This notebook shows how to use CARTOframes for discovering and downloading public datasets from CARTO's Data Observatory.
For more details please visit the Guides. If you'd like further detail on how to discover and explore datasets from the catalog, take a look at Explore CARTO Data Observatory notebook.
The notebook is organized in the following sections:
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 *
from cartoframes.viz import *
from shapely.geometry import box
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.
from cartoframes.auth import set_default_credentials
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.
First, we'll discover the two public datasets applying the public filter:
In addition to the country, category, provider, and geography filters, we can filter public datasets using the public()
filter.
We start by looking for the providers in the US providing 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')>]
We can also discover public demographics datasets in the US.
Note all datasets have the is_public_data
flag that allows to identify public datasets.
Catalog().country('usa').category('demographics').public().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 | wp_population_704f6b75 | Population Mosaics - United States of America ... | Mosaiced 1km resolution global datasets. The m... | demographics | usa | population | worldpop | Grid 1km - United States of America | Global grid at aprox. 1-kilometer resolution (... | yearly | [2020-01-01, 2021-01-01) | None | True | eng | 2020 | Demographics | WorldPop | carto-do-public-data.worldpop.geography_usa_gr... | carto-do-public-data.worldpop.demographics_pop... |
1 | wp_population_22be8012 | Population Mosaics - United States of America ... | Mosaiced 1km resolution global datasets. The m... | demographics | usa | population | worldpop | Grid 1km - United States of America | Global grid at aprox. 1-kilometer resolution (... | yearly | [2019-01-01, 2020-01-01) | None | True | eng | 2019 | Demographics | WorldPop | carto-do-public-data.worldpop.geography_usa_gr... | carto-do-public-data.worldpop.demographics_pop... |
2 | wp_population_55b9b084 | Population Mosaics - United States of America ... | Mosaiced 1km resolution global datasets. The m... | demographics | usa | population | worldpop | Grid 1km - United States of America | Global grid at aprox. 1-kilometer resolution (... | yearly | [2018-01-01, 2019-01-01) | None | True | eng | 2018 | Demographics | WorldPop | carto-do-public-data.worldpop.geography_usa_gr... | carto-do-public-data.worldpop.demographics_pop... |
3 | wp_population_c506ad15 | Population Mosaics - United States of America ... | Mosaiced 1km resolution global datasets. The m... | demographics | usa | population | worldpop | Grid 1km - United States of America | Global grid at aprox. 1-kilometer resolution (... | yearly | [2017-01-01, 2018-01-01) | None | True | eng | 2017 | Demographics | WorldPop | carto-do-public-data.worldpop.geography_usa_gr... | carto-do-public-data.worldpop.demographics_pop... |
4 | wp_population_b2019d83 | Population Mosaics - United States of America ... | Mosaiced 1km resolution global datasets. The m... | demographics | usa | population | worldpop | Grid 1km - United States of America | Global grid at aprox. 1-kilometer resolution (... | yearly | [2016-01-01, 2017-01-01) | None | True | eng | 2016 | Demographics | WorldPop | carto-do-public-data.worldpop.geography_usa_gr... | carto-do-public-data.worldpop.demographics_pop... |
Let's now explore ACS demographics datasets further.
datasets_df = Catalog().country('usa').category('demographics').provider('usa_acs').public().datasets.to_dataframe()
datasets_df.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 are first interested in exploring which datasets provide data at the county level and with a 5 year temporal aggregation.
We select the latest dataset acs_sociodemogr_8c2655e0
.
If you'd like to get a description of the dataset, you can use the Dataset functions to_dict()
, describe()
, head()
, and geom_coverage()
. You can also have access to all its variables as explained on the Explore CARTO Data Observatory notebook.
datasets_df[(datasets_df['geography_name'].str.contains('County')) &
(datasets_df['temporal_aggregation'].str.contains('5yrs'))]\
.sort_values('time_coverage', ascending=False).head()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
165 | acs_sociodemogr_8c2655e0 | 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 ... | 5yrs | [2013-01-01, 2018-01-01) | None | True | eng | 20132017 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_count... | carto-do-public-data.usa_acs.demographics_soci... |
167 | acs_sociodemogr_c6414cc6 | 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 ... | 5yrs | [2012-01-01, 2017-01-01) | None | True | eng | 20122016 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_count... | carto-do-public-data.usa_acs.demographics_soci... |
155 | acs_sociodemogr_18e867ac | 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 ... | 5yrs | [2011-01-01, 2016-01-01) | None | True | eng | 20112015 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_count... | carto-do-public-data.usa_acs.demographics_soci... |
200 | acs_sociodemogr_528f7e8a | 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 ... | 5yrs | [2010-01-01, 2015-01-01) | None | True | eng | 20102014 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_count... | carto-do-public-data.usa_acs.demographics_soci... |
193 | acs_sociodemogr_aa75afd | 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 ... | 5yrs | [2009-01-01, 2014-01-01) | None | True | eng | 20092013 | Demographics | American Community Survey | carto-do-public-data.carto.geography_usa_count... | carto-do-public-data.usa_acs.demographics_soci... |
We are also interested in exploring which datasets provide data at the census tract level and with a 5 year temporal aggregation.
We select the latest dataset acs_sociodemogr_496a0675
.
datasets_df[(datasets_df['geography_name'].str.contains('Census Tract')) &
(datasets_df['temporal_aggregation'].str.contains('5yrs'))]\
.sort_values('time_coverage', ascending=False).head()
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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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... |
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... |
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... |
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... |
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... |
We'll use the county demographics dataset acs_sociodemogr_8c2655e0
we identified above to show how to download only a selection of columns from a dataset. In particular, we'll download all counties with their population.
dataset = Dataset.get('acs_sociodemogr_8c2655e0')
dataset.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 | 30007 | 43.0 | 20132017 | 32.0 | 20.0 | 65.0 | 463.0 | 448.0 | 1151.0 | 2896.0 | 887.0 | 0.0 | 4.0 | 0.0 | 0.0 | 1017.0 | 2936.0 | 5755.0 | 5432.0 | 2859.0 | 0.4452 | 2405.0 | 105.0 | 196.0 | 46.3 | 622.0 | 132.0 | 550.0 | 4762.0 | None | 0.0 | 2621.0 | 152.0 | 112.0 | 377.0 | None | 207.0 | 2743.0 | 196.0 | 117.0 | 75.0 | 54.0 | 135.0 | 131.0 | 160.0 | 163.0 | 184.0 | 908.0 | 210.0 | 213.0 | 63.0 | 171.0 | 191.0 | 129.0 | 66.0 | 55295.0 | None | 84.0 | 146.0 | 368.0 | 44.0 | 293.0 | 0.0 | 112.0 | 135.0 | 205.0 | 124.0 | 117.0 | 123.0 | 90.0 | 91.0 | 113.0 | 165.0 | 153.0 | 218.0 | 250.0 | 196.0 | 116.0 | 137.0 | 74.0 | 114.0 | 173.0 | 158.0 | 52.0 | None | None | 0.0 | 0.0 | 866.0 | 0.0 | 0.0 | 359.0 | 0.0 | 253.0 | 252.0 | 41.0 | 5603.0 | None | 369.0 | 463.0 | 268.0 | 2287.0 | 15.0 | 1552.0 | 152.0 | 336.0 | 96.0 | 32268.0 | 4210.0 | None | 866.0 | 337.0 | 132.0 | 225.0 | 159.0 | 162.0 | 214.0 | 247.0 | 33.0 | 271.0 | 150.0 | 42.0 | 135.0 | 96.0 | 101.0 | 88.0 | 84.0 | 210.0 | 166.0 | 76.0 | 305.0 | 241.0 | 267.0 | 1348.0 | 2029.0 | 20.0 | 2733.0 | 1366.0 | 0.0 | 5.0 | 461.0 | 2492.0 | 2733.0 | 203.0 | 53.0 | 551.0 | 267.0 | 31.0 | 79.0 | 170.0 | 82.0 | 186.0 | 0.0 | 853.0 | 47.0 | 338.0 | 1852.0 | 370.0 | 180.0 | 102.0 | 261.0 | 24.0 | 683.0 | 94.0 | 22.0 | 52.0 | 15.0 | 45.0 | 5.0 | 32.0 | 6.0 | 404.0 | None | 83.0 | 41.0 | 49.0 | 215.0 | 287.0 | 2405.0 | 247.0 | 1122.0 | 404.0 | 5553.0 | 0.0 | 4.0 | 41.0 | 0.0 | 42.0 | 0.0 | 67.0 | 5533.0 | 1564.0 | 2119.0 | 0.0 | 2208.0 | 0.0 | 61.0 | 87.0 | 955.0 | 5634.0 | None | 16.0 | 158.0 | 1987.0 | 147.0 | 335.0 | 337.0 | 625.0 | 92.0 | 13.0 | 26.0 | 2020.0 | 23.6 | 60655.0 | 84.0 | 97.0 | 385.0 | 5711.0 | 11.0 | 34.0 | 291.0 | 231.0 | 0.0 | 873.0 | 380.0 | None | 0.0 | 32.0 | 1212.0 | 179.0 | 955.0 | 141.0 | 471.0 | 141.0 | 64.0 | 271.0 | 94.0 | 195000.0 | 90.0 | 310.0 | 0.0 | 119800.0 | 301300.0 | 460.0 | 491.0 | 90.0 | 186.0 | 640.0 | 18.0 | 0.0 | 163.0 | 0.0 |
1 | 19137 | 106.0 | 20132017 | 17.0 | 42.0 | 291.0 | 1298.0 | 1653.0 | 2349.0 | 4931.0 | 1826.0 | 0.0 | 0.0 | 42.0 | 103.0 | 2094.0 | 5037.0 | 10239.0 | 9640.0 | 5308.0 | 0.4270 | 4614.0 | 149.0 | 256.0 | 44.5 | 774.0 | 385.0 | 482.0 | 8243.0 | None | 11.0 | 4838.0 | 371.0 | 259.0 | 178.0 | None | 310.0 | 5231.0 | 313.0 | 238.0 | 115.0 | 182.0 | 217.0 | 239.0 | 252.0 | 323.0 | 301.0 | 1449.0 | 366.0 | 377.0 | 148.0 | 162.0 | 193.0 | 146.0 | 137.0 | 43674.0 | None | 40.0 | 335.0 | 425.0 | 210.0 | 227.0 | 0.0 | 349.0 | 194.0 | 233.0 | 302.0 | 207.0 | 127.0 | 119.0 | 220.0 | 233.0 | 262.0 | 345.0 | 320.0 | 373.0 | 452.0 | 152.0 | 200.0 | 126.0 | 168.0 | 273.0 | 233.0 | 177.0 | None | None | 0.0 | 0.0 | 867.0 | 0.0 | 0.0 | 1023.0 | 21.0 | 569.0 | 512.0 | 114.0 | 9868.0 | None | 628.0 | 782.0 | 926.0 | 4560.0 | 177.0 | 3080.0 | 371.0 | 530.0 | 282.0 | 25005.0 | 7143.0 | None | 867.0 | 568.0 | 487.0 | 485.0 | 205.0 | 236.0 | 39.0 | 82.0 | 43.0 | 292.0 | 205.0 | 229.0 | 419.0 | 298.0 | 345.0 | 218.0 | 343.0 | 211.0 | 226.0 | 222.0 | 433.0 | 515.0 | 485.0 | 2221.0 | 3045.0 | 136.0 | 5198.0 | 2447.0 | 39.0 | 0.0 | 824.0 | 4793.0 | 5187.0 | 319.0 | 114.0 | 1886.0 | 453.0 | 153.0 | 176.0 | 317.0 | 121.0 | 93.0 | 0.0 | 1534.0 | 246.0 | 617.0 | 3813.0 | 309.0 | 641.0 | 86.0 | 716.0 | 111.0 | 935.0 | 180.0 | 193.0 | 164.0 | 144.0 | 51.0 | 77.0 | 168.0 | 75.0 | 1081.0 | None | 188.0 | 291.0 | 101.0 | 870.0 | 680.0 | 4614.0 | 264.0 | 1552.0 | 1081.0 | 9869.0 | 0.0 | 0.0 | 49.0 | 40.0 | 120.0 | 3.0 | 185.0 | 9756.0 | 2785.0 | 4266.0 | 77.0 | 4318.0 | 0.0 | 27.0 | 32.0 | 1470.0 | 10138.0 | None | 5.0 | 178.0 | 1952.0 | 756.0 | 662.0 | 281.0 | 807.0 | 151.0 | 33.0 | 0.0 | 3131.0 | 26.3 | 86680.0 | 64.0 | 181.0 | 1483.0 | 10014.0 | 142.0 | 67.0 | 122.0 | 633.0 | 0.0 | 856.0 | 1172.0 | None | 21.0 | 550.0 | 2577.0 | 831.0 | 1470.0 | 170.0 | 839.0 | 173.0 | 327.0 | 335.0 | 301.0 | 81800.0 | 245.0 | 949.0 | 29.0 | 49500.0 | 126300.0 | 349.0 | 514.0 | 248.0 | 213.0 | 664.0 | 12.0 | 25.0 | 64.0 | 11.0 |
2 | 20101 | 10.0 | 20132017 | 8.0 | 0.0 | 36.0 | 181.0 | 181.0 | 407.0 | 864.0 | 350.0 | 0.0 | 1.0 | 1.0 | 8.0 | 332.0 | 822.0 | 1702.0 | 1539.0 | 838.0 | 0.4473 | 828.0 | 10.0 | 27.0 | 41.5 | 133.0 | 26.0 | 345.0 | 1379.0 | None | 0.0 | 885.0 | 113.0 | 74.0 | 46.0 | None | 74.0 | 991.0 | 46.0 | 54.0 | 22.0 | 45.0 | 59.0 | 26.0 | 76.0 | 49.0 | 56.0 | 226.0 | 72.0 | 61.0 | 19.0 | 22.0 | 25.0 | 40.0 | 39.0 | 51765.0 | None | 39.0 | 48.0 | 128.0 | 8.0 | 69.0 | 0.0 | 32.0 | 32.0 | 27.0 | 47.0 | 38.0 | 35.0 | 13.0 | 40.0 | 29.0 | 48.0 | 53.0 | 44.0 | 45.0 | 63.0 | 19.0 | 45.0 | 22.0 | 14.0 | 40.0 | 42.0 | 38.0 | None | None | 0.0 | 0.0 | 206.0 | 0.0 | 0.0 | 178.0 | 0.0 | 64.0 | 79.0 | 8.0 | 1589.0 | None | 121.0 | 96.0 | 135.0 | 817.0 | 0.0 | 484.0 | 113.0 | 67.0 | 62.0 | 29768.0 | 1163.0 | None | 206.0 | 98.0 | 93.0 | 64.0 | 38.0 | 47.0 | 15.0 | 15.0 | 0.0 | 9.0 | 35.0 | 32.0 | 42.0 | 50.0 | 67.0 | 67.0 | 35.0 | 32.0 | 35.0 | 12.0 | 79.0 | 109.0 | 139.0 | 401.0 | 462.0 | 12.0 | 917.0 | 302.0 | 6.0 | 2.0 | 165.0 | 844.0 | 917.0 | 43.0 | 8.0 | 410.0 | 42.0 | 10.0 | 48.0 | 45.0 | 11.0 | 18.0 | 0.0 | 344.0 | 16.0 | 163.0 | 732.0 | 112.0 | 73.0 | 25.0 | 106.0 | 7.0 | 207.0 | 27.0 | 16.0 | 18.0 | 13.0 | 19.0 | 8.0 | 0.0 | 10.0 | 185.0 | None | 3.0 | 33.0 | 0.0 | 6.0 | 82.0 | 828.0 | 61.0 | 306.0 | 185.0 | 1623.0 | 0.0 | 1.0 | 0.0 | 0.0 | 13.0 | 0.0 | 52.0 | 1649.0 | 361.0 | 774.0 | 22.0 | 890.0 | 0.0 | 15.0 | 3.0 | 264.0 | 1659.0 | None | 0.0 | 48.0 | 1957.0 | 43.0 | 137.0 | 72.0 | 107.0 | 22.0 | 3.0 | 0.0 | 649.0 | 22.9 | NaN | 39.0 | 95.0 | 179.0 | 1690.0 | 28.0 | 0.0 | 28.0 | 76.0 | 0.0 | 211.0 | 196.0 | None | 0.0 | 61.0 | 448.0 | 61.0 | 264.0 | 30.0 | 84.0 | 34.0 | 20.0 | 117.0 | 51.0 | 76400.0 | 55.0 | 73.0 | 13.0 | 44300.0 | 124400.0 | 253.0 | 198.0 | 65.0 | 24.0 | 551.0 | 0.0 | 13.0 | 48.0 | 4.0 |
3 | 48307 | 133.0 | 20132017 | 1.0 | 93.0 | 220.0 | 883.0 | 1296.0 | 1935.0 | 4171.0 | 1232.0 | 0.0 | 161.0 | 39.0 | 14.0 | 1623.0 | 4096.0 | 8145.0 | 5067.0 | 3974.0 | 0.4495 | 3143.0 | 240.0 | 134.0 | 43.5 | 510.0 | 370.0 | 471.0 | 6496.0 | None | 0.0 | 3658.0 | 2571.0 | 327.0 | 393.0 | None | 263.0 | 4302.0 | 212.0 | 220.0 | 33.0 | 111.0 | 284.0 | 236.0 | 272.0 | 152.0 | 206.0 | 1054.0 | 310.0 | 164.0 | 106.0 | 111.0 | 296.0 | 94.0 | 139.0 | 42367.0 | None | 1.0 | 154.0 | 298.0 | 154.0 | 213.0 | 202.0 | 122.0 | 104.0 | 61.0 | 258.0 | 131.0 | 66.0 | 21.0 | 340.0 | 82.0 | 271.0 | 321.0 | 285.0 | 188.0 | 273.0 | 121.0 | 217.0 | 147.0 | 117.0 | 228.0 | 156.0 | 134.0 | None | None | 0.0 | 0.0 | 671.0 | 18.0 | 0.0 | 867.0 | 2.0 | 483.0 | 334.0 | 60.0 | 5574.0 | None | 327.0 | 391.0 | 217.0 | 3389.0 | 229.0 | 2164.0 | 2571.0 | 453.0 | 228.0 | 23398.0 | 5832.0 | None | 671.0 | 535.0 | 412.0 | 410.0 | 23.0 | 202.0 | 71.0 | 117.0 | 46.0 | 75.0 | 52.0 | 148.0 | 265.0 | 288.0 | 127.0 | 156.0 | 203.0 | 207.0 | 137.0 | 124.0 | 259.0 | 329.0 | 385.0 | 1592.0 | 2716.0 | 207.0 | 3780.0 | 1597.0 | 164.0 | 147.0 | 770.0 | 3450.0 | 3780.0 | 133.0 | 81.0 | 1482.0 | 424.0 | 17.0 | 70.0 | 176.0 | 114.0 | 69.0 | 0.0 | 979.0 | 227.0 | 1159.0 | 2857.0 | 171.0 | 383.0 | 76.0 | 500.0 | 201.0 | 1092.0 | 64.0 | 100.0 | 59.0 | 44.0 | 46.0 | 25.0 | 24.0 | 19.0 | 809.0 | None | 143.0 | 110.0 | 16.0 | 214.0 | 458.0 | 3143.0 | 203.0 | 889.0 | 809.0 | 7863.0 | 0.0 | 161.0 | 6.0 | 18.0 | 39.0 | 0.0 | 137.0 | 6969.0 | 1862.0 | 3281.0 | 23.0 | 3498.0 | 0.0 | 78.0 | 17.0 | 850.0 | 8140.0 | None | 39.0 | 93.0 | 1969.0 | 396.0 | 589.0 | 261.0 | 679.0 | 7.0 | 75.0 | 35.0 | 2398.0 | 32.0 | 53750.0 | 1.0 | 551.0 | 745.0 | 7955.0 | 31.0 | 57.0 | 192.0 | 1229.0 | 0.0 | 556.0 | 647.0 | None | 2.0 | 634.0 | 1809.0 | 600.0 | 850.0 | 133.0 | 829.0 | 231.0 | 206.0 | 383.0 | 363.0 | 82000.0 | 401.0 | 510.0 | 165.0 | 41600.0 | 165300.0 | 602.0 | 719.0 | 214.0 | 265.0 | 721.0 | 0.0 | 165.0 | 169.0 | 0.0 |
4 | 46123 | 66.0 | 20132017 | 8.0 | 21.0 | 118.0 | 544.0 | 1045.0 | 1237.0 | 2781.0 | 649.0 | 0.0 | 0.0 | 9.0 | 59.0 | 1121.0 | 2688.0 | 5480.0 | 4500.0 | 2699.0 | 0.4633 | 2424.0 | 133.0 | 141.0 | 46.5 | 639.0 | 135.0 | 341.0 | 4362.0 | None | 0.0 | 2859.0 | 18.0 | 140.0 | 496.0 | None | 125.0 | 3085.0 | 222.0 | 141.0 | 50.0 | 127.0 | 129.0 | 143.0 | 155.0 | 116.0 | 164.0 | 881.0 | 238.0 | 205.0 | 34.0 | 116.0 | 112.0 | 143.0 | 42.0 | 48409.0 | None | 729.0 | 172.0 | 474.0 | 159.0 | 112.0 | 0.0 | 52.0 | 132.0 | 223.0 | 203.0 | 99.0 | 46.0 | 65.0 | 122.0 | 123.0 | 133.0 | 97.0 | 173.0 | 242.0 | 190.0 | 108.0 | 76.0 | 87.0 | 84.0 | 114.0 | 174.0 | 55.0 | None | None | 0.0 | 0.0 | 643.0 | 0.0 | 0.0 | 878.0 | 37.0 | 272.0 | 269.0 | 66.0 | 5462.0 | None | 310.0 | 392.0 | 319.0 | 2592.0 | 24.0 | 1473.0 | 18.0 | 273.0 | 239.0 | 27613.0 | 3858.0 | None | 643.0 | 347.0 | 158.0 | 167.0 | 120.0 | 92.0 | 17.0 | 56.0 | 39.0 | 55.0 | 143.0 | 143.0 | 174.0 | 110.0 | 132.0 | 111.0 | 119.0 | 168.0 | 91.0 | 124.0 | 227.0 | 270.0 | 279.0 | 1217.0 | 1451.0 | 27.0 | 2911.0 | 1367.0 | 0.0 | 0.0 | 393.0 | 2815.0 | 2911.0 | 175.0 | 32.0 | 1422.0 | 247.0 | 30.0 | 102.0 | 226.0 | 37.0 | 32.0 | 0.0 | 951.0 | 128.0 | 661.0 | 2163.0 | 271.0 | 363.0 | 85.0 | 196.0 | 31.0 | 571.0 | 87.0 | 63.0 | 58.0 | 79.0 | 2.0 | 37.0 | 104.0 | 64.0 | 484.0 | None | 233.0 | 108.0 | 70.0 | 41.0 | 387.0 | 2424.0 | 140.0 | 663.0 | 484.0 | 5334.0 | 0.0 | 0.0 | 38.0 | 52.0 | 75.0 | 0.0 | 125.0 | 4500.0 | 1511.0 | 2410.0 | 16.0 | 2281.0 | 0.0 | 43.0 | 32.0 | 1217.0 | 5425.0 | None | 19.0 | 152.0 | 1966.0 | 220.0 | 338.0 | 210.0 | 261.0 | 83.0 | 56.0 | 16.0 | 1677.0 | 27.5 | 40040.0 | 747.0 | 255.0 | 747.0 | 5294.0 | 95.0 | 39.0 | 118.0 | 408.0 | 0.0 | 580.0 | 674.0 | None | 37.0 | 182.0 | 1086.0 | 340.0 | 1217.0 | 184.0 | 444.0 | 92.0 | 85.0 | 253.0 | 139.0 | 85800.0 | 52.0 | 306.0 | 26.0 | 51600.0 | 156600.0 | 706.0 | 459.0 | 215.0 | 114.0 | 562.0 | 0.0 | 14.0 | 32.0 | 6.0 |
5 | 29155 | 308.0 | 20132017 | 85.0 | 189.0 | 934.0 | 2639.0 | 4859.0 | 4597.0 | 8210.0 | 2043.0 | 0.0 | 4639.0 | 197.0 | 153.0 | 4301.0 | 8474.0 | 17344.0 | 11948.0 | 9134.0 | 0.4862 | 6875.0 | 225.0 | 238.0 | 37.7 | 871.0 | 566.0 | 344.0 | 13251.0 | None | 0.0 | 6326.0 | 421.0 | 614.0 | 742.0 | None | 496.0 | 8178.0 | 747.0 | 421.0 | 226.0 | 282.0 | 479.0 | 431.0 | 449.0 | 422.0 | 526.0 | 2156.0 | 567.0 | 600.0 | 227.0 | 234.0 | 239.0 | 164.0 | 153.0 | 32468.0 | None | 18.0 | 671.0 | 388.0 | 202.0 | 483.0 | 0.0 | 756.0 | 153.0 | 109.0 | 689.0 | 393.0 | 194.0 | 209.0 | 573.0 | 521.0 | 629.0 | 452.0 | 564.0 | 593.0 | 715.0 | 199.0 | 291.0 | 159.0 | 271.0 | 397.0 | 291.0 | 254.0 | None | None | 0.0 | 0.0 | 815.0 | 239.0 | 230.0 | 1273.0 | 23.0 | 877.0 | 1260.0 | 126.0 | 16923.0 | None | 824.0 | 828.0 | 660.0 | 6067.0 | 517.0 | 4342.0 | 421.0 | 1082.0 | 956.0 | 18883.0 | 11212.0 | None | 815.0 | 792.0 | 998.0 | 913.0 | 366.0 | 587.0 | 78.0 | 167.0 | 89.0 | 181.0 | 87.0 | 223.0 | 689.0 | 583.0 | 512.0 | 504.0 | 377.0 | 385.0 | 382.0 | 336.0 | 544.0 | 392.0 | 555.0 | 2758.0 | 6169.0 | 97.0 | 7082.0 | 3602.0 | 19.0 | 19.0 | 1441.0 | 6176.0 | 7082.0 | 159.0 | 72.0 | 1904.0 | 594.0 | 26.0 | 492.0 | 347.0 | 96.0 | 143.0 | 6.0 | 2533.0 | 641.0 | 1303.0 | 5259.0 | 194.0 | 741.0 | 74.0 | 705.0 | 408.0 | 1354.0 | 263.0 | 285.0 | 371.0 | 367.0 | 188.0 | 167.0 | 267.0 | 155.0 | 1164.0 | None | 316.0 | 513.0 | 49.0 | 1328.0 | 866.0 | 6875.0 | 337.0 | 1652.0 | 1164.0 | 16514.0 | 0.0 | 4717.0 | 90.0 | 16.0 | 110.0 | 5.0 | 540.0 | 12113.0 | 4279.0 | 5853.0 | 54.0 | 6098.0 | 91.0 | 206.0 | 25.0 | 1560.0 | 17033.0 | None | 20.0 | 211.0 | 1968.0 | 2120.0 | 1330.0 | 532.0 | 1138.0 | 58.0 | 251.0 | 0.0 | 3631.0 | 28.8 | 105750.0 | 18.0 | 853.0 | 3244.0 | 17042.0 | 313.0 | 89.0 | 313.0 | 2867.0 | 0.0 | 1066.0 | 1780.0 | None | 23.0 | 1577.0 | 2719.0 | 2237.0 | 1560.0 | 282.0 | 1594.0 | 164.0 | 813.0 | 517.0 | 304.0 | 73300.0 | 299.0 | 1565.0 | 89.0 | 37000.0 | 120900.0 | 405.0 | 596.0 | 216.0 | 380.0 | 568.0 | 2.0 | 69.0 | 299.0 | 0.0 |
6 | 21159 | 28.0 | 20132017 | 69.0 | 89.0 | 286.0 | 1464.0 | 3190.0 | 2498.0 | 6744.0 | 1695.0 | 0.0 | 575.0 | 83.0 | 64.0 | 2290.0 | 6830.0 | 12175.0 | 11250.0 | 5431.0 | 0.4422 | 4316.0 | 213.0 | 261.0 | 39.0 | 645.0 | 326.0 | 307.0 | 9909.0 | None | 0.0 | 3032.0 | 226.0 | 323.0 | 1911.0 | None | 342.0 | 5240.0 | 432.0 | 217.0 | 125.0 | 481.0 | 485.0 | 720.0 | 476.0 | 592.0 | 430.0 | 1631.0 | 433.0 | 294.0 | 123.0 | 142.0 | 247.0 | 166.0 | 61.0 | 29239.0 | None | 9.0 | 295.0 | 226.0 | 1441.0 | 225.0 | 0.0 | 552.0 | 34.0 | 8.0 | 334.0 | 229.0 | 101.0 | 118.0 | 303.0 | 308.0 | 384.0 | 316.0 | 401.0 | 419.0 | 466.0 | 74.0 | 255.0 | 158.0 | 176.0 | 203.0 | 158.0 | 150.0 | None | None | 0.0 | 0.0 | 373.0 | 159.0 | 17.0 | 195.0 | 0.0 | 554.0 | 575.0 | 39.0 | 11949.0 | None | 657.0 | 726.0 | 728.0 | 2791.0 | 36.0 | 2940.0 | 226.0 | 481.0 | 782.0 | 14914.0 | 8547.0 | None | 373.0 | 117.0 | 575.0 | 297.0 | 111.0 | 235.0 | 220.0 | 384.0 | 164.0 | 358.0 | 325.0 | 94.0 | 288.0 | 476.0 | 430.0 | 203.0 | 138.0 | 224.0 | 365.0 | 182.0 | 271.0 | 373.0 | 356.0 | 2311.0 | 6325.0 | 34.0 | 3584.0 | 2353.0 | 33.0 | 17.0 | 428.0 | 2799.0 | 3584.0 | 447.0 | 122.0 | 267.0 | 460.0 | 30.0 | 292.0 | 120.0 | 50.0 | 58.0 | 0.0 | 1376.0 | 267.0 | 924.0 | 2292.0 | 197.0 | 520.0 | 0.0 | 360.0 | 347.0 | 1107.0 | 109.0 | 22.0 | 83.0 | 98.0 | 107.0 | 49.0 | 35.0 | 71.0 | 894.0 | None | 107.0 | 53.0 | 105.0 | 45.0 | 736.0 | 4316.0 | 218.0 | 958.0 | 894.0 | 11860.0 | 0.0 | 606.0 | 28.0 | 18.0 | 88.0 | 0.0 | 361.0 | 11397.0 | 3313.0 | 2752.0 | 37.0 | 2976.0 | 54.0 | 143.0 | 41.0 | 875.0 | 12072.0 | None | 55.0 | 19.0 | 1985.0 | 473.0 | 787.0 | 327.0 | 987.0 | 117.0 | 153.0 | 0.0 | 3114.0 | 31.8 | 98955.0 | 9.0 | 141.0 | 1202.0 | 10690.0 | 63.0 | 38.0 | 164.0 | 2339.0 | 0.0 | 553.0 | 775.0 | None | 0.0 | 74.0 | 2195.0 | 1218.0 | 875.0 | 162.0 | 1449.0 | 85.0 | 184.0 | 603.0 | 183.0 | 65500.0 | 165.0 | 319.0 | 24.0 | 30000.0 | 117600.0 | 392.0 | 516.0 | 275.0 | 226.0 | 483.0 | 78.0 | 24.0 | 211.0 | 39.0 |
7 | 31005 | 0.0 | 20132017 | 0.0 | 8.0 | 0.0 | 35.0 | 46.0 | 132.0 | 204.0 | 46.0 | 0.0 | 0.0 | 1.0 | 1.0 | 149.0 | 172.0 | 421.0 | 411.0 | 217.0 | 0.4498 | 177.0 | 9.0 | 3.0 | 43.3 | 54.0 | 27.0 | 388.0 | 298.0 | None | 0.0 | 177.0 | 0.0 | 3.0 | 22.0 | None | 21.0 | 261.0 | 20.0 | 4.0 | 2.0 | 13.0 | 8.0 | 1.0 | 6.0 | 18.0 | 17.0 | 50.0 | 8.0 | 13.0 | 2.0 | 5.0 | 8.0 | 10.0 | 17.0 | 41250.0 | None | 0.0 | 8.0 | 42.0 | 0.0 | 15.0 | 1.0 | 1.0 | 13.0 | 23.0 | 35.0 | 14.0 | 0.0 | 0.0 | 6.0 | 7.0 | 5.0 | 13.0 | 14.0 | 11.0 | 25.0 | 3.0 | 5.0 | 9.0 | 3.0 | 10.0 | 15.0 | 10.0 | None | None | 0.0 | 0.0 | 60.0 | 0.0 | 0.0 | 16.0 | 0.0 | 32.0 | 48.0 | 2.0 | 421.0 | None | 24.0 | 25.0 | 33.0 | 146.0 | 0.0 | 96.0 | 0.0 | 18.0 | 6.0 | 21799.0 | 264.0 | None | 60.0 | 9.0 | 3.0 | 16.0 | 4.0 | 12.0 | 3.0 | 17.0 | 14.0 | 15.0 | 3.0 | 1.0 | 21.0 | 25.0 | 13.0 | 4.0 | 9.0 | 8.0 | 12.0 | 10.0 | 14.0 | 22.0 | 20.0 | 87.0 | 120.0 | 2.0 | 178.0 | 73.0 | 0.0 | 0.0 | 17.0 | 169.0 | 178.0 | 6.0 | 3.0 | 64.0 | 24.0 | 1.0 | 17.0 | 7.0 | 1.0 | 2.0 | 0.0 | 81.0 | 8.0 | 84.0 | 109.0 | 15.0 | 13.0 | 3.0 | 26.0 | 0.0 | 34.0 | 13.0 | 3.0 | 1.0 | 3.0 | 2.0 | 5.0 | 0.0 | 0.0 | 20.0 | None | 9.0 | 0.0 | 0.0 | 13.0 | 21.0 | 177.0 | 7.0 | 25.0 | 20.0 | 415.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.0 | 0.0 | 30.0 | 411.0 | 79.0 | 133.0 | 10.0 | 229.0 | 0.0 | 3.0 | 1.0 | 83.0 | 421.0 | None | 0.0 | 6.0 | 1959.0 | 3.0 | 27.0 | 19.0 | 23.0 | 15.0 | 0.0 | 0.0 | 112.0 | 25.8 | 3040.0 | 0.0 | 31.0 | 65.0 | 421.0 | 2.0 | 0.0 | 3.0 | 13.0 | 0.0 | 59.0 | 37.0 | None | 0.0 | 1.0 | 93.0 | 6.0 | 83.0 | 1.0 | 54.0 | 3.0 | 2.0 | 25.0 | 6.0 | 101500.0 | 9.0 | 21.0 | 2.0 | 57000.0 | 162500.0 | 67.0 | 36.0 | 13.0 | 6.0 | 658.0 | 0.0 | 2.0 | 12.0 | 0.0 |
8 | 01067 | 205.0 | 20132017 | 124.0 | 74.0 | 427.0 | 1953.0 | 2310.0 | 3581.0 | 8208.0 | 2346.0 | 0.0 | 4762.0 | 33.0 | 120.0 | 3517.0 | 8535.0 | 17110.0 | 11757.0 | 8902.0 | 0.4529 | 6727.0 | 176.0 | 317.0 | 43.5 | 1373.0 | 459.0 | 398.0 | 13972.0 | None | 10.0 | 6949.0 | 435.0 | 468.0 | 2311.0 | None | 438.0 | 9055.0 | 594.0 | 307.0 | 171.0 | 396.0 | 369.0 | 350.0 | 658.0 | 418.0 | 528.0 | 2232.0 | 536.0 | 675.0 | 252.0 | 324.0 | 452.0 | 293.0 | 121.0 | 45569.0 | None | 5.0 | 452.0 | 628.0 | 289.0 | 835.0 | 0.0 | 496.0 | 48.0 | 176.0 | 511.0 | 352.0 | 162.0 | 326.0 | 415.0 | 465.0 | 527.0 | 598.0 | 547.0 | 617.0 | 586.0 | 222.0 | 531.0 | 166.0 | 404.0 | 567.0 | 353.0 | 222.0 | None | None | 0.0 | 0.0 | 1087.0 | 291.0 | 314.0 | 605.0 | 0.0 | 750.0 | 892.0 | 146.0 | 16675.0 | None | 757.0 | 839.0 | 925.0 | 6689.0 | 291.0 | 4545.0 | 435.0 | 844.0 | 552.0 | 23983.0 | 12123.0 | None | 1087.0 | 694.0 | 858.0 | 1025.0 | 749.0 | 1067.0 | 154.0 | 395.0 | 241.0 | 445.0 | 301.0 | 288.0 | 504.0 | 566.0 | 332.0 | 278.0 | 457.0 | 308.0 | 323.0 | 412.0 | 616.0 | 694.0 | 614.0 | 3625.0 | 6517.0 | 169.0 | 7455.0 | 3398.0 | 13.0 | 0.0 | 1319.0 | 6865.0 | 7445.0 | 431.0 | 130.0 | 1025.0 | 513.0 | 74.0 | 595.0 | 379.0 | 287.0 | 257.0 | 10.0 | 2182.0 | 185.0 | 2328.0 | 5900.0 | 386.0 | 769.0 | 148.0 | 828.0 | 237.0 | 1843.0 | 149.0 | 96.0 | 162.0 | 91.0 | 35.0 | 109.0 | 75.0 | 51.0 | 1347.0 | None | 151.0 | 163.0 | 0.0 | 787.0 | 918.0 | 6727.0 | 540.0 | 2932.0 | 1347.0 | 16615.0 | 0.0 | 4762.0 | 52.0 | 12.0 | 221.0 | 0.0 | 243.0 | 12144.0 | 4105.0 | 6413.0 | 41.0 | 6183.0 | 2.0 | 218.0 | 113.0 | 2279.0 | 16964.0 | None | 102.0 | 153.0 | 1983.0 | 761.0 | 1033.0 | 987.0 | 1480.0 | 175.0 | 96.0 | 0.0 | 5531.0 | 26.0 | 171195.0 | 5.0 | 343.0 | 1196.0 | 16806.0 | 222.0 | 164.0 | 196.0 | 2348.0 | 0.0 | 1485.0 | 1673.0 | None | 0.0 | 378.0 | 3596.0 | 1129.0 | 2279.0 | 279.0 | 983.0 | 495.0 | 394.0 | 639.0 | 231.0 | 112600.0 | 788.0 | 1238.0 | 89.0 | 62700.0 | 189100.0 | 353.0 | 766.0 | 386.0 | 697.0 | 603.0 | 8.0 | 80.0 | 245.0 | 0.0 |
9 | 41021 | 4.0 | 20132017 | 19.0 | 16.0 | 25.0 | 176.0 | 188.0 | 426.0 | 962.0 | 330.0 | 0.0 | 0.0 | 0.0 | 14.0 | 396.0 | 893.0 | 1910.0 | 1643.0 | 948.0 | 0.4153 | 805.0 | 28.0 | 61.0 | 47.0 | 191.0 | 79.0 | 537.0 | 1529.0 | None | 0.0 | 751.0 | 154.0 | 51.0 | 146.0 | None | 51.0 | 1070.0 | 51.0 | 44.0 | 22.0 | 13.0 | 18.0 | 49.0 | 41.0 | 32.0 | 51.0 | 283.0 | 86.0 | 57.0 | 47.0 | 29.0 | 77.0 | 48.0 | 29.0 | 39831.0 | None | 99.0 | 54.0 | 83.0 | 15.0 | 64.0 | 0.0 | 61.0 | 64.0 | 55.0 | 64.0 | 32.0 | 1.0 | 26.0 | 44.0 | 58.0 | 49.0 | 49.0 | 69.0 | 31.0 | 65.0 | 61.0 | 44.0 | 22.0 | 32.0 | 62.0 | 44.0 | 22.0 | None | None | 0.0 | 0.0 | 200.0 | 0.0 | 0.0 | 191.0 | 0.0 | 102.0 | 101.0 | 14.0 | 1756.0 | None | 114.0 | 130.0 | 77.0 | 684.0 | 15.0 | 512.0 | 154.0 | 96.0 | 52.0 | 24178.0 | 1373.0 | None | 200.0 | 101.0 | 81.0 | 24.0 | 18.0 | 34.0 | 0.0 | 20.0 | 20.0 | 41.0 | 5.0 | 54.0 | 50.0 | 20.0 | 73.0 | 56.0 | 77.0 | 77.0 | 20.0 | 52.0 | 50.0 | 77.0 | 90.0 | 420.0 | 717.0 | 30.0 | 812.0 | 426.0 | 11.0 | 0.0 | 113.0 | 739.0 | 812.0 | 24.0 | 19.0 | 341.0 | 37.0 | 14.0 | 26.0 | 80.0 | 20.0 | 2.0 | 0.0 | 293.0 | 26.0 | 265.0 | 575.0 | 74.0 | 57.0 | 9.0 | 90.0 | 14.0 | 297.0 | 32.0 | 46.0 | 8.0 | 34.0 | 26.0 | 15.0 | 13.0 | 22.0 | 156.0 | None | 0.0 | 6.0 | 6.0 | 22.0 | 111.0 | 805.0 | 85.0 | 257.0 | 156.0 | 1870.0 | 0.0 | 0.0 | 0.0 | 21.0 | 20.0 | 0.0 | 68.0 | 1783.0 | 493.0 | 612.0 | 7.0 | 863.0 | 6.0 | 12.0 | 15.0 | 233.0 | 1903.0 | None | 15.0 | 40.0 | 1957.0 | 109.0 | 128.0 | 64.0 | 169.0 | 5.0 | 13.0 | 0.0 | 515.0 | 25.4 | 11965.0 | 99.0 | 73.0 | 290.0 | 1906.0 | 22.0 | 38.0 | 62.0 | 152.0 | 0.0 | 194.0 | 121.0 | None | 0.0 | 108.0 | 464.0 | 99.0 | 233.0 | 11.0 | 114.0 | 89.0 | 49.0 | 79.0 | 17.0 | 110900.0 | 96.0 | 126.0 | 21.0 | 83300.0 | 162900.0 | 96.0 | 123.0 | 10.0 | 72.0 | 814.0 | 0.0 | 21.0 | 67.0 | 2.0 |
dataset.variables.to_dataframe().head()
slug | name | description | db_type | agg_method | column_name | variable_group_id | dataset_id | id | |
---|---|---|---|---|---|---|---|---|---|
0 | geoid_9d24d904 | 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_69c292c1 | 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_9ba05abf | 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_51ae36c8 | 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_73e15d04 | 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... |
We can explore the variable names in the previous dataframe using the column column_name
. The variable names we are interested in are total_pop
and geom
.
In order to download a dataset, we will use the Dataset function to_dataframe()
. Since we are only interested in downloading two variables of the dataset, we can use a SQL query to express this as shown in the following line of code.
county_pop_df = dataset.to_dataframe(sql_query="select total_pop, geom from $dataset$")
county_pop_df.head()
total_pop | geom | |
---|---|---|
0 | 91518.0 | POLYGON ((-97.03319 29.06433, -97.25027 28.905... |
1 | 16275.0 | POLYGON ((-116.15359 46.34831, -116.12716 46.2... |
2 | 7113.0 | POLYGON ((-79.45176 37.76645, -79.42082 37.789... |
3 | 13843.0 | POLYGON ((-77.18885 38.89627, -77.18972 38.878... |
4 | 24741.0 | POLYGON ((-92.55449 42.64231, -92.31808 42.642... |
We can now visualize the data.
Map(Layer(county_pop_df,
geom_col='geom',
style=color_bins_style('total_pop'),
legends=color_bins_legend('Population'),
encode_data=False),
viewport={'zoom': 3.56, 'lat': 38.521492, 'lng': -99.224130})