ... | -0.785827 | 1.031322 | -1.205449 | 0.180269 | 0.585161 | 0.574214 | -1.306551 | 1.077703 | 0.982872 | 0.279781 |
iso_t1_DPsd_trunc_mz58 | -2.580137 | 0.811229 | -1.118839 | 0.292937 | 1.285882 | -1.566353 | -0.058700 | 1.104828 | 0.493995 | 0.345159 | ... | -1.682016 | 1.226579 | -0.552902 | 0.364202 | 1.350083 | -0.562542 | -0.138621 | 1.097650 | -0.381829 | -0.268570 |
iso_t2_DPsd_trunc_mz58 | NaN | 0.794860 | 0.092600 | 0.089634 | 0.978486 | -1.264454 | -0.100952 | 1.036733 | 0.542787 | -0.137933 | ... | -0.640099 | 1.113219 | -0.957161 | 0.308590 | 1.160745 | 0.007410 | 0.679895 | 0.868153 | -0.097989 | -0.381475 |
lin_j_DPsd_trunc_mz58 | -0.386014 | 0.869291 | -0.652105 | NaN | NaN | -0.730164 | 0.312428 | -0.224457 | 0.177851 | 0.325795 | ... | NaN | 1.317060 | -2.179467 | 0.573569 | 1.131269 | -0.482863 | 0.343152 | 0.532899 | 0.974700 | -1.550512 |
lin_t_DPsd_trunc_mz58 | NaN | 0.904052 | -0.128324 | 0.348596 | 0.441437 | -0.736396 | -0.008309 | 1.159761 | 0.243038 | -0.287189 | ... | -2.200433 | 1.291631 | -0.989890 | 0.668271 | 1.230220 | -0.883423 | 0.168568 | 0.892052 | -0.133994 | -0.581622 |
phase_j_nrm_DPsd_trunc_mz58 | -1.329748 | 1.227999 | -0.868490 | 0.306098 | -1.602138 | -1.189495 | 0.095847 | -0.106883 | 0.173681 | -1.024821 | ... | -0.331097 | 0.591870 | -1.001700 | 0.317694 | 0.375925 | NaN | 0.175273 | 0.684718 | NaN | NaN |
phase_j_psr_DPsd_trunc_mz58 | 0.067268 | 1.004538 | -0.856846 | 0.718332 | -1.760186 | -0.846781 | 0.811338 | 0.298666 | -0.004325 | -0.387652 | ... | -0.660225 | -0.288006 | -0.399035 | -0.090422 | 1.214085 | -0.674306 | 0.855914 | 0.945735 | 0.426026 | -0.504698 |
phase_t_nrm_DPsd_trunc_mz58 | NaN | 1.004355 | -0.967242 | 0.084122 | 0.935139 | -1.110738 | -0.022757 | 1.026597 | 0.129298 | NaN | ... | -1.793738 | 1.221023 | -0.993391 | -0.205448 | 0.821133 | NaN | 0.261642 | 0.950711 | -0.454616 | NaN |
phase_t_psr_DPsd_trunc_mz58 | -2.276363 | 0.941098 | -0.387289 | 0.750056 | 0.810448 | -1.206365 | 0.334312 | -0.696282 | -0.492269 | 0.283389 | ... | -0.077780 | 0.260199 | 0.282691 | -0.058554 | 1.732216 | -0.630644 | 0.711092 | 0.608235 | 0.021168 | -1.134973 |
156 rows × 97 columns
df_to_analyze.count().to_csv('non-null counts 2014-10-15b.csv')
dfa = df_to_analyze
for p in concat_matches(df_to_analyze, '_log'): print p
I5P4_local_trunc_log I8P4_local_trunc_log I5P4_drift_trunc_log I8P4_drift_trunc_log s_iso5t1_DPsd_trunc_log s_iso8t1_DPsd_trunc_log s_iso5t2_DPsd_trunc_log s_iso8t2_DPsd_trunc_log s_lin5t_DPsd_trunc_log s_lin8t_DPsd_trunc_log s_phase5t_DPsd_trunc_log s_phase8t_DPsd_trunc_log s_iso5j_DPsd_trunc_log s_iso8j_DPsd_trunc_log s_lin5j_DPsd_trunc_log s_lin8j_DPsd_trunc_log s_phase5j_DPsd_trunc_log s_phase8j_DPsd_trunc_log s_phase8j_psr_DPsd_trunc_log s_phase8t_psr_DPsd_trunc_log s_phase5j_psr_DPsd_trunc_log s_phase5t_psr_DPsd_trunc_log s_phase5t_nrm_DPsd_trunc_log s_phase8t_nrm_DPsd_trunc_log s_phase5j_nrm_DPsd_trunc_log s_phase8j_nrm_DPsd_trunc_log
paste_1 = ('''
s_iso5t2_DPsd_trunc_log
s_iso8t2_DPsd_trunc_log
s_iso5j_DPsd_trunc_log
s_iso8j_DPsd_trunc_log
s_phase8j_psr_DPsd_trunc_log
s_phase8t_psr_DPsd_trunc_log
s_phase5j_psr_DPsd_trunc_log
s_phase5t_psr_DPsd_trunc_log
''')
design_1 = clean_pasted_vars(paste_1)
scatter_all(dfa[design_1])
8 columns. Proceed? ('s_iso5t2_DPsd_trunc_log', 's_iso8t2_DPsd_trunc_log')
('s_iso5t2_DPsd_trunc_log', 's_iso5j_DPsd_trunc_log')
('s_iso5t2_DPsd_trunc_log', 's_iso8j_DPsd_trunc_log')
('s_iso5t2_DPsd_trunc_log', 's_phase8j_psr_DPsd_trunc_log')
('s_iso5t2_DPsd_trunc_log', 's_phase8t_psr_DPsd_trunc_log')
('s_iso5t2_DPsd_trunc_log', 's_phase5j_psr_DPsd_trunc_log')
('s_iso5t2_DPsd_trunc_log', 's_phase5t_psr_DPsd_trunc_log')
('s_iso8t2_DPsd_trunc_log', 's_iso5j_DPsd_trunc_log')
('s_iso8t2_DPsd_trunc_log', 's_iso8j_DPsd_trunc_log')
('s_iso8t2_DPsd_trunc_log', 's_phase8j_psr_DPsd_trunc_log')
('s_iso8t2_DPsd_trunc_log', 's_phase8t_psr_DPsd_trunc_log')
('s_iso8t2_DPsd_trunc_log', 's_phase5j_psr_DPsd_trunc_log')
('s_iso8t2_DPsd_trunc_log', 's_phase5t_psr_DPsd_trunc_log')
('s_iso5j_DPsd_trunc_log', 's_iso8j_DPsd_trunc_log')
('s_iso5j_DPsd_trunc_log', 's_phase8j_psr_DPsd_trunc_log')
('s_iso5j_DPsd_trunc_log', 's_phase8t_psr_DPsd_trunc_log')
('s_iso5j_DPsd_trunc_log', 's_phase5j_psr_DPsd_trunc_log')
('s_iso5j_DPsd_trunc_log', 's_phase5t_psr_DPsd_trunc_log')
('s_iso8j_DPsd_trunc_log', 's_phase8j_psr_DPsd_trunc_log')
('s_iso8j_DPsd_trunc_log', 's_phase8t_psr_DPsd_trunc_log')
('s_iso8j_DPsd_trunc_log', 's_phase5j_psr_DPsd_trunc_log')
('s_iso8j_DPsd_trunc_log', 's_phase5t_psr_DPsd_trunc_log')
('s_phase8j_psr_DPsd_trunc_log', 's_phase8t_psr_DPsd_trunc_log')
('s_phase8j_psr_DPsd_trunc_log', 's_phase5j_psr_DPsd_trunc_log')
('s_phase8j_psr_DPsd_trunc_log', 's_phase5t_psr_DPsd_trunc_log')
('s_phase8t_psr_DPsd_trunc_log', 's_phase5j_psr_DPsd_trunc_log')
('s_phase8t_psr_DPsd_trunc_log', 's_phase5t_psr_DPsd_trunc_log')
('s_phase5j_psr_DPsd_trunc_log', 's_phase5t_psr_DPsd_trunc_log')
design_2 = clean_pasted_vars('''
s_lin5t_DPsd_trunc_log
s_lin8t_DPsd_trunc_log
s_lin5j_DPsd_trunc_log
s_lin8j_DPsd_trunc_log
''')
scatter_all(dfa[design_2])
('s_lin5t_DPsd_trunc_log', 's_lin8t_DPsd_trunc_log')
('s_lin5t_DPsd_trunc_log', 's_lin5j_DPsd_trunc_log')
('s_lin5t_DPsd_trunc_log', 's_lin8j_DPsd_trunc_log')
('s_lin8t_DPsd_trunc_log', 's_lin5j_DPsd_trunc_log')
('s_lin8t_DPsd_trunc_log', 's_lin8j_DPsd_trunc_log')
('s_lin5j_DPsd_trunc_log', 's_lin8j_DPsd_trunc_log')
design_3 = clean_pasted_vars('''
I5P4_local_trunc_log
I8P4_local_trunc_log
I5P4_drift_trunc_log
I8P4_drift_trunc_log
''')
scatter_all(dfa[design_3])
('I5P4_local_trunc_log', 'I8P4_local_trunc_log')
('I5P4_local_trunc_log', 'I5P4_drift_trunc_log')
('I5P4_local_trunc_log', 'I8P4_drift_trunc_log')
('I8P4_local_trunc_log', 'I5P4_drift_trunc_log')
('I8P4_local_trunc_log', 'I8P4_drift_trunc_log')
('I5P4_drift_trunc_log', 'I8P4_drift_trunc_log')
match('drumlevel').sort(columns='SCAL_qmusic_drumlevel').tail(20)
SCAL_qmusic_drumlevel | |
---|---|
055 | 0 |
057 | 0 |
121 | 0 |
053 | 0 |
052 | 0 |
051 | 0 |
054 | 0 |
048 | 0 |
049 | 0 |
063 | 1 |
033 | 2 |
034 | 2 |
047 | 2 |
082 | 3 |
060 | 3 |
110 | 3 |
119 | 4 |
075 | 4 |
113 | 4 |
064 | NaN |
match('instrumentlevel').sort(columns='SCAL_qmusic_instrumentlevel').median()
SCAL_qmusic_instrumentlevel 2 dtype: float64
def stack_rm_case(case_series):
total_stacked_vars = 12
caseid = case_series.name
#caseid_repeated = [caseid] * total_stacked_vars
#caseid_list = {}
tasktype = {}
targetioi = {}
targetstim = {}
tasktype['s_iso5t2_DPsd_trunc_log'] = 1
tasktype['s_iso8t2_DPsd_trunc_log'] = 1
tasktype['s_iso5j_DPsd_trunc_log'] = 1
tasktype['s_iso8j_DPsd_trunc_log'] = 1
tasktype['s_phase8j_psr_DPsd_trunc_log'] = 2
tasktype['s_phase8t_psr_DPsd_trunc_log'] = 2
tasktype['s_phase5j_psr_DPsd_trunc_log'] = 2
tasktype['s_phase5t_psr_DPsd_trunc_log'] = 2
tasktype['s_phase5t_nrm_DPsd_trunc_log'] = 3
tasktype['s_phase8t_nrm_DPsd_trunc_log'] = 3
tasktype['s_phase5j_nrm_DPsd_trunc_log'] = 3
tasktype['s_phase8j_nrm_DPsd_trunc_log'] = 3
tasktype['s_iso5t2_DPsd_trunc_log'] = 1
tasktype['s_iso8t2_DPsd_trunc_log'] = 1
tasktype['s_iso5j_DPsd_trunc_log'] = 1
tasktype['s_iso8j_DPsd_trunc_log'] = 1
tasktype['s_phase8j_psr_DPsd_trunc_log'] = 2
tasktype['s_phase8t_psr_DPsd_trunc_log'] = 2
tasktype['s_phase5j_psr_DPsd_trunc_log'] = 2
tasktype['s_phase5t_psr_DPsd_trunc_log'] = 2
tasktype['s_phase5t_nrm_DPsd_trunc_log'] = 3
tasktype['s_phase8t_nrm_DPsd_trunc_log'] = 3
tasktype['s_phase5j_nrm_DPsd_trunc_log'] = 3
tasktype['s_phase8j_nrm_DPsd_trunc_log'] = 3
targetioi['s_iso5t2_DPsd_trunc_log'] = 0
targetioi['s_iso8t2_DPsd_trunc_log'] = 1
targetioi['s_iso5j_DPsd_trunc_log'] = 0
targetioi['s_iso8j_DPsd_trunc_log'] = 1
targetioi['s_phase8j_psr_DPsd_trunc_log'] = 1
targetioi['s_phase8t_psr_DPsd_trunc_log'] = 1
targetioi['s_phase5j_psr_DPsd_trunc_log'] = 0
targetioi['s_phase5t_psr_DPsd_trunc_log'] = 0
targetioi['s_phase5t_nrm_DPsd_trunc_log'] = 0
targetioi['s_phase8t_nrm_DPsd_trunc_log'] = 1
targetioi['s_phase5j_nrm_DPsd_trunc_log'] = 0
targetioi['s_phase8j_nrm_DPsd_trunc_log'] = 1
targetstim['s_iso5t2_DPsd_trunc_log'] = 0
targetstim['s_iso8t2_DPsd_trunc_log'] = 0
targetstim['s_iso5j_DPsd_trunc_log'] = 1
targetstim['s_iso8j_DPsd_trunc_log'] = 1
targetstim['s_phase8j_psr_DPsd_trunc_log'] = 1
targetstim['s_phase8t_psr_DPsd_trunc_log'] = 0
targetstim['s_phase5j_psr_DPsd_trunc_log'] = 1
targetstim['s_phase5t_psr_DPsd_trunc_log'] = 0
targetstim['s_phase5t_nrm_DPsd_trunc_log'] = 0
targetstim['s_phase8t_nrm_DPsd_trunc_log'] = 0
targetstim['s_phase5j_nrm_DPsd_trunc_log'] = 1
targetstim['s_phase8j_nrm_DPsd_trunc_log'] = 1
caseid_repeated = {k: caseid for k in tasktype.keys()}
stackedvars = pd.DataFrame({'caseid': caseid_repeated,
'casedata': acase,
'tasktype': tasktype,
'targetioi': targetioi,
'targetstim': targetstim,
},
#index = acase.T.index
)
stackedvars.index.name='original_varname'
case_out = stackedvars.reset_index('original_varname')
return case_out
repmeas = concat_matches(df_to_analyze, 'psr.*log|nrm.*log|iso.t2.*log|iso.j.*log')
cases = [stack_rm_case(repmeas.loc[p]) for p in repmeas.index]
stacked = pd.concat(cases, axis=0)
stacked.index = range(len(stacked))
stacked.index.name = "st_row"
stacked = stacked.reset_index('st_row')
stacked = stacked.set_index('caseid')
df_to_analyze['SCAL_calc_fsiq2']
015 89 016 127 017 108 018 102 019 109 020 105 021 95 022 102 024 99 025 95 026 100 027 105 028 116 029 108 030 115 ... 107 108 108 96 109 97 110 122 111 118 112 87 113 94 114 122 115 123 116 120 117 95 118 108 119 105 120 100 121 97 Name: SCAL_calc_fsiq2, Length: 99, dtype: float64
df_to_analyze.loc['015', staticvar]
89.0
staticvar = 'SCAL_calc_fsiq2'
ids = sorted(set(stacked.index))
for caseid in ids:
print(caseid)
stacked[staticvar] = np.nan
stacked.loc[caseid, staticvar] = df_to_analyze.loc[caseid, staticvar]
#slc = stacked.loc[stacked.caseid=='015']
#slc.somevarname = 'the_value'
#stacked.to_csv('stacked_test.csv')
stacked
015 016 017 018 019 020 021 022 024 025 026 027 028 029 030 032 033 034 035 036 037 038 039 040 041 043 044 046 047 048 049 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121
st_row | original_varname | casedata | targetioi | targetstim | tasktype | SCAL_calc_fsiq2 | |
---|---|---|---|---|---|---|---|
caseid | |||||||
015 | 0 | s_iso5j_DPsd_trunc_log | 1.644933 | 0 | 1 | 1 | NaN |
015 | 1 | s_iso5t2_DPsd_trunc_log | 1.259224 | 0 | 0 | 1 | NaN |
015 | 2 | s_iso8j_DPsd_trunc_log | 1.692863 | 1 | 1 | 1 | NaN |
015 | 3 | s_iso8t2_DPsd_trunc_log | 1.131855 | 1 | 0 | 1 | NaN |
015 | 4 | s_phase5j_nrm_DPsd_trunc_log | 1.542395 | 0 | 1 | 3 | NaN |
015 | 5 | s_phase5j_psr_DPsd_trunc_log | 2.047200 | 0 | 1 | 2 | NaN |
015 | 6 | s_phase5t_nrm_DPsd_trunc_log | 1.237526 | 0 | 0 | 3 | NaN |
015 | 7 | s_phase5t_psr_DPsd_trunc_log | 1.801437 | 0 | 0 | 2 | NaN |
015 | 8 | s_phase8j_nrm_DPsd_trunc_log | 1.356415 | 1 | 1 | 3 | NaN |
015 | 9 | s_phase8j_psr_DPsd_trunc_log | 1.612924 | 1 | 1 | 2 | NaN |
015 | 10 | s_phase8t_nrm_DPsd_trunc_log | 0.987879 | 1 | 0 | 3 | NaN |
015 | 11 | s_phase8t_psr_DPsd_trunc_log | 1.616072 | 1 | 0 | 2 | NaN |
016 | 12 | s_iso5j_DPsd_trunc_log | 1.644933 | 0 | 1 | 1 | NaN |
016 | 13 | s_iso5t2_DPsd_trunc_log | 1.259224 | 0 | 0 | 1 | NaN |
016 | 14 | s_iso8j_DPsd_trunc_log | 1.692863 | 1 | 1 | 1 | NaN |
016 | 15 | s_iso8t2_DPsd_trunc_log | 1.131855 | 1 | 0 | 1 | NaN |
016 | 16 | s_phase5j_nrm_DPsd_trunc_log | 1.542395 | 0 | 1 | 3 | NaN |
016 | 17 | s_phase5j_psr_DPsd_trunc_log | 2.047200 | 0 | 1 | 2 | NaN |
016 | 18 | s_phase5t_nrm_DPsd_trunc_log | 1.237526 | 0 | 0 | 3 | NaN |
016 | 19 | s_phase5t_psr_DPsd_trunc_log | 1.801437 | 0 | 0 | 2 | NaN |
016 | 20 | s_phase8j_nrm_DPsd_trunc_log | 1.356415 | 1 | 1 | 3 | NaN |
016 | 21 | s_phase8j_psr_DPsd_trunc_log | 1.612924 | 1 | 1 | 2 | NaN |
016 | 22 | s_phase8t_nrm_DPsd_trunc_log | 0.987879 | 1 | 0 | 3 | NaN |
016 | 23 | s_phase8t_psr_DPsd_trunc_log | 1.616072 | 1 | 0 | 2 | NaN |
017 | 24 | s_iso5j_DPsd_trunc_log | 1.644933 | 0 | 1 | 1 | NaN |
017 | 25 | s_iso5t2_DPsd_trunc_log | 1.259224 | 0 | 0 | 1 | NaN |
017 | 26 | s_iso8j_DPsd_trunc_log | 1.692863 | 1 | 1 | 1 | NaN |
017 | 27 | s_iso8t2_DPsd_trunc_log | 1.131855 | 1 | 0 | 1 | NaN |
017 | 28 | s_phase5j_nrm_DPsd_trunc_log | 1.542395 | 0 | 1 | 3 | NaN |
017 | 29 | s_phase5j_psr_DPsd_trunc_log | 2.047200 | 0 | 1 | 2 | NaN |
... | ... | ... | ... | ... | ... | ... | ... |
119 | 1158 | s_phase5t_nrm_DPsd_trunc_log | 1.237526 | 0 | 0 | 3 | NaN |
119 | 1159 | s_phase5t_psr_DPsd_trunc_log | 1.801437 | 0 | 0 | 2 | NaN |
119 | 1160 | s_phase8j_nrm_DPsd_trunc_log | 1.356415 | 1 | 1 | 3 | NaN |
119 | 1161 | s_phase8j_psr_DPsd_trunc_log | 1.612924 | 1 | 1 | 2 | NaN |
119 | 1162 | s_phase8t_nrm_DPsd_trunc_log | 0.987879 | 1 | 0 | 3 | NaN |
119 | 1163 | s_phase8t_psr_DPsd_trunc_log | 1.616072 | 1 | 0 | 2 | NaN |
120 | 1164 | s_iso5j_DPsd_trunc_log | 1.644933 | 0 | 1 | 1 | NaN |
120 | 1165 | s_iso5t2_DPsd_trunc_log | 1.259224 | 0 | 0 | 1 | NaN |
120 | 1166 | s_iso8j_DPsd_trunc_log | 1.692863 | 1 | 1 | 1 | NaN |
120 | 1167 | s_iso8t2_DPsd_trunc_log | 1.131855 | 1 | 0 | 1 | NaN |
120 | 1168 | s_phase5j_nrm_DPsd_trunc_log | 1.542395 | 0 | 1 | 3 | NaN |
120 | 1169 | s_phase5j_psr_DPsd_trunc_log | 2.047200 | 0 | 1 | 2 | NaN |
120 | 1170 | s_phase5t_nrm_DPsd_trunc_log | 1.237526 | 0 | 0 | 3 | NaN |
120 | 1171 | s_phase5t_psr_DPsd_trunc_log | 1.801437 | 0 | 0 | 2 | NaN |
120 | 1172 | s_phase8j_nrm_DPsd_trunc_log | 1.356415 | 1 | 1 | 3 | NaN |
120 | 1173 | s_phase8j_psr_DPsd_trunc_log | 1.612924 | 1 | 1 | 2 | NaN |
120 | 1174 | s_phase8t_nrm_DPsd_trunc_log | 0.987879 | 1 | 0 | 3 | NaN |
120 | 1175 | s_phase8t_psr_DPsd_trunc_log | 1.616072 | 1 | 0 | 2 | NaN |
121 | 1176 | s_iso5j_DPsd_trunc_log | 1.644933 | 0 | 1 | 1 | 97 |
121 | 1177 | s_iso5t2_DPsd_trunc_log | 1.259224 | 0 | 0 | 1 | 97 |
121 | 1178 | s_iso8j_DPsd_trunc_log | 1.692863 | 1 | 1 | 1 | 97 |
121 | 1179 | s_iso8t2_DPsd_trunc_log | 1.131855 | 1 | 0 | 1 | 97 |
121 | 1180 | s_phase5j_nrm_DPsd_trunc_log | 1.542395 | 0 | 1 | 3 | 97 |
121 | 1181 | s_phase5j_psr_DPsd_trunc_log | 2.047200 | 0 | 1 | 2 | 97 |
121 | 1182 | s_phase5t_nrm_DPsd_trunc_log | 1.237526 | 0 | 0 | 3 | 97 |
121 | 1183 | s_phase5t_psr_DPsd_trunc_log | 1.801437 | 0 | 0 | 2 | 97 |
121 | 1184 | s_phase8j_nrm_DPsd_trunc_log | 1.356415 | 1 | 1 | 3 | 97 |
121 | 1185 | s_phase8j_psr_DPsd_trunc_log | 1.612924 | 1 | 1 | 2 | 97 |
121 | 1186 | s_phase8t_nrm_DPsd_trunc_log | 0.987879 | 1 | 0 | 3 | 97 |
121 | 1187 | s_phase8t_psr_DPsd_trunc_log | 1.616072 | 1 | 0 | 2 | 97 |
1188 rows × 7 columns
df_to_analyze.loc[caseid, staticvar]
89.0
print("NULL VALUES (INCLUDING REMOVED FOR INCOMPLETE TAP SETS):\n\n")
for c in df_to_analyze:
print(c)
s = df_to_analyze[c]
print(list(s[s.isnull()].index))
print('')
NULL VALUES (INCLUDING REMOVED FOR INCOMPLETE TAP SETS): SCAL_order_500ms_first [] SCAL_sex_femalezero [] SCAL_calc_wasivocab_tscore [] SCAL_calc_wasimatrix_tscore ['053'] SCAL_calc_wasi_tscore_total ['053'] SCAL_calc_fsiq2 ['053'] SCAL_calc_bfi_extraversion [] SCAL_calc_bfi_agreeableness [] SCAL_calc_bfi_conscientiousness [] SCAL_calc_bfi_neuroticism [] SCAL_calc_bfi_openness [] SCAL_qmusic_dancelevel [] SCAL_qmusic_instrumentlevel [] SCAL_qmusic_drumlevel ['064'] SCAL_qmusic_behaviors_12_friendstaste [] SCAL_qmusic_behaviors_13_sharingint [] SCAL_qmusic_behaviors_14_getinterest [] qmusic_calc_anyhours [] qmusic_calc_maxskill ['064'] qmusic_calc_sumskill ['064'] qmusic_calc_socialimp [] SCAL_qmusic_behaviors_07_yourself_ln1p ['093'] SCAL_qmusic_behaviors_08_otherprs_ln1p [] SCAL_qmusic_behaviors_09_danceprv_ln1p [] SCAL_qmusic_dancelevel_ln1p [] SCAL_qmusic_singinghours_nonzero [] SCAL_qmusic_singingtimes_nonzero [] SCAL_qmusic_dancehours_nonzero [] SCAL_qmusic_instrumenthours_nonzero [] SCAL_qmusic_drumhours_nonzero [] SCAL_qmusic_behaviors_09_danceprv_nonzero [] SCAL_qmusic_behaviors_10_dancepub_nonzero [] SCAL_qmusic_gamehoursall_nonzero [] SCAL_qmusic_gamehoursdrumsticks_nonzero [] I5P4_local_trunc ['049'] I8P4_local_trunc ['048'] I8P4_drift_trunc ['048'] I5P4_drift_trunc ['049'] I8L2_local_trunc ['048'] I5L2_local_trunc ['049'] I8L2_drift_trunc ['018', '048', '057', '059', '064', '066', '116'] I5L2_drift_trunc ['016', '022', '033', '036', '044', '049', '052', '053', '056', '061', '077', '078', '084', '085', '089', '092', '096', '097', '101', '105', '107', '109', '110', '111', '115', '116'] I8P4_ints_count ['048'] I5P4_ints_count ['049'] I8L2_ints_count ['048'] I5L2_ints_count ['049'] I8P4_driftperc_trunc ['048'] I8P4_localperc_trunc ['048'] I5P4_driftperc_trunc ['049'] I5P4_localperc_trunc ['049'] s_iso5t1_DPm ['055', '073'] s_iso5t1_DPsd_trunc ['055', '073'] s_iso5t1_DPct ['055', '073'] s_iso8t1_DPm ['073'] s_iso8t1_DPsd_trunc ['073'] s_iso8t1_DPct ['073'] s_iso5t2_DPm ['049'] s_iso5t2_DPsd_trunc ['049'] s_iso5t2_DPct ['049'] s_iso8t2_DPm ['015', '049', '055', '104'] s_iso8t2_DPsd_trunc ['015', '049', '055', '104'] s_iso8t2_DPct ['015', '049', '055', '104'] s_lin5t_DPm ['015', '049', '055', '068', '073', '089'] s_lin5t_DPsd_trunc ['015', '049', '055', '068', '073', '089'] s_lin5t_DPct ['015', '049', '055', '068', '073', '089'] s_lin8t_DPm ['029', '055', '073', '086'] s_lin8t_DPsd_trunc ['029', '055', '073', '086'] s_lin8t_DPct ['029', '055', '073', '086'] s_phase5t_DPm [] s_phase5t_DPsd_trunc [] s_phase5t_DPct [] s_phase8t_DPm [] s_phase8t_DPsd_trunc [] s_phase8t_DPct [] s_iso5j_DPm ['104'] s_iso5j_DPsd_trunc ['104'] s_iso5j_DPct ['104'] s_iso8j_DPm ['015', '049', '055'] s_iso8j_DPsd_trunc ['015', '049', '055'] s_iso8j_DPct ['015', '049', '055'] s_lin5j_DPm ['018', '019', '035', '068', '073', '077', '089', '104', '112'] s_lin5j_DPsd_trunc ['018', '019', '035', '068', '073', '077', '089', '104', '112'] s_lin5j_DPct ['018', '019', '035', '068', '073', '077', '089', '104', '112'] s_lin8j_DPm ['068', '073', '089'] s_lin8j_DPsd_trunc ['068', '073', '089'] s_lin8j_DPct ['068', '073', '089'] s_phase5j_DPm [] s_phase5j_DPsd_trunc [] s_phase5j_DPct [] s_phase8j_DPm [] s_phase8j_DPsd_trunc [] s_phase8j_DPct [] s_phase8j_psr_DPm [] s_phase8j_psr_DPsd_trunc [] s_phase8j_psr_DPct [] s_phase8t_psr_DPm [] s_phase8t_psr_DPsd_trunc [] s_phase8t_psr_DPct [] s_phase5j_psr_DPm [] s_phase5j_psr_DPsd_trunc [] s_phase5j_psr_DPct [] s_phase5t_psr_DPm [] s_phase5t_psr_DPsd_trunc [] s_phase5t_psr_DPct [] s_phase5t_nrm_DPm ['015', '025', '030', '033', '044', '055', '066', '073', '080', '086', '104', '107', '109', '117', '121'] s_phase5t_nrm_DPsd_trunc ['015', '025', '030', '033', '044', '055', '066', '073', '080', '086', '104', '107', '109', '117', '121'] s_phase5t_nrm_DPct ['015', '025', '030', '033', '044', '066', '073', '080', '104', '107', '109', '117', '121'] s_phase8t_nrm_DPm ['015', '025', '049', '055', '068', '073', '086', '089', '098', '104', '109', '117', '121'] s_phase8t_nrm_DPsd_trunc ['015', '025', '049', '055', '068', '073', '086', '089', '098', '104', '109', '117', '121'] s_phase8t_nrm_DPct ['015', '025', '049', '055', '068', '073', '086', '098', '104', '109', '117', '121'] s_phase5j_nrm_DPm ['026', '027', '041', '049', '060', '073', '086', '104', '107', '109', '117', '120', '121'] s_phase5j_nrm_DPsd_trunc ['026', '027', '041', '049', '060', '073', '086', '104', '107', '109', '117', '120', '121'] s_phase5j_nrm_DPct ['026', '027', '041', '049', '060', '073', '104', '107', '109', '117', '120', '121'] s_phase8j_nrm_DPm ['026', '038', '049', '054', '055', '073', '086', '089', '105', '107', '120'] s_phase8j_nrm_DPsd_trunc ['026', '038', '049', '054', '055', '073', '086', '089', '105', '107', '120'] s_phase8j_nrm_DPct ['026', '038', '049', '054', '086', '105', '107', '120'] iso_j_DPsd_trunc_mz58 ['015', '049', '055', '104'] iso_t1_DPsd_trunc_mz58 ['055', '073'] iso_t2_DPsd_trunc_mz58 ['015', '049', '055', '104'] lin_j_DPsd_trunc_mz58 ['018', '019', '035', '068', '073', '077', '089', '104', '112'] lin_t_DPsd_trunc_mz58 ['015', '029', '049', '055', '068', '073', '086', '089'] phase_j_nrm_DPsd_trunc_mz58 ['026', '027', '038', '041', '049', '054', '055', '060', '073', '086', '089', '104', '105', '107', '109', '117', '120', '121'] phase_j_psr_DPsd_trunc_mz58 [] phase_t_nrm_DPsd_trunc_mz58 ['015', '025', '030', '033', '044', '049', '055', '066', '068', '073', '080', '086', '089', '098', '104', '107', '109', '117', '121'] phase_t_psr_DPsd_trunc_mz58 [] IP4_local_trunc_mz58 ['048', '049'] IP4_drift_trunc_mz58 ['048', '049']
dfa = df_to_analyze
get = lambda r: (list(concat_matches(dfo, r).columns), concat_matches(dfo, r))
geta = lambda r: (list(concat_matches(df_to_analyze, r).columns), concat_matches(df_to_analyze, r))
firstcol = lambda df: df.T.iloc[0]
firstcol(match('participant_age')).describe()
count 99.000000 mean 20.939394 std 5.013895 min 18.000000 25% 19.000000 50% 20.000000 75% 21.000000 max 52.000000 dtype: float64
sex = firstcol(match('sex_femalezero'))
is_female = (sex==0)
is_male = (sex==1)
assert is_female[is_female==True].count() == 60
assert is_female[is_female==False].count() == 39
assert is_male[is_male==True].count() == 39
assert is_male[is_male==False].count() == 60
var1 = firstcol(match('participant_age'))
print (" females")
print firstcol(match('participant_age'))[is_female].describe()
print
print (" males")
print firstcol(match('participant_age'))[is_male].describe()
females count 60.00000 mean 21.30000 std 6.01777 min 18.00000 25% 19.00000 50% 19.50000 75% 21.25000 max 52.00000 dtype: float64 males count 39.000000 mean 20.384615 std 2.843417 min 18.000000 25% 19.000000 50% 20.000000 75% 21.000000 max 32.000000 dtype: float64
names, df = get('white')
print 'female'
print df[is_female].sum()
print df[is_female].count()
print
print 'male'
print df[is_male].sum()
print df[is_male].count()
female SCAL_qbasic_ethnicity_white 32 dtype: int64 SCAL_qbasic_ethnicity_white 60 dtype: int64 male SCAL_qbasic_ethnicity_white 16 dtype: int64 SCAL_qbasic_ethnicity_white 39 dtype: int64
match('participant_age').columns
#dfo['SCAL_participant_age'].name
Index([u'SCAL_participant_age'], dtype='object')
names, df = get('I?P4_ints_count')
df.describe()
I5P4_ints_count | I8P4_ints_count | |
---|---|---|
count | 98.000000 | 98.000000 |
mean | 114.469388 | 114.428571 |
std | 4.279426 | 9.169650 |
min | 103.000000 | 78.000000 |
25% | 112.000000 | 112.000000 |
50% | 114.500000 | 115.000000 |
75% | 117.000000 | 119.000000 |
max | 127.000000 | 134.000000 |
names, df = geta('I?P4_drift_trunc$')
df.describe()
I8P4_drift_trunc | I5P4_drift_trunc | |
---|---|---|
count | 98.000000 | 98.000000 |
mean | 3.140968 | 2.634933 |
std | 1.185538 | 0.839702 |
min | 1.477740 | 1.262769 |
25% | 2.272543 | 2.123736 |
50% | 2.785199 | 2.439624 |
75% | 3.754941 | 3.041484 |
max | 6.877687 | 5.521678 |
names, df = geta('s_.*DPsd_trunc$')
dtable = df.describe().T[:14]
reformat = np.round(dtable[['mean', 'std', 'count']], 4)
reformat
mean | std | count | |
---|---|---|---|
s_iso5t1_DPsd_trunc | 4.3521 | 1.2843 | 97 |
s_iso8t1_DPsd_trunc | 4.5325 | 1.7889 | 98 |
s_iso5t2_DPsd_trunc | 4.7942 | 1.6437 | 98 |
s_iso8t2_DPsd_trunc | 4.3974 | 1.6388 | 95 |
s_lin5t_DPsd_trunc | 5.4829 | 1.7679 | 93 |
s_lin8t_DPsd_trunc | 5.1139 | 1.5689 | 95 |
s_phase5t_DPsd_trunc | 6.9577 | 2.5776 | 99 |
s_phase8t_DPsd_trunc | 7.3281 | 4.7130 | 99 |
s_iso5j_DPsd_trunc | 5.7983 | 1.2116 | 98 |
s_iso8j_DPsd_trunc | 6.0582 | 1.4768 | 96 |
s_lin5j_DPsd_trunc | 6.4575 | 1.5829 | 90 |
s_lin8j_DPsd_trunc | 8.0590 | 1.5669 | 96 |
s_phase5j_DPsd_trunc | 8.2631 | 3.0712 | 99 |
s_phase8j_DPsd_trunc | 8.2550 | 4.3892 | 99 |
names, df = geta('nrm_DPsd_trunc$|psr_DPsd_trunc$')
dtable = df.describe().T #[14:-6]
reformat = dtable[['mean', 'std', 'count']]
reformat
mean | std | count | |
---|---|---|---|
s_phase8j_psr_DPsd_trunc | 7.601853 | 2.069916 | 99 |
s_phase8t_psr_DPsd_trunc | 8.044562 | 2.857241 | 99 |
s_phase5j_psr_DPsd_trunc | 9.303088 | 2.055752 | 99 |
s_phase5t_psr_DPsd_trunc | 8.154759 | 2.470972 | 99 |
s_phase5t_nrm_DPsd_trunc | 4.826685 | 1.536312 | 84 |
s_phase8t_nrm_DPsd_trunc | 4.553451 | 1.697381 | 86 |
s_phase5j_nrm_DPsd_trunc | 6.237400 | 1.520756 | 86 |
s_phase8j_nrm_DPsd_trunc | 6.260877 | 1.675251 | 88 |
names, df = get('DPsd')
dtable = df.describe().T[:14]
reformat = np.round(dtable[['mean', 'std', 'count']], 4)
reformat
mean | std | count | |
---|---|---|---|
SMSR_iso5t1_DPsd | 4.3850 | 1.4098 | 97 |
SMSR_iso8t1_DPsd | 4.5325 | 1.7889 | 98 |
SMSR_iso5t2_DPsd | 4.8435 | 1.8394 | 98 |
SMSR_iso8t2_DPsd | 4.4217 | 1.7305 | 95 |
SMSR_lin5t_DPsd | 5.4938 | 1.8040 | 93 |
SMSR_lin8t_DPsd | 5.1355 | 1.6423 | 95 |
SMSR_phase5t_DPsd | 7.0877 | 3.2257 | 99 |
SMSR_phase8t_DPsd | 7.5997 | 5.8158 | 99 |
SMSR_iso5j_DPsd | 5.8287 | 1.3215 | 98 |
SMSR_iso8j_DPsd | 6.0582 | 1.4768 | 96 |
SMSR_lin5j_DPsd | 6.4608 | 1.5929 | 90 |
SMSR_lin8j_DPsd | 8.0682 | 1.5976 | 96 |
SMSR_phase5j_DPsd | 8.3989 | 3.6827 | 99 |
SMSR_phase8j_DPsd | 8.5615 | 5.7565 | 99 |
names, df = get('I5P4_ints_count')
df.std()
I5P4_ints_count 4.279426 dtype: float64
dfa = df_to_analyze
matcha = lambda x: concat_matches(dfa, x)
isips = matcha('P4_drift_trunc|P4_local_trunc')
isips
I5P4_local_trunc | I8P4_local_trunc | I8P4_drift_trunc | I5P4_drift_trunc | I5P4_local_trunc_log | I8P4_local_trunc_log | I5P4_drift_trunc_log | I8P4_drift_trunc_log | |
---|---|---|---|---|---|---|---|---|
015 | 2.918599 | 3.871535 | 5.361354 | 3.622814 | 1.071104 | 1.353651 | 1.287251 | 1.679217 |
016 | 2.692996 | 2.975526 | 2.148091 | 2.134364 | 0.990654 | 1.090421 | 0.758169 | 0.764580 |
017 | 2.764646 | 3.093386 | 3.853557 | 2.614372 | 1.016912 | 1.129266 | 0.961024 | 1.348997 |
018 | 2.080451 | 3.284952 | 2.249890 | 1.649681 | 0.732585 | 1.189352 | 0.500582 | 0.810881 |
019 | 1.397983 | 2.381819 | 2.243927 | 1.262769 | 0.335030 | 0.867865 | 0.233307 | 0.808227 |
020 | 3.357435 | 3.785594 | 4.145644 | 3.145559 | 1.211177 | 1.331203 | 1.145992 | 1.422058 |
021 | 3.454273 | 2.633204 | 4.128266 | 2.873748 | 1.239612 | 0.968201 | 1.055617 | 1.417857 |
022 | 2.634599 | 1.550475 | 2.268349 | 2.029501 | 0.968731 | 0.438561 | 0.707790 | 0.819052 |
024 | 2.834259 | 2.653945 | 3.064198 | 2.445649 | 1.041781 | 0.976047 | 0.894311 | 1.119786 |
025 | 2.568266 | 2.340668 | 4.878173 | 3.253087 | 0.943231 | 0.850436 | 1.179604 | 1.584771 |
026 | 3.350752 | 1.716964 | 1.526251 | 2.556399 | 1.209185 | 0.540557 | 0.938600 | 0.422814 |
027 | 2.534100 | 1.905319 | 2.021396 | 1.719803 | 0.929838 | 0.644649 | 0.542210 | 0.703788 |
028 | 2.693264 | 2.454855 | 2.392972 | 2.529593 | 0.990754 | 0.898068 | 0.928058 | 0.872536 |
029 | 2.740823 | 4.162152 | 3.397715 | 3.012302 | 1.008258 | 1.426032 | 1.102704 | 1.223103 |
030 | 1.836484 | 2.149577 | 2.538334 | 1.459469 | 0.607853 | 0.765271 | 0.378073 | 0.931508 |
032 | 1.604973 | 1.511323 | 1.477740 | 1.508371 | 0.473107 | 0.412985 | 0.411030 | 0.390514 |
033 | 2.628603 | 2.478391 | 2.136364 | 2.062282 | 0.966453 | 0.907609 | 0.723813 | 0.759105 |
034 | 2.239750 | 2.117451 | 1.814533 | 1.989566 | 0.806364 | 0.750213 | 0.687917 | 0.595828 |
035 | 3.150311 | 3.228548 | 3.064296 | 2.369321 | 1.147501 | 1.172033 | 0.862604 | 1.119818 |
036 | 4.481994 | 5.043328 | 5.122094 | 3.699159 | 1.500068 | 1.618066 | 1.308105 | 1.633563 |
037 | 3.387714 | 2.662026 | 4.152781 | 3.619540 | 1.220155 | 0.979088 | 1.286347 | 1.423778 |
038 | 2.249455 | 2.829793 | 2.656698 | 1.680426 | 0.810688 | 1.040204 | 0.519047 | 0.977084 |
039 | 2.780111 | 2.935845 | 2.770641 | 3.031682 | 1.022491 | 1.076995 | 1.109118 | 1.019079 |
040 | 1.510101 | 2.461254 | 3.092411 | 1.467066 | 0.412176 | 0.900671 | 0.383264 | 1.128951 |
041 | 2.145076 | 2.362158 | 2.045128 | 1.845840 | 0.763175 | 0.859575 | 0.612935 | 0.715461 |
043 | 2.488092 | 2.812628 | 5.724121 | 3.740139 | 0.911516 | 1.034119 | 1.319123 | 1.744689 |
044 | 3.350447 | 3.013915 | 2.880284 | 2.462124 | 1.209094 | 1.103240 | 0.901024 | 1.057889 |
046 | 3.210654 | 2.719880 | 3.335428 | 2.691112 | 1.166475 | 1.000588 | 0.989955 | 1.204601 |
047 | 2.780972 | 1.741558 | 1.893753 | 2.608408 | 1.022800 | 0.554780 | 0.958740 | 0.638561 |
048 | 2.057786 | NaN | NaN | 2.157261 | 0.721630 | NaN | 0.768839 | NaN |
... | ... | ... | ... | ... | ... | ... | ... | ... |
091 | 2.371655 | 2.403515 | 3.087463 | 2.414281 | 0.863588 | 0.876932 | 0.881401 | 1.127350 |
092 | 3.221222 | 2.761092 | 3.171285 | 2.436657 | 1.169761 | 1.015626 | 0.890627 | 1.154137 |
093 | 3.187021 | 2.699241 | 2.705529 | 2.975701 | 1.159087 | 0.992971 | 1.090480 | 0.995297 |
094 | 3.895166 | 2.552915 | 2.467728 | 3.166824 | 1.359736 | 0.937236 | 1.152729 | 0.903298 |
095 | 2.855076 | 2.236916 | 2.692721 | 2.346938 | 1.049098 | 0.805098 | 0.853112 | 0.990552 |
096 | 2.758227 | 2.636349 | 2.631248 | 2.430900 | 1.014588 | 0.969395 | 0.888261 | 0.967458 |
097 | 3.288903 | 2.700432 | 2.362413 | 2.863347 | 1.190554 | 0.993412 | 1.051991 | 0.859684 |
098 | 2.470041 | 2.402485 | 1.902927 | 1.794084 | 0.904235 | 0.876504 | 0.584495 | 0.643393 |
099 | 2.549172 | 3.921938 | 4.614650 | 2.129828 | 0.935768 | 1.366586 | 0.756041 | 1.529236 |
100 | 2.891669 | 3.025877 | 2.892949 | 2.279206 | 1.061834 | 1.107201 | 0.823827 | 1.062276 |
101 | 2.653102 | 2.296601 | 4.485799 | 2.192433 | 0.975730 | 0.831430 | 0.785012 | 1.500917 |
102 | 3.197323 | 1.817661 | 2.213733 | 2.416846 | 1.162314 | 0.597551 | 0.882463 | 0.794680 |
103 | 3.243799 | 2.550272 | 2.720966 | 2.747351 | 1.176745 | 0.936200 | 1.010637 | 1.000987 |
104 | 3.037799 | 2.328359 | 2.589216 | 2.791918 | 1.111133 | 0.845164 | 1.026729 | 0.951355 |
105 | 2.873310 | 3.591199 | 4.576603 | 2.544936 | 1.055465 | 1.278486 | 0.934105 | 1.520957 |
107 | 2.465461 | 2.891443 | 2.427825 | 1.809284 | 0.902379 | 1.061756 | 0.592931 | 0.886996 |
108 | 3.134870 | 2.597465 | 2.800888 | 3.308830 | 1.142588 | 0.954536 | 1.196595 | 1.029936 |
109 | 3.009218 | 3.159224 | 3.039583 | 2.322104 | 1.101680 | 1.150326 | 0.842474 | 1.111720 |
110 | 1.940701 | 2.280976 | 1.713848 | 1.549869 | 0.663049 | 0.824603 | 0.438170 | 0.538741 |
111 | 3.034355 | 2.380234 | 3.327398 | 2.317140 | 1.109999 | 0.867199 | 0.840334 | 1.202191 |
112 | 2.629085 | 3.286044 | 4.578238 | 4.305536 | 0.966636 | 1.189684 | 1.459902 | 1.521314 |
113 | 1.898820 | 1.706451 | 3.133905 | 1.431661 | 0.641233 | 0.534416 | 0.358835 | 1.142280 |
114 | 4.873864 | 4.606419 | 3.512176 | 3.896161 | 1.583887 | 1.527451 | 1.359992 | 1.256236 |
115 | 2.944964 | 2.177921 | 1.844496 | 2.387312 | 1.080096 | 0.778371 | 0.870168 | 0.612206 |
116 | 2.272517 | 2.076461 | 1.585251 | 1.767129 | 0.820888 | 0.730665 | 0.569356 | 0.460743 |
117 | 2.884701 | 2.668836 | 3.120648 | 3.003352 | 1.059421 | 0.981642 | 1.099729 | 1.138041 |
118 | 3.168178 | 3.357831 | 2.771240 | 3.208083 | 1.153157 | 1.211295 | 1.165674 | 1.019295 |
119 | 1.649012 | 2.107230 | 1.874337 | 1.540343 | 0.500176 | 0.745374 | 0.432005 | 0.628255 |
120 | 2.400801 | 2.504026 | 2.308642 | 2.400271 | 0.875803 | 0.917900 | 0.875582 | 0.836660 |
121 | 2.143469 | 3.187984 | 3.452782 | 2.112349 | 0.762425 | 1.159389 | 0.747801 | 1.239180 |
99 rows × 8 columns
dfa = df_to_analyze
matcha = lambda x: concat_matches(dfa, x)
#isips = matcha('P4_drift_trunc|P4_local_trunc')
smscols = matcha('^s_.*DPsd_trunc$')
#scatter_all(isips, print_max=3)
#scatter_all(np.log(isips), print_max=3)
smscols.T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s_iso5t1_DPsd_trunc | 8.059057 | 3.423481 | 3.607109 | 3.291210 | 2.583675 | 7.004012 | 4.608983 | 3.167766 | 3.170995 | 3.186261 | ... | 6.123362 | 2.799212 | 4.586392 | 4.075651 | 2.487950 | 5.184207 | 5.405130 | 2.827062 | 5.538719 | 4.245075 |
s_iso8t1_DPsd_trunc | 7.944039 | 2.964738 | 9.560081 | 5.005393 | 2.431597 | 6.505625 | 4.433459 | 2.268350 | 4.452189 | 4.964179 | ... | 8.144077 | 2.344456 | 6.235354 | 3.659721 | 2.334648 | 5.438937 | 3.613148 | 2.767637 | 4.298762 | 5.691151 |
s_iso5t2_DPsd_trunc | 8.438450 | 3.522687 | 3.871627 | 3.373599 | 2.612512 | 7.021192 | 5.208270 | 3.488723 | 3.281671 | 5.276439 | ... | 5.511892 | 2.801852 | 6.319350 | 4.716004 | 3.143240 | 4.653159 | 4.076174 | 3.167036 | 5.855743 | 4.506521 |
s_iso8t2_DPsd_trunc | 9.492087 | 3.101403 | 5.063099 | 5.568829 | 3.404020 | 6.388830 | 4.368410 | 2.339890 | 4.170302 | 4.422124 | ... | 5.838884 | 2.772427 | 6.077328 | 3.511975 | 2.276168 | 4.564913 | 2.928230 | 3.214585 | 3.713862 | 5.989694 |
s_lin5t_DPsd_trunc | 12.332615 | 3.804005 | 4.959168 | 4.888448 | 4.628010 | 7.409937 | 5.736285 | 3.561546 | 4.532819 | 6.311407 | ... | 11.863541 | 3.458209 | 7.417736 | 4.439277 | 3.199540 | 6.582733 | 5.129986 | 4.204554 | 5.773415 | 7.357143 |
s_lin8t_DPsd_trunc | 10.526399 | 3.797363 | 5.997794 | 4.568257 | 4.510050 | 5.714329 | 4.928837 | 3.213383 | 5.215915 | 5.288537 | ... | 7.265519 | 2.892864 | 6.500740 | 3.968508 | 3.315846 | 6.912513 | 4.916196 | 3.477552 | 5.289070 | 5.277031 |
s_phase5t_DPsd_trunc | 13.263134 | 4.636219 | 6.101449 | 6.015203 | 5.880533 | 8.832377 | 5.631446 | 5.549757 | 6.495490 | 8.974706 | ... | 8.618557 | 4.244219 | 7.097735 | 10.690847 | 4.345646 | 9.021404 | 8.359160 | 4.832540 | 6.673500 | 7.992546 |
s_phase8t_DPsd_trunc | 20.380103 | 4.223427 | 6.801106 | 7.011985 | 3.834770 | 7.823846 | 5.464373 | 5.537968 | 5.319286 | 6.812584 | ... | 6.988509 | 5.164350 | 6.926644 | 5.851449 | 4.129328 | 7.146782 | 6.167211 | 4.298461 | 5.503316 | 11.802603 |
s_iso5j_DPsd_trunc | 4.344289 | 5.180662 | 5.260285 | 6.896868 | 6.576153 | 6.369871 | 5.687223 | 5.210024 | 6.350138 | 5.514704 | ... | 6.121927 | 5.109652 | 8.639169 | 6.185684 | 4.871451 | 5.396906 | 8.370299 | 4.820479 | 5.017404 | 5.578518 |
s_iso8j_DPsd_trunc | 10.779028 | 5.435017 | 8.475535 | 5.883866 | 5.660678 | 6.168150 | 5.515570 | 5.788754 | 6.075278 | 6.741509 | ... | 8.035791 | 3.907462 | 6.181960 | 5.100964 | 5.519451 | 4.905857 | 6.811418 | 4.125815 | 4.164116 | 5.553144 |
s_lin5j_DPsd_trunc | 7.501162 | 5.203805 | 6.501155 | NaN | 7.291124 | 6.805953 | 5.055164 | 7.931197 | 5.974806 | 5.822978 | ... | 5.567357 | 4.037070 | 11.463016 | 5.579179 | 4.880630 | 7.704681 | 5.791886 | 6.113417 | 5.339945 | 8.439449 |
s_lin8j_DPsd_trunc | 8.297900 | 6.620878 | 10.113443 | 8.164527 | 9.028881 | 10.058718 | 8.511438 | 7.369508 | 8.030070 | 7.715649 | ... | 8.873398 | 6.363658 | 10.241289 | 7.178777 | 6.117443 | 8.401488 | 7.691800 | 6.781628 | 6.156963 | 11.024891 |
s_phase5j_DPsd_trunc | 11.272936 | 5.897675 | 9.611000 | 8.539146 | 15.480304 | 8.068511 | 6.066509 | 7.779181 | 7.904829 | 8.206434 | ... | 8.633570 | 9.528869 | 9.376242 | 7.038543 | 6.136827 | 9.320015 | 6.556342 | 5.680030 | 7.829466 | 10.310838 |
s_phase8j_DPsd_trunc | 12.682229 | 5.073990 | 8.765685 | 5.700936 | 10.588232 | 11.539131 | 5.894436 | 5.026944 | 6.353962 | 6.817031 | ... | 7.250727 | 5.650887 | 7.474951 | 5.434796 | 6.454431 | 7.585576 | 6.766145 | 5.473712 | 6.684138 | 7.984069 |
s_phase8j_psk_DPsd_trunc | 5.265406 | 5.017459 | 7.267209 | 5.819368 | 12.505387 | 8.127084 | 6.588412 | 4.963010 | 6.031818 | 7.660522 | ... | 8.842019 | 7.837034 | 6.495179 | 7.330560 | 4.875323 | 8.052651 | 5.702284 | 7.037632 | 6.966469 | 8.356615 |
s_phase8j_psr_DPsd_trunc | 5.265406 | 5.017459 | 7.267209 | 5.819368 | 12.505387 | 8.127084 | 6.588412 | 4.963010 | 6.031818 | 7.660522 | ... | 8.842019 | 7.837034 | 6.495179 | 7.330560 | 4.875323 | 8.052651 | 5.702284 | 7.037632 | 6.966469 | 8.356615 |
s_phase8tp_psk_DPsd_trunc | 10.965734 | 4.106756 | 8.509142 | 5.401160 | 4.559657 | 10.939696 | 6.969336 | 9.824751 | 7.201778 | 8.618015 | ... | 6.680462 | 8.436280 | 6.840779 | 8.770963 | 4.564519 | 6.650191 | 5.423142 | 4.591979 | 9.194539 | 8.725255 |
s_phase8t_psr_DPsd_trunc | 10.965734 | 4.106756 | 8.509142 | 5.401160 | 4.559657 | 10.939696 | 6.969336 | 9.824751 | 7.201778 | 8.618015 | ... | 6.680462 | 8.436280 | 6.840779 | 8.770963 | 4.564519 | 6.650191 | 5.423142 | 4.591979 | 9.194539 | 8.725255 |
s_phase5j_psk_DPsd_trunc | 9.671795 | 7.746184 | 13.233118 | 8.123313 | 11.121553 | 11.433945 | 6.963592 | 10.593309 | 10.867236 | 10.456456 | ... | 10.470506 | 10.130621 | 10.167881 | 9.782344 | 7.021825 | 11.598006 | 7.671657 | 5.858844 | 8.079845 | 10.262552 |
s_phase5j_psr_DPsd_trunc | 9.671795 | 7.746184 | 13.233118 | 8.123313 | 11.121553 | 11.433945 | 6.963592 | 10.593309 | 10.867236 | 10.456456 | ... | 10.470506 | 10.130621 | 10.167881 | 9.782344 | 7.021825 | 11.598006 | 7.671657 | 5.858844 | 8.079845 | 10.262552 |
s_phase5t_psk_DPsd_trunc | 15.162014 | 6.058348 | 9.410722 | 6.370397 | 7.103844 | 11.904894 | 7.040196 | 9.685003 | 11.232640 | 9.391291 | ... | 9.352234 | 6.277202 | 7.563332 | 7.484122 | 2.125875 | 11.652432 | 6.887987 | 7.658930 | 6.190892 | 12.641256 |
s_phase5t_psr_DPsd_trunc | 15.162014 | 6.058348 | 9.410722 | 6.370397 | 7.103844 | 11.904894 | 7.040196 | 9.685003 | 11.232640 | 9.391291 | ... | 9.352234 | 6.277202 | 7.563332 | 7.484122 | 2.125875 | 11.652432 | 6.887987 | 7.658930 | 6.190892 | 12.641256 |
s_phase5t_nrm_DPsd_trunc | 9.927312 | 3.838674 | 5.033151 | 4.297641 | 5.041071 | 6.515859 | 5.123415 | 3.202688 | 4.387560 | 6.458833 | ... | 8.683665 | 2.702944 | 6.981320 | 9.376295 | 3.758252 | 5.062213 | 6.683912 | 3.535127 | 5.806555 | 5.977768 |
s_phase8t_nrm_DPsd_trunc | 11.603960 | 3.004894 | 6.854995 | 4.567225 | 2.672316 | 6.204709 | 4.395920 | 2.676518 | 4.489272 | 5.832460 | ... | 6.828493 | 2.992058 | 6.634580 | 3.587443 | 2.923484 | 4.962582 | 4.997630 | 3.253722 | 4.417981 | 8.101868 |
s_phase5j_nrm_DPsd_trunc | 8.743251 | 5.071083 | 7.014337 | 7.038558 | 8.514883 | 6.505715 | 5.969171 | 6.423529 | 7.290552 | 7.161750 | ... | 7.601825 | 5.574598 | 7.316711 | 6.213868 | 5.649286 | 5.908589 | 4.781702 | 5.062602 | 6.617469 | 8.870715 |
s_phase8j_nrm_DPsd_trunc | 8.764165 | 4.396308 | 8.542477 | 5.516430 | 8.587748 | 8.353316 | 5.484358 | 4.902742 | 5.857296 | 6.170993 | ... | 6.737524 | 5.076955 | 7.549187 | 5.142237 | 6.585330 | 6.599854 | 6.341203 | 4.807167 | 6.751792 | 6.183649 |
s_lint_610690_DPsd_trunc | 13.248921 | 3.647491 | 6.254805 | 4.769801 | 5.044258 | 7.952984 | 6.309685 | 3.805393 | 4.389249 | 5.555110 | ... | 12.938677 | 3.283291 | 9.406283 | 4.755764 | 3.414272 | 5.783784 | 5.413948 | 4.346650 | 5.520486 | 5.307701 |
s_linj_610690_DPsd_trunc | 10.788868 | 6.692989 | 14.617349 | NaN | 12.881389 | 7.355279 | 10.090107 | 7.873377 | 7.713142 | 7.518116 | ... | 8.461957 | 6.278382 | 9.227213 | 6.783321 | 5.841060 | 7.550096 | 7.191732 | 4.922011 | 7.405898 | 11.879577 |
s_lint_700800_DPsd_trunc | 13.008334 | 4.188447 | 6.278797 | 5.991372 | 4.800754 | 6.452870 | 5.338793 | 3.525517 | 5.302861 | 5.957211 | ... | 7.897561 | 3.474329 | 9.170531 | 4.447257 | 3.353788 | 8.064835 | 3.622448 | 3.628472 | 5.876414 | 9.321889 |
s_lint_500600_DPsd_trunc | 12.486970 | 3.763389 | 7.094297 | 4.338958 | 3.428162 | 6.109977 | 7.413205 | 3.699728 | 4.834872 | 6.247794 | ... | 9.205697 | 3.589876 | 9.466895 | 3.924627 | 2.970704 | 5.239500 | 6.837897 | 5.603176 | 5.913442 | 4.700504 |
s_linj_700800_DPsd_trunc | 8.344733 | 5.658566 | 8.351930 | NaN | 4.806744 | 7.484739 | 7.572755 | 5.879357 | 6.601137 | 6.673417 | ... | 8.888837 | 5.307915 | 12.374330 | 5.832539 | 5.775269 | 5.086778 | 5.948107 | 5.018592 | 4.189604 | 10.555065 |
s_linj_500600_DPsd_trunc | 9.660953 | 6.813903 | 14.502102 | NaN | 9.550193 | 11.361092 | 10.270239 | 11.104630 | 8.447056 | 6.763741 | ... | 11.726403 | 7.186386 | 13.197983 | 8.916590 | 6.994755 | 8.385819 | 9.242493 | 10.248535 | 5.844371 | 8.385738 |
32 rows × 99 columns
dft1 = df_to_analyze['s_phase5t_s4a_DPm_trunc']
dft2 = df_to_analyze['s_phase8j_s4a_DPm_trunc']
#dft2.corr(dft1)
dft1.corr(dft2)
0.065833593780387181
#mna = match('5._DPm|8._DPm|5.2_DPm|8.2_DPm')
#mna.to_csv('perc_negative_asynchrony_20141008.csv')
phase_sections_means = match('a_DPm|b_DPm')
phase_sections_sd = match('a_DPsd|b_DPsd')
match('nonzero').T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SCAL_qmusic_singinghours_nonzero | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
SCAL_qmusic_singingtimes_nonzero | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
SCAL_qmusic_dancehours_nonzero | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | ... | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 |
SCAL_qmusic_instrumenthours_nonzero | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 |
SCAL_qmusic_drumhours_nonzero | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
SCAL_qmusic_behaviors_09_danceprv_nonzero | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | ... | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 |
SCAL_qmusic_behaviors_10_dancepub_nonzero | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | ... | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
SCAL_qmusic_gamehoursall_nonzero | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | ... | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 |
SCAL_qmusic_gamehoursdrumsticks_nonzero | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
9 rows × 99 columns
#for c in range(35):
# s = phase_sections_sd.ix[:,c]
# m = phase_sections_means.ix[:,c]
# print phase_sections_sd.columns[c]
# print phase_sections_means.columns[c]
# print s.corr(m)
#matchq('behaviors_')
pasted = '''
SCAL_sex_femalezero
SCAL_calc_wasivocab_totalrawscore
SCAL_calc_wasimatrix_totalscore
SCAL_calc_wasivocab_tscore
SCAL_calc_wasimatrix_tscore
SCAL_calc_wasi_tscore_total
SCAL_calc_fsiq2
SCAL_calc_bfi_extraversion
SCAL_calc_bfi_agreeableness
SCAL_calc_bfi_conscientiousness
SCAL_calc_bfi_neuroticism
SCAL_calc_bfi_openness
SCAL_session_taskorder
SCAL_order_500ms_first
SCAL_order_rhythmfirst
SCAL_qbasic_hearingdeficityn
SCAL_qbasic_injuriesyn
SCAL_qbasic_exerciseyn
SCAL_qbasic_neurodisorderyn
SCAL_qmusic_singingyn
SCAL_qmusic_singinghours --> NONZERO
SCAL_qmusic_singingtimes --> NONZERO
SCAL_qmusic_dancelevel --> LN1P
SCAL_qmusic_instrumentlevel --> good
SCAL_qmusic_dancehours --> NONZERO
SCAL_qmusic_instrumenthours --> NONZERO
SCAL_qmusic_danceyn
SCAL_qmusic_instrumentyn
SCAL_qmusic_gameyn
SCAL_qmusic_drumsyn
SCAL_qmusic_gamenames --> string
SCAL_qmusic_gamehoursall --> NONZERO
SCAL_qmusic_gamehoursdrumsticks --> NONZERO
SCAL_qmusic_drumstyles --> string
SCAL_qmusic_drumhours --> NONZERO
SCAL_qmusic_drumlevel --> NONZERO
SCAL_qmusic_behaviors_07_yourself --> LN1P
SCAL_qmusic_behaviors_08_otherprs --> LN1P
SCAL_qmusic_behaviors_09_danceprv --> LN1P
SCAL_qmusic_behaviors_10_dancepub --> NONZERO
SCAL_qmusic_behaviors_11_urgemove --> NONZERO
SCAL_qmusic_behaviors_12_friendstaste --> good
SCAL_qmusic_behaviors_13_sharingint --> good
SCAL_qmusic_behaviors_14_getinterest --> good
'''
tolist = pasted.split('\n')
nonzero = filter(lambda i: i.split(" ")[-1] == "NONZERO", tolist)
nonzero = [i.split(" ")[0] for i in nonzero]
assert len(nonzero) == pasted.count('NONZERO')
LN1P = filter(lambda i: i.split(" ")[-1] == "LN1P", tolist)
LN1P = [i.split(" ")[0] for i in LN1P]
assert len(LN1P) == pasted.count('LN1P')
tolist = [i.replace("--> good", "") for i in tolist]
tolist = filter(lambda i: "-->" not in i, tolist)
tolist = [i.strip() for i in tolist]
tolist = filter(lambda i: i != "", tolist)
LN1P
['SCAL_qmusic_dancelevel', 'SCAL_qmusic_behaviors_07_yourself', 'SCAL_qmusic_behaviors_08_otherprs', 'SCAL_qmusic_behaviors_09_danceprv']
match = lambda x: concat_matches(dfo, x)
df_q = match('SCAL_qbasic|SCAL_qmusic')
matchq = lambda x: concat_matches(df_q, x)
rnot = lambda r: '^((?!' + r + ').)*$'
#scales = concat_matches(scales, '^((?!notes).)*$') #hacky "does not contain 'notes' matcher
scales_keep = dfo[['SCAL_qmusic_instrumentlevel',
'SCAL_qmusic_behaviors_12_friendstaste',
]]
plist = lambda l: '\n'.join(l)
print plist(match('SCAL_').columns)
#print('\n'.join(list(match('SCAL_').columns)))
SCAL_session_day SCAL_session_time SCAL_session_isfemale SCAL_exclusion_jitterlinearmissing SCAL_exclusion_rhythmadminerror SCAL_sex_femalezero SCAL_participant_age SCAL_calc_wasivocab_totalrawscore SCAL_calc_wasimatrix_totalscore SCAL_calc_wasivocab_tscore SCAL_calc_wasimatrix_tscore SCAL_calc_wasi_tscore_total SCAL_calc_fsiq2 SCAL_calc_bfi_extraversion SCAL_calc_bfi_agreeableness SCAL_calc_bfi_conscientiousness SCAL_calc_bfi_neuroticism SCAL_calc_bfi_openness SCAL_calc_qmusic_socialimportance SCAL_session_taskorder SCAL_order_500ms_first SCAL_order_rhythmfirst SCAL_notes_csv_cleaning SCAL_notes_adminerror SCAL_notes_methodchange SCAL_notes_participantissue SCAL_notes_observations SCAL_notes_inclusion SCAL_notes_language SCAL_notes_temp SCAL_notes_wasivocab SCAL_notes_wasimatrix SCAL_notes_bfi SCAL_notes_qbasic_hearing SCAL_notes_qbasic_injuries SCAL_notes_qbasic_exercise SCAL_notes_qbasic_neurodisorder SCAL_notes_qbasic_physexclusion SCAL_notes_qbasic_nonstraight SCAL_notes_qbasic_heightweight SCAL_notes_qbasic_handedness SCAL_notes_qmusic_dance SCAL_notes_qmusic_instrument SCAL_notes_qmusic_otherexper SCAL_notes_qmusic_behaviors SCAL_notes_qmusic_singing SCAL_wasivocab_itemscore01_fish SCAL_wasivocab_itemscore02_shovel SCAL_wasivocab_itemscore03_shell SCAL_wasivocab_itemscore04_shirt SCAL_wasivocab_itemscore05_car SCAL_wasivocab_itemscore06_lamp SCAL_wasivocab_itemscore07_bird SCAL_wasivocab_itemscore08_tongue SCAL_wasivocab_itemscore09_pet SCAL_wasivocab_itemscore10_lunch SCAL_wasivocab_itemscore11_bell SCAL_wasivocab_itemscore12_calendar SCAL_wasivocab_itemscore13_alligator SCAL_wasivocab_itemscore14_dance SCAL_wasivocab_itemscore15_summer SCAL_wasivocab_itemscore16_reveal SCAL_wasivocab_itemscore17_decade SCAL_wasivocab_itemscore18_entertain SCAL_wasivocab_itemscore19_tradition SCAL_wasivocab_itemscore20_enthusiastic SCAL_wasivocab_itemscore21_improvise SCAL_wasivocab_itemscore22_haste SCAL_wasivocab_itemscore23_trend SCAL_wasivocab_itemscore24_impulse SCAL_wasivocab_itemscore25_ruminate SCAL_wasivocab_itemscore26_mollify SCAL_wasivocab_itemscore27_extirpate SCAL_wasivocab_itemscore28_panacea SCAL_wasivocab_itemscore29_perfunctory SCAL_wasivocab_itemscore30_insipid SCAL_wasivocab_itemscore31_pavid SCAL_wasimatrix_itemscore01 SCAL_wasimatrix_itemscore02 SCAL_wasimatrix_itemscore03 SCAL_wasimatrix_itemscore04 SCAL_wasimatrix_itemscore05 SCAL_wasimatrix_itemscore06 SCAL_wasimatrix_itemscore07 SCAL_wasimatrix_itemscore08 SCAL_wasimatrix_itemscore09 SCAL_wasimatrix_itemscore10 SCAL_wasimatrix_itemscore11 SCAL_wasimatrix_itemscore12 SCAL_wasimatrix_itemscore13 SCAL_wasimatrix_itemscore14 SCAL_wasimatrix_itemscore15 SCAL_wasimatrix_itemscore16 SCAL_wasimatrix_itemscore17 SCAL_wasimatrix_itemscore18 SCAL_wasimatrix_itemscore19 SCAL_wasimatrix_itemscore20 SCAL_wasimatrix_itemscore21 SCAL_wasimatrix_itemscore22 SCAL_wasimatrix_itemscore23 SCAL_wasimatrix_itemscore24 SCAL_wasimatrix_itemscore25 SCAL_wasimatrix_itemscore26 SCAL_wasimatrix_itemscore27 SCAL_wasimatrix_itemscore28 SCAL_wasimatrix_itemscore29 SCAL_wasimatrix_itemscore30 SCAL_qbasic_isfemale SCAL_qbasic_age SCAL_qbasic_ethnicity_selected SCAL_qbasic_ethnicity_white SCAL_qbasic_ethnicity_nativeam SCAL_qbasic_ethnicity_hispanic SCAL_qbasic_ethnicity_hawaiianpac SCAL_qbasic_ethnicity_black SCAL_qbasic_ethnicity_eastasian SCAL_qbasic_ethnicity_southasian SCAL_qbasic_ethnicity_middleeastern SCAL_qbasic_ethnicity_noneofthese SCAL_qbasic_ethnicityother SCAL_qbasic_ethnicitynotes SCAL_qbasic_relationshipyn SCAL_qbasic_relationshipyears SCAL_qbasic_relationshipmonths SCAL_calc_qbasic_rel_totalmonths SCAL_qbasic_marriedyn SCAL_qbasic_livingwithyn SCAL_qbasic_straightyn SCAL_qbasic_totalheightin SCAL_qbasic_weightlbs SCAL_qbasic_handednessa SCAL_qbasic_handednessb SCAL_qbasic_handednessc SCAL_qbasic_handednessd SCAL_qbasic_handednesse SCAL_qbasic_hearingdeficityn SCAL_qbasic_injuriesyn SCAL_qbasic_exerciseyn SCAL_qbasic_neurodisorderyn SCAL_qmusic_singingyn SCAL_qmusic_singinghours SCAL_qmusic_singingtimes SCAL_qmusic_danceyn SCAL_qmusic_dancestyle SCAL_qmusic_dancelevel SCAL_qmusic_dancehours SCAL_qmusic_instrumentyn SCAL_qmusic_instrumentlist SCAL_qmusic_instrumentlevel SCAL_qmusic_instrumenthours SCAL_qmusic_gameyn SCAL_qmusic_gamenames SCAL_qmusic_gamehoursall SCAL_qmusic_gamehoursdrumsticks SCAL_qmusic_drumsyn SCAL_qmusic_drumstyles SCAL_qmusic_drumhours SCAL_qmusic_drumlevel SCAL_qmusic_behaviors_07_yourself SCAL_qmusic_behaviors_08_otherprs SCAL_qmusic_behaviors_09_danceprv SCAL_qmusic_behaviors_10_dancepub SCAL_qmusic_behaviors_11_urgemove SCAL_qmusic_behaviors_12_friendstaste SCAL_qmusic_behaviors_13_sharingint SCAL_qmusic_behaviors_14_getinterest SCAL_bfi_item01 SCAL_bfi_item02 SCAL_bfi_item03 SCAL_bfi_item04 SCAL_bfi_item05 SCAL_bfi_item06 SCAL_bfi_item07 SCAL_bfi_item08 SCAL_bfi_item09 SCAL_bfi_item10 SCAL_bfi_item11 SCAL_bfi_item12 SCAL_bfi_item13 SCAL_bfi_item14 SCAL_bfi_item15 SCAL_bfi_item16 SCAL_bfi_item17 SCAL_bfi_item18 SCAL_bfi_item19 SCAL_bfi_item20 SCAL_bfi_item21 SCAL_bfi_item22 SCAL_bfi_item23 SCAL_bfi_item24 SCAL_bfi_item25 SCAL_bfi_item26 SCAL_bfi_item27 SCAL_bfi_item28 SCAL_bfi_item29 SCAL_bfi_item30 SCAL_bfi_item31 SCAL_bfi_item32 SCAL_bfi_item33 SCAL_bfi_item34 SCAL_bfi_item35 SCAL_bfi_item36 SCAL_bfi_item37 SCAL_bfi_item38 SCAL_bfi_item39 SCAL_bfi_item40 SCAL_bfi_item41 SCAL_bfi_item42 SCAL_bfi_item43 SCAL_bfi_item44 SCAL_qmusic_singinghours_nonzero SCAL_qmusic_singingtimes_nonzero SCAL_qmusic_dancehours_nonzero SCAL_qmusic_instrumenthours_nonzero SCAL_qmusic_drumhours_nonzero SCAL_qmusic_behaviors_09_danceprv_nonzero SCAL_qmusic_behaviors_10_dancepub_nonzero SCAL_qmusic_gamehoursall_nonzero SCAL_qmusic_gamehoursdrumsticks_nonzero SCAL_qmusic_behaviors_07_yourself_ln1p SCAL_qmusic_behaviors_08_otherprs_ln1p SCAL_qmusic_behaviors_09_danceprv_ln1p SCAL_qmusic_dancelevel_ln1p SCAL_qmusic_dancelevel_tophalf SCAL_orders_500 SCAL_orders_800 SCAL_orders_iso SCAL_orders_phase SCAL_orders_linear SCAL_order_iso5t1 SCAL_order_iso8t1 SCAL_order_iso5t2 SCAL_order_iso8t2 SCAL_order_psh5t SCAL_order_psh8t SCAL_order_lin5t SCAL_order_lin8t SCAL_order_iso5j SCAL_order_iso8j SCAL_order_psh5j SCAL_order_psh8j SCAL_order_lin5j SCAL_order_lin8j SCAL_order_isip5 SCAL_order_isip8
dfo['SCAL_orders_psh_first'] = (dfo.SCAL_orders_phase==0).astype(int)
dfo['SCAL_orders_lin_first'] = (dfo.SCAL_orders_linear==0).astype(int)
dfo['SCAL_orders_iso_first'] = (dfo.SCAL_orders_iso==0).astype(int)
match('orders').head(4).T
015 | 016 | 017 | 018 | |
---|---|---|---|---|
SCAL_orders_500 | 1 | 0 | 0 | 0 |
SCAL_orders_800 | 0 | 1 | 1 | 1 |
SCAL_orders_iso | 1 | 0 | 1 | 1 |
SCAL_orders_phase | 2 | 2 | 2 | 0 |
SCAL_orders_linear | 0 | 1 | 0 | 2 |
SCAL_orders_psh_first | 0 | 0 | 0 | 1 |
SCAL_orders_lin_first | 1 | 0 | 1 | 0 |
SCAL_orders_iso_first | 0 | 1 | 0 | 0 |
dff = dfo[tolist]
dff.T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SCAL_session_isfemale | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | ... | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 |
SCAL_participant_age | 21 | 19 | 23 | 19 | 19 | 18 | 19 | 23 | 18 | 21 | ... | 20 | 19 | 19 | 19 | 20 | 19 | 20 | 19 | 24 | 18 |
SCAL_calc_wasivocab_totalrawscore | 37 | 48 | 41 | 37 | 40 | 41 | 39 | 42 | 33 | 36 | ... | 30 | 33 | 44 | 47 | 39 | 34 | 42 | 38 | 33 | 36 |
SCAL_calc_wasimatrix_totalscore | 15 | 22 | 23 | 22 | 23 | 20 | 17 | 19 | 23 | 20 | ... | 19 | 20 | 25 | 22 | 27 | 20 | 22 | 23 | 24 | 20 |
SCAL_calc_wasivocab_tscore | 49 | 78 | 55 | 50 | 55 | 57 | 53 | 57 | 44 | 47 | ... | 39 | 44 | 63 | 74 | 52 | 46 | 57 | 51 | 43 | 48 |
SCAL_calc_wasimatrix_tscore | 38 | 53 | 54 | 53 | 55 | 49 | 42 | 46 | 55 | 48 | ... | 46 | 49 | 62 | 53 | 71 | 49 | 52 | 55 | 57 | 49 |
SCAL_calc_wasi_tscore_total | 87 | 131 | 109 | 103 | 110 | 106 | 95 | 103 | 99 | 95 | ... | 85 | 93 | 125 | 127 | 123 | 95 | 109 | 106 | 100 | 97 |
SCAL_calc_fsiq2 | 89 | 127 | 108 | 102 | 109 | 105 | 95 | 102 | 99 | 95 | ... | 87 | 94 | 122 | 123 | 120 | 95 | 108 | 105 | 100 | 97 |
SCAL_calc_bfi_extraversion | 2.125 | 4 | 2.75 | 3 | 4.5 | 1.75 | 2.125 | 3.25 | 2.5 | 2.625 | ... | 3.875 | 4.5 | 3 | 3.125 | 4.875 | 4.375 | 4.375 | 4.25 | 3 | 3.25 |
SCAL_calc_bfi_agreeableness | 3.666667 | 4.111111 | 2.888889 | 4.111111 | 4.444444 | 4.222222 | 3.888889 | 3.555556 | 5 | 2.222222 | ... | 5 | 4.444444 | 4.444444 | 2.888889 | 3.666667 | 4.555556 | 4.111111 | 3.666667 | 3.111111 | 3.333333 |
SCAL_calc_bfi_conscientiousness | 3.777778 | 2.777778 | 3.555556 | 4.555556 | 4 | 3.111111 | 3.888889 | 4.444444 | 3.444444 | 2.555556 | ... | 4.555556 | 4 | 2.888889 | 2.888889 | 3.444444 | 3.555556 | 2.888889 | 3.444444 | 3.222222 | 3.555556 |
SCAL_calc_bfi_neuroticism | 3.125 | 3.25 | 3 | 3 | 1.75 | 2.625 | 3.875 | 3.25 | 2.125 | 3.5 | ... | 2.25 | 1.375 | 4.375 | 2.5 | 2.125 | 3.875 | 2.625 | 2.875 | 3.25 | 3.142857 |
SCAL_calc_bfi_openness | 2.8 | 3.7 | 3.7 | 3.6 | 3.3 | 2.5 | 2.7 | 4.7 | 4.2 | 3.2 | ... | 4.3 | 3.6 | 3.6 | 4.3 | 4.4 | 3 | 3.9 | 4.3 | 4.9 | 4.2 |
SCAL_session_taskorder | 3. Lin, Iso, Jump | 1. Iso, Lin, Jump | 3. Lin, Iso, Jump | 5. Jump, Iso, Lin | 3. Lin, Iso, Jump | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 2. Iso, Jump, Lin | ... | 2. Iso, Jump, Lin | 5. Jump, Iso, Lin | 5. Jump, Iso, Lin | 2. Iso, Jump, Lin | 3. Lin, Iso, Jump | 6. Jump, Lin, Iso | 5. Jump, Iso, Lin | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 6. Jump, Lin, Iso |
SCAL_order_500ms_first | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
SCAL_order_rhythmfirst | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | ... | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
SCAL_qbasic_hearingdeficityn | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
SCAL_qbasic_injuriesyn | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
SCAL_qbasic_exerciseyn | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
SCAL_qbasic_neurodisorderyn | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
SCAL_qmusic_singingyn | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
SCAL_qmusic_instrumentlevel | 0 | 3 | 3 | 0 | 2 | 0 | 1 | 4 | 2 | 0 | ... | 0 | 4 | 4 | 4 | 3 | 0 | 3 | 3 | 0 | 2 |
SCAL_qmusic_danceyn | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | ... | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 |
SCAL_qmusic_instrumentyn | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | ... | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 |
SCAL_qmusic_gameyn | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
SCAL_qmusic_drumsyn | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
SCAL_qmusic_behaviors_12_friendstaste | 3 | 3 | 1 | 3 | 4 | 3 | 2 | 2 | 4 | 4 | ... | 7 | 4 | 2 | 3 | 5 | 5 | 4 | 6 | 5 | 5 |
SCAL_qmusic_behaviors_13_sharingint | 4 | 5 | 1 | 4 | 2 | 4 | 3 | 5 | 1 | 3 | ... | 5 | 4 | 2 | 4 | 7 | 6 | 5 | 7 | 5 | 5 |
SCAL_qmusic_behaviors_14_getinterest | 5 | 5 | 4 | 4 | 2 | 4 | 2 | 3 | 7 | 4 | ... | 5 | 5 | 3 | 1 | 7 | 5 | 5 | 7 | 5 | 6 |
29 rows × 99 columns
match('order').T
015 | 016 | 017 | 018 | 019 | 020 | 021 | 022 | 024 | 025 | ... | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SCAL_session_taskorder | 3. Lin, Iso, Jump | 1. Iso, Lin, Jump | 3. Lin, Iso, Jump | 5. Jump, Iso, Lin | 3. Lin, Iso, Jump | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 2. Iso, Jump, Lin | ... | 2. Iso, Jump, Lin | 5. Jump, Iso, Lin | 5. Jump, Iso, Lin | 2. Iso, Jump, Lin | 3. Lin, Iso, Jump | 6. Jump, Lin, Iso | 5. Jump, Iso, Lin | 6. Jump, Lin, Iso | 1. Iso, Lin, Jump | 6. Jump, Lin, Iso |
SCAL_order_500ms_first | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
SCAL_order_rhythmfirst | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | ... | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 |
SCAL_notes_qbasic_neurodisorder | ADD & general anxiety | ADHD | ... | ||||||||||||||||||
SCAL_qbasic_neurodisorderyn | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
SCAL_orders_500 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | ... | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
SCAL_orders_800 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
SCAL_orders_iso | 1 | 0 | 1 | 1 | 1 | 2 | 0 | 2 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 0 | 2 |
SCAL_orders_phase | 2 | 2 | 2 | 0 | 2 | 0 | 2 | 0 | 2 | 1 | ... | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 2 | 0 |
SCAL_orders_linear | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 1 | 1 | 2 | ... | 2 | 2 | 2 | 2 | 0 | 1 | 2 | 1 | 1 | 1 |
SCAL_order_iso5t1 | 2 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | ... | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 1 |
SCAL_order_iso8t1 | 1 | 2 | 2 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | ... | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 1 | 2 |
SCAL_order_iso5t2 | 6 | 3 | 5 | 5 | 5 | 8 | 4 | 8 | 3 | 4 | ... | 4 | 6 | 6 | 4 | 5 | 7 | 5 | 7 | 4 | 7 |
SCAL_order_iso8t2 | 5 | 4 | 6 | 6 | 6 | 7 | 3 | 7 | 4 | 3 | ... | 3 | 5 | 5 | 3 | 6 | 8 | 6 | 8 | 3 | 8 |
SCAL_order_psh5t | 8 | 7 | 7 | 3 | 7 | 4 | 8 | 4 | 7 | 6 | ... | 6 | 4 | 4 | 6 | 7 | 3 | 3 | 3 | 8 | 3 |
SCAL_order_psh8t | 7 | 8 | 8 | 4 | 8 | 3 | 7 | 3 | 8 | 5 | ... | 5 | 3 | 3 | 5 | 8 | 4 | 4 | 4 | 7 | 4 |
SCAL_order_lin5t | 4 | 5 | 3 | 7 | 3 | 6 | 6 | 6 | 5 | 8 | ... | 8 | 8 | 8 | 8 | 3 | 5 | 7 | 5 | 6 | 5 |
SCAL_order_lin8t | 3 | 6 | 4 | 8 | 4 | 5 | 5 | 5 | 6 | 7 | ... | 7 | 7 | 7 | 7 | 4 | 6 | 8 | 6 | 5 | 6 |
SCAL_order_iso5j | 12 | 9 | 11 | 11 | 11 | 14 | 10 | 14 | 9 | 10 | ... | 10 | 12 | 12 | 10 | 11 | 13 | 11 | 13 | 10 | 13 |
SCAL_order_iso8j | 11 | 10 | 12 | 12 | 12 | 13 | 9 | 13 | 10 | 9 | ... | 9 | 11 | 11 | 9 | 12 | 14 | 12 | 14 | 9 | 14 |
SCAL_order_psh5j | 14 | 13 | 13 | 9 | 13 | 10 | 14 | 10 | 13 | 12 | ... | 12 | 10 | 10 | 12 | 13 | 9 | 9 | 9 | 14 | 9 |
SCAL_order_psh8j | 13 | 14 | 14 | 10 | 14 | 9 | 13 | 9 | 14 | 11 | ... | 11 | 9 | 9 | 11 | 14 | 10 | 10 | 10 | 13 | 10 |
SCAL_order_lin5j | 10 | 11 | 9 | 13 | 9 | 12 | 12 | 12 | 11 | 14 | ... | 14 | 14 | 14 | 14 | 9 | 11 | 13 | 11 | 12 | 11 |
SCAL_order_lin8j | 9 | 12 | 10 | 14 | 10 | 11 | 11 | 11 | 12 | 13 | ... | 13 | 13 | 13 | 13 | 10 | 12 | 14 | 12 | 11 | 12 |
SCAL_order_isip5 | 16 | 15 | 15 | 15 | 15 | 16 | 16 | 16 | 15 | 16 | ... | 16 | 16 | 16 | 16 | 15 | 15 | 15 | 15 | 16 | 15 |
SCAL_order_isip8 | 15 | 16 | 16 | 16 | 16 | 15 | 15 | 15 | 16 | 15 | ... | 15 | 15 | 15 | 15 | 16 | 16 | 16 | 16 | 15 | 16 |
SCAL_orders_psh_first | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | ... | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 |
SCAL_orders_lin_first | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
SCAL_orders_iso_first | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | ... | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
29 rows × 99 columns
hrs = dfo.SCAL_qmusic_danceyn
hrs[hrs > 0].count()
total = dfo.SCAL_qmusic_drumhours + dfo.SCAL_qmusic_instrumenthours + dfo.SCAL_qmusic_dancehours
def filter_outliers(series):
# Tabachnik & fidell call +- 3.29 SD a removable/truncatable outlier
return series[np.abs(series) <= 3.29 * series.std()]
trunc_count = 0
def truncate_outliers(series):
# Tabachnik & fidell call +- 3.29 SD a removable/truncatable outlier
maxval = series.mean() + 3.29 * series.std()
minval = series.mean() - 3.29 * series.std()
trunc_count = 0
def trunc(val):
if val > maxval:
trunc_count += 1
return maxval
elif val < minval:
trunc_count += 1
return minval
else:
return val
s = series.apply(trunc)
print('truncated {} of {} cases.'.format(trunc_count, len(s)))
return s
truncate_outliers(total).hist()
dfo
SCAL_session_day | SCAL_session_time | SCAL_session_isfemale | SCAL_exclusion_jitterlinearmissing | SCAL_exclusion_rhythmadminerror | SCAL_sex_femalezero | SCAL_participant_age | SCAL_calc_wasivocab_totalrawscore | SCAL_calc_wasimatrix_totalscore | SCAL_calc_wasivocab_tscore | ... | SMSR_phase8t_s4a_DPsd_inv | SMSR_phase5j_s4a_DPsd_inv | SMSR_phase8j_s4a_DPsd_inv | SMSR_phase5t_s4b_DPsd_inv | SMSR_phase8t_s4b_DPsd_inv | SMSR_phase5j_s4b_DPsd_inv | SMSR_phase8j_s4b_DPsd_inv | SCAL_orders_psh_first | SCAL_orders_lin_first | SCAL_orders_iso_first | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
015 | 20140228 | 1:00pm | 1 | 0 | 0 | 0 | 21 | 37 | 15 | 49 | ... | 0.053945 | 0.109104 | 0.141834 | 0.044401 | 0.044053 | 0.080712 | 0.110317 | 0 | 1 | 0 |
016 | 20140303 | 9:10am | 0 | 0 | 0 | 1 | 19 | 48 | 22 | 78 | ... | 0.307656 | 0.126311 | 0.228519 | 0.293895 | 0.322954 | 0.253038 | 0.277177 | 0 | 0 | 1 |
017 | 20140303 | 10:30am | 0 | 0 | 0 | 1 | 23 | 41 | 23 | 55 | ... | 0.238160 | NaN | 0.267141 | 0.273082 | 0.369306 | 0.073817 | 0.143151 | 0 | 1 | 0 |
018 | 20140303 | 1:00pm | 0 | 0 | 0 | 1 | 19 | 37 | 22 | 50 | ... | 0.280125 | 0.268595 | 0.472431 | 0.218647 | 0.329568 | 0.206339 | 0.196048 | 1 | 0 | 0 |
019 | 20140303 | 2:20pm | 0 | 0 | 0 | 1 | 19 | 40 | 23 | 55 | ... | 0.218318 | 0.142223 | 0.230305 | 0.225257 | 0.360575 | 0.315614 | 0.268594 | 0 | 1 | 0 |
020 | 20140303 | 3:37pm | 0 | 0 | 0 | 1 | 18 | 41 | 20 | 57 | ... | 0.200960 | 0.094811 | 0.091210 | 0.144236 | 0.251791 | 0.144323 | 0.139888 | 1 | 0 | 0 |
021 | 20140304 | 9:40am | 1 | 0 | 0 | 0 | 19 | 39 | 17 | 53 | ... | 0.165609 | 0.387882 | 0.122095 | 0.239746 | 0.255567 | 0.267809 | 0.184375 | 0 | 0 | 1 |
022 | 20140304 | 12:30nn | 1 | 0 | 0 | 0 | 23 | 42 | 19 | 57 | ... | 0.218622 | 0.103781 | 0.524508 | 0.268215 | 0.806137 | 0.166897 | 0.263083 | 1 | 0 | 0 |
024 | 20140304 | 3:20pm | 1 | 0 | 0 | 0 | 18 | 33 | 23 | 44 | ... | 0.290449 | 0.162213 | 0.138587 | 0.305706 | 0.218173 | 0.200656 | 0.309556 | 0 | 0 | 1 |
025 | 20140304 | 4:50pm | 0 | 0 | 0 | 1 | 21 | 36 | 20 | 47 | ... | NaN | 0.096158 | 0.110128 | 0.187185 | 0.166658 | 0.353406 | 0.204062 | 0 | 0 | 1 |
026 | 20140305 | 8:00am | 0 | 0 | 0 | 1 | 20 | 35 | 23 | 46 | ... | 0.315938 | 0.172583 | 0.242976 | 0.267496 | 0.427044 | 0.319051 | 0.164220 | 1 | 0 | 0 |
027 | 20140305 | 9:10am | 0 | 0 | 0 | 1 | 19 | 39 | 22 | 53 | ... | 0.448790 | 0.335749 | 0.126193 | 0.517326 | 0.352330 | 0.160887 | 0.331544 | 0 | 1 | 0 |
028 | 20140305 | 3:40pm | 0 | 0 | 0 | 1 | 18 | 44 | 23 | 63 | ... | 0.197235 | 0.109011 | 0.164658 | 0.155067 | 0.320924 | 0.167243 | 0.166183 | 0 | 0 | 1 |
029 | 20140306 | 8:20am | 1 | 0 | 0 | 0 | 20 | 39 | 24 | 52 | ... | 0.192119 | 0.238632 | 0.098325 | 0.226234 | 0.100256 | 0.243489 | 0.150339 | 1 | 0 | 0 |
030 | 20140306 | 12:40nn | 1 | 0 | 0 | 0 | 43 | 46 | 23 | 61 | ... | 0.354506 | 0.247562 | 0.110378 | 0.271160 | 0.230343 | 0.239115 | 0.180716 | 0 | 1 | 0 |
032 | 20140306 | 3:30pm | 1 | 0 | 0 | 0 | 18 | 41 | 23 | 57 | ... | 0.288649 | 0.167086 | 0.493896 | 0.574432 | 0.358773 | 0.292778 | 0.391160 | 0 | 0 | 1 |
033 | 20140307 | 1:00pm | 0 | 0 | 0 | 1 | 19 | 43 | 24 | 61 | ... | 0.142608 | 0.091577 | 0.105533 | 0.283750 | 0.321588 | 0.215825 | 0.247249 | 0 | 0 | 1 |
034 | 20140307 | 2:20pm | 0 | 0 | 0 | 1 | 19 | 39 | 21 | 53 | ... | 0.253809 | 0.300227 | 0.213578 | 0.292100 | 0.267982 | 0.386933 | 0.173508 | 1 | 0 | 0 |
035 | 20140310 | 10:30am | 1 | 0 | 0 | 0 | 20 | 42 | 22 | 57 | ... | 0.332722 | NaN | 0.165752 | 0.208685 | 0.224142 | 0.201219 | 0.174894 | 1 | 0 | 0 |
036 | 20140310 | 2:20pm | 1 | 0 | 0 | 0 | 19 | 32 | 19 | 43 | ... | 0.385685 | 0.122914 | 0.096976 | 0.130206 | 0.152122 | 0.098771 | 0.088321 | 0 | 1 | 0 |
037 | 20140311 | 9:40am | 1 | 0 | 0 | 0 | 18 | 41 | 28 | 57 | ... | 0.087951 | 0.089036 | 0.096754 | 0.156003 | 0.199734 | 0.440983 | 0.250549 | 0 | 1 | 0 |
038 | 20140311 | 3:10pm | 0 | 0 | 0 | 1 | 18 | 41 | 22 | 57 | ... | 0.186885 | 0.213108 | 0.196591 | 0.403707 | 0.215995 | 0.313550 | 0.071964 | 1 | 0 | 0 |
039 | 20140312 | 12:00nn | 0 | 0 | 0 | 1 | 21 | 27 | 25 | 35 | ... | 0.221292 | 0.124040 | 0.118965 | 0.371071 | 0.508101 | 0.140520 | 0.216045 | 0 | 0 | 1 |
040 | 20140325 | 5:10pm | 0 | 0 | 0 | 1 | 21 | 39 | 26 | 52 | ... | 0.505729 | 0.160670 | 0.152935 | 0.336509 | 0.284273 | 0.499273 | 0.151792 | 0 | 0 | 1 |
041 | 20140326 | 1:00pm | 1 | 0 | 0 | 0 | 23 | 40 | 23 | 53 | ... | 0.662521 | NaN | 0.121985 | 0.416434 | 0.431053 | 0.093620 | 0.195735 | 0 | 0 | 1 |
043 | 20140328 | 11:00am | 1 | 0 | 0 | 0 | 19 | 29 | 15 | 38 | ... | 0.126257 | 0.206833 | 0.166734 | 0.173898 | 0.198134 | 0.170268 | 0.141374 | 1 | 0 | 0 |
044 | 20140331 | 10:30am | 1 | 0 | 0 | 0 | 18 | 40 | 22 | 55 | ... | 0.120922 | 0.100960 | 0.196223 | 0.176374 | 0.238373 | 0.157676 | 0.170921 | 0 | 1 | 0 |
046 | 20140331 | 1:30pm | 0 | 0 | 0 | 1 | 18 | 38 | 17 | 51 | ... | NaN | 0.107789 | 0.147044 | 0.156625 | 0.385439 | 0.229132 | 0.246827 | 0 | 1 | 0 |
047 | 20140401 | 9:10am | 0 | 0 | 0 | 1 | 18 | 41 | 26 | 57 | ... | NaN | 0.352100 | 0.113883 | 0.230409 | 0.195420 | 0.213305 | 0.143730 | 1 | 0 | 0 |
048 | 20140401 | 2:50pm | 0 | 0 | 0 | 1 | 32 | 41 | 23 | 53 | ... | 0.082037 | 0.211633 | 0.181116 | 0.351736 | 0.140725 | 0.200187 | 0.232906 | 0 | 0 | 1 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
091 | 20140430 | 10:30am | 1 | 0 | 0 | 0 | 23 | 40 | 23 | 53 | ... | 0.514594 | NaN | 0.133380 | 0.267509 | 0.457058 | 0.289365 | 0.363775 | 1 | 0 | 0 |
092 | 20140430 | 12:00nn | 1 | 0 | 0 | 0 | 20 | 36 | 21 | 47 | ... | 1.463008 | 0.160003 | 0.141283 | 0.297839 | 0.311048 | 0.405914 | 0.283209 | 1 | 0 | 0 |
093 | 20140430 | 1:30pm | 1 | 0 | 0 | 0 | 19 | 34 | 20 | 46 | ... | 0.126370 | 0.139741 | 0.158888 | 0.195304 | 0.175792 | 0.327514 | 0.207166 | 1 | 0 | 0 |
094 | 20140501 | 7:35am | 1 | 0 | 0 | 0 | 22 | 35 | 20 | 46 | ... | 0.157942 | 0.570216 | 0.183847 | 0.226480 | 0.116993 | 0.158132 | 0.130149 | 0 | 0 | 1 |
095 | 20140501 | 9:00am | 0 | 0 | 0 | 1 | 19 | 43 | 21 | 61 | ... | 0.623089 | 0.129448 | 0.157132 | 0.512506 | 0.308305 | 0.147790 | 0.277586 | 1 | 0 | 0 |
096 | 20140501 | 10:35am | 1 | 0 | 0 | 0 | 22 | 40 | 26 | 53 | ... | 0.117796 | 0.158067 | 0.135327 | 0.231670 | 0.375871 | 0.314143 | 0.166200 | 0 | 1 | 0 |
097 | 20140502 | 9:00am | 0 | 0 | 0 | 1 | 19 | 39 | 21 | 53 | ... | 0.167973 | 0.088569 | 0.106628 | 0.195924 | 0.328464 | 0.174956 | 0.169626 | 0 | 1 | 0 |
098 | 20140502 | 10:25am | 0 | 0 | 0 | 1 | 21 | 38 | 26 | 50 | ... | 0.276621 | NaN | 0.230370 | 0.265347 | 0.420993 | 0.171184 | 0.278959 | 1 | 0 | 0 |
099 | 20140502 | 1:00pm | 0 | 0 | 0 | 1 | 19 | 36 | 18 | 48 | ... | 0.136994 | 0.107712 | 0.276791 | 0.426067 | 0.214582 | 0.288515 | 0.197530 | 0 | 0 | 1 |
100 | 20140502 | 2:50pm | 1 | 0 | 0 | 0 | 18 | 39 | 25 | 53 | ... | 0.413154 | 0.115105 | 0.154248 | 0.249031 | 0.207041 | 0.296783 | 0.164985 | 0 | 0 | 1 |
101 | 20140502 | 5:35pm | 0 | 0 | 0 | 1 | 19 | 40 | 19 | 55 | ... | 0.238751 | 0.102274 | 0.224906 | 0.203175 | 0.223450 | 0.235341 | 0.160918 | 0 | 0 | 1 |
102 | 20140503 | 9:30am | 1 | 0 | 0 | 0 | 18 | 41 | 21 | 57 | ... | 0.252654 | 0.113860 | 0.187137 | 0.360029 | 0.286429 | 0.236990 | 0.267650 | 1 | 0 | 0 |
103 | 20140503 | 10:50am | 0 | 0 | 0 | 1 | 21 | 41 | 20 | 55 | ... | 0.091374 | 0.109908 | 0.086229 | 0.285006 | 0.328613 | 0.147275 | 0.190954 | 0 | 1 | 0 |
104 | 20140505 | 1:30pm | 1 | 0 | 0 | 0 | 21 | 29 | 21 | 37 | ... | 0.193879 | 0.097889 | NaN | 0.068193 | 0.105806 | 0.058434 | 0.108806 | 1 | 0 | 0 |
105 | 20140505 | 5:50pm | 1 | 0 | 0 | 0 | 19 | 37 | 22 | 50 | ... | 0.226203 | 0.055243 | 0.097951 | 0.183074 | 0.253580 | 0.133442 | 0.099407 | 0 | 0 | 1 |
107 | 20140506 | 12:00nn | 0 | 0 | 0 | 1 | 20 | 43 | 21 | 59 | ... | 0.315815 | 0.168736 | 0.123241 | 0.284850 | 0.254540 | 0.292223 | 0.174521 | 0 | 0 | 1 |
108 | 20140507 | 12:00nn | 1 | 0 | 0 | 0 | 19 | 37 | 19 | 50 | ... | 0.121067 | 0.127023 | 0.066406 | 0.125400 | 0.258994 | 0.138733 | 0.187978 | 0 | 0 | 1 |
109 | 20140507 | 1:30pm | 1 | 0 | 0 | 0 | 21 | 34 | 22 | 45 | ... | NaN | 0.197451 | 0.256688 | 0.332181 | 0.248028 | 0.230421 | 0.304478 | 0 | 1 | 0 |
110 | 20140507 | 3:00pm | 0 | 0 | 0 | 1 | 19 | 47 | 21 | 74 | ... | 0.746870 | 0.240477 | 0.175004 | 0.264562 | 0.400076 | 0.157301 | 0.301736 | 1 | 0 | 0 |
111 | 20140507 | 5:00pm | 1 | 0 | 0 | 0 | 20 | 41 | 26 | 55 | ... | 0.180501 | 0.153378 | 0.201756 | 0.282680 | 0.342289 | 0.218383 | 0.228682 | 0 | 1 | 0 |
112 | 20140508 | 12:20nn | 1 | 0 | 0 | 0 | 20 | 30 | 19 | 39 | ... | 0.099765 | 0.086206 | 0.141479 | 0.201735 | 0.362215 | 0.215882 | 0.137384 | 0 | 0 | 1 |
113 | 20140508 | 2:00pm | 0 | 0 | 0 | 1 | 19 | 33 | 20 | 44 | ... | 0.225395 | 0.903250 | 0.122948 | 0.536510 | 0.361752 | 0.085727 | 0.363479 | 1 | 0 | 0 |
114 | 20140508 | 5:00pm | 1 | 0 | 0 | 0 | 19 | 44 | 25 | 63 | ... | 0.140522 | 0.155581 | 0.097414 | 0.183771 | 0.173245 | 0.130977 | 0.136999 | 1 | 0 | 0 |
115 | 20140508 | 6:30pm | 1 | 0 | 0 | 0 | 19 | 47 | 22 | 74 | ... | 0.212135 | 0.125535 | 0.137462 | 0.161361 | 0.261306 | 0.281474 | 0.248404 | 0 | 0 | 1 |
116 | 20140509 | 7:30am | 0 | 0 | 0 | 1 | 20 | 39 | 27 | 52 | ... | 0.209886 | 0.114240 | 0.128472 | 0.467552 | 0.446516 | 0.247116 | 0.263422 | 0 | 1 | 0 |
117 | 20140509 | 10:30am | 1 | 0 | 0 | 0 | 19 | 34 | 20 | 46 | ... | 0.253321 | 1.172359 | 0.110528 | 0.374751 | 0.127035 | 0.130191 | 0.133639 | 1 | 0 | 0 |
118 | 20140509 | 12:00nn | 0 | 0 | 0 | 1 | 20 | 42 | 22 | 57 | ... | 0.233624 | 0.137326 | 0.156730 | 0.327467 | 0.249650 | 0.140216 | 0.182431 | 1 | 0 | 0 |
119 | 20140509 | 3:00pm | 1 | 0 | 0 | 0 | 19 | 38 | 23 | 51 | ... | 0.296574 | 0.142627 | 0.101641 | 0.353110 | 0.353557 | 0.222948 | 0.247663 | 1 | 0 | 0 |
120 | 20140509 | 4:30pm | 0 | 0 | 0 | 1 | 24 | 33 | 24 | 43 | ... | 0.136025 | 0.138683 | 0.160969 | 0.344341 | 0.216403 | 0.263427 | 0.192488 | 0 | 0 | 1 |
121 | 20140509 | 7:30pm | 1 | 0 | 0 | 0 | 18 | 36 | 20 | 48 | ... | NaN | 0.212650 | 0.142823 | 0.273276 | 0.200202 | 0.174823 | 0.182236 | 1 | 0 | 0 |
99 rows × 638 columns
#dfo.scales.bfi_item39.hist()
dfo.sms.phase5t_DPsd.apply(lambda x: 1/x).hist()
<matplotlib.axes.AxesSubplot at 0xf4e81d0>
def variable_labels_syntax(varlist):
var_labels = "VARIABLE LABELS \n{vlist}."
vl_item = " {var} '{label}'\n"
vl_list = '\n'.join([vl_item.format(var=v, label=l) for (v, l) in varlist])
return var_labels.format(vlist=vl_list)
#testing
print variable_labels_syntax(varlist = [("fff", "sssss")])
VARIABLE LABELS fff 'sssss' .
bfi={}
bfi['E'] = ['1', '6R', '11', '16', '21R', '26', '31R', '36']
bfi['A'] = ['2R', '7', '12R', '17', '22', '27R', '32', '37R', '42']
bfi['C'] = ['3', '8R', '13', '18R', '23R', '28', '33', '38', '43R']
bfi['N'] = ['4', '9R', '14', '19', '24R', '29', '34R', '39']
bfi['O'] = ['5', '10', '15', '20', '25', '30', '35R', '40', '41R', '44']
bfi_score = {}
for k, v in bfi.items():
for i in v:
reverse_scored = 'R' in i
if reverse_scored:
i = i[:-1]
item = int(i)
bfi_score[item] = {'factor': k,
'reverse_scored': reverse_scored}
bfi_score
{1: {'factor': 'E', 'reverse_scored': False}, 2: {'factor': 'A', 'reverse_scored': True}, 3: {'factor': 'C', 'reverse_scored': False}, 4: {'factor': 'N', 'reverse_scored': False}, 5: {'factor': 'O', 'reverse_scored': False}, 6: {'factor': 'E', 'reverse_scored': True}, 7: {'factor': 'A', 'reverse_scored': False}, 8: {'factor': 'C', 'reverse_scored': True}, 9: {'factor': 'N', 'reverse_scored': True}, 10: {'factor': 'O', 'reverse_scored': False}, 11: {'factor': 'E', 'reverse_scored': False}, 12: {'factor': 'A', 'reverse_scored': True}, 13: {'factor': 'C', 'reverse_scored': False}, 14: {'factor': 'N', 'reverse_scored': False}, 15: {'factor': 'O', 'reverse_scored': False}, 16: {'factor': 'E', 'reverse_scored': False}, 17: {'factor': 'A', 'reverse_scored': False}, 18: {'factor': 'C', 'reverse_scored': True}, 19: {'factor': 'N', 'reverse_scored': False}, 20: {'factor': 'O', 'reverse_scored': False}, 21: {'factor': 'E', 'reverse_scored': True}, 22: {'factor': 'A', 'reverse_scored': False}, 23: {'factor': 'C', 'reverse_scored': True}, 24: {'factor': 'N', 'reverse_scored': True}, 25: {'factor': 'O', 'reverse_scored': False}, 26: {'factor': 'E', 'reverse_scored': False}, 27: {'factor': 'A', 'reverse_scored': True}, 28: {'factor': 'C', 'reverse_scored': False}, 29: {'factor': 'N', 'reverse_scored': False}, 30: {'factor': 'O', 'reverse_scored': False}, 31: {'factor': 'E', 'reverse_scored': True}, 32: {'factor': 'A', 'reverse_scored': False}, 33: {'factor': 'C', 'reverse_scored': False}, 34: {'factor': 'N', 'reverse_scored': True}, 35: {'factor': 'O', 'reverse_scored': True}, 36: {'factor': 'E', 'reverse_scored': False}, 37: {'factor': 'A', 'reverse_scored': True}, 38: {'factor': 'C', 'reverse_scored': False}, 39: {'factor': 'N', 'reverse_scored': False}, 40: {'factor': 'O', 'reverse_scored': False}, 41: {'factor': 'O', 'reverse_scored': True}, 42: {'factor': 'A', 'reverse_scored': False}, 43: {'factor': 'C', 'reverse_scored': True}, 44: {'factor': 'O', 'reverse_scored': False}}
print('ALTER TYPE')
print(' (F8.2)\n'.join(others) + ' (F8.2)')
print('.')
# Oops - these aren't the values in the dfo_flat output. Need to do this there instead,
# or import from the CSV I made there.
ALTER TYPE session_isfemale (F8.2) exclusion_jitterlinearmissing (F8.2) exclusion_rhythmadminerror (F8.2) sex_femalezero (F8.2) participant_age (F8.2) calc_wasivocab_totalrawscore (F8.2) calc_wasimatrix_totalscore (F8.2) calc_wasivocab_tscore (F8.2) calc_wasimatrix_tscore (F8.2) calc_wasi_tscore_total (F8.2) calc_fsiq2 (F8.2) calc_bfi_extraversion (F8.2) calc_bfi_agreeableness (F8.2) calc_bfi_conscientiousness (F8.2) calc_bfi_neuroticism (F8.2) calc_bfi_openness (F8.2) calc_qmusic_socialimportance (F8.2) order_500ms_first (F8.2) order_rhythmfirst (F8.2) wasivocab_itemscore01_fish (F8.2) wasivocab_itemscore02_shovel (F8.2) wasivocab_itemscore03_shell (F8.2) wasivocab_itemscore04_shirt (F8.2) wasivocab_itemscore05_car (F8.2) wasivocab_itemscore06_lamp (F8.2) wasivocab_itemscore07_bird (F8.2) wasivocab_itemscore08_tongue (F8.2) wasivocab_itemscore09_pet (F8.2) wasivocab_itemscore10_lunch (F8.2) wasivocab_itemscore11_bell (F8.2) wasivocab_itemscore12_calendar (F8.2) wasivocab_itemscore13_alligator (F8.2) wasivocab_itemscore14_dance (F8.2) wasivocab_itemscore15_summer (F8.2) wasivocab_itemscore16_reveal (F8.2) wasivocab_itemscore17_decade (F8.2) wasivocab_itemscore18_entertain (F8.2) wasivocab_itemscore19_tradition (F8.2) wasivocab_itemscore20_enthusiastic (F8.2) wasivocab_itemscore21_improvise (F8.2) wasivocab_itemscore22_haste (F8.2) wasivocab_itemscore23_trend (F8.2) wasivocab_itemscore24_impulse (F8.2) wasivocab_itemscore25_ruminate (F8.2) wasivocab_itemscore26_mollify (F8.2) wasivocab_itemscore27_extirpate (F8.2) wasivocab_itemscore28_panacea (F8.2) wasivocab_itemscore29_perfunctory (F8.2) wasivocab_itemscore30_insipid (F8.2) wasivocab_itemscore31_pavid (F8.2) wasimatrix_itemscore01 (F8.2) wasimatrix_itemscore02 (F8.2) wasimatrix_itemscore03 (F8.2) wasimatrix_itemscore04 (F8.2) wasimatrix_itemscore05 (F8.2) wasimatrix_itemscore06 (F8.2) wasimatrix_itemscore07 (F8.2) wasimatrix_itemscore08 (F8.2) wasimatrix_itemscore09 (F8.2) wasimatrix_itemscore10 (F8.2) wasimatrix_itemscore11 (F8.2) wasimatrix_itemscore12 (F8.2) wasimatrix_itemscore13 (F8.2) wasimatrix_itemscore14 (F8.2) wasimatrix_itemscore15 (F8.2) wasimatrix_itemscore16 (F8.2) wasimatrix_itemscore17 (F8.2) wasimatrix_itemscore18 (F8.2) wasimatrix_itemscore19 (F8.2) wasimatrix_itemscore20 (F8.2) wasimatrix_itemscore21 (F8.2) wasimatrix_itemscore22 (F8.2) wasimatrix_itemscore23 (F8.2) wasimatrix_itemscore24 (F8.2) wasimatrix_itemscore25 (F8.2) wasimatrix_itemscore26 (F8.2) wasimatrix_itemscore27 (F8.2) wasimatrix_itemscore28 (F8.2) wasimatrix_itemscore29 (F8.2) wasimatrix_itemscore30 (F8.2) qbasic_isfemale (F8.2) qbasic_age (F8.2) qbasic_ethnicity_white (F8.2) qbasic_ethnicity_nativeam (F8.2) qbasic_ethnicity_hispanic (F8.2) qbasic_ethnicity_hawaiianpac (F8.2) qbasic_ethnicity_black (F8.2) qbasic_ethnicity_eastasian (F8.2) qbasic_ethnicity_southasian (F8.2) qbasic_ethnicity_middleeastern (F8.2) qbasic_ethnicity_noneofthese (F8.2) qbasic_relationshipyn (F8.2) qbasic_relationshipyears (F8.2) qbasic_relationshipmonths (F8.2) calc_qbasic_rel_totalmonths (F8.2) qbasic_marriedyn (F8.2) qbasic_livingwithyn (F8.2) qbasic_straightyn (F8.2) qbasic_totalheightin (F8.2) qbasic_weightlbs (F8.2) qbasic_handednessa (F8.2) qbasic_handednessb (F8.2) qbasic_handednessc (F8.2) qbasic_handednessd (F8.2) qbasic_handednesse (F8.2) qbasic_hearingdeficityn (F8.2) qbasic_injuriesyn (F8.2) qbasic_exerciseyn (F8.2) qbasic_neurodisorderyn (F8.2) qmusic_singingyn (F8.2) qmusic_singinghours (F8.2) qmusic_singingtimes (F8.2) qmusic_danceyn (F8.2) qmusic_dancelevel (F8.2) qmusic_dancehours (F8.2) qmusic_instrumentyn (F8.2) qmusic_instrumentlevel (F8.2) qmusic_instrumenthours (F8.2) qmusic_gameyn (F8.2) qmusic_gamehoursall (F8.2) qmusic_gamehoursdrumsticks (F8.2) qmusic_drumsyn (F8.2) qmusic_drumhours (F8.2) qmusic_drumlevel (F8.2) qmusic_behaviors_07_yourself (F8.2) qmusic_behaviors_08_otherprs (F8.2) qmusic_behaviors_09_danceprv (F8.2) qmusic_behaviors_10_dancepub (F8.2) qmusic_behaviors_11_urgemove (F8.2) qmusic_behaviors_12_friendstaste (F8.2) qmusic_behaviors_13_sharingint (F8.2) qmusic_behaviors_14_getinterest (F8.2) bfi_item01 (F8.2) bfi_item02 (F8.2) bfi_item03 (F8.2) bfi_item04 (F8.2) bfi_item05 (F8.2) bfi_item06 (F8.2) bfi_item07 (F8.2) bfi_item08 (F8.2) bfi_item09 (F8.2) bfi_item10 (F8.2) bfi_item11 (F8.2) bfi_item12 (F8.2) bfi_item13 (F8.2) bfi_item14 (F8.2) bfi_item15 (F8.2) bfi_item16 (F8.2) bfi_item17 (F8.2) bfi_item18 (F8.2) bfi_item19 (F8.2) bfi_item20 (F8.2) bfi_item21 (F8.2) bfi_item22 (F8.2) bfi_item23 (F8.2) bfi_item24 (F8.2) bfi_item25 (F8.2) bfi_item26 (F8.2) bfi_item27 (F8.2) bfi_item28 (F8.2) bfi_item29 (F8.2) bfi_item30 (F8.2) bfi_item31 (F8.2) bfi_item32 (F8.2) bfi_item33 (F8.2) bfi_item34 (F8.2) bfi_item35 (F8.2) bfi_item36 (F8.2) bfi_item37 (F8.2) bfi_item38 (F8.2) bfi_item39 (F8.2) bfi_item40 (F8.2) bfi_item41 (F8.2) bfi_item42 (F8.2) bfi_item43 (F8.2) bfi_item44 (F8.2) qmusic_singinghours_nonzero (F8.2) qmusic_singingtimes_nonzero (F8.2) qmusic_dancehours_nonzero (F8.2) qmusic_instrumenthours_nonzero (F8.2) qmusic_drumhours_nonzero (F8.2) qmusic_behaviors_09_danceprv_nonzero (F8.2) qmusic_behaviors_10_dancepub_nonzero (F8.2) qmusic_gamehoursall_nonzero (F8.2) qmusic_gamehoursdrumsticks_nonzero (F8.2) qmusic_behaviors_07_yourself_ln1p (F8.2) qmusic_behaviors_08_otherprs_ln1p (F8.2) qmusic_behaviors_09_danceprv_ln1p (F8.2) qmusic_dancelevel_ln1p (F8.2) qmusic_dancelevel_tophalf (F8.2) order_iso500t1 (F8.2) order_iso800t1 (F8.2) order_iso500t2 (F8.2) order_iso800t2 (F8.2) order_phase500 (F8.2) order_phase800 (F8.2) order_linear500 (F8.2) order_linear800 (F8.2) order_isip500 (F8.2) order_isip800 (F8.2) .
varlist = []
for k, v in bfi_score.items():
name = "SCAL_bfi_item" + str(k)
factor = v['factor']
label = "BFI item {n} ({f})".format(n=k, f=factor)
return
#bfi_vars
BFI item 1 (E) BFI item 2 (A) BFI item 3 (C) BFI item 4 (N) BFI item 5 (O) BFI item 6 (E) BFI item 7 (A) BFI item 8 (C) BFI item 9 (N) BFI item 10 (O) BFI item 11 (E) BFI item 12 (A) BFI item 13 (C) BFI item 14 (N) BFI item 15 (O) BFI item 16 (E) BFI item 17 (A) BFI item 18 (C) BFI item 19 (N) BFI item 20 (O) BFI item 21 (E) BFI item 22 (A) BFI item 23 (C) BFI item 24 (N) BFI item 25 (O) BFI item 26 (E) BFI item 27 (A) BFI item 28 (C) BFI item 29 (N) BFI item 30 (O) BFI item 31 (E) BFI item 32 (A) BFI item 33 (C) BFI item 34 (N) BFI item 35 (O) BFI item 36 (E) BFI item 37 (A) BFI item 38 (C) BFI item 39 (N) BFI item 40 (O) BFI item 41 (O) BFI item 42 (A) BFI item 43 (C) BFI item 44 (O)