{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "7LKLhQBzJMOz" }, "source": [ "## Access public data from CARTO's Data Observatory.\n", "\n", "This notebook shows how to use CARTOframes for discovering and downloading **public** datasets from CARTO's [Data Observatory](https://carto.com/spatial-data-catalog/).\n", "\n", "For more details please visit the [Guides](https://carto.com/developers/cartoframes/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.\n", "\n", "The notebook is organized in the following sections:\n", " - [Discover datasets applying the public filter](#section1)\n", " - [Download a selection of variables of a dataset](#section2)\n", " - [Download a dataset applying spatial filtering. Filter by bounding box or polygon](#section3)" ] }, { "cell_type": "markdown", "metadata": { "id": "V4-0hJjiVsen" }, "source": [ "### Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "6vLRH4fEEkms" }, "outputs": [], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "pd.set_option('display.max_columns', None)\n", "\n", "from cartoframes.auth import set_default_credentials\n", "from cartoframes.data.observatory import *\n", "from cartoframes.viz import *\n", "from shapely.geometry import box" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Set CARTO default credentials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n", "\n", "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", "\n", "set_default_credentials('creds.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note about credentials**\n", "\n", "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](https://carto.com/developers/cartoframes/guides/Authentication/)." ] }, { "cell_type": "markdown", "metadata": { "id": "gTkF0eWzjlOw" }, "source": [ "### Discover and download a Public Dataset\n", "\n", "First, we'll discover the two public datasets applying the public filter:\n", "1. Public dataset providing total population per county in US.\n", "2. Public dataset providing sociodemographics data at the census tract level for the US." ] }, { "cell_type": "markdown", "metadata": { "id": "0Vhi0kdatNfG" }, "source": [ "\n", "#### Dicover datasets applying the public filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the country, category, provider, and geography filters, we can filter public datasets using the `public()` filter.\n", "\n", "We start by looking for the providers in the US providing public demographics data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "id": "iNDxjNRgr_FC", "outputId": "bb1e4690-7918-485f-9924-ccd7e777d272" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You can find more entities with the Global country filter. To apply that filter run:\n", "\tCatalog().country('glo')\n" ] }, { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Catalog().country('usa').category('demographics').public().providers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also discover public demographics datasets in the US.\n", "\n", "Note all datasets have the `is_public_data` flag that allows to identify public datasets." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "2bImQIlTqBzf", "outputId": "b8b0b4f3-064d-4f60-bdd9-c3181360d00a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You can find more entities with the Global country filter. To apply that filter run:\n", "\tCatalog().country('glo')\n" ] }, { "data": { "text/html": [ "
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slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
0wp_population_704f6b75Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2020-01-01, 2021-01-01)NoneTrueeng2020DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
1wp_population_22be8012Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2019-01-01, 2020-01-01)NoneTrueeng2019DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
2wp_population_55b9b084Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2018-01-01, 2019-01-01)NoneTrueeng2018DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
3wp_population_c506ad15Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2017-01-01, 2018-01-01)NoneTrueeng2017DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
4wp_population_b2019d83Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2016-01-01, 2017-01-01)NoneTrueeng2016DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
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" ], "text/plain": [ " slug name \\\n", "0 wp_population_704f6b75 Population Mosaics - United States of America ... \n", "1 wp_population_22be8012 Population Mosaics - United States of America ... \n", "2 wp_population_55b9b084 Population Mosaics - United States of America ... \n", "3 wp_population_c506ad15 Population Mosaics - United States of America ... \n", "4 wp_population_b2019d83 Population Mosaics - United States of America ... \n", "\n", " description category_id country_id \\\n", "0 Mosaiced 1km resolution global datasets. The m... demographics usa \n", "1 Mosaiced 1km resolution global datasets. The m... demographics usa \n", "2 Mosaiced 1km resolution global datasets. The m... demographics usa \n", "3 Mosaiced 1km resolution global datasets. The m... demographics usa \n", "4 Mosaiced 1km resolution global datasets. The m... demographics usa \n", "\n", " data_source_id provider_id geography_name \\\n", "0 population worldpop Grid 1km - United States of America \n", "1 population worldpop Grid 1km - United States of America \n", "2 population worldpop Grid 1km - United States of America \n", "3 population worldpop Grid 1km - United States of America \n", "4 population worldpop Grid 1km - United States of America \n", "\n", " geography_description temporal_aggregation \\\n", "0 Global grid at aprox. 1-kilometer resolution (... yearly \n", "1 Global grid at aprox. 1-kilometer resolution (... yearly \n", "2 Global grid at aprox. 1-kilometer resolution (... yearly \n", "3 Global grid at aprox. 1-kilometer resolution (... yearly \n", "4 Global grid at aprox. 1-kilometer resolution (... yearly \n", "\n", " time_coverage update_frequency is_public_data lang version \\\n", "0 [2020-01-01, 2021-01-01) None True eng 2020 \n", "1 [2019-01-01, 2020-01-01) None True eng 2019 \n", "2 [2018-01-01, 2019-01-01) None True eng 2018 \n", "3 [2017-01-01, 2018-01-01) None True eng 2017 \n", "4 [2016-01-01, 2017-01-01) None True eng 2016 \n", "\n", " category_name provider_name \\\n", "0 Demographics WorldPop \n", "1 Demographics WorldPop \n", "2 Demographics WorldPop \n", "3 Demographics WorldPop \n", "4 Demographics WorldPop \n", "\n", " geography_id \\\n", "0 carto-do-public-data.worldpop.geography_usa_gr... \n", "1 carto-do-public-data.worldpop.geography_usa_gr... \n", "2 carto-do-public-data.worldpop.geography_usa_gr... \n", "3 carto-do-public-data.worldpop.geography_usa_gr... \n", "4 carto-do-public-data.worldpop.geography_usa_gr... \n", "\n", " id \n", "0 carto-do-public-data.worldpop.demographics_pop... \n", "1 carto-do-public-data.worldpop.demographics_pop... \n", "2 carto-do-public-data.worldpop.demographics_pop... \n", "3 carto-do-public-data.worldpop.demographics_pop... \n", "4 carto-do-public-data.worldpop.demographics_pop... " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Catalog().country('usa').category('demographics').public().datasets.to_dataframe().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now explore [ACS demographics datasets](https://carto.com/spatial-data-catalog/browser/?category=demographics&provider=usa_acs) further." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 467 }, "id": "_Ga6cYNb4vZC", "outputId": "95cae3e0-3710-44ef-b126-06ea5ba85a0c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You can find more entities with the Global country filter. To apply that filter run:\n", "\tCatalog().country('glo')\n" ] }, { "data": { "text/html": [ "
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slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
0acs_sociodemogr_a0c48b07Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...yearly[2007-01-01, 2008-01-01)NoneTrueeng2007DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
1acs_sociodemogr_a03fb95fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCongressional District - United States of Amer...Shoreline clipped TIGER/Line boundaries. More ...yearly[2017-01-01, 2018-01-01)NoneTrueeng2017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_congr...carto-do-public-data.usa_acs.demographics_soci...
2acs_sociodemogr_e7b702b0Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...3yrs[2006-01-01, 2009-01-01)NoneTrueeng20062008DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
3acs_sociodemogr_e1e92d8dSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...yearly[2013-01-01, 2014-01-01)NoneTrueeng2013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
4acs_sociodemogr_2960a7d7Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...yearly[2018-01-01, 2019-01-01)NoneTrueeng2018DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
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165acs_sociodemogr_8c2655e0Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2013-01-01, 2018-01-01)NoneTrueeng20132017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
167acs_sociodemogr_c6414cc6Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2012-01-01, 2017-01-01)NoneTrueeng20122016DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
155acs_sociodemogr_18e867acSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2011-01-01, 2016-01-01)NoneTrueeng20112015DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
200acs_sociodemogr_528f7e8aSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2010-01-01, 2015-01-01)NoneTrueeng20102014DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
193acs_sociodemogr_aa75afdSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2009-01-01, 2014-01-01)NoneTrueeng20092013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
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" ], "text/plain": [ " slug \\\n", "165 acs_sociodemogr_8c2655e0 \n", "167 acs_sociodemogr_c6414cc6 \n", "155 acs_sociodemogr_18e867ac \n", "200 acs_sociodemogr_528f7e8a \n", "193 acs_sociodemogr_aa75afd \n", "\n", " name \\\n", "165 Sociodemographics - United States of America (... \n", "167 Sociodemographics - United States of America (... \n", "155 Sociodemographics - United States of America (... \n", "200 Sociodemographics - United States of America (... \n", "193 Sociodemographics - United States of America (... \n", "\n", " description category_id \\\n", "165 The American Community Survey (ACS) is an ongo... demographics \n", "167 The American Community Survey (ACS) is an ongo... demographics \n", "155 The American Community Survey (ACS) is an ongo... demographics \n", "200 The American Community Survey (ACS) is an ongo... demographics \n", "193 The American Community Survey (ACS) is an ongo... demographics \n", "\n", " country_id data_source_id provider_id \\\n", "165 usa sociodemographics usa_acs \n", "167 usa sociodemographics usa_acs \n", "155 usa sociodemographics usa_acs \n", "200 usa sociodemographics usa_acs \n", "193 usa sociodemographics usa_acs \n", "\n", " geography_name \\\n", "165 County - United States of America (2015) \n", "167 County - United States of America (2015) \n", "155 County - United States of America (2015) \n", "200 County - United States of America (2015) \n", "193 County - United States of America (2015) \n", "\n", " geography_description temporal_aggregation \\\n", "165 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "167 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "155 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "200 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "193 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "\n", " time_coverage update_frequency is_public_data lang version \\\n", "165 [2013-01-01, 2018-01-01) None True eng 20132017 \n", "167 [2012-01-01, 2017-01-01) None True eng 20122016 \n", "155 [2011-01-01, 2016-01-01) None True eng 20112015 \n", "200 [2010-01-01, 2015-01-01) None True eng 20102014 \n", "193 [2009-01-01, 2014-01-01) None True eng 20092013 \n", "\n", " category_name provider_name \\\n", "165 Demographics American Community Survey \n", "167 Demographics American Community Survey \n", "155 Demographics American Community Survey \n", "200 Demographics American Community Survey \n", "193 Demographics American Community Survey \n", "\n", " geography_id \\\n", "165 carto-do-public-data.carto.geography_usa_count... \n", "167 carto-do-public-data.carto.geography_usa_count... \n", "155 carto-do-public-data.carto.geography_usa_count... \n", "200 carto-do-public-data.carto.geography_usa_count... \n", "193 carto-do-public-data.carto.geography_usa_count... \n", "\n", " id \n", "165 carto-do-public-data.usa_acs.demographics_soci... \n", "167 carto-do-public-data.usa_acs.demographics_soci... \n", "155 carto-do-public-data.usa_acs.demographics_soci... \n", "200 carto-do-public-data.usa_acs.demographics_soci... \n", "193 carto-do-public-data.usa_acs.demographics_soci... " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datasets_df[(datasets_df['geography_name'].str.contains('County')) & \n", " (datasets_df['temporal_aggregation'].str.contains('5yrs'))]\\\n", " .sort_values('time_coverage', ascending=False).head()" ] }, { "cell_type": "markdown", "metadata": { "id": "NSq6azNp6E6Z" }, "source": [ "We are also interested in exploring which datasets provide data at the **census tract** level and with a **5 year temporal aggregation**.\n", "\n", "We select the latest [dataset](https://carto.com/spatial-data-catalog/browser/dataset/acs_sociodemogr_6bf5c7f4/) `acs_sociodemogr_496a0675`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 707 }, "id": "rSgq4Ncy5y9r", "outputId": "24ea831e-6218-41d9-87b6-95dd7ec18e36" }, "outputs": [ { "data": { "text/html": [ "
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slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
68acs_sociodemogr_496a0675Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2013-01-01, 2018-01-01)NoneTrueeng20132017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
6acs_sociodemogr_30d1f53Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2012-01-01, 2017-01-01)NoneTrueeng20122016DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
219acs_sociodemogr_dda43439Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2011-01-01, 2016-01-01)NoneTrueeng20112015DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
180acs_sociodemogr_97c32d1fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2010-01-01, 2015-01-01)NoneTrueeng20102014DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
46acs_sociodemogr_cfeb0968Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2009-01-01, 2014-01-01)NoneTrueeng20092013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
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" ], "text/plain": [ " slug \\\n", "68 acs_sociodemogr_496a0675 \n", "6 acs_sociodemogr_30d1f53 \n", "219 acs_sociodemogr_dda43439 \n", "180 acs_sociodemogr_97c32d1f \n", "46 acs_sociodemogr_cfeb0968 \n", "\n", " name \\\n", "68 Sociodemographics - United States of America (... \n", "6 Sociodemographics - United States of America (... \n", "219 Sociodemographics - United States of America (... \n", "180 Sociodemographics - United States of America (... \n", "46 Sociodemographics - United States of America (... \n", "\n", " description category_id \\\n", "68 The American Community Survey (ACS) is an ongo... demographics \n", "6 The American Community Survey (ACS) is an ongo... demographics \n", "219 The American Community Survey (ACS) is an ongo... demographics \n", "180 The American Community Survey (ACS) is an ongo... demographics \n", "46 The American Community Survey (ACS) is an ongo... demographics \n", "\n", " country_id data_source_id provider_id \\\n", "68 usa sociodemographics usa_acs \n", "6 usa sociodemographics usa_acs \n", "219 usa sociodemographics usa_acs \n", "180 usa sociodemographics usa_acs \n", "46 usa sociodemographics usa_acs \n", "\n", " geography_name \\\n", "68 Census Tract - United States of America (2015) \n", "6 Census Tract - United States of America (2015) \n", "219 Census Tract - United States of America (2015) \n", "180 Census Tract - United States of America (2015) \n", "46 Census Tract - United States of America (2015) \n", "\n", " geography_description temporal_aggregation \\\n", "68 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "6 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "219 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "180 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "46 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", "\n", " time_coverage update_frequency is_public_data lang version \\\n", "68 [2013-01-01, 2018-01-01) None True eng 20132017 \n", "6 [2012-01-01, 2017-01-01) None True eng 20122016 \n", "219 [2011-01-01, 2016-01-01) None True eng 20112015 \n", "180 [2010-01-01, 2015-01-01) None True eng 20102014 \n", "46 [2009-01-01, 2014-01-01) None True eng 20092013 \n", "\n", " category_name provider_name \\\n", "68 Demographics American Community Survey \n", "6 Demographics American Community Survey \n", "219 Demographics American Community Survey \n", "180 Demographics American Community Survey \n", "46 Demographics American Community Survey \n", "\n", " geography_id \\\n", "68 carto-do-public-data.carto.geography_usa_censu... \n", "6 carto-do-public-data.carto.geography_usa_censu... \n", "219 carto-do-public-data.carto.geography_usa_censu... \n", "180 carto-do-public-data.carto.geography_usa_censu... \n", "46 carto-do-public-data.carto.geography_usa_censu... \n", "\n", " id \n", "68 carto-do-public-data.usa_acs.demographics_soci... \n", "6 carto-do-public-data.usa_acs.demographics_soci... \n", "219 carto-do-public-data.usa_acs.demographics_soci... \n", "180 carto-do-public-data.usa_acs.demographics_soci... \n", "46 carto-do-public-data.usa_acs.demographics_soci... " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datasets_df[(datasets_df['geography_name'].str.contains('Census Tract')) & \n", " (datasets_df['temporal_aggregation'].str.contains('5yrs'))]\\\n", " .sort_values('time_coverage', ascending=False).head()" ] }, { "cell_type": "markdown", "metadata": { "id": "kVFwrN7BtAgW" }, "source": [ "\n", "#### Download a selection of variables of a dataset\n", "\n", "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." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "ItuC51LTs9RI" }, "outputs": [], "source": [ "dataset = Dataset.get('acs_sociodemogr_8c2655e0')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 367 }, "id": "79_zTMLEzijt", "outputId": "d08c7699-9f1a-4112-c59a-ce56749e190c" }, "outputs": [ { "data": { "text/html": [ "
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62115928.02013201769.089.0286.01464.03190.02498.06744.01695.00.0575.083.064.02290.06830.012175.011250.05431.00.44224316.0213.0261.039.0645.0326.0307.09909.0None0.03032.0226.0323.01911.0None342.05240.0432.0217.0125.0481.0485.0720.0476.0592.0430.01631.0433.0294.0123.0142.0247.0166.061.029239.0None9.0295.0226.01441.0225.00.0552.034.08.0334.0229.0101.0118.0303.0308.0384.0316.0401.0419.0466.074.0255.0158.0176.0203.0158.0150.0NoneNone0.00.0373.0159.017.0195.00.0554.0575.039.011949.0None657.0726.0728.02791.036.02940.0226.0481.0782.014914.08547.0None373.0117.0575.0297.0111.0235.0220.0384.0164.0358.0325.094.0288.0476.0430.0203.0138.0224.0365.0182.0271.0373.0356.02311.06325.034.03584.02353.033.017.0428.02799.03584.0447.0122.0267.0460.030.0292.0120.050.058.00.01376.0267.0924.02292.0197.0520.00.0360.0347.01107.0109.022.083.098.0107.049.035.071.0894.0None107.053.0105.045.0736.04316.0218.0958.0894.011860.00.0606.028.018.088.00.0361.011397.03313.02752.037.02976.054.0143.041.0875.012072.0None55.019.01985.0473.0787.0327.0987.0117.0153.00.03114.031.898955.09.0141.01202.010690.063.038.0164.02339.00.0553.0775.0None0.074.02195.01218.0875.0162.01449.085.0184.0603.0183.065500.0165.0319.024.030000.0117600.0392.0516.0275.0226.0483.078.024.0211.039.0
7310050.0201320170.08.00.035.046.0132.0204.046.00.00.01.01.0149.0172.0421.0411.0217.00.4498177.09.03.043.354.027.0388.0298.0None0.0177.00.03.022.0None21.0261.020.04.02.013.08.01.06.018.017.050.08.013.02.05.08.010.017.041250.0None0.08.042.00.015.01.01.013.023.035.014.00.00.06.07.05.013.014.011.025.03.05.09.03.010.015.010.0NoneNone0.00.060.00.00.016.00.032.048.02.0421.0None24.025.033.0146.00.096.00.018.06.021799.0264.0None60.09.03.016.04.012.03.017.014.015.03.01.021.025.013.04.09.08.012.010.014.022.020.087.0120.02.0178.073.00.00.017.0169.0178.06.03.064.024.01.017.07.01.02.00.081.08.084.0109.015.013.03.026.00.034.013.03.01.03.02.05.00.00.020.0None9.00.00.013.021.0177.07.025.020.0415.00.00.00.00.03.00.030.0411.079.0133.010.0229.00.03.01.083.0421.0None0.06.01959.03.027.019.023.015.00.00.0112.025.83040.00.031.065.0421.02.00.03.013.00.059.037.0None0.01.093.06.083.01.054.03.02.025.06.0101500.09.021.02.057000.0162500.067.036.013.06.0658.00.02.012.00.0
801067205.020132017124.074.0427.01953.02310.03581.08208.02346.00.04762.033.0120.03517.08535.017110.011757.08902.00.45296727.0176.0317.043.51373.0459.0398.013972.0None10.06949.0435.0468.02311.0None438.09055.0594.0307.0171.0396.0369.0350.0658.0418.0528.02232.0536.0675.0252.0324.0452.0293.0121.045569.0None5.0452.0628.0289.0835.00.0496.048.0176.0511.0352.0162.0326.0415.0465.0527.0598.0547.0617.0586.0222.0531.0166.0404.0567.0353.0222.0NoneNone0.00.01087.0291.0314.0605.00.0750.0892.0146.016675.0None757.0839.0925.06689.0291.04545.0435.0844.0552.023983.012123.0None1087.0694.0858.01025.0749.01067.0154.0395.0241.0445.0301.0288.0504.0566.0332.0278.0457.0308.0323.0412.0616.0694.0614.03625.06517.0169.07455.03398.013.00.01319.06865.07445.0431.0130.01025.0513.074.0595.0379.0287.0257.010.02182.0185.02328.05900.0386.0769.0148.0828.0237.01843.0149.096.0162.091.035.0109.075.051.01347.0None151.0163.00.0787.0918.06727.0540.02932.01347.016615.00.04762.052.012.0221.00.0243.012144.04105.06413.041.06183.02.0218.0113.02279.016964.0None102.0153.01983.0761.01033.0987.01480.0175.096.00.05531.026.0171195.05.0343.01196.016806.0222.0164.0196.02348.00.01485.01673.0None0.0378.03596.01129.02279.0279.0983.0495.0394.0639.0231.0112600.0788.01238.089.062700.0189100.0353.0766.0386.0697.0603.08.080.0245.00.0
9410214.02013201719.016.025.0176.0188.0426.0962.0330.00.00.00.014.0396.0893.01910.01643.0948.00.4153805.028.061.047.0191.079.0537.01529.0None0.0751.0154.051.0146.0None51.01070.051.044.022.013.018.049.041.032.051.0283.086.057.047.029.077.048.029.039831.0None99.054.083.015.064.00.061.064.055.064.032.01.026.044.058.049.049.069.031.065.061.044.022.032.062.044.022.0NoneNone0.00.0200.00.00.0191.00.0102.0101.014.01756.0None114.0130.077.0684.015.0512.0154.096.052.024178.01373.0None200.0101.081.024.018.034.00.020.020.041.05.054.050.020.073.056.077.077.020.052.050.077.090.0420.0717.030.0812.0426.011.00.0113.0739.0812.024.019.0341.037.014.026.080.020.02.00.0293.026.0265.0575.074.057.09.090.014.0297.032.046.08.034.026.015.013.022.0156.0None0.06.06.022.0111.0805.085.0257.0156.01870.00.00.00.021.020.00.068.01783.0493.0612.07.0863.06.012.015.0233.01903.0None15.040.01957.0109.0128.064.0169.05.013.00.0515.025.411965.099.073.0290.01906.022.038.062.0152.00.0194.0121.0None0.0108.0464.099.0233.011.0114.089.049.079.017.0110900.096.0126.021.083300.0162900.096.0123.010.072.0814.00.021.067.02.0
\n", "
" ], "text/plain": [ " geoid no_car do_date male_20 male_21 no_cars one_car poverty \\\n", "0 30007 43.0 20132017 32.0 20.0 65.0 463.0 448.0 \n", "1 19137 106.0 20132017 17.0 42.0 291.0 1298.0 1653.0 \n", "2 20101 10.0 20132017 8.0 0.0 36.0 181.0 181.0 \n", "3 48307 133.0 20132017 1.0 93.0 220.0 883.0 1296.0 \n", "4 46123 66.0 20132017 8.0 21.0 118.0 544.0 1045.0 \n", "5 29155 308.0 20132017 85.0 189.0 934.0 2639.0 4859.0 \n", "6 21159 28.0 20132017 69.0 89.0 286.0 1464.0 3190.0 \n", "7 31005 0.0 20132017 0.0 8.0 0.0 35.0 46.0 \n", "8 01067 205.0 20132017 124.0 74.0 427.0 1953.0 2310.0 \n", "9 41021 4.0 20132017 19.0 16.0 25.0 176.0 188.0 \n", "\n", " children male_pop two_cars asian_pop black_pop female_20 female_21 \\\n", "0 1151.0 2896.0 887.0 0.0 4.0 0.0 0.0 \n", "1 2349.0 4931.0 1826.0 0.0 0.0 42.0 103.0 \n", "2 407.0 864.0 350.0 0.0 1.0 1.0 8.0 \n", "3 1935.0 4171.0 1232.0 0.0 161.0 39.0 14.0 \n", "4 1237.0 2781.0 649.0 0.0 0.0 9.0 59.0 \n", "5 4597.0 8210.0 2043.0 0.0 4639.0 197.0 153.0 \n", "6 2498.0 6744.0 1695.0 0.0 575.0 83.0 64.0 \n", "7 132.0 204.0 46.0 0.0 0.0 1.0 1.0 \n", "8 3581.0 8208.0 2346.0 0.0 4762.0 33.0 120.0 \n", "9 426.0 962.0 330.0 0.0 0.0 0.0 14.0 \n", "\n", " in_school pop_25_64 total_pop white_pop female_pop gini_index \\\n", "0 1017.0 2936.0 5755.0 5432.0 2859.0 0.4452 \n", "1 2094.0 5037.0 10239.0 9640.0 5308.0 0.4270 \n", "2 332.0 822.0 1702.0 1539.0 838.0 0.4473 \n", "3 1623.0 4096.0 8145.0 5067.0 3974.0 0.4495 \n", "4 1121.0 2688.0 5480.0 4500.0 2699.0 0.4633 \n", "5 4301.0 8474.0 17344.0 11948.0 9134.0 0.4862 \n", "6 2290.0 6830.0 12175.0 11250.0 5431.0 0.4422 \n", "7 149.0 172.0 421.0 411.0 217.0 0.4498 \n", "8 3517.0 8535.0 17110.0 11757.0 8902.0 0.4529 \n", "9 396.0 893.0 1910.0 1643.0 948.0 0.4153 \n", "\n", " households male_60_61 male_62_64 median_age three_cars male_5_to_9 \\\n", "0 2405.0 105.0 196.0 46.3 622.0 132.0 \n", "1 4614.0 149.0 256.0 44.5 774.0 385.0 \n", "2 828.0 10.0 27.0 41.5 133.0 26.0 \n", "3 3143.0 240.0 134.0 43.5 510.0 370.0 \n", "4 2424.0 133.0 141.0 46.5 639.0 135.0 \n", "5 6875.0 225.0 238.0 37.7 871.0 566.0 \n", "6 4316.0 213.0 261.0 39.0 645.0 326.0 \n", "7 177.0 9.0 3.0 43.3 54.0 27.0 \n", "8 6727.0 176.0 317.0 43.5 1373.0 459.0 \n", "9 805.0 28.0 61.0 47.0 191.0 79.0 \n", "\n", " median_rent pop_16_over pop_widowed armed_forces employed_pop \\\n", "0 550.0 4762.0 None 0.0 2621.0 \n", "1 482.0 8243.0 None 11.0 4838.0 \n", "2 345.0 1379.0 None 0.0 885.0 \n", "3 471.0 6496.0 None 0.0 3658.0 \n", "4 341.0 4362.0 None 0.0 2859.0 \n", "5 344.0 13251.0 None 0.0 6326.0 \n", "6 307.0 9909.0 None 0.0 3032.0 \n", "7 388.0 298.0 None 0.0 177.0 \n", "8 398.0 13972.0 None 10.0 6949.0 \n", "9 537.0 1529.0 None 0.0 751.0 \n", "\n", " hispanic_pop male_under_5 mobile_homes pop_divorced female_5_to_9 \\\n", "0 152.0 112.0 377.0 None 207.0 \n", "1 371.0 259.0 178.0 None 310.0 \n", "2 113.0 74.0 46.0 None 74.0 \n", "3 2571.0 327.0 393.0 None 263.0 \n", "4 18.0 140.0 496.0 None 125.0 \n", "5 421.0 614.0 742.0 None 496.0 \n", "6 226.0 323.0 1911.0 None 342.0 \n", "7 0.0 3.0 22.0 None 21.0 \n", "8 435.0 468.0 2311.0 None 438.0 \n", "9 154.0 51.0 146.0 None 51.0 \n", "\n", " housing_units male_10_to_14 male_15_to_17 male_18_to_19 male_22_to_24 \\\n", "0 2743.0 196.0 117.0 75.0 54.0 \n", "1 5231.0 313.0 238.0 115.0 182.0 \n", "2 991.0 46.0 54.0 22.0 45.0 \n", "3 4302.0 212.0 220.0 33.0 111.0 \n", "4 3085.0 222.0 141.0 50.0 127.0 \n", "5 8178.0 747.0 421.0 226.0 282.0 \n", "6 5240.0 432.0 217.0 125.0 481.0 \n", "7 261.0 20.0 4.0 2.0 13.0 \n", "8 9055.0 594.0 307.0 171.0 396.0 \n", "9 1070.0 51.0 44.0 22.0 13.0 \n", "\n", " male_25_to_29 male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 \\\n", "0 135.0 131.0 160.0 163.0 184.0 \n", "1 217.0 239.0 252.0 323.0 301.0 \n", "2 59.0 26.0 76.0 49.0 56.0 \n", "3 284.0 236.0 272.0 152.0 206.0 \n", "4 129.0 143.0 155.0 116.0 164.0 \n", "5 479.0 431.0 449.0 422.0 526.0 \n", "6 485.0 720.0 476.0 592.0 430.0 \n", "7 8.0 1.0 6.0 18.0 17.0 \n", "8 369.0 350.0 658.0 418.0 528.0 \n", "9 18.0 49.0 41.0 32.0 51.0 \n", "\n", " male_45_to_64 male_50_to_54 male_55_to_59 male_65_to_66 male_67_to_69 \\\n", "0 908.0 210.0 213.0 63.0 171.0 \n", "1 1449.0 366.0 377.0 148.0 162.0 \n", "2 226.0 72.0 61.0 19.0 22.0 \n", "3 1054.0 310.0 164.0 106.0 111.0 \n", "4 881.0 238.0 205.0 34.0 116.0 \n", "5 2156.0 567.0 600.0 227.0 234.0 \n", "6 1631.0 433.0 294.0 123.0 142.0 \n", "7 50.0 8.0 13.0 2.0 5.0 \n", "8 2232.0 536.0 675.0 252.0 324.0 \n", "9 283.0 86.0 57.0 47.0 29.0 \n", "\n", " male_70_to_74 male_75_to_79 male_80_to_84 median_income pop_separated \\\n", "0 191.0 129.0 66.0 55295.0 None \n", "1 193.0 146.0 137.0 43674.0 None \n", "2 25.0 40.0 39.0 51765.0 None \n", "3 296.0 94.0 139.0 42367.0 None \n", "4 112.0 143.0 42.0 48409.0 None \n", "5 239.0 164.0 153.0 32468.0 None \n", "6 247.0 166.0 61.0 29239.0 None \n", "7 8.0 10.0 17.0 41250.0 None \n", "8 452.0 293.0 121.0 45569.0 None \n", "9 77.0 48.0 29.0 39831.0 None \n", "\n", " amerindian_pop female_under_5 four_more_cars group_quarters \\\n", "0 84.0 146.0 368.0 44.0 \n", "1 40.0 335.0 425.0 210.0 \n", "2 39.0 48.0 128.0 8.0 \n", "3 1.0 154.0 298.0 154.0 \n", "4 729.0 172.0 474.0 159.0 \n", "5 18.0 671.0 388.0 202.0 \n", "6 9.0 295.0 226.0 1441.0 \n", "7 0.0 8.0 42.0 0.0 \n", "8 5.0 452.0 628.0 289.0 \n", "9 99.0 54.0 83.0 15.0 \n", "\n", " masters_degree other_race_pop unemployed_pop walked_to_work \\\n", "0 293.0 0.0 112.0 135.0 \n", "1 227.0 0.0 349.0 194.0 \n", "2 69.0 0.0 32.0 32.0 \n", "3 213.0 202.0 122.0 104.0 \n", "4 112.0 0.0 52.0 132.0 \n", "5 483.0 0.0 756.0 153.0 \n", "6 225.0 0.0 552.0 34.0 \n", "7 15.0 1.0 1.0 13.0 \n", "8 835.0 0.0 496.0 48.0 \n", "9 64.0 0.0 61.0 64.0 \n", "\n", " worked_at_home female_10_to_14 female_15_to_17 female_18_to_19 \\\n", "0 205.0 124.0 117.0 123.0 \n", "1 233.0 302.0 207.0 127.0 \n", "2 27.0 47.0 38.0 35.0 \n", "3 61.0 258.0 131.0 66.0 \n", "4 223.0 203.0 99.0 46.0 \n", "5 109.0 689.0 393.0 194.0 \n", "6 8.0 334.0 229.0 101.0 \n", "7 23.0 35.0 14.0 0.0 \n", "8 176.0 511.0 352.0 162.0 \n", "9 55.0 64.0 32.0 1.0 \n", "\n", " female_22_to_24 female_25_to_29 female_30_to_34 female_35_to_39 \\\n", "0 90.0 91.0 113.0 165.0 \n", "1 119.0 220.0 233.0 262.0 \n", "2 13.0 40.0 29.0 48.0 \n", "3 21.0 340.0 82.0 271.0 \n", "4 65.0 122.0 123.0 133.0 \n", "5 209.0 573.0 521.0 629.0 \n", "6 118.0 303.0 308.0 384.0 \n", "7 0.0 6.0 7.0 5.0 \n", "8 326.0 415.0 465.0 527.0 \n", "9 26.0 44.0 58.0 49.0 \n", "\n", " female_40_to_44 female_45_to_49 female_50_to_54 female_55_to_59 \\\n", "0 153.0 218.0 250.0 196.0 \n", "1 345.0 320.0 373.0 452.0 \n", "2 53.0 44.0 45.0 63.0 \n", "3 321.0 285.0 188.0 273.0 \n", "4 97.0 173.0 242.0 190.0 \n", "5 452.0 564.0 593.0 715.0 \n", "6 316.0 401.0 419.0 466.0 \n", "7 13.0 14.0 11.0 25.0 \n", "8 598.0 547.0 617.0 586.0 \n", "9 49.0 69.0 31.0 65.0 \n", "\n", " female_60_to_61 female_62_to_64 female_65_to_66 female_67_to_69 \\\n", "0 116.0 137.0 74.0 114.0 \n", "1 152.0 200.0 126.0 168.0 \n", "2 19.0 45.0 22.0 14.0 \n", "3 121.0 217.0 147.0 117.0 \n", "4 108.0 76.0 87.0 84.0 \n", "5 199.0 291.0 159.0 271.0 \n", "6 74.0 255.0 158.0 176.0 \n", "7 3.0 5.0 9.0 3.0 \n", "8 222.0 531.0 166.0 404.0 \n", "9 61.0 44.0 22.0 32.0 \n", "\n", " female_70_to_74 female_75_to_79 female_80_to_84 pop_15_and_over \\\n", "0 173.0 158.0 52.0 None \n", "1 273.0 233.0 177.0 None \n", "2 40.0 42.0 38.0 None \n", "3 228.0 156.0 134.0 None \n", "4 114.0 174.0 55.0 None \n", "5 397.0 291.0 254.0 None \n", "6 203.0 158.0 150.0 None \n", "7 10.0 15.0 10.0 None \n", "8 567.0 353.0 222.0 None \n", "9 62.0 44.0 22.0 None \n", "\n", " pop_now_married asian_male_45_54 asian_male_55_64 bachelors_degree \\\n", "0 None 0.0 0.0 866.0 \n", "1 None 0.0 0.0 867.0 \n", "2 None 0.0 0.0 206.0 \n", "3 None 0.0 0.0 671.0 \n", "4 None 0.0 0.0 643.0 \n", "5 None 0.0 0.0 815.0 \n", "6 None 0.0 0.0 373.0 \n", "7 None 0.0 0.0 60.0 \n", "8 None 0.0 0.0 1087.0 \n", "9 None 0.0 0.0 200.0 \n", "\n", " black_male_45_54 black_male_55_64 commute_5_9_mins commuters_by_bus \\\n", "0 0.0 0.0 359.0 0.0 \n", "1 0.0 0.0 1023.0 21.0 \n", "2 0.0 0.0 178.0 0.0 \n", "3 18.0 0.0 867.0 2.0 \n", "4 0.0 0.0 878.0 37.0 \n", "5 239.0 230.0 1273.0 23.0 \n", "6 159.0 17.0 195.0 0.0 \n", "7 0.0 0.0 16.0 0.0 \n", "8 291.0 314.0 605.0 0.0 \n", "9 0.0 0.0 191.0 0.0 \n", "\n", " in_grades_1_to_4 in_grades_5_to_8 male_85_and_over not_hispanic_pop \\\n", "0 253.0 252.0 41.0 5603.0 \n", "1 569.0 512.0 114.0 9868.0 \n", "2 64.0 79.0 8.0 1589.0 \n", "3 483.0 334.0 60.0 5574.0 \n", "4 272.0 269.0 66.0 5462.0 \n", "5 877.0 1260.0 126.0 16923.0 \n", "6 554.0 575.0 39.0 11949.0 \n", "7 32.0 48.0 2.0 421.0 \n", "8 750.0 892.0 146.0 16675.0 \n", "9 102.0 101.0 14.0 1756.0 \n", "\n", " pop_5_years_over white_male_45_54 white_male_55_64 associates_degree \\\n", "0 None 369.0 463.0 268.0 \n", "1 None 628.0 782.0 926.0 \n", "2 None 121.0 96.0 135.0 \n", "3 None 327.0 391.0 217.0 \n", "4 None 310.0 392.0 319.0 \n", "5 None 824.0 828.0 660.0 \n", "6 None 657.0 726.0 728.0 \n", "7 None 24.0 25.0 33.0 \n", "8 None 757.0 839.0 925.0 \n", "9 None 114.0 130.0 77.0 \n", "\n", " commuters_16_over dwellings_2_units family_households hispanic_any_race \\\n", "0 2287.0 15.0 1552.0 152.0 \n", "1 4560.0 177.0 3080.0 371.0 \n", "2 817.0 0.0 484.0 113.0 \n", "3 3389.0 229.0 2164.0 2571.0 \n", "4 2592.0 24.0 1473.0 18.0 \n", "5 6067.0 517.0 4342.0 421.0 \n", "6 2791.0 36.0 2940.0 226.0 \n", "7 146.0 0.0 96.0 0.0 \n", "8 6689.0 291.0 4545.0 435.0 \n", "9 684.0 15.0 512.0 154.0 \n", "\n", " in_grades_9_to_12 income_less_10000 income_per_capita pop_25_years_over \\\n", "0 336.0 96.0 32268.0 4210.0 \n", "1 530.0 282.0 25005.0 7143.0 \n", "2 67.0 62.0 29768.0 1163.0 \n", "3 453.0 228.0 23398.0 5832.0 \n", "4 273.0 239.0 27613.0 3858.0 \n", "5 1082.0 956.0 18883.0 11212.0 \n", "6 481.0 782.0 14914.0 8547.0 \n", "7 18.0 6.0 21799.0 264.0 \n", "8 844.0 552.0 23983.0 12123.0 \n", "9 96.0 52.0 24178.0 1373.0 \n", "\n", " pop_never_married bachelors_degree_2 commute_10_14_mins \\\n", "0 None 866.0 337.0 \n", "1 None 867.0 568.0 \n", "2 None 206.0 98.0 \n", "3 None 671.0 535.0 \n", "4 None 643.0 347.0 \n", "5 None 815.0 792.0 \n", "6 None 373.0 117.0 \n", "7 None 60.0 9.0 \n", "8 None 1087.0 694.0 \n", "9 None 200.0 101.0 \n", "\n", " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", "0 132.0 225.0 159.0 \n", "1 487.0 485.0 205.0 \n", "2 93.0 64.0 38.0 \n", "3 412.0 410.0 23.0 \n", "4 158.0 167.0 120.0 \n", "5 998.0 913.0 366.0 \n", "6 575.0 297.0 111.0 \n", "7 3.0 16.0 4.0 \n", "8 858.0 1025.0 749.0 \n", "9 81.0 24.0 18.0 \n", "\n", " commute_30_34_mins commute_35_39_mins commute_35_44_mins \\\n", "0 162.0 214.0 247.0 \n", "1 236.0 39.0 82.0 \n", "2 47.0 15.0 15.0 \n", "3 202.0 71.0 117.0 \n", "4 92.0 17.0 56.0 \n", "5 587.0 78.0 167.0 \n", "6 235.0 220.0 384.0 \n", "7 12.0 3.0 17.0 \n", "8 1067.0 154.0 395.0 \n", "9 34.0 0.0 20.0 \n", "\n", " commute_40_44_mins commute_45_59_mins commute_60_89_mins \\\n", "0 33.0 271.0 150.0 \n", "1 43.0 292.0 205.0 \n", "2 0.0 9.0 35.0 \n", "3 46.0 75.0 52.0 \n", "4 39.0 55.0 143.0 \n", "5 89.0 181.0 87.0 \n", "6 164.0 358.0 325.0 \n", "7 14.0 15.0 3.0 \n", "8 241.0 445.0 301.0 \n", "9 20.0 41.0 5.0 \n", "\n", " female_85_and_over income_10000_14999 income_15000_19999 \\\n", "0 42.0 135.0 96.0 \n", "1 229.0 419.0 298.0 \n", "2 32.0 42.0 50.0 \n", "3 148.0 265.0 288.0 \n", "4 143.0 174.0 110.0 \n", "5 223.0 689.0 583.0 \n", "6 94.0 288.0 476.0 \n", "7 1.0 21.0 25.0 \n", "8 288.0 504.0 566.0 \n", "9 54.0 50.0 20.0 \n", "\n", " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", "0 101.0 88.0 84.0 \n", "1 345.0 218.0 343.0 \n", "2 67.0 67.0 35.0 \n", "3 127.0 156.0 203.0 \n", "4 132.0 111.0 119.0 \n", "5 512.0 504.0 377.0 \n", "6 430.0 203.0 138.0 \n", "7 13.0 4.0 9.0 \n", "8 332.0 278.0 457.0 \n", "9 73.0 56.0 77.0 \n", "\n", " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", "0 210.0 166.0 76.0 \n", "1 211.0 226.0 222.0 \n", "2 32.0 35.0 12.0 \n", "3 207.0 137.0 124.0 \n", "4 168.0 91.0 124.0 \n", "5 385.0 382.0 336.0 \n", "6 224.0 365.0 182.0 \n", "7 8.0 12.0 10.0 \n", "8 308.0 323.0 412.0 \n", "9 77.0 20.0 52.0 \n", "\n", " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", "0 305.0 241.0 267.0 \n", "1 433.0 515.0 485.0 \n", "2 79.0 109.0 139.0 \n", "3 259.0 329.0 385.0 \n", "4 227.0 270.0 279.0 \n", "5 544.0 392.0 555.0 \n", "6 271.0 373.0 356.0 \n", "7 14.0 22.0 20.0 \n", "8 616.0 694.0 614.0 \n", "9 50.0 77.0 90.0 \n", "\n", " married_households not_in_labor_force not_us_citizen_pop \\\n", "0 1348.0 2029.0 20.0 \n", "1 2221.0 3045.0 136.0 \n", "2 401.0 462.0 12.0 \n", "3 1592.0 2716.0 207.0 \n", "4 1217.0 1451.0 27.0 \n", "5 2758.0 6169.0 97.0 \n", "6 2311.0 6325.0 34.0 \n", "7 87.0 120.0 2.0 \n", "8 3625.0 6517.0 169.0 \n", "9 420.0 717.0 30.0 \n", "\n", " pop_in_labor_force high_school_diploma hispanic_male_45_54 \\\n", "0 2733.0 1366.0 0.0 \n", "1 5198.0 2447.0 39.0 \n", "2 917.0 302.0 6.0 \n", "3 3780.0 1597.0 164.0 \n", "4 2911.0 1367.0 0.0 \n", "5 7082.0 3602.0 19.0 \n", "6 3584.0 2353.0 33.0 \n", "7 178.0 73.0 0.0 \n", "8 7455.0 3398.0 13.0 \n", "9 812.0 426.0 11.0 \n", "\n", " hispanic_male_55_64 occupation_services workers_16_and_over \\\n", "0 5.0 461.0 2492.0 \n", "1 0.0 824.0 4793.0 \n", "2 2.0 165.0 844.0 \n", "3 147.0 770.0 3450.0 \n", "4 0.0 393.0 2815.0 \n", "5 19.0 1441.0 6176.0 \n", "6 17.0 428.0 2799.0 \n", "7 0.0 17.0 169.0 \n", "8 0.0 1319.0 6865.0 \n", "9 0.0 113.0 739.0 \n", "\n", " civilian_labor_force commute_60_more_mins commute_90_more_mins \\\n", "0 2733.0 203.0 53.0 \n", "1 5187.0 319.0 114.0 \n", "2 917.0 43.0 8.0 \n", "3 3780.0 133.0 81.0 \n", "4 2911.0 175.0 32.0 \n", "5 7082.0 159.0 72.0 \n", "6 3584.0 447.0 122.0 \n", "7 178.0 6.0 3.0 \n", "8 7445.0 431.0 130.0 \n", "9 812.0 24.0 19.0 \n", "\n", " commute_less_10_mins commuters_by_carpool employed_information \\\n", "0 551.0 267.0 31.0 \n", "1 1886.0 453.0 153.0 \n", "2 410.0 42.0 10.0 \n", "3 1482.0 424.0 17.0 \n", "4 1422.0 247.0 30.0 \n", "5 1904.0 594.0 26.0 \n", "6 267.0 460.0 30.0 \n", "7 64.0 24.0 1.0 \n", "8 1025.0 513.0 74.0 \n", "9 341.0 37.0 14.0 \n", "\n", " in_undergrad_college income_100000_124999 income_125000_149999 \\\n", "0 79.0 170.0 82.0 \n", "1 176.0 317.0 121.0 \n", "2 48.0 45.0 11.0 \n", "3 70.0 176.0 114.0 \n", "4 102.0 226.0 37.0 \n", "5 492.0 347.0 96.0 \n", "6 292.0 120.0 50.0 \n", "7 17.0 7.0 1.0 \n", "8 595.0 379.0 287.0 \n", "9 26.0 80.0 20.0 \n", "\n", " income_150000_199999 male_male_households nonfamily_households \\\n", "0 186.0 0.0 853.0 \n", "1 93.0 0.0 1534.0 \n", "2 18.0 0.0 344.0 \n", "3 69.0 0.0 979.0 \n", "4 32.0 0.0 951.0 \n", "5 143.0 6.0 2533.0 \n", "6 58.0 0.0 1376.0 \n", "7 2.0 0.0 81.0 \n", "8 257.0 10.0 2182.0 \n", "9 2.0 0.0 293.0 \n", "\n", " rent_over_50_percent vacant_housing_units commuters_drove_alone \\\n", "0 47.0 338.0 1852.0 \n", "1 246.0 617.0 3813.0 \n", "2 16.0 163.0 732.0 \n", "3 227.0 1159.0 2857.0 \n", "4 128.0 661.0 2163.0 \n", "5 641.0 1303.0 5259.0 \n", "6 267.0 924.0 2292.0 \n", "7 8.0 84.0 109.0 \n", "8 185.0 2328.0 5900.0 \n", "9 26.0 265.0 575.0 \n", "\n", " employed_construction employed_retail_trade income_200000_or_more \\\n", "0 370.0 180.0 102.0 \n", "1 309.0 641.0 86.0 \n", "2 112.0 73.0 25.0 \n", "3 171.0 383.0 76.0 \n", "4 271.0 363.0 85.0 \n", "5 194.0 741.0 74.0 \n", "6 197.0 520.0 0.0 \n", "7 15.0 13.0 3.0 \n", "8 386.0 769.0 148.0 \n", "9 74.0 57.0 9.0 \n", "\n", " less_one_year_college male_45_64_grade_9_12 one_year_more_college \\\n", "0 261.0 24.0 683.0 \n", "1 716.0 111.0 935.0 \n", "2 106.0 7.0 207.0 \n", "3 500.0 201.0 1092.0 \n", "4 196.0 31.0 571.0 \n", "5 705.0 408.0 1354.0 \n", "6 360.0 347.0 1107.0 \n", "7 26.0 0.0 34.0 \n", "8 828.0 237.0 1843.0 \n", "9 90.0 14.0 297.0 \n", "\n", " rent_10_to_15_percent rent_15_to_20_percent rent_20_to_25_percent \\\n", "0 94.0 22.0 52.0 \n", "1 180.0 193.0 164.0 \n", "2 27.0 16.0 18.0 \n", "3 64.0 100.0 59.0 \n", "4 87.0 63.0 58.0 \n", "5 263.0 285.0 371.0 \n", "6 109.0 22.0 83.0 \n", "7 13.0 3.0 1.0 \n", "8 149.0 96.0 162.0 \n", "9 32.0 46.0 8.0 \n", "\n", " rent_25_to_30_percent rent_30_to_35_percent rent_35_to_40_percent \\\n", "0 15.0 45.0 5.0 \n", "1 144.0 51.0 77.0 \n", "2 13.0 19.0 8.0 \n", "3 44.0 46.0 25.0 \n", "4 79.0 2.0 37.0 \n", "5 367.0 188.0 167.0 \n", "6 98.0 107.0 49.0 \n", "7 3.0 2.0 5.0 \n", "8 91.0 35.0 109.0 \n", "9 34.0 26.0 15.0 \n", "\n", " rent_40_to_50_percent rent_under_10_percent sales_office_employed \\\n", "0 32.0 6.0 404.0 \n", "1 168.0 75.0 1081.0 \n", "2 0.0 10.0 185.0 \n", "3 24.0 19.0 809.0 \n", "4 104.0 64.0 484.0 \n", "5 267.0 155.0 1164.0 \n", "6 35.0 71.0 894.0 \n", "7 0.0 0.0 20.0 \n", "8 75.0 51.0 1347.0 \n", "9 13.0 22.0 156.0 \n", "\n", " speak_spanish_at_home two_or_more_races_pop dwellings_3_to_4_units \\\n", "0 None 83.0 41.0 \n", "1 None 188.0 291.0 \n", "2 None 3.0 33.0 \n", "3 None 143.0 110.0 \n", "4 None 233.0 108.0 \n", "5 None 316.0 513.0 \n", "6 None 107.0 53.0 \n", "7 None 9.0 0.0 \n", "8 None 151.0 163.0 \n", "9 None 0.0 6.0 \n", "\n", " dwellings_5_to_9_units employed_manufacturing male_45_64_high_school \\\n", "0 49.0 215.0 287.0 \n", "1 101.0 870.0 680.0 \n", "2 0.0 6.0 82.0 \n", "3 16.0 214.0 458.0 \n", "4 70.0 41.0 387.0 \n", "5 49.0 1328.0 866.0 \n", "6 105.0 45.0 736.0 \n", "7 0.0 13.0 21.0 \n", "8 0.0 787.0 918.0 \n", "9 6.0 22.0 111.0 \n", "\n", " occupied_housing_units male_45_64_some_college mortgaged_housing_units \\\n", "0 2405.0 247.0 1122.0 \n", "1 4614.0 264.0 1552.0 \n", "2 828.0 61.0 306.0 \n", "3 3143.0 203.0 889.0 \n", "4 2424.0 140.0 663.0 \n", "5 6875.0 337.0 1652.0 \n", "6 4316.0 218.0 958.0 \n", "7 177.0 7.0 25.0 \n", "8 6727.0 540.0 2932.0 \n", "9 805.0 85.0 257.0 \n", "\n", " occupation_sales_office population_3_years_over asian_including_hispanic \\\n", "0 404.0 5553.0 0.0 \n", "1 1081.0 9869.0 0.0 \n", "2 185.0 1623.0 0.0 \n", "3 809.0 7863.0 0.0 \n", "4 484.0 5334.0 0.0 \n", "5 1164.0 16514.0 0.0 \n", "6 894.0 11860.0 0.0 \n", "7 20.0 415.0 0.0 \n", "8 1347.0 16615.0 0.0 \n", "9 156.0 1870.0 0.0 \n", "\n", " black_including_hispanic dwellings_10_to_19_units \\\n", "0 4.0 41.0 \n", "1 0.0 49.0 \n", "2 1.0 0.0 \n", "3 161.0 6.0 \n", "4 0.0 38.0 \n", "5 4717.0 90.0 \n", "6 606.0 28.0 \n", "7 0.0 0.0 \n", "8 4762.0 52.0 \n", "9 0.0 0.0 \n", "\n", " dwellings_20_to_49_units employed_wholesale_trade \\\n", "0 0.0 42.0 \n", "1 40.0 120.0 \n", "2 0.0 13.0 \n", "3 18.0 39.0 \n", "4 52.0 75.0 \n", "5 16.0 110.0 \n", "6 18.0 88.0 \n", "7 0.0 3.0 \n", "8 12.0 221.0 \n", "9 21.0 20.0 \n", "\n", " female_female_households rent_burden_not_computed \\\n", "0 0.0 67.0 \n", "1 3.0 185.0 \n", "2 0.0 52.0 \n", "3 0.0 137.0 \n", "4 0.0 125.0 \n", "5 5.0 540.0 \n", "6 0.0 361.0 \n", "7 0.0 30.0 \n", "8 0.0 243.0 \n", "9 0.0 68.0 \n", "\n", " white_including_hispanic high_school_including_ged \\\n", "0 5533.0 1564.0 \n", "1 9756.0 2785.0 \n", "2 1649.0 361.0 \n", "3 6969.0 1862.0 \n", "4 4500.0 1511.0 \n", "5 12113.0 4279.0 \n", "6 11397.0 3313.0 \n", "7 411.0 79.0 \n", "8 12144.0 4105.0 \n", "9 1783.0 493.0 \n", "\n", " commuters_by_car_truck_van dwellings_1_units_attached \\\n", "0 2119.0 0.0 \n", "1 4266.0 77.0 \n", "2 774.0 22.0 \n", "3 3281.0 23.0 \n", "4 2410.0 16.0 \n", "5 5853.0 54.0 \n", "6 2752.0 37.0 \n", "7 133.0 10.0 \n", "8 6413.0 41.0 \n", "9 612.0 7.0 \n", "\n", " dwellings_1_units_detached dwellings_50_or_more_units \\\n", "0 2208.0 0.0 \n", "1 4318.0 0.0 \n", "2 890.0 0.0 \n", "3 3498.0 0.0 \n", "4 2281.0 0.0 \n", "5 6098.0 91.0 \n", "6 2976.0 54.0 \n", "7 229.0 0.0 \n", "8 6183.0 2.0 \n", "9 863.0 6.0 \n", "\n", " housing_built_2000_to_2004 male_45_64_graduate_degree \\\n", "0 61.0 87.0 \n", "1 27.0 32.0 \n", "2 15.0 3.0 \n", "3 78.0 17.0 \n", "4 43.0 32.0 \n", "5 206.0 25.0 \n", "6 143.0 41.0 \n", "7 3.0 1.0 \n", "8 218.0 113.0 \n", "9 12.0 15.0 \n", "\n", " occupation_management_arts population_1_year_and_over \\\n", "0 955.0 5634.0 \n", "1 1470.0 10138.0 \n", "2 264.0 1659.0 \n", "3 850.0 8140.0 \n", "4 1217.0 5425.0 \n", "5 1560.0 17033.0 \n", "6 875.0 12072.0 \n", "7 83.0 421.0 \n", "8 2279.0 16964.0 \n", "9 233.0 1903.0 \n", "\n", " speak_only_english_at_home housing_built_2005_or_later \\\n", "0 None 16.0 \n", "1 None 5.0 \n", "2 None 0.0 \n", "3 None 39.0 \n", "4 None 19.0 \n", "5 None 20.0 \n", "6 None 55.0 \n", "7 None 0.0 \n", "8 None 102.0 \n", "9 None 15.0 \n", "\n", " male_45_64_bachelors_degree median_year_structure_built \\\n", "0 158.0 1987.0 \n", "1 178.0 1952.0 \n", "2 48.0 1957.0 \n", "3 93.0 1969.0 \n", "4 152.0 1966.0 \n", "5 211.0 1968.0 \n", "6 19.0 1985.0 \n", "7 6.0 1959.0 \n", "8 153.0 1983.0 \n", "9 40.0 1957.0 \n", "\n", " children_in_single_female_hh families_with_young_children \\\n", "0 147.0 335.0 \n", "1 756.0 662.0 \n", "2 43.0 137.0 \n", "3 396.0 589.0 \n", "4 220.0 338.0 \n", "5 2120.0 1330.0 \n", "6 473.0 787.0 \n", "7 3.0 27.0 \n", "8 761.0 1033.0 \n", "9 109.0 128.0 \n", "\n", " graduate_professional_degree households_retirement_income \\\n", "0 337.0 625.0 \n", "1 281.0 807.0 \n", "2 72.0 107.0 \n", "3 261.0 679.0 \n", "4 210.0 261.0 \n", "5 532.0 1138.0 \n", "6 327.0 987.0 \n", "7 19.0 23.0 \n", "8 987.0 1480.0 \n", "9 64.0 169.0 \n", "\n", " male_45_64_associates_degree male_45_64_less_than_9_grade \\\n", "0 92.0 13.0 \n", "1 151.0 33.0 \n", "2 22.0 3.0 \n", "3 7.0 75.0 \n", "4 83.0 56.0 \n", "5 58.0 251.0 \n", "6 117.0 153.0 \n", "7 15.0 0.0 \n", "8 175.0 96.0 \n", "9 5.0 13.0 \n", "\n", " million_dollar_housing_units owner_occupied_housing_units \\\n", "0 26.0 2020.0 \n", "1 0.0 3131.0 \n", "2 0.0 649.0 \n", "3 35.0 2398.0 \n", "4 16.0 1677.0 \n", "5 0.0 3631.0 \n", "6 0.0 3114.0 \n", "7 0.0 112.0 \n", "8 0.0 5531.0 \n", "9 0.0 515.0 \n", "\n", " percent_income_spent_on_rent aggregate_travel_time_to_work \\\n", "0 23.6 60655.0 \n", "1 26.3 86680.0 \n", "2 22.9 NaN \n", "3 32.0 53750.0 \n", "4 27.5 40040.0 \n", "5 28.8 105750.0 \n", "6 31.8 98955.0 \n", "7 25.8 3040.0 \n", "8 26.0 171195.0 \n", "9 25.4 11965.0 \n", "\n", " amerindian_including_hispanic housing_built_1939_or_earlier \\\n", "0 84.0 97.0 \n", "1 64.0 181.0 \n", "2 39.0 95.0 \n", "3 1.0 551.0 \n", "4 747.0 255.0 \n", "5 18.0 853.0 \n", "6 9.0 141.0 \n", "7 0.0 31.0 \n", "8 5.0 343.0 \n", "9 99.0 73.0 \n", "\n", " housing_units_renter_occupied pop_determined_poverty_status \\\n", "0 385.0 5711.0 \n", "1 1483.0 10014.0 \n", "2 179.0 1690.0 \n", "3 745.0 7955.0 \n", "4 747.0 5294.0 \n", "5 3244.0 17042.0 \n", "6 1202.0 10690.0 \n", "7 65.0 421.0 \n", "8 1196.0 16806.0 \n", "9 290.0 1906.0 \n", "\n", " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", "0 11.0 34.0 \n", "1 142.0 67.0 \n", "2 28.0 0.0 \n", "3 31.0 57.0 \n", "4 95.0 39.0 \n", "5 313.0 89.0 \n", "6 63.0 38.0 \n", "7 2.0 0.0 \n", "8 222.0 164.0 \n", "9 22.0 38.0 \n", "\n", " employed_public_administration less_than_high_school_graduate \\\n", "0 291.0 231.0 \n", "1 122.0 633.0 \n", "2 28.0 76.0 \n", "3 192.0 1229.0 \n", "4 118.0 408.0 \n", "5 313.0 2867.0 \n", "6 164.0 2339.0 \n", "7 3.0 13.0 \n", "8 196.0 2348.0 \n", "9 62.0 152.0 \n", "\n", " commuters_by_subway_or_elevated bachelors_degree_or_higher_25_64 \\\n", "0 0.0 873.0 \n", "1 0.0 856.0 \n", "2 0.0 211.0 \n", "3 0.0 556.0 \n", "4 0.0 580.0 \n", "5 0.0 1066.0 \n", "6 0.0 553.0 \n", "7 0.0 59.0 \n", "8 0.0 1485.0 \n", "9 0.0 194.0 \n", "\n", " employed_education_health_social speak_spanish_at_home_low_english \\\n", "0 380.0 None \n", "1 1172.0 None \n", "2 196.0 None \n", "3 647.0 None \n", "4 674.0 None \n", "5 1780.0 None \n", "6 775.0 None \n", "7 37.0 None \n", "8 1673.0 None \n", "9 121.0 None \n", "\n", " commuters_by_public_transportation different_house_year_ago_same_city \\\n", "0 0.0 32.0 \n", "1 21.0 550.0 \n", "2 0.0 61.0 \n", "3 2.0 634.0 \n", "4 37.0 182.0 \n", "5 23.0 1577.0 \n", "6 0.0 74.0 \n", "7 0.0 1.0 \n", "8 0.0 378.0 \n", "9 0.0 108.0 \n", "\n", " some_college_and_associates_degree households_public_asst_or_food_stamps \\\n", "0 1212.0 179.0 \n", "1 2577.0 831.0 \n", "2 448.0 61.0 \n", "3 1809.0 600.0 \n", "4 1086.0 340.0 \n", "5 2719.0 2237.0 \n", "6 2195.0 1218.0 \n", "7 93.0 6.0 \n", "8 3596.0 1129.0 \n", "9 464.0 99.0 \n", "\n", " management_business_sci_arts_employed \\\n", "0 955.0 \n", "1 1470.0 \n", "2 264.0 \n", "3 850.0 \n", "4 1217.0 \n", "5 1560.0 \n", "6 875.0 \n", "7 83.0 \n", "8 2279.0 \n", "9 233.0 \n", "\n", " employed_finance_insurance_real_estate \\\n", "0 141.0 \n", "1 170.0 \n", "2 30.0 \n", "3 133.0 \n", "4 184.0 \n", "5 282.0 \n", "6 162.0 \n", "7 1.0 \n", "8 279.0 \n", "9 11.0 \n", "\n", " different_house_year_ago_different_city \\\n", "0 471.0 \n", "1 839.0 \n", "2 84.0 \n", "3 829.0 \n", "4 444.0 \n", "5 1594.0 \n", "6 1449.0 \n", "7 54.0 \n", "8 983.0 \n", "9 114.0 \n", "\n", " employed_science_management_admin_waste \\\n", "0 141.0 \n", "1 173.0 \n", "2 34.0 \n", "3 231.0 \n", "4 92.0 \n", "5 164.0 \n", "6 85.0 \n", "7 3.0 \n", "8 495.0 \n", "9 89.0 \n", "\n", " one_parent_families_with_young_children \\\n", "0 64.0 \n", "1 327.0 \n", "2 20.0 \n", "3 206.0 \n", "4 85.0 \n", "5 813.0 \n", "6 184.0 \n", "7 2.0 \n", "8 394.0 \n", "9 49.0 \n", "\n", " two_parent_families_with_young_children \\\n", "0 271.0 \n", "1 335.0 \n", "2 117.0 \n", "3 383.0 \n", "4 253.0 \n", "5 517.0 \n", "6 603.0 \n", "7 25.0 \n", "8 639.0 \n", "9 79.0 \n", "\n", " employed_other_services_not_public_admin \\\n", "0 94.0 \n", "1 301.0 \n", "2 51.0 \n", "3 363.0 \n", "4 139.0 \n", "5 304.0 \n", "6 183.0 \n", "7 6.0 \n", "8 231.0 \n", "9 17.0 \n", "\n", " owner_occupied_housing_units_median_value \\\n", "0 195000.0 \n", "1 81800.0 \n", "2 76400.0 \n", "3 82000.0 \n", "4 85800.0 \n", "5 73300.0 \n", "6 65500.0 \n", "7 101500.0 \n", "8 112600.0 \n", "9 110900.0 \n", "\n", " employed_transportation_warehousing_utilities \\\n", "0 90.0 \n", "1 245.0 \n", "2 55.0 \n", "3 401.0 \n", "4 52.0 \n", "5 299.0 \n", "6 165.0 \n", "7 9.0 \n", "8 788.0 \n", "9 96.0 \n", "\n", " occupation_production_transportation_material \\\n", "0 310.0 \n", "1 949.0 \n", "2 73.0 \n", "3 510.0 \n", "4 306.0 \n", "5 1565.0 \n", "6 319.0 \n", "7 21.0 \n", "8 1238.0 \n", "9 126.0 \n", "\n", " father_one_parent_families_with_young_children \\\n", "0 0.0 \n", "1 29.0 \n", "2 13.0 \n", "3 165.0 \n", "4 26.0 \n", "5 89.0 \n", "6 24.0 \n", "7 2.0 \n", "8 89.0 \n", "9 21.0 \n", "\n", " owner_occupied_housing_units_lower_value_quartile \\\n", "0 119800.0 \n", "1 49500.0 \n", "2 44300.0 \n", "3 41600.0 \n", "4 51600.0 \n", "5 37000.0 \n", "6 30000.0 \n", "7 57000.0 \n", "8 62700.0 \n", "9 83300.0 \n", "\n", " owner_occupied_housing_units_upper_value_quartile \\\n", "0 301300.0 \n", "1 126300.0 \n", "2 124400.0 \n", "3 165300.0 \n", "4 156600.0 \n", "5 120900.0 \n", "6 117600.0 \n", "7 162500.0 \n", "8 189100.0 \n", "9 162900.0 \n", "\n", " employed_agriculture_forestry_fishing_hunting_mining \\\n", "0 460.0 \n", "1 349.0 \n", "2 253.0 \n", "3 602.0 \n", "4 706.0 \n", "5 405.0 \n", "6 392.0 \n", "7 67.0 \n", "8 353.0 \n", "9 96.0 \n", "\n", " occupation_natural_resources_construction_maintenance \\\n", "0 491.0 \n", "1 514.0 \n", "2 198.0 \n", "3 719.0 \n", "4 459.0 \n", "5 596.0 \n", "6 516.0 \n", "7 36.0 \n", "8 766.0 \n", "9 123.0 \n", "\n", " two_parents_in_labor_force_families_with_young_children \\\n", "0 90.0 \n", "1 248.0 \n", "2 65.0 \n", "3 214.0 \n", "4 215.0 \n", "5 216.0 \n", "6 275.0 \n", "7 13.0 \n", "8 386.0 \n", "9 10.0 \n", "\n", " employed_arts_entertainment_recreation_accommodation_food \\\n", "0 186.0 \n", "1 213.0 \n", "2 24.0 \n", "3 265.0 \n", "4 114.0 \n", "5 380.0 \n", "6 226.0 \n", "7 6.0 \n", "8 697.0 \n", "9 72.0 \n", "\n", " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", "0 640.0 \n", "1 664.0 \n", "2 551.0 \n", "3 721.0 \n", "4 562.0 \n", "5 568.0 \n", "6 483.0 \n", "7 658.0 \n", "8 603.0 \n", "9 814.0 \n", "\n", " two_parents_not_in_labor_force_families_with_young_children \\\n", "0 18.0 \n", "1 12.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 2.0 \n", "6 78.0 \n", "7 0.0 \n", "8 8.0 \n", "9 0.0 \n", "\n", " father_in_labor_force_one_parent_families_with_young_children \\\n", "0 0.0 \n", "1 25.0 \n", "2 13.0 \n", "3 165.0 \n", "4 14.0 \n", "5 69.0 \n", "6 24.0 \n", "7 2.0 \n", "8 80.0 \n", "9 21.0 \n", "\n", " two_parents_father_in_labor_force_families_with_young_children \\\n", "0 163.0 \n", "1 64.0 \n", "2 48.0 \n", "3 169.0 \n", "4 32.0 \n", "5 299.0 \n", "6 211.0 \n", "7 12.0 \n", "8 245.0 \n", "9 67.0 \n", "\n", " two_parents_mother_in_labor_force_families_with_young_children \n", "0 0.0 \n", "1 11.0 \n", "2 4.0 \n", "3 0.0 \n", "4 6.0 \n", "5 0.0 \n", "6 39.0 \n", "7 0.0 \n", "8 0.0 \n", "9 2.0 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": 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slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
0geoid_9d24d904geoidUS Census Block Groups GeoidsSTRINGNonegeoidNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
1do_date_69c292c1do_dateFirst day of the year the survey was issuedDATENonedo_dateNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
2total_pop_9ba05abfTotal PopulationTotal Population. The total number of all peop...FLOATSUMtotal_popNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
3households_51ae36c8Number of householdsHouseholds. A count of the number of household...FLOATSUMhouseholdsNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
4male_pop_73e15d04male_popMale Population. The number of people within e...FLOATSUMmale_popcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
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" ], "text/plain": [ " slug name \\\n", "0 geoid_9d24d904 geoid \n", "1 do_date_69c292c1 do_date \n", "2 total_pop_9ba05abf Total Population \n", "3 households_51ae36c8 Number of households \n", "4 male_pop_73e15d04 male_pop \n", "\n", " description db_type agg_method \\\n", "0 US Census Block Groups Geoids STRING None \n", "1 First day of the year the survey was issued DATE None \n", "2 Total Population. The total number of all peop... FLOAT SUM \n", "3 Households. A count of the number of household... FLOAT SUM \n", "4 Male Population. The number of people within e... FLOAT SUM \n", "\n", " column_name variable_group_id \\\n", "0 geoid None \n", "1 do_date None \n", "2 total_pop None \n", "3 households None \n", "4 male_pop carto-do-public-data.usa_acs.demographics_soci... \n", "\n", " dataset_id \\\n", "0 carto-do-public-data.usa_acs.demographics_soci... \n", "1 carto-do-public-data.usa_acs.demographics_soci... \n", "2 carto-do-public-data.usa_acs.demographics_soci... \n", "3 carto-do-public-data.usa_acs.demographics_soci... \n", "4 carto-do-public-data.usa_acs.demographics_soci... \n", "\n", " id \n", "0 carto-do-public-data.usa_acs.demographics_soci... \n", "1 carto-do-public-data.usa_acs.demographics_soci... \n", "2 carto-do-public-data.usa_acs.demographics_soci... \n", "3 carto-do-public-data.usa_acs.demographics_soci... \n", "4 carto-do-public-data.usa_acs.demographics_soci... " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.variables.to_dataframe().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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`.\n", "\n", "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." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "Jce9Hfys0td6" }, "outputs": [], "source": [ "county_pop_df = dataset.to_dataframe(sql_query=\"select total_pop, geom from $dataset$\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 197 }, "id": "Zu1A2UET2fY7", "outputId": "ce64b55c-1b7b-4ecc-cac6-6d1acd34683a" }, "outputs": [ { "data": { "text/html": [ "
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total_popgeom
091518.0POLYGON ((-97.03319 29.06433, -97.25027 28.905...
116275.0POLYGON ((-116.15359 46.34831, -116.12716 46.2...
27113.0POLYGON ((-79.45176 37.76645, -79.42082 37.789...
313843.0POLYGON ((-77.18885 38.89627, -77.18972 38.878...
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" ], "text/plain": [ " total_pop geom\n", "0 91518.0 POLYGON ((-97.03319 29.06433, -97.25027 28.905...\n", "1 16275.0 POLYGON ((-116.15359 46.34831, -116.12716 46.2...\n", "2 7113.0 POLYGON ((-79.45176 37.76645, -79.42082 37.789...\n", "3 13843.0 POLYGON ((-77.18885 38.89627, -77.18972 38.878...\n", "4 24741.0 POLYGON ((-92.55449 42.64231, -92.31808 42.642..." ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "county_pop_df.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "Xvmj4nXV_p-w" }, "source": [ "##### Visualize dataset\n", "\n", "We can now visualize the data." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 674 }, "id": "LXld0r-G2mS7", "outputId": "276dce9d-65af-4de1-f0b8-ec4ca407fc91" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " None\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " Static map image\n", " \n", " \n", "
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    \n", "\n", "\n", "\n", "\n", "\n", "\">\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Map(Layer(county_pop_df, \n", " geom_col='geom',\n", " style=color_bins_style('total_pop'),\n", " legends=color_bins_legend('Population'),\n", " encode_data=False),\n", " viewport={'zoom': 3.56, 'lat': 38.521492, 'lng': -99.224130})" ] }, { "cell_type": "markdown", "metadata": { "id": "Vwildiv-tKf_" }, "source": [ "\n", "#### Download a dataset applying spatial filtering. Filter by bounding box or polygon\n", "\n", "We will now work with the ACS census tract demographics dataset identified previously `acs_sociodemogr_496a0675`.\n", "\n", "In this case, we are interested in all variables, but we don't want the data for the whole US. We can filter a dataset by bounding box or polygon very easily.\n", "\n", "You can use the following link to calculate the bounding box you want to use as filter:\n", " http://bboxfinder.com" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "LyMTJKjp_L-O" }, "outputs": [], "source": [ "dataset = Dataset.get('acs_sociodemogr_496a0675')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 367 }, "id": "dTCHAaQpAEe8", "outputId": "0f1afacd-e4ad-472b-be7d-0f7ad4c5df25" }, "outputs": [ { "data": { "text/html": [ "
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    \n", "
    " ], "text/plain": [ " geoid no_car do_date male_20 male_21 no_cars one_car poverty \\\n", "0 47157005000 76.0 20132017 0.0 0.0 151.0 122.0 552.0 \n", "1 72051990021 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "2 72071990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "3 01097990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "4 18127980001 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "5 47157980100 0.0 20132017 0.0 6.0 0.0 0.0 58.0 \n", "6 55071990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "7 25025990101 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "8 55025991703 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "9 26041990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", "\n", " children male_pop two_cars asian_pop black_pop female_20 female_21 \\\n", "0 316.0 415.0 64.0 0.0 1004.0 19.0 42.0 \n", "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "5 0.0 38.0 0.0 0.0 4.0 0.0 0.0 \n", "6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "7 0.0 58.0 0.0 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", " in_school pop_25_64 total_pop white_pop female_pop gini_index \\\n", "0 331.0 328.0 1042.0 16.0 627.0 0.4372 \n", "1 0.0 0.0 0.0 0.0 0.0 NaN \n", "2 0.0 0.0 0.0 0.0 0.0 NaN \n", "3 0.0 0.0 0.0 0.0 0.0 NaN \n", "4 0.0 0.0 0.0 0.0 0.0 NaN \n", "5 0.0 54.0 74.0 70.0 36.0 NaN \n", "6 0.0 0.0 0.0 0.0 0.0 NaN \n", "7 29.0 23.0 71.0 0.0 13.0 NaN \n", "8 0.0 0.0 0.0 0.0 0.0 NaN \n", "9 0.0 0.0 0.0 0.0 0.0 NaN \n", "\n", " households male_60_61 male_62_64 median_age three_cars male_5_to_9 \\\n", "0 343.0 0.0 2.0 28.9 0.0 38.0 \n", "1 0.0 0.0 0.0 NaN 0.0 0.0 \n", "2 0.0 0.0 0.0 NaN 0.0 0.0 \n", "3 0.0 0.0 0.0 NaN 0.0 0.0 \n", "4 0.0 0.0 0.0 NaN 0.0 0.0 \n", "5 0.0 0.0 0.0 31.3 0.0 0.0 \n", "6 0.0 0.0 0.0 NaN 0.0 0.0 \n", "7 13.0 0.0 0.0 24.0 13.0 0.0 \n", "8 0.0 0.0 0.0 NaN 0.0 0.0 \n", "9 0.0 0.0 0.0 NaN 0.0 0.0 \n", "\n", " median_rent pop_16_over pop_widowed armed_forces employed_pop \\\n", "0 391.0 749.0 None 0.0 233.0 \n", "1 NaN 0.0 None 0.0 0.0 \n", "2 NaN 0.0 None 0.0 0.0 \n", "3 NaN 0.0 None 0.0 0.0 \n", "4 NaN 0.0 None 0.0 0.0 \n", "5 NaN 74.0 None 0.0 0.0 \n", "6 NaN 0.0 None 0.0 0.0 \n", "7 NaN 71.0 None 0.0 71.0 \n", "8 NaN 0.0 None 0.0 0.0 \n", "9 NaN 0.0 None 0.0 0.0 \n", "\n", " hispanic_pop male_under_5 mobile_homes pop_divorced female_5_to_9 \\\n", "0 0.0 44.0 5.0 None 76.0 \n", "1 0.0 0.0 0.0 None 0.0 \n", "2 0.0 0.0 0.0 None 0.0 \n", "3 0.0 0.0 0.0 None 0.0 \n", "4 0.0 0.0 0.0 None 0.0 \n", "5 0.0 0.0 0.0 None 0.0 \n", "6 0.0 0.0 0.0 None 0.0 \n", "7 71.0 0.0 0.0 None 0.0 \n", "8 0.0 0.0 0.0 None 0.0 \n", "9 0.0 0.0 0.0 None 0.0 \n", "\n", " housing_units male_10_to_14 male_15_to_17 male_18_to_19 male_22_to_24 \\\n", "0 465.0 27.0 26.0 30.0 22.0 \n", "1 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 6.0 0.0 \n", "6 0.0 0.0 0.0 0.0 0.0 \n", "7 13.0 0.0 0.0 16.0 19.0 \n", "8 0.0 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 0.0 \n", "\n", " male_25_to_29 male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 \\\n", "0 11.0 13.0 31.0 11.0 15.0 \n", "1 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 0.0 \n", "5 0.0 6.0 12.0 4.0 0.0 \n", "6 0.0 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 0.0 \n", "\n", " male_45_to_64 male_50_to_54 male_55_to_59 male_65_to_66 male_67_to_69 \\\n", "0 79.0 21.0 41.0 0.0 5.0 \n", "1 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 0.0 0.0 \n", "7 23.0 0.0 23.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 0.0 \n", "\n", " male_70_to_74 male_75_to_79 male_80_to_84 median_income pop_separated \\\n", "0 26.0 5.0 31.0 14410.0 None \n", "1 0.0 0.0 0.0 NaN None \n", "2 0.0 0.0 0.0 NaN None \n", "3 0.0 0.0 0.0 NaN None \n", "4 0.0 0.0 0.0 NaN None \n", "5 0.0 4.0 0.0 NaN None \n", "6 0.0 0.0 0.0 NaN None \n", "7 0.0 0.0 0.0 NaN None \n", "8 0.0 0.0 0.0 NaN None \n", "9 0.0 0.0 0.0 NaN None \n", "\n", " amerindian_pop female_under_5 four_more_cars group_quarters \\\n", "0 0.0 24.0 6.0 45.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 74.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " masters_degree other_race_pop unemployed_pop walked_to_work \\\n", "0 13.0 0.0 71.0 7.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " worked_at_home female_10_to_14 female_15_to_17 female_18_to_19 \\\n", "0 0.0 47.0 34.0 17.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " female_22_to_24 female_25_to_29 female_30_to_34 female_35_to_39 \\\n", "0 32.0 34.0 13.0 38.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 4.0 18.0 6.0 0.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 13.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " female_40_to_44 female_45_to_49 female_50_to_54 female_55_to_59 \\\n", "0 19.0 25.0 26.0 22.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 5.0 3.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " female_60_to_61 female_62_to_64 female_65_to_66 female_67_to_69 \\\n", "0 0.0 6.0 22.0 38.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " female_70_to_74 female_75_to_79 female_80_to_84 pop_15_and_over \\\n", "0 36.0 13.0 6.0 None \n", "1 0.0 0.0 0.0 None \n", "2 0.0 0.0 0.0 None \n", "3 0.0 0.0 0.0 None \n", "4 0.0 0.0 0.0 None \n", "5 0.0 0.0 0.0 None \n", "6 0.0 0.0 0.0 None \n", "7 0.0 0.0 0.0 None \n", "8 0.0 0.0 0.0 None \n", "9 0.0 0.0 0.0 None \n", "\n", " pop_now_married asian_male_45_54 asian_male_55_64 bachelors_degree \\\n", "0 None 0.0 0.0 0.0 \n", "1 None 0.0 0.0 0.0 \n", "2 None 0.0 0.0 0.0 \n", "3 None 0.0 0.0 0.0 \n", "4 None 0.0 0.0 0.0 \n", "5 None 0.0 0.0 0.0 \n", "6 None 0.0 0.0 0.0 \n", "7 None 0.0 0.0 0.0 \n", "8 None 0.0 0.0 0.0 \n", "9 None 0.0 0.0 0.0 \n", "\n", " black_male_45_54 black_male_55_64 commute_5_9_mins commuters_by_bus \\\n", "0 23.0 35.0 7.0 52.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 71.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " in_grades_1_to_4 in_grades_5_to_8 male_85_and_over not_hispanic_pop \\\n", "0 90.0 41.0 16.0 1042.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 74.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " pop_5_years_over white_male_45_54 white_male_55_64 associates_degree \\\n", "0 None 13.0 3.0 28.0 \n", "1 None 0.0 0.0 0.0 \n", "2 None 0.0 0.0 0.0 \n", "3 None 0.0 0.0 0.0 \n", "4 None 0.0 0.0 0.0 \n", "5 None 0.0 0.0 0.0 \n", "6 None 0.0 0.0 0.0 \n", "7 None 0.0 0.0 0.0 \n", "8 None 0.0 0.0 0.0 \n", "9 None 0.0 0.0 0.0 \n", "\n", " commuters_16_over dwellings_2_units family_households hispanic_any_race \\\n", "0 233.0 13.0 183.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 0.0 \n", "7 71.0 0.0 13.0 71.0 \n", "8 0.0 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 0.0 \n", "\n", " in_grades_9_to_12 income_less_10000 income_per_capita pop_25_years_over \\\n", "0 111.0 79.0 8747.0 564.0 \n", "1 0.0 0.0 NaN 0.0 \n", "2 0.0 0.0 NaN 0.0 \n", "3 0.0 0.0 NaN 0.0 \n", "4 0.0 0.0 NaN 0.0 \n", "5 0.0 0.0 5896.0 58.0 \n", "6 0.0 0.0 NaN 0.0 \n", "7 0.0 0.0 17785.0 23.0 \n", "8 0.0 0.0 NaN 0.0 \n", "9 0.0 0.0 NaN 0.0 \n", "\n", " pop_never_married bachelors_degree_2 commute_10_14_mins \\\n", "0 None 0.0 4.0 \n", "1 None NaN 0.0 \n", "2 None NaN 0.0 \n", "3 None 0.0 0.0 \n", "4 None 0.0 0.0 \n", "5 None 0.0 0.0 \n", "6 None 0.0 0.0 \n", "7 None 0.0 0.0 \n", "8 None 0.0 0.0 \n", "9 None 0.0 0.0 \n", "\n", " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", "0 59.0 108.0 10.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " commute_30_34_mins commute_35_39_mins commute_35_44_mins \\\n", "0 20.0 0.0 7.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 36.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " commute_40_44_mins commute_45_59_mins commute_60_89_mins \\\n", "0 7.0 13.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 35.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " female_85_and_over income_10000_14999 income_15000_19999 \\\n", "0 38.0 105.0 44.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", "0 38.0 13.0 18.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", "0 11.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", "0 7.0 10.0 18.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 13.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " married_households not_in_labor_force not_us_citizen_pop \\\n", "0 26.0 445.0 0.0 \n", "1 0.0 0.0 NaN \n", "2 0.0 0.0 NaN \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 74.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 71.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " pop_in_labor_force high_school_diploma hispanic_male_45_54 \\\n", "0 304.0 188.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 6.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 71.0 23.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " hispanic_male_55_64 occupation_services workers_16_and_over \\\n", "0 0.0 58.0 233.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 23.0 13.0 71.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " civilian_labor_force commute_60_more_mins commute_90_more_mins \\\n", "0 304.0 5.0 5.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 71.0 35.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " commute_less_10_mins commuters_by_carpool employed_information \\\n", "0 7.0 21.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " in_undergrad_college income_100000_124999 income_125000_149999 \\\n", "0 63.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 29.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " income_150000_199999 male_male_households nonfamily_households \\\n", "0 0.0 0.0 160.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " rent_over_50_percent vacant_housing_units commuters_drove_alone \\\n", "0 38.0 122.0 141.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " employed_construction employed_retail_trade income_200000_or_more \\\n", "0 24.0 19.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " less_one_year_college male_45_64_grade_9_12 one_year_more_college \\\n", "0 22.0 18.0 85.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 18.0 0.0 4.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " rent_10_to_15_percent rent_15_to_20_percent rent_20_to_25_percent \\\n", "0 11.0 42.0 39.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 13.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " rent_25_to_30_percent rent_30_to_35_percent rent_35_to_40_percent \\\n", "0 33.0 47.0 9.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " rent_40_to_50_percent rent_under_10_percent sales_office_employed \\\n", "0 41.0 0.0 30.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " speak_spanish_at_home two_or_more_races_pop dwellings_3_to_4_units \\\n", "0 None 22.0 63.0 \n", "1 None 0.0 0.0 \n", "2 None 0.0 0.0 \n", "3 None 0.0 0.0 \n", "4 None 0.0 0.0 \n", "5 None 0.0 0.0 \n", "6 None 0.0 0.0 \n", "7 None 0.0 13.0 \n", "8 None 0.0 0.0 \n", "9 None 0.0 0.0 \n", "\n", " dwellings_5_to_9_units employed_manufacturing male_45_64_high_school \\\n", "0 69.0 16.0 41.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 23.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " occupied_housing_units male_45_64_some_college mortgaged_housing_units \\\n", "0 343.0 10.0 20.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 13.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " occupation_sales_office population_3_years_over asian_including_hispanic \\\n", "0 30.0 999.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 74.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 71.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 \n", "\n", " black_including_hispanic dwellings_10_to_19_units \\\n", "0 1004.0 40.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 4.0 0.0 \n", "6 0.0 0.0 \n", "7 19.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " dwellings_20_to_49_units employed_wholesale_trade \\\n", "0 25.0 2.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " female_female_households rent_burden_not_computed \\\n", "0 0.0 18.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " white_including_hispanic high_school_including_ged \\\n", "0 16.0 235.0 \n", "1 0.0 NaN \n", "2 0.0 NaN \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 70.0 18.0 \n", "6 0.0 0.0 \n", "7 52.0 23.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " commuters_by_car_truck_van dwellings_1_units_attached \\\n", "0 162.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " dwellings_1_units_detached dwellings_50_or_more_units \\\n", "0 195.0 55.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " housing_built_2000_to_2004 male_45_64_graduate_degree \\\n", "0 10.0 1.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " occupation_management_arts population_1_year_and_over \\\n", "0 54.0 1019.0 \n", "1 0.0 NaN \n", "2 0.0 NaN \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 74.0 \n", "6 0.0 0.0 \n", "7 16.0 71.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " speak_only_english_at_home housing_built_2005_or_later \\\n", "0 None 0.0 \n", "1 None 0.0 \n", "2 None 0.0 \n", "3 None 0.0 \n", "4 None 0.0 \n", "5 None 0.0 \n", "6 None 0.0 \n", "7 None 0.0 \n", "8 None 0.0 \n", "9 None 0.0 \n", "\n", " male_45_64_bachelors_degree median_year_structure_built \\\n", "0 0.0 1981.0 \n", "1 0.0 NaN \n", "2 0.0 NaN \n", "3 0.0 NaN \n", "4 0.0 NaN \n", "5 0.0 NaN \n", "6 0.0 NaN \n", "7 0.0 NaN \n", "8 0.0 NaN \n", "9 0.0 NaN \n", "\n", " children_in_single_female_hh families_with_young_children \\\n", "0 278.0 75.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " graduate_professional_degree households_retirement_income \\\n", "0 13.0 47.0 \n", "1 NaN 0.0 \n", "2 NaN 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " male_45_64_associates_degree male_45_64_less_than_9_grade \\\n", "0 7.0 2.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " million_dollar_housing_units owner_occupied_housing_units \\\n", "0 0.0 65.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " percent_income_spent_on_rent aggregate_travel_time_to_work \\\n", "0 30.5 None \n", "1 NaN None \n", "2 NaN None \n", "3 NaN None \n", "4 NaN None \n", "5 NaN None \n", "6 NaN None \n", "7 NaN None \n", "8 NaN None \n", "9 NaN None \n", "\n", " amerindian_including_hispanic housing_built_1939_or_earlier \\\n", "0 0.0 81.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " housing_units_renter_occupied pop_determined_poverty_status \\\n", "0 278.0 1042.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 74.0 \n", "6 0.0 0.0 \n", "7 13.0 71.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", "0 14.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " employed_public_administration less_than_high_school_graduate \\\n", "0 4.0 181.0 \n", "1 0.0 NaN \n", "2 0.0 NaN \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 18.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " commuters_by_subway_or_elevated bachelors_degree_or_higher_25_64 \\\n", "0 0.0 1.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " employed_education_health_social speak_spanish_at_home_low_english \\\n", "0 57.0 None \n", "1 0.0 None \n", "2 0.0 None \n", "3 0.0 None \n", "4 0.0 None \n", "5 0.0 None \n", "6 0.0 None \n", "7 29.0 None \n", "8 0.0 None \n", "9 0.0 None \n", "\n", " commuters_by_public_transportation different_house_year_ago_same_city \\\n", "0 52.0 38.0 \n", "1 0.0 NaN \n", "2 0.0 NaN \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 0.0 0.0 \n", "6 0.0 0.0 \n", "7 71.0 0.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " some_college_and_associates_degree households_public_asst_or_food_stamps \\\n", "0 135.0 147.0 \n", "1 NaN 0.0 \n", "2 NaN 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "5 22.0 0.0 \n", "6 0.0 0.0 \n", "7 0.0 13.0 \n", "8 0.0 0.0 \n", "9 0.0 0.0 \n", "\n", " management_business_sci_arts_employed \\\n", "0 54.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 16.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " employed_finance_insurance_real_estate \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " different_house_year_ago_different_city \\\n", "0 15.0 \n", "1 NaN \n", "2 NaN \n", "3 0.0 \n", "4 0.0 \n", "5 63.0 \n", "6 0.0 \n", "7 19.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " employed_science_management_admin_waste \\\n", "0 33.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " one_parent_families_with_young_children \\\n", "0 75.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " two_parent_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " employed_other_services_not_public_admin \\\n", "0 14.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 23.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " owner_occupied_housing_units_median_value \\\n", "0 None \n", "1 None \n", "2 None \n", "3 None \n", "4 None \n", "5 None \n", "6 None \n", "7 None \n", "8 None \n", "9 None \n", "\n", " employed_transportation_warehousing_utilities \\\n", "0 43.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 19.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " occupation_production_transportation_material \\\n", "0 80.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 19.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " father_one_parent_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " owner_occupied_housing_units_lower_value_quartile \\\n", "0 26600.0 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", "\n", " owner_occupied_housing_units_upper_value_quartile \\\n", "0 231300.0 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", "\n", " employed_agriculture_forestry_fishing_hunting_mining \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " occupation_natural_resources_construction_maintenance \\\n", "0 11.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 23.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " two_parents_in_labor_force_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " employed_arts_entertainment_recreation_accommodation_food \\\n", "0 21.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", "0 507.0 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", "\n", " two_parents_not_in_labor_force_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " father_in_labor_force_one_parent_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " two_parents_father_in_labor_force_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 \n", "\n", " two_parents_mother_in_labor_force_families_with_young_children \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "8 0.0 \n", "9 0.0 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "_o3YCUYnAJQw" }, "source": [ "Just like we did in the previous section, we can use SQL queries to specify the bounding box or polygon we are interested in.\n", "\n", "- If you'd like to filter by bounding box, you need to use the SQL geography function `ST_IntersectsBox`.\n", "- If you'd like to filter by polygon, you need to use the SQL geography function `ST_Intersects`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 257 }, "id": "q0RKwncP3KUo", "outputId": "cb081331-0b6a-4ad9-9150-daa1c0edc37d" }, "outputs": [ { "data": { "text/html": [ "
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    \n", "
    " ], "text/plain": [ " geoid do_date total_pop households male_pop female_pop \\\n", "0 36047053500 2013-01-01 4053.0 1052.0 2072.0 1981.0 \n", "1 36061011300 2013-01-01 115.0 62.0 90.0 25.0 \n", "2 36081019900 2013-01-01 830.0 193.0 652.0 178.0 \n", "\n", " median_age male_under_5 male_5_to_9 male_10_to_14 male_15_to_17 \\\n", "0 20.2 554.0 258.0 120.0 79.0 \n", "1 30.8 0.0 0.0 0.0 0.0 \n", "2 36.2 5.0 0.0 26.0 0.0 \n", "\n", " male_18_to_19 male_20 male_21 male_22_to_24 male_25_to_29 \\\n", "0 61.0 0.0 51.0 192.0 185.0 \n", "1 0.0 0.0 0.0 22.0 15.0 \n", "2 16.0 13.0 18.0 22.0 96.0 \n", "\n", " male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 male_50_to_54 \\\n", "0 67.0 49.0 57.0 16.0 31.0 \n", "1 19.0 6.0 10.0 0.0 4.0 \n", "2 102.0 57.0 89.0 51.0 62.0 \n", "\n", " male_55_to_59 male_60_to_61 male_62_to_64 male_65_to_66 male_67_to_69 \\\n", "0 140.0 6.0 51.0 31.0 56.0 \n", "1 7.0 7.0 0.0 0.0 0.0 \n", "2 25.0 10.0 5.0 26.0 9.0 \n", "\n", " male_70_to_74 male_75_to_79 male_80_to_84 male_85_and_over \\\n", "0 0.0 59.0 0.0 9.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 11.0 5.0 4.0 \n", "\n", " female_under_5 female_5_to_9 female_10_to_14 female_15_to_17 \\\n", "0 466.0 308.0 86.0 70.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 13.0 31.0 8.0 \n", "\n", " female_18_to_19 female_20 female_21 female_22_to_24 female_25_to_29 \\\n", "0 16.0 39.0 33.0 226.0 140.0 \n", "1 0.0 0.0 0.0 0.0 8.0 \n", "2 0.0 0.0 3.0 16.0 27.0 \n", "\n", " female_30_to_34 female_35_to_39 female_40_to_44 female_45_to_49 \\\n", "0 88.0 20.0 60.0 27.0 \n", "1 8.0 5.0 4.0 0.0 \n", "2 5.0 20.0 0.0 47.0 \n", "\n", " female_50_to_54 female_55_to_59 female_60_to_61 female_62_to_64 \\\n", "0 44.0 139.0 13.0 51.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 4.0 0.0 \n", "\n", " female_65_to_66 female_67_to_69 female_70_to_74 female_75_to_79 \\\n", "0 53.0 36.0 0.0 52.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 4.0 0.0 0.0 \n", "\n", " female_80_to_84 female_85_and_over white_pop population_1_year_and_over \\\n", "0 14.0 0.0 3705.0 3854.0 \n", "1 0.0 0.0 21.0 115.0 \n", "2 0.0 0.0 140.0 830.0 \n", "\n", " population_3_years_over pop_5_years_over pop_15_and_over pop_16_over \\\n", "0 3407.0 NaN NaN 2241.0 \n", "1 115.0 NaN NaN 115.0 \n", "2 825.0 NaN NaN 755.0 \n", "\n", " pop_25_years_over pop_25_64 pop_never_married pop_now_married \\\n", "0 1494.0 1184.0 NaN NaN \n", "1 93.0 93.0 NaN NaN \n", "2 659.0 600.0 NaN NaN \n", "\n", " pop_separated pop_widowed pop_divorced not_us_citizen_pop black_pop \\\n", "0 NaN NaN NaN 136.0 197.0 \n", "1 NaN NaN NaN 37.0 26.0 \n", "2 NaN NaN NaN 103.0 196.0 \n", "\n", " asian_pop hispanic_pop amerindian_pop other_race_pop \\\n", "0 0.0 137.0 14.0 0.0 \n", "1 65.0 0.0 0.0 0.0 \n", "2 68.0 426.0 0.0 0.0 \n", "\n", " two_or_more_races_pop white_including_hispanic black_including_hispanic \\\n", "0 0.0 3745.0 197.0 \n", "1 3.0 21.0 26.0 \n", "2 0.0 241.0 224.0 \n", "\n", " asian_including_hispanic amerindian_including_hispanic hispanic_any_race \\\n", "0 0.0 14.0 137.0 \n", "1 65.0 0.0 0.0 \n", "2 68.0 8.0 426.0 \n", "\n", " not_hispanic_pop asian_male_45_54 asian_male_55_64 black_male_45_54 \\\n", "0 3916.0 0.0 0.0 0.0 \n", "1 115.0 0.0 0.0 0.0 \n", "2 404.0 6.0 0.0 26.0 \n", "\n", " black_male_55_64 hispanic_male_45_54 hispanic_male_55_64 \\\n", "0 0.0 0.0 34.0 \n", "1 14.0 0.0 0.0 \n", "2 9.0 32.0 23.0 \n", "\n", " white_male_45_54 white_male_55_64 median_income income_per_capita \\\n", "0 47.0 163.0 27027.0 9140.0 \n", "1 4.0 0.0 110000.0 108737.0 \n", "2 49.0 8.0 38977.0 15956.0 \n", "\n", " income_less_10000 income_10000_14999 income_15000_19999 \\\n", "0 34.0 243.0 76.0 \n", "1 3.0 4.0 0.0 \n", "2 8.0 13.0 0.0 \n", "\n", " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", "0 113.0 92.0 105.0 \n", "1 0.0 0.0 0.0 \n", "2 12.0 12.0 23.0 \n", "\n", " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", "0 118.0 52.0 21.0 \n", "1 0.0 0.0 9.0 \n", "2 33.0 0.0 5.0 \n", "\n", " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", "0 35.0 69.0 41.0 \n", "1 0.0 4.0 11.0 \n", "2 5.0 13.0 46.0 \n", "\n", " income_100000_124999 income_125000_149999 income_150000_199999 \\\n", "0 35.0 18.0 0.0 \n", "1 0.0 10.0 5.0 \n", "2 0.0 15.0 0.0 \n", "\n", " income_200000_or_more households_retirement_income \\\n", "0 0.0 47.0 \n", "1 16.0 0.0 \n", "2 8.0 21.0 \n", "\n", " pop_determined_poverty_status poverty gini_index housing_units \\\n", "0 4053.0 2042.0 0.3801 1110.0 \n", "1 115.0 28.0 0.5676 78.0 \n", "2 467.0 39.0 0.4302 287.0 \n", "\n", " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", "0 1150.0 \n", "1 3021.0 \n", "2 1309.0 \n", "\n", " owner_occupied_housing_units_lower_value_quartile \\\n", "0 312500.0 \n", "1 797800.0 \n", "2 327100.0 \n", "\n", " owner_occupied_housing_units_median_value \\\n", "0 740400.0 \n", "1 904400.0 \n", "2 387500.0 \n", "\n", " owner_occupied_housing_units_upper_value_quartile occupied_housing_units \\\n", "0 905400.0 1052.0 \n", "1 1023400.0 62.0 \n", "2 659700.0 193.0 \n", "\n", " housing_units_renter_occupied vacant_housing_units \\\n", "0 953.0 58.0 \n", "1 33.0 16.0 \n", "2 164.0 94.0 \n", "\n", " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", "0 37.0 0.0 \n", "1 0.0 0.0 \n", "2 13.0 0.0 \n", "\n", " dwellings_1_units_detached dwellings_1_units_attached dwellings_2_units \\\n", "0 12.0 12.0 138.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 27.0 41.0 \n", "\n", " dwellings_3_to_4_units dwellings_5_to_9_units dwellings_10_to_19_units \\\n", "0 357.0 47.0 101.0 \n", "1 3.0 5.0 4.0 \n", "2 116.0 84.0 5.0 \n", "\n", " dwellings_20_to_49_units dwellings_50_or_more_units mobile_homes \\\n", "0 352.0 91.0 0.0 \n", "1 8.0 58.0 0.0 \n", "2 10.0 4.0 0.0 \n", "\n", " housing_built_2005_or_later housing_built_2000_to_2004 \\\n", "0 0.0 0.0 \n", "1 0.0 5.0 \n", "2 0.0 0.0 \n", "\n", " housing_built_1939_or_earlier median_year_structure_built \\\n", "0 66.0 1939.0 \n", "1 0.0 2001.0 \n", "2 51.0 1939.0 \n", "\n", " married_households nonfamily_households family_households \\\n", "0 773.0 170.0 882.0 \n", "1 0.0 62.0 0.0 \n", "2 66.0 73.0 120.0 \n", "\n", " households_public_asst_or_food_stamps male_male_households \\\n", "0 633.0 0.0 \n", "1 3.0 0.0 \n", "2 21.0 0.0 \n", "\n", " female_female_households children children_in_single_female_hh \\\n", "0 0.0 1941.0 126.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 83.0 0.0 \n", "\n", " median_rent percent_income_spent_on_rent rent_burden_not_computed \\\n", "0 1053.0 49.7 34.0 \n", "1 3021.0 22.5 0.0 \n", "2 1196.0 26.8 13.0 \n", "\n", " rent_over_50_percent rent_40_to_50_percent rent_35_to_40_percent \\\n", "0 456.0 117.0 79.0 \n", "1 3.0 4.0 0.0 \n", "2 45.0 0.0 11.0 \n", "\n", " rent_30_to_35_percent rent_25_to_30_percent rent_20_to_25_percent \\\n", "0 32.0 52.0 99.0 \n", "1 0.0 8.0 3.0 \n", "2 5.0 23.0 5.0 \n", "\n", " rent_15_to_20_percent rent_10_to_15_percent rent_under_10_percent \\\n", "0 38.0 46.0 0.0 \n", "1 11.0 0.0 4.0 \n", "2 38.0 20.0 4.0 \n", "\n", " owner_occupied_housing_units million_dollar_housing_units \\\n", "0 99.0 10.0 \n", "1 29.0 8.0 \n", "2 29.0 0.0 \n", "\n", " mortgaged_housing_units different_house_year_ago_different_city \\\n", "0 14.0 38.0 \n", "1 12.0 5.0 \n", "2 12.0 203.0 \n", "\n", " different_house_year_ago_same_city families_with_young_children \\\n", "0 112.0 1211.0 \n", "1 19.0 0.0 \n", "2 88.0 5.0 \n", "\n", " two_parent_families_with_young_children \\\n", "0 1064.0 \n", "1 0.0 \n", "2 5.0 \n", "\n", " two_parents_in_labor_force_families_with_young_children \\\n", "0 223.0 \n", "1 0.0 \n", "2 0.0 \n", "\n", " two_parents_father_in_labor_force_families_with_young_children \\\n", "0 732.0 \n", "1 0.0 \n", "2 5.0 \n", "\n", " two_parents_mother_in_labor_force_families_with_young_children \\\n", "0 109.0 \n", "1 0.0 \n", "2 0.0 \n", "\n", " two_parents_not_in_labor_force_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "\n", " one_parent_families_with_young_children \\\n", "0 147.0 \n", "1 0.0 \n", "2 0.0 \n", "\n", " father_one_parent_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "\n", " father_in_labor_force_one_parent_families_with_young_children \\\n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "\n", " commute_5_9_mins commute_less_10_mins commute_10_14_mins \\\n", "0 128.0 140.0 299.0 \n", "1 5.0 5.0 27.0 \n", "2 21.0 21.0 55.0 \n", "\n", " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", "0 235.0 161.0 24.0 \n", "1 21.0 15.0 14.0 \n", "2 14.0 23.0 0.0 \n", "\n", " commute_30_34_mins commute_35_39_mins commute_40_44_mins \\\n", "0 64.0 14.0 20.0 \n", "1 4.0 0.0 0.0 \n", "2 60.0 26.0 28.0 \n", "\n", " commute_35_44_mins commute_45_59_mins commute_60_more_mins \\\n", "0 34.0 90.0 37.0 \n", "1 0.0 0.0 0.0 \n", "2 54.0 29.0 42.0 \n", "\n", " commute_60_89_mins commute_90_more_mins commuters_16_over \\\n", "0 12.0 25.0 1084.0 \n", "1 0.0 0.0 86.0 \n", "2 22.0 20.0 298.0 \n", "\n", " walked_to_work worked_at_home no_car no_cars one_car two_cars \\\n", "0 674.0 101.0 909.0 797.0 214.0 41.0 \n", "1 52.0 4.0 69.0 54.0 8.0 0.0 \n", "2 43.0 0.0 118.0 94.0 89.0 10.0 \n", "\n", " three_cars four_more_cars aggregate_travel_time_to_work \\\n", "0 0.0 0.0 NaN \n", "1 0.0 0.0 NaN \n", "2 0.0 0.0 NaN \n", "\n", " commuters_by_public_transportation commuters_by_bus \\\n", "0 135.0 36.0 \n", "1 34.0 0.0 \n", "2 173.0 39.0 \n", "\n", " commuters_by_car_truck_van commuters_by_carpool \\\n", "0 275.0 64.0 \n", "1 0.0 0.0 \n", "2 82.0 0.0 \n", "\n", " commuters_by_subway_or_elevated commuters_drove_alone group_quarters \\\n", "0 99.0 211.0 0.0 \n", "1 34.0 0.0 32.0 \n", "2 134.0 82.0 363.0 \n", "\n", " associates_degree bachelors_degree high_school_diploma \\\n", "0 0.0 23.0 611.0 \n", "1 3.0 48.0 0.0 \n", "2 35.0 52.0 169.0 \n", "\n", " less_one_year_college masters_degree one_year_more_college \\\n", "0 83.0 71.0 150.0 \n", "1 7.0 3.0 0.0 \n", "2 27.0 4.0 93.0 \n", "\n", " less_than_high_school_graduate high_school_including_ged \\\n", "0 486.0 656.0 \n", "1 19.0 6.0 \n", "2 184.0 264.0 \n", "\n", " bachelors_degree_2 bachelors_degree_or_higher_25_64 \\\n", "0 23.0 110.0 \n", "1 48.0 58.0 \n", "2 52.0 56.0 \n", "\n", " graduate_professional_degree some_college_and_associates_degree \\\n", "0 96.0 233.0 \n", "1 10.0 10.0 \n", "2 4.0 155.0 \n", "\n", " male_45_64_associates_degree male_45_64_bachelors_degree \\\n", "0 0.0 0.0 \n", "1 0.0 4.0 \n", "2 11.0 20.0 \n", "\n", " male_45_64_graduate_degree male_45_64_less_than_9_grade \\\n", "0 12.0 37.0 \n", "1 0.0 7.0 \n", "2 0.0 6.0 \n", "\n", " male_45_64_grade_9_12 male_45_64_high_school male_45_64_some_college \\\n", "0 41.0 104.0 50.0 \n", "1 0.0 0.0 7.0 \n", "2 29.0 69.0 18.0 \n", "\n", " male_45_to_64 employed_pop unemployed_pop pop_in_labor_force \\\n", "0 244.0 1196.0 55.0 1251.0 \n", "1 18.0 90.0 6.0 96.0 \n", "2 153.0 298.0 36.0 334.0 \n", "\n", " not_in_labor_force workers_16_and_over armed_forces \\\n", "0 990.0 1185.0 0.0 \n", "1 19.0 90.0 0.0 \n", "2 421.0 298.0 0.0 \n", "\n", " civilian_labor_force employed_agriculture_forestry_fishing_hunting_mining \\\n", "0 1251.0 0.0 \n", "1 96.0 0.0 \n", "2 334.0 0.0 \n", "\n", " employed_arts_entertainment_recreation_accommodation_food \\\n", "0 179.0 \n", "1 13.0 \n", "2 60.0 \n", "\n", " employed_construction employed_education_health_social \\\n", "0 45.0 301.0 \n", "1 4.0 4.0 \n", "2 4.0 66.0 \n", "\n", " employed_finance_insurance_real_estate employed_information \\\n", "0 34.0 11.0 \n", "1 20.0 7.0 \n", "2 10.0 4.0 \n", "\n", " employed_manufacturing employed_other_services_not_public_admin \\\n", "0 58.0 133.0 \n", "1 0.0 0.0 \n", "2 5.0 65.0 \n", "\n", " employed_public_administration employed_retail_trade \\\n", "0 0.0 225.0 \n", "1 0.0 4.0 \n", "2 0.0 6.0 \n", "\n", " employed_science_management_admin_waste \\\n", "0 66.0 \n", "1 27.0 \n", "2 0.0 \n", "\n", " employed_transportation_warehousing_utilities employed_wholesale_trade \\\n", "0 77.0 67.0 \n", "1 11.0 0.0 \n", "2 78.0 0.0 \n", "\n", " occupation_management_arts \\\n", "0 351.0 \n", "1 67.0 \n", "2 31.0 \n", "\n", " occupation_natural_resources_construction_maintenance \\\n", "0 44.0 \n", "1 0.0 \n", "2 42.0 \n", "\n", " occupation_production_transportation_material occupation_sales_office \\\n", "0 110.0 527.0 \n", "1 4.0 16.0 \n", "2 85.0 33.0 \n", "\n", " occupation_services management_business_sci_arts_employed \\\n", "0 164.0 351.0 \n", "1 3.0 67.0 \n", "2 107.0 31.0 \n", "\n", " sales_office_employed in_grades_1_to_4 in_grades_5_to_8 \\\n", "0 527.0 427.0 170.0 \n", "1 16.0 0.0 0.0 \n", "2 33.0 18.0 39.0 \n", "\n", " in_grades_9_to_12 in_school in_undergrad_college \\\n", "0 200.0 1462.0 135.0 \n", "1 0.0 8.0 0.0 \n", "2 34.0 124.0 20.0 \n", "\n", " speak_only_english_at_home speak_spanish_at_home \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "\n", " speak_spanish_at_home_low_english do_label do_perimeter do_area \\\n", "0 NaN 535.0 1588.147 129710.372 \n", "1 NaN 113.0 1737.382 175691.837 \n", "2 NaN 199.0 6528.538 1679379.837 \n", "\n", " do_num_vertices geom \n", "0 8 POLYGON ((-73.96328 40.70773, -73.96485 40.707... \n", "1 5 POLYGON ((-73.99163 40.75471, -73.98595 40.752... \n", "2 29 POLYGON ((-73.94216 40.73568, -73.94037 40.733... " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sql_query = \"SELECT * FROM $dataset$ WHERE ST_IntersectsBox(geom, -74.044467,40.706128,-73.891345,40.837690)\"\n", "ct_demog_df = dataset.to_dataframe(sql_query=sql_query)\n", "ct_demog_df.head(3)" ] }, { "cell_type": "markdown", "metadata": { "id": "I85IxaWNASib" }, "source": [ "##### Visualize dataset" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 674 }, "id": "jdpXmibP34Yx", "outputId": "e8276b0c-a1d8-4625-9e84-0ce14fcbacd3" }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " None\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " Static map image\n", " \n", " \n", "
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