This notebook shows how to use CARTOframes to discover and explore datasets from CARTO's Data Observatory.
If you haven't installed CARTOframes yet, please visit our installation guide.
The notebook is organized in the following sections:
Want to learn more? Learn how to access and download datasets on the following notebooks Access Public Data and Access Premium Data.
import geopandas as gpd
import pandas as pd
pd.set_option('display.max_columns', None)
from cartoframes.auth import set_default_credentials
from cartoframes.data.observatory import *
In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.
Please, visit the Authentication guide for further detail.
set_default_credentials('creds.json')
Note about credentials
For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section Setting your credentials of the Authentication guide.
CARTO's data catalog consists of a set of datasets organized by:
In addition, datasets are classified in public and premium data.
This classification can be used to explore the Catalog and narrow down your search for the dataset most suitable for your analysis.
For example, you may start exploring by country, then filter by category and finally select a dataset based on the provider. Alternatively, you could start exploring by category or provider.
In this subsection we explore each of the four classification categories.
Note results can be displayed in Python list format or in Pandas DataFrame format.
Catalog().countries.to_dataframe().head(10)
id | name | |
---|---|---|
0 | abw | Aruba |
1 | afg | Afghanistan |
2 | ago | Angola |
3 | aia | Anguilla |
4 | ala | Åland Islands |
5 | alb | Albania |
6 | and | Andorra |
7 | are | United Arab Emirates |
8 | arg | Argentina |
9 | arm | Armenia |
Catalog().categories.to_dataframe()
id | name | |
---|---|---|
0 | behavioral | Behavioral |
1 | covid19 | Covid-19 |
2 | demographics | Demographics |
3 | derived | Derived |
4 | environmental | Environmental |
5 | financial | Financial |
6 | housing | Housing |
7 | human_mobility | Human Mobility |
8 | points_of_interest | Points of Interest |
9 | road_traffic | Road Traffic |
Catalog().providers.to_dataframe().head(10)
id | name | |
---|---|---|
0 | mastercard | Mastercard |
1 | vodafone | Vodafone |
2 | experian | Experian |
3 | here | HERE |
4 | tomtom | TomTom |
5 | foursquare | Foursquare |
6 | precisely | Precisely |
7 | mbi | Michael Bauer International |
8 | unacast | Unacast |
9 | safegraph | SafeGraph |
Catalog().geographies.to_dataframe().head()
slug | name | description | country_id | provider_id | geom_type | geom_coverage | update_frequency | is_public_data | lang | version | provider_name | id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | mbi_pc_4_digit_5bc747b4 | PC 4-digit - New Zealand | MBI Digital Boundaries for New Zealand at PC 4... | nzl | mbi | MULTIPOLYGON | None | None | False | eng | 2020 | Michael Bauer International | carto-do.mbi.geography_nzl_pc4digit_2020 |
1 | mbi_statistical_b9e153d7 | Statistical Districts - Czechia | MBI Digital Boundaries for Czech Republic at S... | cze | mbi | MULTIPOLYGON | None | None | False | eng | 2020 | Michael Bauer International | carto-do.mbi.geography_cze_statisticaldistrict... |
2 | mbi_zillahs_11fffa06 | Zillahs - Pakistan | MBI Digital Boundaries for Pakistan at Zillahs... | pak | mbi | MULTIPOLYGON | None | None | False | eng | 2020 | Michael Bauer International | carto-do.mbi.geography_pak_zillahs_2020 |
3 | tigr_cbsa_2ae03906 | Core-based Statistical Area - United States of... | Core-based Statistical Area TIGER/Line Shapefi... | usa | usa_tiger | MULTIPOLYGON | None | None | True | eng | 2019 | Tiger/Line geographic data from the U.S. Censu... | carto-do-public-data.usa_tiger.geography_usa_c... |
4 | tigr_place_89fd760b | Census Place - United States of America (2017) | Census Place TIGER/Line Shapefiles from the Un... | usa | usa_tiger | MULTIPOLYGON | None | None | True | eng | 2017 | Tiger/Line geographic data from the U.S. Censu... | carto-do-public-data.usa_tiger.geography_usa_p... |
Let's now take a look at how to combine category and provider filters to get the public demographics datasets available in the US from ACS at the census tract level.
Categories available in the US.
Catalog().country('usa').categories.to_dataframe()
You can find more entities with the Global country filter. To apply that filter run: Catalog().country('glo')
id | name | |
---|---|---|
0 | behavioral | Behavioral |
1 | covid19 | Covid-19 |
2 | demographics | Demographics |
3 | derived | Derived |
4 | environmental | Environmental |
5 | financial | Financial |
6 | housing | Housing |
7 | human_mobility | Human Mobility |
8 | points_of_interest | Points of Interest |
9 | road_traffic | Road Traffic |
Providers available in the US.
Catalog().country('usa').providers.to_dataframe().head()
You can find more entities with the Global country filter. To apply that filter run: Catalog().country('glo')
id | name | |
---|---|---|
0 | mastercard | Mastercard |
1 | experian | Experian |
2 | here | HERE |
3 | tomtom | TomTom |
4 | foursquare | Foursquare |
List of providers available in the US offering demographics data.
Catalog().country('usa').category('demographics').providers
You can find more entities with the Global country filter. To apply that filter run: Catalog().country('glo')
[<Provider.get('experian')>, <Provider.get('mbi')>, <Provider.get('ags')>, <Provider.get('worldpop')>, <Provider.get('usa_acs')>, <Provider.get('usa_bls')>, <Provider.get('gbr_cdrc')>]
List of providers available in the US offering public demographics data.
Catalog().country('usa').category('demographics').public().providers
You can find more entities with the Global country filter. To apply that filter run: Catalog().country('glo')
[<Provider.get('worldpop')>, <Provider.get('usa_acs')>, <Provider.get('usa_bls')>, <Provider.get('gbr_cdrc')>]
Let's now take a look at all the demographics datasets provided by ACS in the US.
Catalog().country('usa').category('demographics').provider('usa_acs').datasets.to_dataframe().head()
You can find more entities with the Global country filter. To apply that filter run: Catalog().country('glo')
slug | name | description | category_id | country_id | data_source_id | provider_id | geography_name | geography_description | temporal_aggregation | time_coverage | update_frequency | is_public_data | lang | version | category_name | provider_name | geography_id | id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | acs_sociodemogr_a0c48b07 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | County - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | yearly | [2007-01-01, 2008-01-01) | None | True | eng | 2007 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_count... | carto-do-public-data.usa_acs.demographics_soci... |
1 | acs_sociodemogr_a03fb95f | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Congressional District - United States of Amer... | Shoreline clipped TIGER/Line boundaries. More ... | yearly | [2017-01-01, 2018-01-01) | None | True | eng | 2017 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_congr... | carto-do-public-data.usa_acs.demographics_soci... |
2 | acs_sociodemogr_e7b702b0 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Core-based Statistical Area - United States of... | Shoreline clipped TIGER/Line boundaries. More ... | 3yrs | [2006-01-01, 2009-01-01) | None | True | eng | 20062008 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_cbsa_... | carto-do-public-data.usa_acs.demographics_soci... |
3 | acs_sociodemogr_e1e92d8d | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Core-based Statistical Area - United States of... | Shoreline clipped TIGER/Line boundaries. More ... | yearly | [2013-01-01, 2014-01-01) | None | True | eng | 2013 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_cbsa_... | carto-do-public-data.usa_acs.demographics_soci... |
4 | acs_sociodemogr_2960a7d7 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | County - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | yearly | [2018-01-01, 2019-01-01) | None | True | eng | 2018 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_count... | carto-do-public-data.usa_acs.demographics_soci... |
We can explore the types of geographies for which the datasets are available. In order to filter by a specific type of geography, we have to apply a filter to the geography_name column just like we would for a string column on a Pandas DataFrame.
datasets_acs_df = Catalog().country('usa').category('demographics').provider('usa_acs').datasets.to_dataframe()
You can find more entities with the Global country filter. To apply that filter run: Catalog().country('glo')
datasets_acs_df['geography_name'].unique()
array(['County - United States of America (2015)', 'Congressional District - United States of America (2019)', 'Core-based Statistical Area - United States of America (2019)', 'Census Tract - United States of America (2015)', 'Public Use Microdata Area - United States of America (2019)', 'Census Place - United States of America (2019)', 'State - United States of America (2015)', 'School District (secondary) - United States of America (2019)', 'School District (elementary) - United States of America (2019)', 'School District (unified) - United States of America (2019)', '5-digit Zip Code Tabulation Area - United States of America (2015)', 'Census Block Group - United States of America (2015)'], dtype=object)
datasets_acs_df[datasets_acs_df['geography_name'].str.contains('Census Tract')]
slug | name | description | category_id | country_id | data_source_id | provider_id | geography_name | geography_description | temporal_aggregation | time_coverage | update_frequency | is_public_data | lang | version | category_name | provider_name | geography_id | id | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6 | acs_sociodemogr_30d1f53 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2012-01-01, 2017-01-01) | None | True | eng | 20122016 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
46 | acs_sociodemogr_cfeb0968 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2009-01-01, 2014-01-01) | None | True | eng | 20092013 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
63 | acs_sociodemogr_9ed5d625 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2007-01-01, 2012-01-01) | None | True | eng | 20072011 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
68 | acs_sociodemogr_496a0675 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2013-01-01, 2018-01-01) | None | True | eng | 20132017 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
69 | acs_sociodemogr_d4b2cf03 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2006-01-01, 2011-01-01) | None | True | eng | 20062010 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
180 | acs_sociodemogr_97c32d1f | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2010-01-01, 2015-01-01) | None | True | eng | 20102014 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
219 | acs_sociodemogr_dda43439 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2011-01-01, 2016-01-01) | None | True | eng | 20112015 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
238 | acs_sociodemogr_6bf5c7f4 | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | None | None | True | eng | 20142018 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
251 | acs_sociodemogr_858c104e | Sociodemographics - United States of America (... | The American Community Survey (ACS) is an ongo... | demographics | usa | sociodemographics | usa_acs | Census Tract - United States of America (2015) | Shoreline clipped TIGER/Line boundaries. More ... | 5yrs | [2008-01-01, 2013-01-01) | None | True | eng | 20082012 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_censu... | carto-do-public-data.usa_acs.demographics_soci... |
We select the dataset acs_sociodemogr_496a0675 from the list of datasets above because it is the one with the latest data update.
CARTOframes allows you to get a first glimpse of the dataset so that you can make sure it's the right dataset for your analysis. This includes:
describe()
function in Pandas.sample_ds = Dataset.get('acs_sociodemogr_496a0675')
sample_ds.to_dict()
{'slug': 'acs_sociodemogr_496a0675', 'name': 'Sociodemographics - United States of America (Census Tract, 2017, 5yrs)', 'description': 'The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html', 'category_id': 'demographics', 'country_id': 'usa', 'data_source_id': 'sociodemographics', 'provider_id': 'usa_acs', 'geography_name': 'Census Tract - United States of America (2015)', 'geography_description': 'Shoreline clipped TIGER/Line boundaries. More info: https://carto.com/blog/tiger-shoreline-clip/', 'temporal_aggregation': '5yrs', 'time_coverage': '[2013-01-01, 2018-01-01)', 'update_frequency': None, 'is_public_data': True, 'lang': 'eng', 'version': '20132017', 'category_name': 'Demographics', 'provider_name': 'American Community Survey', 'geography_id': 'carto-do-public-data.carto.geography_usa_censustract_2015', 'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20132017'}
sample_ds.variables.to_dataframe().head(5)
slug | name | description | db_type | agg_method | column_name | variable_group_id | dataset_id | id | |
---|---|---|---|---|---|---|---|---|---|
0 | geoid_e99a58c1 | geoid | US Census Block Groups Geoids | STRING | None | geoid | None | carto-do-public-data.usa_acs.demographics_soci... | carto-do-public-data.usa_acs.demographics_soci... |
1 | do_date_45f076c2 | do_date | First day of the year the survey was issued | DATE | None | do_date | None | carto-do-public-data.usa_acs.demographics_soci... | carto-do-public-data.usa_acs.demographics_soci... |
2 | total_pop_17ea032f | Total Population | Total Population. The total number of all peop... | FLOAT | SUM | total_pop | None | carto-do-public-data.usa_acs.demographics_soci... | carto-do-public-data.usa_acs.demographics_soci... |
3 | households_a12defd5 | Number of households | Households. A count of the number of household... | FLOAT | SUM | households | None | carto-do-public-data.usa_acs.demographics_soci... | carto-do-public-data.usa_acs.demographics_soci... |
4 | male_pop_eac43e5a | male_pop | Male Population. The number of people within e... | FLOAT | SUM | male_pop | carto-do-public-data.usa_acs.demographics_soci... | carto-do-public-data.usa_acs.demographics_soci... | carto-do-public-data.usa_acs.demographics_soci... |
sample_ds.head()
geoid | no_car | do_date | male_20 | male_21 | no_cars | one_car | poverty | children | male_pop | two_cars | asian_pop | black_pop | female_20 | female_21 | in_school | pop_25_64 | total_pop | white_pop | female_pop | gini_index | households | male_60_61 | male_62_64 | median_age | three_cars | male_5_to_9 | median_rent | pop_16_over | pop_widowed | armed_forces | employed_pop | hispanic_pop | male_under_5 | mobile_homes | pop_divorced | female_5_to_9 | housing_units | male_10_to_14 | male_15_to_17 | male_18_to_19 | male_22_to_24 | male_25_to_29 | male_30_to_34 | male_35_to_39 | male_40_to_44 | male_45_to_49 | male_45_to_64 | male_50_to_54 | male_55_to_59 | male_65_to_66 | male_67_to_69 | male_70_to_74 | male_75_to_79 | male_80_to_84 | median_income | pop_separated | amerindian_pop | female_under_5 | four_more_cars | group_quarters | masters_degree | other_race_pop | unemployed_pop | walked_to_work | worked_at_home | female_10_to_14 | female_15_to_17 | female_18_to_19 | female_22_to_24 | female_25_to_29 | female_30_to_34 | female_35_to_39 | female_40_to_44 | female_45_to_49 | female_50_to_54 | female_55_to_59 | female_60_to_61 | female_62_to_64 | female_65_to_66 | female_67_to_69 | female_70_to_74 | female_75_to_79 | female_80_to_84 | pop_15_and_over | pop_now_married | asian_male_45_54 | asian_male_55_64 | bachelors_degree | black_male_45_54 | black_male_55_64 | commute_5_9_mins | commuters_by_bus | in_grades_1_to_4 | in_grades_5_to_8 | male_85_and_over | not_hispanic_pop | pop_5_years_over | white_male_45_54 | white_male_55_64 | associates_degree | commuters_16_over | dwellings_2_units | family_households | hispanic_any_race | in_grades_9_to_12 | income_less_10000 | income_per_capita | pop_25_years_over | pop_never_married | bachelors_degree_2 | commute_10_14_mins | commute_15_19_mins | commute_20_24_mins | commute_25_29_mins | commute_30_34_mins | commute_35_39_mins | commute_35_44_mins | commute_40_44_mins | commute_45_59_mins | commute_60_89_mins | female_85_and_over | income_10000_14999 | income_15000_19999 | income_20000_24999 | income_25000_29999 | income_30000_34999 | income_35000_39999 | income_40000_44999 | income_45000_49999 | income_50000_59999 | income_60000_74999 | income_75000_99999 | married_households | not_in_labor_force | not_us_citizen_pop | pop_in_labor_force | high_school_diploma | hispanic_male_45_54 | hispanic_male_55_64 | occupation_services | workers_16_and_over | civilian_labor_force | commute_60_more_mins | commute_90_more_mins | commute_less_10_mins | commuters_by_carpool | employed_information | in_undergrad_college | income_100000_124999 | income_125000_149999 | income_150000_199999 | male_male_households | nonfamily_households | rent_over_50_percent | vacant_housing_units | commuters_drove_alone | employed_construction | employed_retail_trade | income_200000_or_more | less_one_year_college | male_45_64_grade_9_12 | one_year_more_college | rent_10_to_15_percent | rent_15_to_20_percent | rent_20_to_25_percent | rent_25_to_30_percent | rent_30_to_35_percent | rent_35_to_40_percent | rent_40_to_50_percent | rent_under_10_percent | sales_office_employed | speak_spanish_at_home | two_or_more_races_pop | dwellings_3_to_4_units | dwellings_5_to_9_units | employed_manufacturing | male_45_64_high_school | occupied_housing_units | male_45_64_some_college | mortgaged_housing_units | occupation_sales_office | population_3_years_over | asian_including_hispanic | black_including_hispanic | dwellings_10_to_19_units | dwellings_20_to_49_units | employed_wholesale_trade | female_female_households | rent_burden_not_computed | white_including_hispanic | high_school_including_ged | commuters_by_car_truck_van | dwellings_1_units_attached | dwellings_1_units_detached | dwellings_50_or_more_units | housing_built_2000_to_2004 | male_45_64_graduate_degree | occupation_management_arts | population_1_year_and_over | speak_only_english_at_home | housing_built_2005_or_later | male_45_64_bachelors_degree | median_year_structure_built | children_in_single_female_hh | families_with_young_children | graduate_professional_degree | households_retirement_income | male_45_64_associates_degree | male_45_64_less_than_9_grade | million_dollar_housing_units | owner_occupied_housing_units | percent_income_spent_on_rent | aggregate_travel_time_to_work | amerindian_including_hispanic | housing_built_1939_or_earlier | housing_units_renter_occupied | pop_determined_poverty_status | vacant_housing_units_for_rent | vacant_housing_units_for_sale | employed_public_administration | less_than_high_school_graduate | commuters_by_subway_or_elevated | bachelors_degree_or_higher_25_64 | employed_education_health_social | speak_spanish_at_home_low_english | commuters_by_public_transportation | different_house_year_ago_same_city | some_college_and_associates_degree | households_public_asst_or_food_stamps | management_business_sci_arts_employed | employed_finance_insurance_real_estate | different_house_year_ago_different_city | employed_science_management_admin_waste | one_parent_families_with_young_children | two_parent_families_with_young_children | employed_other_services_not_public_admin | owner_occupied_housing_units_median_value | employed_transportation_warehousing_utilities | occupation_production_transportation_material | father_one_parent_families_with_young_children | owner_occupied_housing_units_lower_value_quartile | owner_occupied_housing_units_upper_value_quartile | employed_agriculture_forestry_fishing_hunting_mining | occupation_natural_resources_construction_maintenance | two_parents_in_labor_force_families_with_young_children | employed_arts_entertainment_recreation_accommodation_food | renter_occupied_housing_units_paying_cash_median_gross_rent | two_parents_not_in_labor_force_families_with_young_children | father_in_labor_force_one_parent_families_with_young_children | two_parents_father_in_labor_force_families_with_young_children | two_parents_mother_in_labor_force_families_with_young_children | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 47157005000 | 76.0 | 20132017 | 0.0 | 0.0 | 151.0 | 122.0 | 552.0 | 316.0 | 415.0 | 64.0 | 0.0 | 1004.0 | 19.0 | 42.0 | 331.0 | 328.0 | 1042.0 | 16.0 | 627.0 | 0.4372 | 343.0 | 0.0 | 2.0 | 28.9 | 0.0 | 38.0 | 391.0 | 749.0 | None | 0.0 | 233.0 | 0.0 | 44.0 | 5.0 | None | 76.0 | 465.0 | 27.0 | 26.0 | 30.0 | 22.0 | 11.0 | 13.0 | 31.0 | 11.0 | 15.0 | 79.0 | 21.0 | 41.0 | 0.0 | 5.0 | 26.0 | 5.0 | 31.0 | 14410.0 | None | 0.0 | 24.0 | 6.0 | 45.0 | 13.0 | 0.0 | 71.0 | 7.0 | 0.0 | 47.0 | 34.0 | 17.0 | 32.0 | 34.0 | 13.0 | 38.0 | 19.0 | 25.0 | 26.0 | 22.0 | 0.0 | 6.0 | 22.0 | 38.0 | 36.0 | 13.0 | 6.0 | None | None | 0.0 | 0.0 | 0.0 | 23.0 | 35.0 | 7.0 | 52.0 | 90.0 | 41.0 | 16.0 | 1042.0 | None | 13.0 | 3.0 | 28.0 | 233.0 | 13.0 | 183.0 | 0.0 | 111.0 | 79.0 | 8747.0 | 564.0 | None | 0.0 | 4.0 | 59.0 | 108.0 | 10.0 | 20.0 | 0.0 | 7.0 | 7.0 | 13.0 | 0.0 | 38.0 | 105.0 | 44.0 | 38.0 | 13.0 | 18.0 | 11.0 | 0.0 | 0.0 | 7.0 | 10.0 | 18.0 | 26.0 | 445.0 | 0.0 | 304.0 | 188.0 | 0.0 | 0.0 | 58.0 | 233.0 | 304.0 | 5.0 | 5.0 | 7.0 | 21.0 | 0.0 | 63.0 | 0.0 | 0.0 | 0.0 | 0.0 | 160.0 | 38.0 | 122.0 | 141.0 | 24.0 | 19.0 | 0.0 | 22.0 | 18.0 | 85.0 | 11.0 | 42.0 | 39.0 | 33.0 | 47.0 | 9.0 | 41.0 | 0.0 | 30.0 | None | 22.0 | 63.0 | 69.0 | 16.0 | 41.0 | 343.0 | 10.0 | 20.0 | 30.0 | 999.0 | 0.0 | 1004.0 | 40.0 | 25.0 | 2.0 | 0.0 | 18.0 | 16.0 | 235.0 | 162.0 | 0.0 | 195.0 | 55.0 | 10.0 | 1.0 | 54.0 | 1019.0 | None | 0.0 | 0.0 | 1981.0 | 278.0 | 75.0 | 13.0 | 47.0 | 7.0 | 2.0 | 0.0 | 65.0 | 30.5 | None | 0.0 | 81.0 | 278.0 | 1042.0 | 14.0 | 0.0 | 4.0 | 181.0 | 0.0 | 1.0 | 57.0 | None | 52.0 | 38.0 | 135.0 | 147.0 | 54.0 | 0.0 | 15.0 | 33.0 | 75.0 | 0.0 | 14.0 | None | 43.0 | 80.0 | 0.0 | 26600.0 | 231300.0 | 0.0 | 11.0 | 0.0 | 21.0 | 507.0 | 0.0 | 0.0 | 0.0 | 0.0 |
1 | 72051990021 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | None | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | None | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
2 | 72071990000 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | None | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | None | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
3 | 01097990000 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
4 | 18127980001 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
5 | 47157980100 | 0.0 | 20132017 | 0.0 | 6.0 | 0.0 | 0.0 | 58.0 | 0.0 | 38.0 | 0.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0.0 | 54.0 | 74.0 | 70.0 | 36.0 | NaN | 0.0 | 0.0 | 0.0 | 31.3 | 0.0 | 0.0 | NaN | 74.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 | 0.0 | 0.0 | 6.0 | 12.0 | 4.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 74.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.0 | 18.0 | 6.0 | 0.0 | 0.0 | 0.0 | 5.0 | 3.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 74.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5896.0 | 58.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 74.0 | 0.0 | 0.0 | 6.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 18.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 74.0 | 0.0 | 4.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 70.0 | 18.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 74.0 | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 74.0 | 0.0 | 0.0 | 0.0 | 18.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 22.0 | 0.0 | 0.0 | 0.0 | 63.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
6 | 55071990000 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
7 | 25025990101 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 58.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 29.0 | 23.0 | 71.0 | 0.0 | 13.0 | NaN | 13.0 | 0.0 | 0.0 | 24.0 | 13.0 | 0.0 | NaN | 71.0 | None | 0.0 | 71.0 | 71.0 | 0.0 | 0.0 | None | 0.0 | 13.0 | 0.0 | 0.0 | 16.0 | 19.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 23.0 | 0.0 | 23.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 13.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 71.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 71.0 | 0.0 | 13.0 | 71.0 | 0.0 | 0.0 | 17785.0 | 23.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 36.0 | 0.0 | 0.0 | 0.0 | 0.0 | 35.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 13.0 | 0.0 | 0.0 | 0.0 | 71.0 | 71.0 | 23.0 | 0.0 | 23.0 | 13.0 | 71.0 | 71.0 | 35.0 | 0.0 | 0.0 | 0.0 | 0.0 | 29.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 13.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 13.0 | 0.0 | 0.0 | 23.0 | 13.0 | 0.0 | 0.0 | 0.0 | 71.0 | 0.0 | 19.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 52.0 | 23.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 16.0 | 71.0 | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 13.0 | 71.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 29.0 | None | 71.0 | 0.0 | 0.0 | 13.0 | 16.0 | 0.0 | 19.0 | 0.0 | 0.0 | 0.0 | 23.0 | None | 19.0 | 19.0 | 0.0 | NaN | NaN | 0.0 | 23.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
8 | 55025991703 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
9 | 26041990000 | 0.0 | 20132017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | NaN | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | None | 0.0 | 0.0 | 0.0 | NaN | NaN | 0.0 | 0.0 | 0.0 | 0.0 | NaN | 0.0 | 0.0 | 0.0 | 0.0 |
sample_ds.counts()
rows 7.400100e+04 cells 1.864825e+07 null_cells 7.901830e+05 null_cells_percent 4.237303e+00 dtype: float64
sample_ds.fields_by_type()
float 250 string 2 dtype: int64
sample_ds.describe()
total_pop | households | male_pop | female_pop | median_age | male_under_5 | male_5_to_9 | male_10_to_14 | male_15_to_17 | male_18_to_19 | male_20 | male_21 | male_22_to_24 | male_25_to_29 | male_30_to_34 | male_35_to_39 | male_40_to_44 | male_45_to_49 | male_50_to_54 | male_55_to_59 | male_65_to_66 | male_67_to_69 | male_70_to_74 | male_75_to_79 | male_80_to_84 | male_85_and_over | female_under_5 | female_5_to_9 | female_10_to_14 | female_15_to_17 | female_18_to_19 | female_20 | female_21 | female_22_to_24 | female_25_to_29 | female_30_to_34 | female_35_to_39 | female_40_to_44 | female_45_to_49 | female_50_to_54 | female_55_to_59 | female_60_to_61 | female_62_to_64 | female_65_to_66 | female_67_to_69 | female_70_to_74 | female_75_to_79 | female_80_to_84 | female_85_and_over | white_pop | population_1_year_and_over | population_3_years_over | pop_5_years_over | pop_15_and_over | pop_16_over | pop_25_years_over | pop_25_64 | pop_never_married | pop_now_married | pop_separated | pop_widowed | pop_divorced | not_us_citizen_pop | black_pop | asian_pop | hispanic_pop | amerindian_pop | other_race_pop | two_or_more_races_pop | hispanic_any_race | not_hispanic_pop | asian_male_45_54 | asian_male_55_64 | black_male_45_54 | black_male_55_64 | hispanic_male_45_54 | hispanic_male_55_64 | white_male_45_54 | white_male_55_64 | median_income | income_per_capita | income_less_10000 | income_10000_14999 | income_15000_19999 | income_20000_24999 | income_25000_29999 | income_30000_34999 | income_35000_39999 | income_40000_44999 | income_45000_49999 | income_50000_59999 | income_60000_74999 | income_75000_99999 | income_100000_124999 | income_125000_149999 | income_150000_199999 | income_200000_or_more | pop_determined_poverty_status | poverty | gini_index | housing_units | renter_occupied_housing_units_paying_cash_median_gross_rent | owner_occupied_housing_units_lower_value_quartile | owner_occupied_housing_units_median_value | owner_occupied_housing_units_upper_value_quartile | occupied_housing_units | housing_units_renter_occupied | vacant_housing_units | vacant_housing_units_for_rent | vacant_housing_units_for_sale | dwellings_1_units_detached | dwellings_1_units_attached | dwellings_2_units | dwellings_3_to_4_units | dwellings_5_to_9_units | dwellings_10_to_19_units | dwellings_20_to_49_units | dwellings_50_or_more_units | mobile_homes | housing_built_2005_or_later | housing_built_2000_to_2004 | housing_built_1939_or_earlier | median_year_structure_built | married_households | nonfamily_households | family_households | households_public_asst_or_food_stamps | male_male_households | female_female_households | children | children_in_single_female_hh | median_rent | percent_income_spent_on_rent | rent_burden_not_computed | rent_over_50_percent | rent_40_to_50_percent | rent_35_to_40_percent | rent_30_to_35_percent | rent_25_to_30_percent | rent_20_to_25_percent | rent_15_to_20_percent | rent_10_to_15_percent | rent_under_10_percent | owner_occupied_housing_units | million_dollar_housing_units | mortgaged_housing_units | different_house_year_ago_different_city | different_house_year_ago_same_city | families_with_young_children | two_parent_families_with_young_children | two_parents_in_labor_force_families_with_young_children | two_parents_father_in_labor_force_families_with_young_children | two_parents_mother_in_labor_force_families_with_young_children | two_parents_not_in_labor_force_families_with_young_children | one_parent_families_with_young_children | father_one_parent_families_with_young_children | father_in_labor_force_one_parent_families_with_young_children | commute_less_10_mins | commute_10_14_mins | commute_15_19_mins | commute_20_24_mins | commute_25_29_mins | commute_30_34_mins | commute_35_44_mins | commute_45_59_mins | commute_60_more_mins | commuters_16_over | walked_to_work | worked_at_home | no_car | no_cars | one_car | two_cars | three_cars | four_more_cars | aggregate_travel_time_to_work | commuters_by_public_transportation | commuters_by_bus | commuters_by_car_truck_van | commuters_by_carpool | commuters_by_subway_or_elevated | commuters_drove_alone | group_quarters | associates_degree | bachelors_degree | high_school_diploma | less_one_year_college | masters_degree | one_year_more_college | less_than_high_school_graduate | high_school_including_ged | bachelors_degree_2 | bachelors_degree_or_higher_25_64 | graduate_professional_degree | some_college_and_associates_degree | male_45_64_associates_degree | male_45_64_bachelors_degree | male_45_64_graduate_degree | male_45_64_less_than_9_grade | male_45_64_grade_9_12 | male_45_64_high_school | male_45_64_some_college | male_45_to_64 | employed_pop | unemployed_pop | pop_in_labor_force | not_in_labor_force | workers_16_and_over | armed_forces | civilian_labor_force | employed_agriculture_forestry_fishing_hunting_mining | employed_arts_entertainment_recreation_accommodation_food | employed_construction | employed_education_health_social | employed_finance_insurance_real_estate | employed_information | employed_manufacturing | employed_other_services_not_public_admin | employed_public_administration | employed_retail_trade | employed_science_management_admin_waste | employed_transportation_warehousing_utilities | employed_wholesale_trade | occupation_management_arts | occupation_natural_resources_construction_maintenance | occupation_production_transportation_material | occupation_sales_office | occupation_services | management_business_sci_arts_employed | sales_office_employed | in_grades_1_to_4 | in_grades_5_to_8 | in_grades_9_to_12 | in_school | in_undergrad_college | speak_only_english_at_home | speak_spanish_at_home | speak_spanish_at_home_low_english | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
avg | 4384.716 | 1622.255 | 2157.711 | 2227.005 | 39.301 | 138.383 | 142.308 | 144.575 | 88.048 | 60.372 | 32.765 | 32.174 | 92.851 | 155.252 | 148.350 | 138.830 | 137.546 | 141.625 | 148.273 | 142.355 | 44.901 | 57.965 | 73.062 | 50.088 | 33.343 | 28.863 | 132.231 | 136.600 | 138.316 | 84.020 | 57.567 | 30.890 | 30.419 | 88.340 | 150.569 | 146.878 | 139.650 | 139.306 | 144.672 | 153.890 | 151.454 | 57.492 | 79.403 | 49.830 | 65.202 | 85.536 | 62.539 | 47.082 | 55.122 | 2666.237 | 4342.539 | 4225.816 | None | None | 3495.006 | 2954.858 | 2301.326 | None | None | None | None | None | 305.762 | 533.077 | 229.615 | 810.032 | 28.362 | 9.694 | 100.731 | 810.032 | 3574.684 | 14.916 | 11.694 | 34.401 | 29.042 | 47.007 | 30.485 | 188.315 | 192.633 | 61206.505 | 30651.804 | 112.029 | 79.903 | 79.066 | 81.211 | 76.990 | 78.111 | 72.823 | 72.989 | 64.370 | 124.887 | 160.500 | 198.418 | 139.128 | 87.014 | 93.800 | 101.016 | 4276.775 | 637.742 | 0.426 | 1850.797 | 1058.678 | 176568.474 | 244327.960 | 332960.368 | 1622.255 | 586.179 | 228.542 | 38.847 | 18.632 | 1143.346 | 109.266 | 67.630 | 81.129 | 87.975 | 82.481 | 65.918 | 96.432 | 115.067 | 16.103 | 42.215 | 94.030 | 1971.950 | 782.945 | 552.684 | 1069.572 | 220.147 | 2.848 | 2.910 | 1004.481 | 254.948 | 896.061 | 30.757 | 45.496 | 138.332 | 49.808 | 36.222 | 49.367 | 62.232 | 68.746 | 67.926 | 46.913 | 21.136 | 1036.076 | 14.981 | 655.655 | 411.542 | 195.111 | 312.742 | 201.810 | 118.298 | 74.180 | 6.341 | 2.992 | 110.932 | 25.601 | 22.815 | 243.340 | 261.580 | 294.358 | 280.068 | 122.279 | 263.641 | 131.140 | 156.067 | 171.718 | 1924.192 | 55.157 | 95.262 | 87.285 | 144.230 | 540.504 | 604.804 | 229.571 | 103.146 | 59341.504 | 103.101 | 51.148 | 1729.272 | 184.784 | 38.183 | 1544.488 | 109.820 | 245.286 | 564.903 | 690.444 | 182.175 | 246.894 | 429.059 | 375.563 | 808.881 | 566.375 | 743.548 | 349.192 | 860.344 | 43.028 | 100.065 | 64.985 | 30.097 | 42.240 | 164.811 | 112.808 | 558.033 | 2049.152 | 145.686 | 2208.707 | 1286.299 | 2019.454 | 13.869 | 2194.838 | 38.275 | 198.444 | 129.972 | 473.374 | 134.648 | 43.153 | 210.403 | 100.384 | 96.162 | 233.863 | 231.125 | 104.331 | 55.019 | 766.585 | 182.001 | 249.131 | 482.802 | 368.633 | 766.585 | 482.802 | 222.900 | 225.327 | 232.226 | 1116.528 | 255.724 | None | None | None |
max | 65528.000 | 21429.000 | 32266.000 | 33262.000 | 85.700 | 3297.000 | 3464.000 | 3137.000 | 2416.000 | 3909.000 | 2845.000 | 3025.000 | 6083.000 | 3956.000 | 2143.000 | 3146.000 | 3718.000 | 2652.000 | 2300.000 | 1741.000 | 1244.000 | 3225.000 | 4708.000 | 3287.000 | 1666.000 | 667.000 | 2829.000 | 3509.000 | 3667.000 | 1601.000 | 4815.000 | 1586.000 | 1330.000 | 2045.000 | 2410.000 | 2840.000 | 3450.000 | 3586.000 | 3628.000 | 1756.000 | 1445.000 | 873.000 | 2105.000 | 2147.000 | 3661.000 | 5268.000 | 2481.000 | 1166.000 | 1337.000 | 38684.000 | 64803.000 | 62438.000 | None | None | 44642.000 | 39486.000 | 34258.000 | None | None | None | None | None | 12232.000 | 16655.000 | 13712.000 | 28683.000 | 9644.000 | 1385.000 | 3611.000 | 28683.000 | 50477.000 | 1301.000 | 745.000 | 1094.000 | 1122.000 | 1464.000 | 777.000 | 2189.000 | 2545.000 | 250001.000 | 220253.000 | 1550.000 | 930.000 | 761.000 | 1009.000 | 876.000 | 864.000 | 1530.000 | 1118.000 | 861.000 | 2054.000 | 2789.000 | 3368.000 | 2704.000 | 2157.000 | 3878.000 | 5386.000 | 65332.000 | 9440.000 | 0.829 | 26526.000 | 3501.000 | 2000001.000 | 2000001.000 | 2000001.000 | 21429.000 | 8039.000 | 11924.000 | 4472.000 | 547.000 | 25527.000 | 4280.000 | 1999.000 | 1629.000 | 1863.000 | 5280.000 | 4283.000 | 11518.000 | 3314.000 | 2502.000 | 7206.000 | 6177.000 | 2016.000 | 15105.000 | 6883.000 | 16813.000 | 2325.000 | 355.000 | 174.000 | 23608.000 | 2863.000 | 3501.000 | 50.000 | 1100.000 | 3487.000 | 972.000 | 641.000 | 860.000 | 999.000 | 1382.000 | 1879.000 | 1098.000 | 912.000 | 20473.000 | 1453.000 | 12672.000 | 13064.000 | 5347.000 | 6700.000 | 6517.000 | 3989.000 | 3888.000 | 1308.000 | 285.000 | 1418.000 | 502.000 | 502.000 | 11621.000 | 5838.000 | 7095.000 | 4886.000 | 2205.000 | 4880.000 | 3178.000 | 5390.000 | 6070.000 | 26482.000 | 11621.000 | 7043.000 | 8111.000 | 5847.000 | 15548.000 | 12012.000 | 3231.000 | 1107.000 | 973160.000 | 7759.000 | 3940.000 | 25012.000 | 3023.000 | 5945.000 | 21989.000 | 16421.000 | 4070.000 | 13767.000 | 8215.000 | 4037.000 | 7767.000 | 5621.000 | 5363.000 | 8929.000 | 13767.000 | 22856.000 | 10374.000 | 12745.000 | 855.000 | 2547.000 | 2448.000 | 900.000 | 1626.000 | 2084.000 | 1628.000 | 6993.000 | 28945.000 | 1607.000 | 30552.000 | 34142.000 | 28252.000 | 21214.000 | 30552.000 | 4197.000 | 5211.000 | 1950.000 | 9127.000 | 2853.000 | 1141.000 | 3993.000 | 1175.000 | 1816.000 | 2808.000 | 5115.000 | 1465.000 | 1294.000 | 19097.000 | 3973.000 | 2495.000 | 6162.000 | 3948.000 | 19097.000 | 6162.000 | 5927.000 | 5674.000 | 4965.000 | 23989.000 | 12985.000 | None | None | None |
min | 0.000 | 0.000 | 0.000 | 0.000 | 7.700 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | None | None | 0.000 | 0.000 | 0.000 | None | None | None | None | None | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 2499.000 | 32.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.000 | 99.000 | 9999.000 | 9999.000 | 9999.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 1939.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 99.000 | 10.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 65.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | None | None | None |
sum | 324473370.000 | 120048527.000 | 159672750.000 | 164800620.000 | 2879750.600 | 10240491.000 | 10530919.000 | 10698700.000 | 6515667.000 | 4467600.000 | 2424606.000 | 2380945.000 | 6871085.000 | 11488834.000 | 10978057.000 | 10273584.000 | 10178564.000 | 10480362.000 | 10972330.000 | 10534395.000 | 3322707.000 | 4289470.000 | 5406639.000 | 3706536.000 | 2467409.000 | 2135919.000 | 9785223.000 | 10108532.000 | 10235546.000 | 6217528.000 | 4259986.000 | 2285886.000 | 2251005.000 | 6537216.000 | 11142266.000 | 10869144.000 | 10334203.000 | 10308819.000 | 10705845.000 | 11387986.000 | 11207768.000 | 4254466.000 | 5875871.000 | 3687460.000 | 4825029.000 | 6329715.000 | 4627940.000 | 3484080.000 | 4079106.000 | 197304205.000 | 317248555.000 | 312714581.000 | None | None | 258633976.000 | 218662435.000 | 170300425.000 | None | None | None | None | None | 22337765.000 | 39448215.000 | 16991765.000 | 59943182.000 | 2098847.000 | 717393.000 | 7454186.000 | 59943182.000 | 264530188.000 | 1103826.000 | 865352.000 | 2545740.000 | 2149154.000 | 3478537.000 | 2255952.000 | 13935498.000 | 14255050.000 | 4464586091.000 | 2245428568.000 | 8290228.000 | 5912909.000 | 5850945.000 | 6009672.000 | 5697363.000 | 5780308.000 | 5388955.000 | 5401277.000 | 4763457.000 | 9241728.000 | 11877176.000 | 14683147.000 | 10295604.000 | 6439142.000 | 6941320.000 | 7475296.000 | 316485642.000 | 47193565.000 | 31111.100 | 136960864.000 | 76128487.000 | 12589332219.000 | 17587459517.000 | 23968817977.000 | 120048527.000 | 43377863.000 | 16912337.000 | 2874708.000 | 1378777.000 | 84608778.000 | 8085807.000 | 5004687.000 | 6003655.000 | 6510210.000 | 6103648.000 | 4877964.000 | 7136049.000 | 8515077.000 | 1191642.000 | 3123981.000 | 6958328.000 | 142948661.000 | 57938689.000 | 40899159.000 | 79149368.000 | 16291126.000 | 210721.000 | 215333.000 | 74332606.000 | 18866414.000 | 64273567.000 | 2225701.700 | 3366768.000 | 10236720.000 | 3685848.000 | 2680486.000 | 3653236.000 | 4605232.000 | 5087309.000 | 5026567.000 | 3471635.000 | 1564062.000 | 76670664.000 | 1108620.000 | 48519146.000 | 30065590.000 | 14253996.000 | 23143255.000 | 14934156.000 | 8754150.000 | 5489360.000 | 469236.000 | 221410.000 | 8209099.000 | 1894468.000 | 1688343.000 | 18007425.000 | 19357159.000 | 21782820.000 | 20725344.000 | 9048793.000 | 19509679.000 | 9704493.000 | 11549099.000 | 12707300.000 | 142392112.000 | 4081654.000 | 7049498.000 | 6459181.000 | 10673160.000 | 39997864.000 | 44756070.000 | 16988505.000 | 7632928.000 | 2970754365.000 | 7629599.000 | 3785002.000 | 127967857.000 | 13674224.000 | 2825557.000 | 114293633.000 | 8126758.000 | 18151427.000 | 41803378.000 | 51093567.000 | 13481107.000 | 18270373.000 | 31750818.000 | 27437114.000 | 59093612.000 | 41377068.000 | 55023314.000 | 25510535.000 | 62853315.000 | 3184139.000 | 7404907.000 | 4808988.000 | 2227181.000 | 3125781.000 | 12196143.000 | 8347879.000 | 41295018.000 | 151639301.000 | 10780902.000 | 163446545.000 | 95187431.000 | 149441610.000 | 1026342.000 | 162420203.000 | 2832376.000 | 14685032.000 | 9618071.000 | 35030129.000 | 9964050.000 | 3193371.000 | 15570023.000 | 7428501.000 | 7116087.000 | 17306059.000 | 17103509.000 | 7720629.000 | 4071464.000 | 56728086.000 | 13468231.000 | 18435915.000 | 35727854.000 | 27279215.000 | 56728086.000 | 35727854.000 | 16494816.000 | 16674443.000 | 17184966.000 | 82624168.000 | 18923849.000 | None | None | None |
range | 65528.000 | 21429.000 | 32266.000 | 33262.000 | 78.000 | 3297.000 | 3464.000 | 3137.000 | 2416.000 | 3909.000 | 2845.000 | 3025.000 | 6083.000 | 3956.000 | 2143.000 | 3146.000 | 3718.000 | 2652.000 | 2300.000 | 1741.000 | 1244.000 | 3225.000 | 4708.000 | 3287.000 | 1666.000 | 667.000 | 2829.000 | 3509.000 | 3667.000 | 1601.000 | 4815.000 | 1586.000 | 1330.000 | 2045.000 | 2410.000 | 2840.000 | 3450.000 | 3586.000 | 3628.000 | 1756.000 | 1445.000 | 873.000 | 2105.000 | 2147.000 | 3661.000 | 5268.000 | 2481.000 | 1166.000 | 1337.000 | 38684.000 | 64803.000 | 62438.000 | None | None | 44642.000 | 39486.000 | 34258.000 | None | None | None | None | None | 12232.000 | 16655.000 | 13712.000 | 28683.000 | 9644.000 | 1385.000 | 3611.000 | 28683.000 | 50477.000 | 1301.000 | 745.000 | 1094.000 | 1122.000 | 1464.000 | 777.000 | 2189.000 | 2545.000 | 247502.000 | 220221.000 | 1550.000 | 930.000 | 761.000 | 1009.000 | 876.000 | 864.000 | 1530.000 | 1118.000 | 861.000 | 2054.000 | 2789.000 | 3368.000 | 2704.000 | 2157.000 | 3878.000 | 5386.000 | 65332.000 | 9440.000 | 0.828 | 26526.000 | 3402.000 | 1990002.000 | 1990002.000 | 1990002.000 | 21429.000 | 8039.000 | 11924.000 | 4472.000 | 547.000 | 25527.000 | 4280.000 | 1999.000 | 1629.000 | 1863.000 | 5280.000 | 4283.000 | 11518.000 | 3314.000 | 2502.000 | 7206.000 | 6177.000 | 77.000 | 15105.000 | 6883.000 | 16813.000 | 2325.000 | 355.000 | 174.000 | 23608.000 | 2863.000 | 3402.000 | 40.000 | 1100.000 | 3487.000 | 972.000 | 641.000 | 860.000 | 999.000 | 1382.000 | 1879.000 | 1098.000 | 912.000 | 20473.000 | 1453.000 | 12672.000 | 13064.000 | 5347.000 | 6700.000 | 6517.000 | 3989.000 | 3888.000 | 1308.000 | 285.000 | 1418.000 | 502.000 | 502.000 | 11621.000 | 5838.000 | 7095.000 | 4886.000 | 2205.000 | 4880.000 | 3178.000 | 5390.000 | 6070.000 | 26482.000 | 11621.000 | 7043.000 | 8111.000 | 5847.000 | 15548.000 | 12012.000 | 3231.000 | 1107.000 | 973095.000 | 7759.000 | 3940.000 | 25012.000 | 3023.000 | 5945.000 | 21989.000 | 16421.000 | 4070.000 | 13767.000 | 8215.000 | 4037.000 | 7767.000 | 5621.000 | 5363.000 | 8929.000 | 13767.000 | 22856.000 | 10374.000 | 12745.000 | 855.000 | 2547.000 | 2448.000 | 900.000 | 1626.000 | 2084.000 | 1628.000 | 6993.000 | 28945.000 | 1607.000 | 30552.000 | 34142.000 | 28252.000 | 21214.000 | 30552.000 | 4197.000 | 5211.000 | 1950.000 | 9127.000 | 2853.000 | 1141.000 | 3993.000 | 1175.000 | 1816.000 | 2808.000 | 5115.000 | 1465.000 | 1294.000 | 19097.000 | 3973.000 | 2495.000 | 6162.000 | 3948.000 | 19097.000 | 6162.000 | 5927.000 | 5674.000 | 4965.000 | 23989.000 | 12985.000 | None | None | None |
stdev | 2228.937 | 793.617 | 1120.561 | 1146.240 | 7.751 | 110.491 | 112.164 | 113.200 | 71.887 | 107.593 | 54.899 | 52.519 | 94.760 | 125.158 | 115.504 | 105.708 | 103.406 | 99.139 | 95.758 | 87.386 | 36.859 | 47.085 | 59.938 | 46.417 | 33.901 | 33.031 | 106.250 | 109.686 | 110.042 | 69.231 | 122.202 | 54.004 | 50.175 | 79.572 | 118.100 | 109.718 | 104.010 | 102.262 | 99.840 | 95.956 | 90.648 | 42.795 | 55.337 | 40.184 | 51.256 | 67.010 | 52.313 | 42.668 | 57.938 | 1907.504 | 2201.732 | 2139.467 | None | None | 1719.905 | 1474.748 | 1216.138 | None | None | None | None | None | 453.551 | 909.323 | 503.608 | 1282.778 | 178.956 | 34.688 | 127.044 | 1282.778 | 2056.417 | 39.752 | 31.828 | 63.033 | 53.957 | 83.689 | 58.783 | 152.387 | 145.516 | 30901.058 | 16126.937 | 106.300 | 69.849 | 63.590 | 62.059 | 58.300 | 58.626 | 55.053 | 54.989 | 49.507 | 84.174 | 105.981 | 137.296 | 114.717 | 85.138 | 107.818 | 164.783 | 2216.522 | 561.814 | 0.064 | 895.836 | 464.256 | 164802.630 | 218956.686 | 286614.050 | 793.617 | 479.149 | 313.122 | 67.020 | 28.307 | 754.309 | 195.291 | 113.359 | 116.582 | 132.416 | 151.111 | 144.087 | 299.168 | 225.078 | 47.328 | 103.868 | 116.601 | 17.553 | 505.011 | 363.756 | 585.972 | 196.613 | 8.899 | 7.653 | 676.013 | 240.274 | 458.720 | 7.795 | 49.744 | 143.299 | 55.747 | 43.031 | 55.987 | 66.949 | 72.524 | 72.061 | 53.122 | 31.293 | 654.343 | 55.371 | 479.093 | 393.284 | 249.243 | 231.674 | 182.928 | 114.817 | 90.506 | 18.145 | 11.524 | 114.155 | 37.744 | 35.345 | 225.753 | 196.725 | 213.210 | 208.958 | 110.217 | 215.589 | 129.281 | 160.217 | 190.405 | 1072.317 | 140.448 | 104.734 | 231.521 | 229.842 | 331.457 | 379.998 | 171.130 | 94.484 | 34229.680 | 264.763 | 108.631 | 1041.140 | 144.773 | 196.741 | 947.467 | 444.664 | 169.441 | 482.882 | 409.367 | 126.778 | 255.769 | 262.970 | 354.004 | 479.144 | 484.280 | 706.282 | 380.169 | 506.211 | 40.479 | 100.991 | 86.981 | 49.654 | 45.710 | 115.910 | 81.153 | 303.356 | 1138.865 | 109.220 | 1203.354 | 715.005 | 1129.133 | 161.617 | 1192.208 | 88.749 | 160.030 | 111.691 | 301.579 | 130.573 | 54.003 | 182.628 | 76.194 | 99.876 | 155.195 | 201.500 | 90.187 | 54.514 | 615.676 | 151.202 | 186.202 | 298.801 | 236.678 | 615.676 | 298.801 | 168.745 | 169.223 | 168.723 | 766.285 | 408.312 | None | None | None |
q1 | 2627.000 | 998.000 | 1276.000 | 1318.000 | 32.800 | 56.000 | 57.000 | 59.000 | 32.000 | 14.000 | 0.000 | 0.000 | 31.000 | 61.000 | 61.000 | 60.000 | 60.000 | 66.000 | 72.000 | 71.000 | 15.000 | 22.000 | 28.000 | 16.000 | 8.000 | 4.000 | 53.000 | 54.000 | 55.000 | 30.000 | 12.000 | 0.000 | 0.000 | 30.000 | 60.000 | 64.000 | 62.000 | 62.000 | 68.000 | 77.000 | 77.000 | 23.000 | 35.000 | 18.000 | 26.000 | 36.000 | 22.000 | 14.000 | 14.000 | 951.000 | 2604.000 | 2519.000 | None | None | 2125.000 | 1782.000 | 1328.000 | None | None | None | None | None | 22.000 | 16.000 | 0.000 | 66.000 | 0.000 | 0.000 | 16.000 | 66.000 | 1943.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 53.000 | 60.000 | 36974.000 | 19023.000 | 34.000 | 23.000 | 26.000 | 30.000 | 29.000 | 30.000 | 28.000 | 29.000 | 24.000 | 58.000 | 76.000 | 86.000 | 47.000 | 19.000 | 14.000 | 8.000 | 2559.000 | 212.000 | 0.374 | 1153.000 | 692.000 | 65700.000 | 98800.000 | 149100.000 | 998.000 | 209.000 | 64.000 | 0.000 | 0.000 | 522.000 | 7.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 9.000 | 1956.000 | 381.000 | 273.000 | 612.000 | 62.000 | 0.000 | 0.000 | 508.000 | 73.000 | 529.000 | 24.200 | 9.000 | 29.000 | 7.000 | 0.000 | 7.000 | 11.000 | 14.000 | 15.000 | 8.000 | 0.000 | 496.000 | 0.000 | 274.000 | 140.000 | 6.000 | 141.000 | 70.000 | 34.000 | 12.000 | 0.000 | 0.000 | 23.000 | 0.000 | 0.000 | 84.000 | 104.000 | 126.000 | 110.000 | 36.000 | 95.000 | 33.000 | 38.000 | 42.000 | 1072.000 | 0.000 | 23.000 | 7.000 | 29.000 | 278.000 | 301.000 | 85.000 | 24.000 | 33150.000 | 0.000 | 0.000 | 894.000 | 74.000 | 0.000 | 780.000 | 0.000 | 109.000 | 191.000 | 342.000 | 80.000 | 62.000 | 222.000 | 115.000 | 400.000 | 192.000 | 220.000 | 81.000 | 457.000 | 11.000 | 24.000 | 8.000 | 0.000 | 7.000 | 67.000 | 47.000 | 316.000 | 1143.000 | 60.000 | 1245.000 | 746.000 | 1115.000 | 0.000 | 1235.000 | 0.000 | 82.000 | 45.000 | 234.000 | 41.000 | 7.000 | 65.000 | 40.000 | 29.000 | 111.000 | 81.000 | 37.000 | 13.000 | 290.000 | 63.000 | 96.000 | 243.000 | 186.000 | 290.000 | 243.000 | 97.000 | 97.000 | 102.000 | 560.000 | 84.000 | None | None | None |
q3 | 4606.000 | 1714.000 | 2254.000 | 2334.000 | 41.100 | 138.000 | 142.000 | 145.000 | 88.000 | 52.000 | 26.000 | 26.000 | 87.000 | 151.000 | 146.000 | 138.000 | 138.000 | 144.000 | 152.000 | 148.000 | 45.000 | 59.000 | 74.000 | 50.000 | 33.000 | 27.000 | 132.000 | 137.000 | 139.000 | 85.000 | 46.000 | 23.000 | 24.000 | 84.000 | 149.000 | 146.000 | 140.000 | 141.000 | 148.000 | 159.000 | 159.000 | 58.000 | 81.000 | 50.000 | 66.000 | 87.000 | 63.000 | 46.000 | 51.000 | 2942.000 | 4525.000 | 4421.000 | None | None | 3666.000 | 3115.000 | 2398.000 | None | None | None | None | None | 198.000 | 269.000 | 97.000 | 448.000 | 2.000 | 0.000 | 88.000 | 448.000 | 3809.000 | 0.000 | 0.000 | 16.000 | 13.000 | 23.000 | 13.000 | 200.000 | 210.000 | 60825.000 | 30221.000 | 104.000 | 79.000 | 80.000 | 82.000 | 78.000 | 80.000 | 74.000 | 74.000 | 65.000 | 128.000 | 167.000 | 207.000 | 141.000 | 83.000 | 81.000 | 60.000 | 4496.000 | 605.000 | 0.435 | 1949.000 | 1055.000 | 153300.000 | 210800.000 | 286700.000 | 1714.000 | 570.000 | 188.000 | 31.000 | 15.000 | 1248.000 | 62.000 | 41.000 | 59.000 | 60.000 | 43.000 | 27.000 | 20.000 | 27.000 | 0.000 | 21.000 | 81.000 | 1978.000 | 813.000 | 555.000 | 1126.000 | 210.000 | 0.000 | 0.000 | 1019.000 | 233.000 | 889.000 | 31.400 | 41.000 | 126.000 | 45.000 | 32.000 | 44.000 | 57.000 | 63.000 | 62.000 | 42.000 | 17.000 | 1105.000 | 0.000 | 679.000 | 383.000 | 166.000 | 313.000 | 197.000 | 113.000 | 66.000 | 0.000 | 0.000 | 100.000 | 19.000 | 17.000 | 231.000 | 261.000 | 297.000 | 283.000 | 121.000 | 261.000 | 124.000 | 145.000 | 145.000 | 2014.000 | 34.000 | 87.000 | 48.000 | 109.000 | 556.000 | 636.000 | 240.000 | 102.000 | 60350.000 | 34.000 | 20.000 | 1824.000 | 183.000 | 0.000 | 1637.000 | 20.000 | 252.000 | 550.000 | 728.000 | 189.000 | 216.000 | 438.000 | 339.000 | 850.000 | 547.000 | 683.000 | 295.000 | 902.000 | 42.000 | 92.000 | 49.000 | 19.000 | 39.000 | 172.000 | 115.000 | 582.000 | 2156.000 | 145.000 | 2319.000 | 1325.000 | 2115.000 | 0.000 | 2300.000 | 17.000 | 194.000 | 126.000 | 487.000 | 125.000 | 37.000 | 204.000 | 101.000 | 87.000 | 238.000 | 220.000 | 101.000 | 53.000 | 747.000 | 181.000 | 253.000 | 501.000 | 374.000 | 747.000 | 501.000 | 224.000 | 226.000 | 236.000 | 1120.000 | 212.000 | None | None | None |
median | 3627.000 | 1357.000 | 1773.000 | 1842.000 | 37.100 | 94.000 | 98.000 | 99.000 | 59.000 | 31.000 | 13.000 | 13.000 | 56.000 | 103.000 | 101.000 | 96.000 | 97.000 | 103.000 | 111.000 | 108.000 | 29.000 | 39.000 | 50.000 | 32.000 | 19.000 | 15.000 | 90.000 | 94.000 | 94.000 | 56.000 | 27.000 | 11.000 | 11.000 | 56.000 | 102.000 | 102.000 | 100.000 | 99.000 | 106.000 | 118.000 | 117.000 | 40.000 | 58.000 | 34.000 | 45.000 | 60.000 | 41.000 | 29.000 | 30.000 | 1989.000 | 3590.000 | 3499.000 | None | None | 2921.000 | 2453.000 | 1880.000 | None | None | None | None | None | 82.000 | 82.000 | 28.000 | 187.000 | 0.000 | 0.000 | 47.000 | 187.000 | 2906.000 | 0.000 | 0.000 | 0.000 | 0.000 | 5.000 | 0.000 | 127.000 | 135.000 | 48811.000 | 24552.000 | 64.000 | 48.000 | 51.000 | 55.000 | 52.000 | 53.000 | 49.000 | 50.000 | 44.000 | 92.000 | 120.000 | 145.000 | 92.000 | 48.000 | 40.000 | 26.000 | 3523.000 | 383.000 | 0.405 | 1547.000 | 851.000 | 101100.000 | 147900.000 | 203100.000 | 1357.000 | 374.000 | 120.000 | 10.000 | 0.000 | 915.000 | 26.000 | 15.000 | 23.000 | 19.000 | 9.000 | 0.000 | 0.000 | 0.000 | 0.000 | 4.000 | 40.000 | 1969.000 | 599.000 | 409.000 | 870.000 | 129.000 | 0.000 | 0.000 | 765.000 | 144.000 | 691.000 | 28.100 | 24.000 | 70.000 | 22.000 | 15.000 | 22.000 | 30.000 | 35.000 | 35.000 | 23.000 | 7.000 | 812.000 | 0.000 | 472.000 | 251.000 | 67.000 | 223.000 | 130.000 | 71.000 | 35.000 | 0.000 | 0.000 | 57.000 | 5.000 | 0.000 | 150.000 | 180.000 | 207.000 | 193.000 | 74.000 | 174.000 | 73.000 | 82.000 | 84.000 | 1548.000 | 16.000 | 52.000 | 23.000 | 62.000 | 411.000 | 471.000 | 162.000 | 60.000 | 45995.000 | 8.000 | 2.000 | 1362.000 | 124.000 | 0.000 | 1211.000 | 5.000 | 178.000 | 342.000 | 534.000 | 132.000 | 126.000 | 325.000 | 214.000 | 630.000 | 345.000 | 408.000 | 165.000 | 672.000 | 25.000 | 52.000 | 24.000 | 6.000 | 21.000 | 118.000 | 79.000 | 450.000 | 1651.000 | 100.000 | 1793.000 | 1026.000 | 1622.000 | 0.000 | 1780.000 | 0.000 | 133.000 | 82.000 | 358.000 | 78.000 | 19.000 | 129.000 | 69.000 | 55.000 | 172.000 | 141.000 | 66.000 | 31.000 | 495.000 | 120.000 | 168.000 | 369.000 | 274.000 | 495.000 | 369.000 | 158.000 | 161.000 | 165.000 | 833.000 | 142.000 | None | None | None |
interquartile_range | 1979.000 | 716.000 | 978.000 | 1016.000 | 8.300 | 82.000 | 85.000 | 86.000 | 56.000 | 38.000 | 26.000 | 26.000 | 56.000 | 90.000 | 85.000 | 78.000 | 78.000 | 78.000 | 80.000 | 77.000 | 30.000 | 37.000 | 46.000 | 34.000 | 25.000 | 23.000 | 79.000 | 83.000 | 84.000 | 55.000 | 34.000 | 23.000 | 24.000 | 54.000 | 89.000 | 82.000 | 78.000 | 79.000 | 80.000 | 82.000 | 82.000 | 35.000 | 46.000 | 32.000 | 40.000 | 51.000 | 41.000 | 32.000 | 37.000 | 1991.000 | 1921.000 | 1902.000 | None | None | 1541.000 | 1333.000 | 1070.000 | None | None | None | None | None | 176.000 | 253.000 | 97.000 | 382.000 | 2.000 | 0.000 | 72.000 | 382.000 | 1866.000 | 0.000 | 0.000 | 16.000 | 13.000 | 23.000 | 13.000 | 147.000 | 150.000 | 23851.000 | 11198.000 | 70.000 | 56.000 | 54.000 | 52.000 | 49.000 | 50.000 | 46.000 | 45.000 | 41.000 | 70.000 | 91.000 | 121.000 | 94.000 | 64.000 | 67.000 | 52.000 | 1937.000 | 393.000 | 0.061 | 796.000 | 363.000 | 87600.000 | 112000.000 | 137600.000 | 716.000 | 361.000 | 124.000 | 31.000 | 15.000 | 726.000 | 55.000 | 41.000 | 59.000 | 60.000 | 43.000 | 27.000 | 20.000 | 27.000 | 0.000 | 21.000 | 72.000 | 22.000 | 432.000 | 282.000 | 514.000 | 148.000 | 0.000 | 0.000 | 511.000 | 160.000 | 360.000 | 7.200 | 32.000 | 97.000 | 38.000 | 32.000 | 37.000 | 46.000 | 49.000 | 47.000 | 34.000 | 17.000 | 609.000 | 0.000 | 405.000 | 243.000 | 160.000 | 172.000 | 127.000 | 79.000 | 54.000 | 0.000 | 0.000 | 77.000 | 19.000 | 17.000 | 147.000 | 157.000 | 171.000 | 173.000 | 85.000 | 166.000 | 91.000 | 107.000 | 103.000 | 942.000 | 34.000 | 64.000 | 41.000 | 80.000 | 278.000 | 335.000 | 155.000 | 78.000 | 27200.000 | 34.000 | 20.000 | 930.000 | 109.000 | 0.000 | 857.000 | 20.000 | 143.000 | 359.000 | 386.000 | 109.000 | 154.000 | 216.000 | 224.000 | 450.000 | 355.000 | 463.000 | 214.000 | 445.000 | 31.000 | 68.000 | 41.000 | 19.000 | 32.000 | 105.000 | 68.000 | 266.000 | 1013.000 | 85.000 | 1074.000 | 579.000 | 1000.000 | 0.000 | 1065.000 | 17.000 | 112.000 | 81.000 | 253.000 | 84.000 | 30.000 | 139.000 | 61.000 | 58.000 | 127.000 | 139.000 | 64.000 | 40.000 | 457.000 | 118.000 | 157.000 | 258.000 | 188.000 | 457.000 | 258.000 | 127.000 | 129.000 | 134.000 | 560.000 | 128.000 | None | None | None |
sample_ds.geom_coverage()