CARTOframes

A Python package for integrating CARTO maps, analysis, and data services into data science workflows.

This version includes breaking changes, check the CHANGELOG for more information

Data discovery

Introduction

The Data Observatory is a spatial data repository that enables Data Scientists to augment their data and broaden their analysis. It offers a wide range of datasets from around the globe.

This guide is intended for those who want to start augmenting their own data using CARTOframes and wish to explore our public Data Observatory catalog to find datasets that best fit their use cases and analyses.

Note: The catalog is public and you don’t need a CARTO account to search for available datasets

Looking for demographics and financial data in the US in the catalog

In this guide we walk through the Data Observatory catalog looking for demographics and financial data in the US.

The catalog is comprised of thousands of curated spatial datasets, so when searching for data the easiest way to find what you are looking for is make use of a faceted search. A faceted (or hierarchical) search allows you to narrow down search results by applying multiple filters based on faceted classification of catalog datasets.

Datasets are organized in three main hierarchies:

  • Country
  • Category
  • Geography (or spatial resolution)

For our analysis we are looking for demographics and financial datasets in the US with a spatial resolution at block group level.

Firstly we can start by discovering which available geographies (or spatial resolutions) we have for demographics data in the US, by filtering the catalog by country and category and listing the available geographies.

Let’s start exploring the available categories of data for the US:

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from cartoframes.data.observatory import Catalog
Catalog().country('usa').categories
[<Category.get('road_traffic')>,
  <Category.get('points_of_interest')>,
  <Category.get('human_mobility')>,
  <Category.get('financial')>,
  <Category.get('environmental')>,
  <Category.get('demographics')>]

For the case of the US, the Data Observatory provides six different categories of datasets. Let’s take a look at the spatial resolutions available for the demographics category (which on this occasion contains the population data we need).

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from cartoframes.data.observatory import Catalog
geographies = Catalog().country('usa').category('demographics').geographies
geographies
[<Geography.get('ags_blockgroup_1c63771c')>,
  <Geography.get('ags_q17_4739be4f')>,
  <Geography.get('mbi_blockgroups_1ab060a')>,
  <Geography.get('mbi_counties_141b61cd')>,
  <Geography.get('mbi_county_subd_e8e6ea23')>,
  <Geography.get('mbi_pc_5_digit_4b1682a6')>,
  <Geography.get('od_blockclippe_9c508438')>,
  <Geography.get('od_blockgroupc_3ab29c84')>,
  <Geography.get('od_cbsaclipped_b6a32adc')>,
  <Geography.get('od_censustract_5962fe30')>,
  <Geography.get('od_congression_6774ebb')>,
  <Geography.get('od_countyclipp_caef1ec9')>,
  <Geography.get('od_placeclippe_48a89947')>,
  <Geography.get('od_pumaclipped_b065909')>,
  <Geography.get('od_schooldistr_6d5c417f')>,
  <Geography.get('od_schooldistr_f70c7e28')>,
  <Geography.get('od_schooldistr_75493a16')>,
  <Geography.get('od_stateclippe_8d79f5be')>,
  <Geography.get('od_zcta5clippe_6b6ff33c')>,
  <Geography.get('usct_censustract_784cc2ed')>]

Let’s filter the geographies by those that contain information at the level of blockgroup. For that purpose we are converting the geographies to a pandas DataFrame and search for the string blockgroup in the id of the geographies:

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df = geographies.to_dataframe()
df[df['id'].str.contains('blockgroup', case=False, na=False)]
available_in country_id description geom_coverage geom_type id is_public_data lang name provider_id provider_name slug summary_json update_frequency version
0 [bq] usa None 0106000020E61000000800000001030000000100000009... MULTIPOLYGON carto-do.ags.geography_usa_blockgroup_2015 False eng USA Census Block Group ags Applied Geographic Solutions ags_blockgroup_1c63771c None None 2015
2 None usa MBI Digital Boundaries for USA at Blockgroups ... 01060000005A0100000103000000010000002900000013... MULTIPOLYGON carto-do.mbi.geography_usa_blockgroups_2019 False eng USA - Blockgroups mbi Michael Bauer International mbi_blockgroups_1ab060a None None 2019
7 None usa None 0106000020E61000000800000001030000000100000009... MULTIPOLYGON carto-do-public-data.tiger.geography_usa_block... True eng Topologically Integrated Geographic Encoding a... open_data Open Data od_blockgroupc_3ab29c84 None None 2015

We have three available datasets, from three different providers: Michael Bauer International, Open Data and AGS. For this example, we are going to look for demographic datasets for the AGS blockgroups geography ags_blockgroup_1c63771c:

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datasets = Catalog().country('usa').category('demographics').geography('ags_blockgroup_1c63771c').datasets
datasets
[<Dataset.get('ags_sociodemogr_e92b1637')>,
    <Dataset.get('ags_consumerspe_fe5d060a')>,
    <Dataset.get('ags_retailpoten_ddf56a1a')>,
    <Dataset.get('ags_consumerpro_e8344e2e')>,
    <Dataset.get('ags_businesscou_a8310a11')>,
    <Dataset.get('ags_crimerisk_9ec89442')>]

Let’s continue with the data discovery. We have 6 datasets in the US with demographics information at the level of AGS blockgroups:

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datasets.to_dataframe()
available_in category_id category_name country_id data_source_id description geography_description geography_id geography_name id ... lang name provider_id provider_name slug summary_json temporal_aggregation time_coverage update_frequency version
0 [bq] demographics Demographics usa sociodemographic Census and ACS sociodemographic data estimated... None carto-do.ags.geography_usa_blockgroup_2015 USA Census Block Group carto-do.ags.demographics_sociodemographic_usa... ... eng Sociodemographic ags Applied Geographic Solutions ags_sociodemogr_e92b1637 {'counts': {'rows': 217182, 'cells': 22369746,... yearly [2019-01-01,2020-01-01) None 2019
1 [bq] demographics Demographics usa consumerspending The Consumer Expenditure database consists of ... None carto-do.ags.geography_usa_blockgroup_2015 USA Census Block Group carto-do.ags.demographics_consumerspending_usa... ... eng Consumer Spending ags Applied Geographic Solutions ags_consumerspe_fe5d060a {'counts': {'rows': 217182, 'cells': 28016478,... yearly [2018-01-01,2019-01-01) None 2018
2 [bq] demographics Demographics usa retailpotential The retail potential database consists of aver... None carto-do.ags.geography_usa_blockgroup_2015 USA Census Block Group carto-do.ags.demographics_retailpotential_usa_... ... eng Retail Potential ags Applied Geographic Solutions ags_retailpoten_ddf56a1a {'counts': {'rows': 217182, 'cells': 28668024,... yearly [2018-01-01,2019-01-01) None 2018
3 [bq] demographics Demographics usa consumerprofiles Segmentation of the population in sixty-eight ... None carto-do.ags.geography_usa_blockgroup_2015 USA Census Block Group carto-do.ags.demographics_consumerprofiles_usa... ... eng Consumer Profiles ags Applied Geographic Solutions ags_consumerpro_e8344e2e {'counts': {'rows': 217182, 'cells': 31057026,... yearly [2018-01-01,2019-01-01) None 2018
4 [bq] demographics Demographics usa businesscounts Business Counts database is a geographic summa... None carto-do.ags.geography_usa_blockgroup_2015 USA Census Block Group carto-do.ags.demographics_businesscounts_usa_b... ... eng Business Counts ags Applied Geographic Solutions ags_businesscou_a8310a11 {'counts': {'rows': 217182, 'cells': 25627476,... yearly [2018-01-01,2019-01-01) None 2018
5 [bq] demographics Demographics usa crimerisk Using advanced statistical methodologies and a... None carto-do.ags.geography_usa_blockgroup_2015 USA Census Block Group carto-do.ags.demographics_crimerisk_usa_blockg... ... eng Crime Risk ags Applied Geographic Solutions ags_crimerisk_9ec89442 {'counts': {'rows': 217182, 'cells': 3040548, ... yearly [2018-01-01,2019-01-01) None 2018

6 rows × 21 columns

They include different information: consumer spending, retail potential, consumer profiles, etc.

At first glance, it looks like the dataset with data_source_id: sociodemographic might contain the population information we are looking for. Let’s try to understand a little bit better what data this dataset contains by looking at its variables:

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from cartoframes.data.observatory import Dataset
dataset = Dataset.get('ags_sociodemogr_e92b1637')
variables = dataset.variables
variables
[<Variable.get('HINCYMED65_310bc888')> #'Median Household Income: Age 65-74 (2019A)',
<Variable.get('HINCYMED55_1a269b4b')> #'Median Household Income: Age 55-64 (2019A)',
<Variable.get('HINCYMED45_33daa0a')> #'Median Household Income: Age 45-54 (2019A)',
<Variable.get('HINCYMED35_4c7c3ccd')> #'Median Household Income: Age 35-44 (2019A)',
<Variable.get('HINCYMED25_55670d8c')> #'Median Household Income: Age 25-34 (2019A)',
<Variable.get('HINCYMED24_22603d1a')> #'Median Household Income: Age < 25 (2019A)',
<Variable.get('HINCYGT200_e552a738')> #'Household Income > $200000 (2019A)',
<Variable.get('HINCY6075_1933e114')> #'Household Income $60000-$74999 (2019A)',
<Variable.get('HINCY4550_f7ad7d79')> #'Household Income $45000-$49999 (2019A)',
<Variable.get('HINCY4045_98177a5c')> #'Household Income $40000-$44999 (2019A)',
<Variable.get('HINCY3540_73617481')> #'Household Income $35000-$39999 (2019A)',
<Variable.get('HINCY2530_849c8523')> #'Household Income $25000-$29999 (2019A)',
<Variable.get('HINCY2025_eb268206')> #'Household Income $20000-$24999 (2019A)',
<Variable.get('HINCY1520_8f321b8c')> #'Household Income $15000-$19999 (2019A)',
<Variable.get('HINCY12550_f5b5f848')> #'Household Income $125000-$149999 (2019A)',
<Variable.get('HHSCYMCFCH_9bddf3b1')> #'Families married couple w children (2019A)',
<Variable.get('HHSCYLPMCH_e844cd91')> #'Families male no wife w children (2019A)',
<Variable.get('HHSCYLPFCH_e4112270')> #'Families female no husband children (2019A)',
<Variable.get('HHDCYMEDAG_69c53f22')> #'Median Age of Householder (2019A)',
<Variable.get('HHDCYFAM_85548592')> #'Family Households (2019A)',
<Variable.get('HHDCYAVESZ_f4a95c6f')> #'Average Household Size (2019A)',
<Variable.get('HHDCY_23e8e012')> #'Households (2019A)',
<Variable.get('EDUCYSHSCH_5c444deb')> #'Pop 25+ 9th-12th grade no diploma (2019A)',
<Variable.get('EDUCYLTGR9_cbcfcc89')> #'Pop 25+ less than 9th grade (2019A)',
<Variable.get('EDUCYHSCH_b236c803')> #'Pop 25+ HS graduate (2019A)',
<Variable.get('EDUCYGRAD_d0179ccb')> #'Pop 25+ graduate or prof school degree (2019A)',
<Variable.get('EDUCYBACH_c2295f79')> #'Pop 25+ Bachelors degree (2019A)',
<Variable.get('DWLCYVACNT_4d5e33e9')> #'Housing units vacant (2019A)',
<Variable.get('DWLCYRENT_239f79ae')> #'Occupied units renter (2019A)',
<Variable.get('DWLCYOWNED_a34794a5')> #'Occupied units owner (2019A)',
<Variable.get('AGECYMED_b6eaafb4')> #'Median Age (2019A)',
<Variable.get('AGECYGT85_b9d8a94d')> #'Population age 85+ (2019A)',
<Variable.get('AGECYGT25_433741c7')> #'Population Age 25+ (2019A)',
<Variable.get('AGECYGT15_681a1204')> #'Population Age 15+ (2019A)',
<Variable.get('AGECY8084_b25d4aed')> #'Population age 80-84 (2019A)',
<Variable.get('AGECY7579_15dcf822')> #'Population age 75-79 (2019A)',
<Variable.get('AGECY7074_6da64674')> #'Population age 70-74 (2019A)',
<Variable.get('AGECY6064_cc011050')> #'Population age 60-64 (2019A)',
<Variable.get('AGECY5559_8de3522b')> #'Population age 55-59 (2019A)',
<Variable.get('AGECY5054_f599ec7d')> #'Population age 50-54 (2019A)',
<Variable.get('AGECY4549_2c44040f')> #'Population age 45-49 (2019A)',
<Variable.get('AGECY4044_543eba59')> #'Population age 40-44 (2019A)',
<Variable.get('AGECY3034_86a81427')> #'Population age 30-34 (2019A)',
<Variable.get('AGECY2529_5f75fc55')> #'Population age 25-29 (2019A)',
<Variable.get('AGECY1519_66ed0078')> #'Population age 15-19 (2019A)',
<Variable.get('AGECY0509_c74a565c')> #'Population age 5-9 (2019A)',
<Variable.get('AGECY0004_bf30e80a')> #'Population age 0-4 (2019A)',
<Variable.get('EDUCYSCOLL_1e8c4828')> #'Pop 25+ college no diploma (2019A)',
<Variable.get('MARCYMARR_26e07b7')> #'Now Married (2019A)',
<Variable.get('AGECY2024_270f4203')> #'Population age 20-24 (2019A)',
<Variable.get('AGECY1014_1e97be2e')> #'Population age 10-14 (2019A)',
<Variable.get('AGECY3539_fed2aa71')> #'Population age 35-39 (2019A)',
<Variable.get('EDUCYASSOC_fa1bcf13')> #'Pop 25+ Associate degree (2019A)',
<Variable.get('HINCY1015_d2be7e2b')> #'Household Income $10000-$14999 (2019A)',
<Variable.get('HINCYLT10_745f9119')> #'Household Income < $10000 (2019A)',
<Variable.get('POPPY_946f4ed6')> #'Population (2024A)',
<Variable.get('INCPYMEDHH_e8930404')> #'Median household income (2024A)',
<Variable.get('AGEPYMED_91aa42e6')> #'Median Age (2024A)',
<Variable.get('DWLPY_819e5af0')> #'Housing units (2024A)',
<Variable.get('INCPYAVEHH_6e0d7b43')> #'Average household Income (2024A)',
<Variable.get('INCPYPCAP_ec5fd8ca')> #'Per capita income (2024A)',
<Variable.get('HHDPY_4207a180')> #'Households (2024A)',
<Variable.get('VPHCYNONE_22cb7350')> #'Households: No Vehicle Available (2019A)',
<Variable.get('VPHCYGT1_a052056d')> #'Households: Two or More Vehicles Available (2019A)',
<Variable.get('VPHCY1_53dc760f')> #'Households: One Vehicle Available (2019A)',
<Variable.get('UNECYRATE_b3dc32ba')> #'Unemployment Rate (2019A)',
<Variable.get('SEXCYMAL_ca14d4b8')> #'Population male (2019A)',
<Variable.get('SEXCYFEM_d52acecb')> #'Population female (2019A)',
<Variable.get('RCHCYWHNHS_9206188d')> #'Non Hispanic White (2019A)',
<Variable.get('RCHCYOTNHS_d8592ce9')> #'Non Hispanic Other Race (2019A)',
<Variable.get('RCHCYMUNHS_1a2518ec')> #'Non Hispanic Multiple Race (2019A)',
<Variable.get('RCHCYHANHS_dbe5754')> #'Non Hispanic Hawaiian/Pacific Islander (2019A)',
<Variable.get('RCHCYBLNHS_b5649728')> #'Non Hispanic Black (2019A)',
<Variable.get('RCHCYASNHS_fabeaa31')> #'Non Hispanic Asian (2019A)',
<Variable.get('RCHCYAMNHS_4a788a9d')> #'Non Hispanic American Indian (2019A)',
<Variable.get('POPCYGRPI_147af7a9')> #'Institutional Group Quarters Population (2019A)',
<Variable.get('POPCYGRP_74c19673')> #'Population in Group Quarters (2019A)',
<Variable.get('POPCY_f5800f44')> #'Population (2019A)',
<Variable.get('MARCYWIDOW_7a2977e0')> #'Widowed (2019A)',
<Variable.get('MARCYSEP_9024e7e5')> #'Separated (2019A)',
<Variable.get('MARCYNEVER_c82856b0')> #'Never Married (2019A)',
<Variable.get('MARCYDIVOR_32a11923')> #'Divorced (2019A)',
<Variable.get('LNIEXSPAN_9a19f7f7')> #'SPANISH SPEAKING HOUSEHOLDS',
<Variable.get('LNIEXISOL_d776b2f7')> #'LINGUISTICALLY ISOLATED HOUSEHOLDS (NON-ENGLISH SP...',
<Variable.get('LBFCYUNEM_1e711de4')> #'Pop 16+ civilian unemployed (2019A)',
<Variable.get('LBFCYNLF_c4c98350')> #'Pop 16+ not in labor force (2019A)',
<Variable.get('INCCYMEDHH_bea58257')> #'Median household income (2019A)',
<Variable.get('INCCYMEDFA_59fa177d')> #'Median family income (2019A)',
<Variable.get('INCCYAVEHH_383bfd10')> #'Average household Income (2019A)',
<Variable.get('HUSEXAPT_988f452f')> #'UNITS IN STRUCTURE: 20 OR MORE',
<Variable.get('HUSEX1DET_3684405c')> #'UNITS IN STRUCTURE: 1 DETACHED',
<Variable.get('HOOEXMED_c2d4b5b')> #'Median Value of Owner Occupied Housing Units',
<Variable.get('HISCYHISP_f3b3a31e')> #'Population Hispanic (2019A)',
<Variable.get('HINCYMED75_2810f9c9')> #'Median Household Income: Age 75+ (2019A)',
<Variable.get('HINCY15020_21e894dd')> #'Household Income $150000-$199999 (2019A)',
<Variable.get('BLOCKGROUP_16298bd5')> #'Geographic Identifier',
<Variable.get('LBFCYLBF_59ce7ab0')> #'Population In Labor Force (2019A)',
<Variable.get('LBFCYARM_8c06223a')> #'Pop 16+ in Armed Forces (2019A)',
<Variable.get('DWLCY_e0711b62')> #'Housing units (2019A)',
<Variable.get('LBFCYPOP16_53fa921c')> #'Population Age 16+ (2019A)',
<Variable.get('LBFCYEMPL_c9c22a0')> #'Pop 16+ civilian employed (2019A)',
<Variable.get('INCCYPCAP_691da8ff')> #'Per capita income (2019A)',
<Variable.get('RNTEXMED_2e309f54')> #'Median Cash Rent',
<Variable.get('HINCY3035_4a81d422')> #'Household Income $30000-$34999 (2019A)',
<Variable.get('HINCY5060_62f78b34')> #'Household Income $50000-$59999 (2019A)',
<Variable.get('HINCY10025_665c9060')> #'Household Income $100000-$124999 (2019A)',
<Variable.get('HINCY75100_9d5c69c8')> #'Household Income $75000-$99999 (2019A)',
<Variable.get('AGECY6569_b47bae06')> #'Population age 65-69 (2019A)']
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vdf = variables.to_dataframe()
vdf
agg_method column_name dataset_id db_type description id name slug starred summary_json variable_group_id
0 AVG HINCYMED65 carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Household Income: Age 65-74 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYMED65 HINCYMED65_310bc888 False {'head': [67500, 0, 0, 50000, 0, 0, 0, 0, 0, 0... None
1 AVG HINCYMED55 carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Household Income: Age 55-64 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYMED55 HINCYMED55_1a269b4b False {'head': [67500, 87500, 0, 30000, 0, 0, 0, 0, ... None
2 AVG HINCYMED45 carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Household Income: Age 45-54 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYMED45 HINCYMED45_33daa0a False {'head': [67500, 0, 0, 60000, 0, 0, 0, 0, 0, 0... None
3 AVG HINCYMED35 carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Household Income: Age 35-44 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYMED35 HINCYMED35_4c7c3ccd False {'head': [0, 87500, 0, 5000, 0, 0, 0, 0, 0, 0]... None
4 AVG HINCYMED25 carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Household Income: Age 25-34 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYMED25 HINCYMED25_55670d8c False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
5 AVG HINCYMED24 carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Household Income: Age < 25 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYMED24 HINCYMED24_22603d1a False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
6 AVG HINCYGT200 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income > $200000 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYGT200 HINCYGT200_e552a738 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
7 AVG HINCY6075 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $60000-$74999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY6075 HINCY6075_1933e114 False {'head': [5, 0, 0, 2, 0, 0, 0, 0, 0, 0], 'tail... None
8 AVG HINCY4550 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $45000-$49999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY4550 HINCY4550_f7ad7d79 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
9 AVG HINCY4045 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $40000-$44999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY4045 HINCY4045_98177a5c False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
10 AVG HINCY3540 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $35000-$39999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY3540 HINCY3540_73617481 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
11 AVG HINCY2530 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $25000-$29999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY2530 HINCY2530_849c8523 False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
12 AVG HINCY2025 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $20000-$24999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY2025 HINCY2025_eb268206 False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
13 AVG HINCY1520 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $15000-$19999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY1520 HINCY1520_8f321b8c False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
14 AVG HINCY12550 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $125000-$149999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY12550 HINCY12550_f5b5f848 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
15 SUM HHSCYMCFCH carto-do.ags.demographics_sociodemographic_usa... INTEGER Families married couple w children (2019A) carto-do.ags.demographics_sociodemographic_usa... HHSCYMCFCH HHSCYMCFCH_9bddf3b1 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
16 SUM HHSCYLPMCH carto-do.ags.demographics_sociodemographic_usa... INTEGER Families male no wife w children (2019A) carto-do.ags.demographics_sociodemographic_usa... HHSCYLPMCH HHSCYLPMCH_e844cd91 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
17 SUM HHSCYLPFCH carto-do.ags.demographics_sociodemographic_usa... INTEGER Families female no husband children (2019A) carto-do.ags.demographics_sociodemographic_usa... HHSCYLPFCH HHSCYLPFCH_e4112270 False {'head': [0, 1, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
18 AVG HHDCYMEDAG carto-do.ags.demographics_sociodemographic_usa... FLOAT Median Age of Householder (2019A) carto-do.ags.demographics_sociodemographic_usa... HHDCYMEDAG HHDCYMEDAG_69c53f22 False {'head': [61.5, 54, 0, 61.5, 0, 0, 0, 0, 0, 0]... None
19 SUM HHDCYFAM carto-do.ags.demographics_sociodemographic_usa... INTEGER Family Households (2019A) carto-do.ags.demographics_sociodemographic_usa... HHDCYFAM HHDCYFAM_85548592 False {'head': [1, 2, 0, 6, 0, 0, 0, 0, 0, 0], 'tail... None
20 AVG HHDCYAVESZ carto-do.ags.demographics_sociodemographic_usa... FLOAT Average Household Size (2019A) carto-do.ags.demographics_sociodemographic_usa... HHDCYAVESZ HHDCYAVESZ_f4a95c6f False {'head': [1.2, 2.5, 0, 2, 0, 0, 0, 0, 0, 0], '... None
21 SUM HHDCY carto-do.ags.demographics_sociodemographic_usa... INTEGER Households (2019A) carto-do.ags.demographics_sociodemographic_usa... HHDCY HHDCY_23e8e012 False {'head': [5, 2, 0, 11, 0, 0, 0, 0, 0, 0], 'tai... None
22 SUM EDUCYSHSCH carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ 9th-12th grade no diploma (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYSHSCH EDUCYSHSCH_5c444deb False {'head': [0, 0, 0, 4, 4, 0, 0, 0, 0, 0], 'tail... None
23 SUM EDUCYLTGR9 carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ less than 9th grade (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYLTGR9 EDUCYLTGR9_cbcfcc89 False {'head': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
24 SUM EDUCYHSCH carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ HS graduate (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYHSCH EDUCYHSCH_b236c803 False {'head': [5, 0, 0, 8, 14, 0, 0, 0, 0, 0], 'tai... None
25 SUM EDUCYGRAD carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ graduate or prof school degree (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYGRAD EDUCYGRAD_d0179ccb False {'head': [0, 0, 0, 1, 3, 0, 0, 0, 0, 0], 'tail... None
26 SUM EDUCYBACH carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ Bachelors degree (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYBACH EDUCYBACH_c2295f79 False {'head': [0, 0, 0, 1, 7, 0, 0, 0, 0, 0], 'tail... None
27 SUM DWLCYVACNT carto-do.ags.demographics_sociodemographic_usa... INTEGER Housing units vacant (2019A) carto-do.ags.demographics_sociodemographic_usa... DWLCYVACNT DWLCYVACNT_4d5e33e9 False {'head': [0, 0, 0, 10, 0, 0, 0, 0, 0, 0], 'tai... None
28 SUM DWLCYRENT carto-do.ags.demographics_sociodemographic_usa... INTEGER Occupied units renter (2019A) carto-do.ags.demographics_sociodemographic_usa... DWLCYRENT DWLCYRENT_239f79ae False {'head': [0, 0, 0, 6, 0, 0, 0, 0, 0, 0], 'tail... None
29 SUM DWLCYOWNED carto-do.ags.demographics_sociodemographic_usa... INTEGER Occupied units owner (2019A) carto-do.ags.demographics_sociodemographic_usa... DWLCYOWNED DWLCYOWNED_a34794a5 False {'head': [5, 2, 0, 5, 0, 0, 0, 0, 0, 0], 'tail... None
... ... ... ... ... ... ... ... ... ... ... ...
78 SUM MARCYWIDOW carto-do.ags.demographics_sociodemographic_usa... INTEGER Widowed (2019A) carto-do.ags.demographics_sociodemographic_usa... MARCYWIDOW MARCYWIDOW_7a2977e0 False {'head': [0, 0, 0, 2, 0, 0, 0, 0, 0, 0], 'tail... None
79 SUM MARCYSEP carto-do.ags.demographics_sociodemographic_usa... INTEGER Separated (2019A) carto-do.ags.demographics_sociodemographic_usa... MARCYSEP MARCYSEP_9024e7e5 False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
80 SUM MARCYNEVER carto-do.ags.demographics_sociodemographic_usa... INTEGER Never Married (2019A) carto-do.ags.demographics_sociodemographic_usa... MARCYNEVER MARCYNEVER_c82856b0 False {'head': [0, 1, 0, 13, 959, 0, 0, 0, 0, 0], 't... None
81 SUM MARCYDIVOR carto-do.ags.demographics_sociodemographic_usa... INTEGER Divorced (2019A) carto-do.ags.demographics_sociodemographic_usa... MARCYDIVOR MARCYDIVOR_32a11923 False {'head': [0, 0, 0, 4, 0, 0, 0, 0, 0, 0], 'tail... None
82 SUM LNIEXSPAN carto-do.ags.demographics_sociodemographic_usa... INTEGER SPANISH SPEAKING HOUSEHOLDS carto-do.ags.demographics_sociodemographic_usa... LNIEXSPAN LNIEXSPAN_9a19f7f7 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
83 SUM LNIEXISOL carto-do.ags.demographics_sociodemographic_usa... INTEGER LINGUISTICALLY ISOLATED HOUSEHOLDS (NON-ENGLIS... carto-do.ags.demographics_sociodemographic_usa... LNIEXISOL LNIEXISOL_d776b2f7 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
84 SUM LBFCYUNEM carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ civilian unemployed (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYUNEM LBFCYUNEM_1e711de4 False {'head': [0, 0, 0, 0, 32, 0, 0, 0, 0, 0], 'tai... None
85 SUM LBFCYNLF carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ not in labor force (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYNLF LBFCYNLF_c4c98350 False {'head': [6, 1, 0, 10, 581, 0, 0, 0, 0, 0], 't... None
86 AVG INCCYMEDHH carto-do.ags.demographics_sociodemographic_usa... INTEGER Median household income (2019A) carto-do.ags.demographics_sociodemographic_usa... INCCYMEDHH INCCYMEDHH_bea58257 False {'head': [67499, 87499, 0, 27499, 0, 0, 0, 0, ... None
87 AVG INCCYMEDFA carto-do.ags.demographics_sociodemographic_usa... INTEGER Median family income (2019A) carto-do.ags.demographics_sociodemographic_usa... INCCYMEDFA INCCYMEDFA_59fa177d False {'head': [67499, 87499, 0, 49999, 0, 0, 0, 0, ... None
88 AVG INCCYAVEHH carto-do.ags.demographics_sociodemographic_usa... INTEGER Average household Income (2019A) carto-do.ags.demographics_sociodemographic_usa... INCCYAVEHH INCCYAVEHH_383bfd10 False {'head': [64504, 82566, 0, 33294, 0, 0, 0, 0, ... None
89 SUM HUSEXAPT carto-do.ags.demographics_sociodemographic_usa... INTEGER UNITS IN STRUCTURE: 20 OR MORE carto-do.ags.demographics_sociodemographic_usa... HUSEXAPT HUSEXAPT_988f452f False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
90 SUM HUSEX1DET carto-do.ags.demographics_sociodemographic_usa... INTEGER UNITS IN STRUCTURE: 1 DETACHED carto-do.ags.demographics_sociodemographic_usa... HUSEX1DET HUSEX1DET_3684405c False {'head': [2, 2, 0, 9, 0, 0, 0, 0, 0, 0], 'tail... None
91 AVG HOOEXMED carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Value of Owner Occupied Housing Units carto-do.ags.demographics_sociodemographic_usa... HOOEXMED HOOEXMED_c2d4b5b False {'head': [63749, 124999, 0, 74999, 0, 0, 0, 0,... None
92 SUM HISCYHISP carto-do.ags.demographics_sociodemographic_usa... INTEGER Population Hispanic (2019A) carto-do.ags.demographics_sociodemographic_usa... HISCYHISP HISCYHISP_f3b3a31e False {'head': [0, 0, 0, 0, 36, 0, 0, 0, 0, 0], 'tai... None
93 AVG HINCYMED75 carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Household Income: Age 75+ (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCYMED75 HINCYMED75_2810f9c9 False {'head': [67500, 0, 0, 12500, 0, 0, 0, 0, 0, 0... None
94 AVG HINCY15020 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $150000-$199999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY15020 HINCY15020_21e894dd False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
95 None BLOCKGROUP carto-do.ags.demographics_sociodemographic_usa... STRING Geographic Identifier carto-do.ags.demographics_sociodemographic_usa... BLOCKGROUP BLOCKGROUP_16298bd5 False {'head': ['010159819011', '010159819021', '010... None
96 SUM LBFCYLBF carto-do.ags.demographics_sociodemographic_usa... INTEGER Population In Labor Force (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYLBF LBFCYLBF_59ce7ab0 False {'head': [0, 2, 0, 10, 378, 0, 0, 0, 0, 0], 't... None
97 SUM LBFCYARM carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ in Armed Forces (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYARM LBFCYARM_8c06223a False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
98 SUM DWLCY carto-do.ags.demographics_sociodemographic_usa... INTEGER Housing units (2019A) carto-do.ags.demographics_sociodemographic_usa... DWLCY DWLCY_e0711b62 False {'head': [5, 2, 0, 21, 0, 0, 0, 0, 0, 0], 'tai... None
99 SUM LBFCYPOP16 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population Age 16+ (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYPOP16 LBFCYPOP16_53fa921c False {'head': [6, 3, 0, 20, 959, 0, 0, 0, 0, 0], 't... None
100 SUM LBFCYEMPL carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ civilian employed (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYEMPL LBFCYEMPL_c9c22a0 False {'head': [0, 2, 0, 10, 346, 0, 0, 0, 0, 0], 't... None
101 AVG INCCYPCAP carto-do.ags.demographics_sociodemographic_usa... INTEGER Per capita income (2019A) carto-do.ags.demographics_sociodemographic_usa... INCCYPCAP INCCYPCAP_691da8ff False {'head': [53754, 33026, 0, 16647, 3753, 0, 0, ... None
102 AVG RNTEXMED carto-do.ags.demographics_sociodemographic_usa... INTEGER Median Cash Rent carto-do.ags.demographics_sociodemographic_usa... RNTEXMED RNTEXMED_2e309f54 False {'head': [0, 0, 0, 449, 0, 0, 0, 0, 0, 0], 'ta... None
103 AVG HINCY3035 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $30000-$34999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY3035 HINCY3035_4a81d422 False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
104 AVG HINCY5060 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $50000-$59999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY5060 HINCY5060_62f78b34 False {'head': [0, 0, 0, 2, 0, 0, 0, 0, 0, 0], 'tail... None
105 AVG HINCY10025 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $100000-$124999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY10025 HINCY10025_665c9060 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
106 AVG HINCY75100 carto-do.ags.demographics_sociodemographic_usa... INTEGER Household Income $75000-$99999 (2019A) carto-do.ags.demographics_sociodemographic_usa... HINCY75100 HINCY75100_9d5c69c8 False {'head': [0, 2, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
107 SUM AGECY6569 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 65-69 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY6569 AGECY6569_b47bae06 False {'head': [2, 0, 0, 7, 0, 0, 0, 0, 0, 0], 'tail... None

108 rows × 11 columns

We can see there are several variables related to population, so this is the Dataset we are looking for.

1
vdf[vdf['description'].str.contains('pop', case=False, na=False)]
agg_method column_name dataset_id db_type description id name slug starred summary_json variable_group_id
22 SUM EDUCYSHSCH carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ 9th-12th grade no diploma (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYSHSCH EDUCYSHSCH_5c444deb False {'head': [0, 0, 0, 4, 4, 0, 0, 0, 0, 0], 'tail... None
23 SUM EDUCYLTGR9 carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ less than 9th grade (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYLTGR9 EDUCYLTGR9_cbcfcc89 False {'head': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
24 SUM EDUCYHSCH carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ HS graduate (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYHSCH EDUCYHSCH_b236c803 False {'head': [5, 0, 0, 8, 14, 0, 0, 0, 0, 0], 'tai... None
25 SUM EDUCYGRAD carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ graduate or prof school degree (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYGRAD EDUCYGRAD_d0179ccb False {'head': [0, 0, 0, 1, 3, 0, 0, 0, 0, 0], 'tail... None
26 SUM EDUCYBACH carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ Bachelors degree (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYBACH EDUCYBACH_c2295f79 False {'head': [0, 0, 0, 1, 7, 0, 0, 0, 0, 0], 'tail... None
31 SUM AGECYGT85 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 85+ (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECYGT85 AGECYGT85_b9d8a94d False {'head': [1, 0, 0, 2, 2, 0, 0, 0, 0, 0], 'tail... None
32 SUM AGECYGT25 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population Age 25+ (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECYGT25 AGECYGT25_433741c7 False {'head': [6, 3, 0, 18, 41, 0, 0, 0, 0, 0], 'ta... None
33 SUM AGECYGT15 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population Age 15+ (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECYGT15 AGECYGT15_681a1204 False {'head': [6, 3, 0, 20, 959, 0, 0, 0, 0, 0], 't... None
34 SUM AGECY8084 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 80-84 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY8084 AGECY8084_b25d4aed False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
35 SUM AGECY7579 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 75-79 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY7579 AGECY7579_15dcf822 False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
36 SUM AGECY7074 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 70-74 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY7074 AGECY7074_6da64674 False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
37 SUM AGECY6064 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 60-64 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY6064 AGECY6064_cc011050 False {'head': [1, 2, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
38 SUM AGECY5559 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 55-59 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY5559 AGECY5559_8de3522b False {'head': [1, 0, 0, 2, 0, 0, 0, 0, 0, 0], 'tail... None
39 SUM AGECY5054 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 50-54 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY5054 AGECY5054_f599ec7d False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
40 SUM AGECY4549 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 45-49 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY4549 AGECY4549_2c44040f False {'head': [1, 0, 0, 3, 3, 0, 0, 0, 0, 0], 'tail... None
41 SUM AGECY4044 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 40-44 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY4044 AGECY4044_543eba59 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
42 SUM AGECY3034 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 30-34 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY3034 AGECY3034_86a81427 False {'head': [0, 0, 0, 0, 5, 0, 0, 0, 0, 0], 'tail... None
43 SUM AGECY2529 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 25-29 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY2529 AGECY2529_5f75fc55 False {'head': [0, 0, 0, 0, 31, 0, 0, 0, 0, 0], 'tai... None
44 SUM AGECY1519 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 15-19 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY1519 AGECY1519_66ed0078 False {'head': [0, 0, 0, 1, 488, 0, 0, 0, 0, 0], 'ta... None
45 SUM AGECY0509 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 5-9 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY0509 AGECY0509_c74a565c False {'head': [0, 0, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
46 SUM AGECY0004 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 0-4 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY0004 AGECY0004_bf30e80a False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
47 SUM EDUCYSCOLL carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ college no diploma (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYSCOLL EDUCYSCOLL_1e8c4828 False {'head': [0, 2, 0, 3, 10, 0, 0, 0, 0, 0], 'tai... None
49 SUM AGECY2024 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 20-24 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY2024 AGECY2024_270f4203 False {'head': [0, 0, 0, 1, 430, 0, 0, 0, 0, 0], 'ta... None
50 SUM AGECY1014 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 10-14 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY1014 AGECY1014_1e97be2e False {'head': [0, 2, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
51 SUM AGECY3539 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 35-39 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY3539 AGECY3539_fed2aa71 False {'head': [0, 1, 0, 1, 0, 0, 0, 0, 0, 0], 'tail... None
52 SUM EDUCYASSOC carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 25+ Associate degree (2019A) carto-do.ags.demographics_sociodemographic_usa... EDUCYASSOC EDUCYASSOC_fa1bcf13 False {'head': [0, 0, 0, 1, 3, 0, 0, 0, 0, 0], 'tail... None
55 SUM POPPY carto-do.ags.demographics_sociodemographic_usa... FLOAT Population (2024A) carto-do.ags.demographics_sociodemographic_usa... POPPY POPPY_946f4ed6 False {'head': [0, 0, 8, 0, 0, 0, 4, 0, 2, 59], 'tai... None
66 SUM SEXCYMAL carto-do.ags.demographics_sociodemographic_usa... INTEGER Population male (2019A) carto-do.ags.demographics_sociodemographic_usa... SEXCYMAL SEXCYMAL_ca14d4b8 False {'head': [1, 2, 0, 13, 374, 0, 0, 0, 0, 0], 't... None
67 SUM SEXCYFEM carto-do.ags.demographics_sociodemographic_usa... INTEGER Population female (2019A) carto-do.ags.demographics_sociodemographic_usa... SEXCYFEM SEXCYFEM_d52acecb False {'head': [5, 3, 0, 9, 585, 0, 0, 0, 0, 0], 'ta... None
75 SUM POPCYGRPI carto-do.ags.demographics_sociodemographic_usa... INTEGER Institutional Group Quarters Population (2019A) carto-do.ags.demographics_sociodemographic_usa... POPCYGRPI POPCYGRPI_147af7a9 False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
76 SUM POPCYGRP carto-do.ags.demographics_sociodemographic_usa... INTEGER Population in Group Quarters (2019A) carto-do.ags.demographics_sociodemographic_usa... POPCYGRP POPCYGRP_74c19673 False {'head': [0, 0, 0, 0, 959, 0, 0, 0, 0, 0], 'ta... None
77 SUM POPCY carto-do.ags.demographics_sociodemographic_usa... INTEGER Population (2019A) carto-do.ags.demographics_sociodemographic_usa... POPCY POPCY_f5800f44 False {'head': [6, 5, 0, 22, 959, 0, 0, 0, 0, 0], 't... None
84 SUM LBFCYUNEM carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ civilian unemployed (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYUNEM LBFCYUNEM_1e711de4 False {'head': [0, 0, 0, 0, 32, 0, 0, 0, 0, 0], 'tai... None
85 SUM LBFCYNLF carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ not in labor force (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYNLF LBFCYNLF_c4c98350 False {'head': [6, 1, 0, 10, 581, 0, 0, 0, 0, 0], 't... None
92 SUM HISCYHISP carto-do.ags.demographics_sociodemographic_usa... INTEGER Population Hispanic (2019A) carto-do.ags.demographics_sociodemographic_usa... HISCYHISP HISCYHISP_f3b3a31e False {'head': [0, 0, 0, 0, 36, 0, 0, 0, 0, 0], 'tai... None
96 SUM LBFCYLBF carto-do.ags.demographics_sociodemographic_usa... INTEGER Population In Labor Force (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYLBF LBFCYLBF_59ce7ab0 False {'head': [0, 2, 0, 10, 378, 0, 0, 0, 0, 0], 't... None
97 SUM LBFCYARM carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ in Armed Forces (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYARM LBFCYARM_8c06223a False {'head': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'tail... None
99 SUM LBFCYPOP16 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population Age 16+ (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYPOP16 LBFCYPOP16_53fa921c False {'head': [6, 3, 0, 20, 959, 0, 0, 0, 0, 0], 't... None
100 SUM LBFCYEMPL carto-do.ags.demographics_sociodemographic_usa... INTEGER Pop 16+ civilian employed (2019A) carto-do.ags.demographics_sociodemographic_usa... LBFCYEMPL LBFCYEMPL_c9c22a0 False {'head': [0, 2, 0, 10, 346, 0, 0, 0, 0, 0], 't... None
107 SUM AGECY6569 carto-do.ags.demographics_sociodemographic_usa... INTEGER Population age 65-69 (2019A) carto-do.ags.demographics_sociodemographic_usa... AGECY6569 AGECY6569_b47bae06 False {'head': [2, 0, 0, 7, 0, 0, 0, 0, 0, 0], 'tail... None

We can follow the very same process to discover financial datasets, so let’s see how it works. Firstly, we list the geographies available for the category financial in the US:

1
Catalog().country('usa').category('financial').geographies
[<Geography.get('mc_block_9ebc626c')>,
  <Geography.get('mc_blockgroup_c4b8da4c')>,
  <Geography.get('mc_county_31cde2d')>,
  <Geography.get('mc_state_cc31b9d1')>,
  <Geography.get('mc_tract_3704a85c')>,
  <Geography.get('mc_zipcode_263079e3')>]
  

We can clearly identify a geography at block group resolution, provided by Mastercard:

1
2
from cartoframes.data.observatory import Geography
Geography.get('mc_blockgroup_c4b8da4c').to_dict()
{'id': 'carto-do.mastercard.geography_usa_blockgroup_2019',
  'slug': 'mc_blockgroup_c4b8da4c',
  'name': 'USA Census Block Groups',
  'description': None,
  'country_id': 'usa',
  'provider_id': 'mastercard',
  'provider_name': 'Mastercard',
  'lang': 'eng',
  'geom_type': 'MULTIPOLYGON',
  'update_frequency': None,
  'version': '2019',
  'is_public_data': False}
  

Now we can list the available datasets provided by Mastercard for the US Census block groups spatial resolution:

1
Catalog().country('usa').category('financial').geography('mc_blockgroup_c4b8da4c').datasets.to_dataframe()
available_in category_id category_name country_id data_source_id description geography_description geography_id geography_name id ... lang name provider_id provider_name slug summary_json temporal_aggregation time_coverage update_frequency version
0 None financial Financial usa mrli MRLI scores validate, evaluate and benchmark t... None carto-do.mastercard.geography_usa_blockgroup_2019 USA Census Block Groups carto-do.mastercard.financial_mrli_usa_blockgr... ... eng MRLI Data for Census Block Groups mastercard Mastercard mc_mrli_35402a9d {'counts': {'rows': 1072383, 'cells': 22520043... monthly None monthly 2019

1 rows × 21 columns

Finally, let’s check the variables available in the dataset:

1
Dataset.get('mc_mrli_35402a9d').variables
[<Variable.get('transactions_st_d22b3489')> #'Same as transactions_score, but only comparing ran...',
  <Variable.get('region_id_3c7d0d92')> #'Region identifier (construction varies depending o...',
  <Variable.get('category_8c84b3a7')> #'Industry/sector categories (Total Retail, Retail e...',
  <Variable.get('month_57cd6f80')> #'Name of the month the data refers to',
  <Variable.get('region_type_d875e9e7')> #'Administrative boundary type (block, block group, ...',
  <Variable.get('stability_state_8af6b92')> #'Same as stability_score, but only comparing rankin...',
  <Variable.get('sales_score_49d02f1e')> #'Rank based on the average monthly sales for the pr...',
  <Variable.get('stability_score_6756cb72')> #'Rank based on the change in merchants between the ...',
  <Variable.get('ticket_size_sta_3bfd5114')> #'Same as ticket_size_score, but only comparing rank...',
  <Variable.get('sales_metro_sco_e088134d')> #'Same as sales_score, but only comparing ranking wi...',
  <Variable.get('transactions_me_628f6065')> #'Same as transactions_score, but only comparing ran...',
  <Variable.get('growth_score_68b3f9ac')> #'Rank based on the percent change in sales between ...',
  <Variable.get('ticket_size_met_8b5905f8')> #'Same as ticket_size_score, but only comparing rank...',
  <Variable.get('ticket_size_sco_21f7820a')> #'Rank based on the average monthly sales for the pr...',
  <Variable.get('growth_state_sc_11870b1c')> #'Same as growth_score, but only comparing ranking w...',
  <Variable.get('stability_metro_b80b3f7e')> #'Same as stability_score, but only comparing rankin...',
  <Variable.get('growth_metro_sc_a1235ff0')> #'Same as growth_score, but only comparing ranking w...',
  <Variable.get('sales_state_sco_502c47a1')> #'Same as sales_score, but only comparing ranking wi...',
  <Variable.get('transactions_sc_ee976f1e')> #'Rank based on the average number of transactions f...']

Dataset and variables metadata

The Data Observatory catalog is not only a repository of curated spatial datasets, it also contains valuable information that helps us to understand better the underlying data for every dataset, so you can take an informed decision on what data best fits your problem.

Some of the augmented metadata you can find for each dataset in the catalog is:

  • head and tail methods to get a glimpse of the actual data. This helps you to understand the available columns, data types, etc. Allowing to start modelling your problem right away.
  • geom_coverage to visualize the geographical coverage of the data in the Dataset on a map.
  • counts, fields_by_type and a full describe method with stats of the actual values in the dataset, such as: average, stdev, quantiles, min, max, median for each of the variables of the dataset.

You don’t need a subscription to a dataset to be able to query the augmented metadata, it’s just publicly available for anyone exploring the Data Observatory catalog.

Let’s take a look at some of that information, starting by getting a glimpse of the ten first or last rows of the actual data of the dataset:

1
2
from cartoframes.data.observatory import Dataset
dataset = Dataset.get('ags_sociodemogr_e92b1637')
1
dataset.head()
DWLCY HHDCY POPCY VPHCY1 AGECYMED HHDCYFAM HOOEXMED HUSEXAPT LBFCYARM LBFCYLBF ... MARCYDIVOR MARCYNEVER MARCYWIDOW RCHCYAMNHS RCHCYASNHS RCHCYBLNHS RCHCYHANHS RCHCYMUNHS RCHCYOTNHS RCHCYWHNHS
0 5 5 6 0 64.00 1 63749 0 0 0 ... 0 0 0 0 0 0 0 0 0 6
1 2 2 5 1 36.50 2 124999 0 0 2 ... 0 1 0 0 0 3 0 0 0 2
2 0 0 0 0 0.00 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
3 21 11 22 4 64.00 6 74999 0 0 10 ... 4 13 2 0 0 22 0 0 0 0
4 0 0 959 0 18.91 0 0 0 0 378 ... 0 959 0 5 53 230 0 25 0 609
5 0 0 0 0 0.00 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
6 0 0 0 0 0.00 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
7 0 0 0 0 0.00 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
8 0 0 0 0 0.00 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
9 0 0 0 0 0.00 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

10 rows × 101 columns

Alternatively, you can get the last ten ones with dataset.tail()

An overview of the coverage of the dataset

1
dataset.geom_coverage()

Some stats about the dataset:

1
dataset.counts()
rows                    217182
cells                 22369746
null_cells                   0
null_cells_percent           0
dtype: int64
1
dataset.fields_by_type()
float       4
string      1
integer    96
dtype: int64
1
dataset.describe()
HINCYMED65 HINCYMED55 HINCYMED45 HINCYMED35 HINCYMED25 HINCYMED24 HINCYGT200 HINCY6075 HINCY4550 HINCY4045 ... DWLCY LBFCYPOP16 LBFCYEMPL INCCYPCAP RNTEXMED HINCY3035 HINCY5060 HINCY10025 HINCY75100 AGECY6569
avg 6.195559e+04 7.513449e+04 8.297294e+04 7.907689e+04 6.610137e+04 4.765168e+04 4.236225e+01 5.938193e+01 2.406235e+01 2.483668e+01 ... 6.420374e+02 1.218212e+03 7.402907e+02 3.451758e+04 9.315027e+02 2.416786e+01 4.542230e+01 4.876603e+01 8.272891e+01 8.051784e+01
max 3.500000e+05 3.500000e+05 3.500000e+05 3.500000e+05 3.500000e+05 3.500000e+05 4.812000e+03 3.081000e+03 9.530000e+02 1.293000e+03 ... 2.800700e+04 4.707100e+04 3.202300e+04 2.898428e+06 3.999000e+03 7.290000e+02 1.981000e+03 3.231000e+03 4.432000e+03 7.777000e+03
min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
sum 1.345564e+10 1.631786e+10 1.802023e+10 1.717408e+10 1.435603e+10 1.034909e+10 9.200319e+06 1.289669e+07 5.225909e+06 5.394080e+06 ... 1.394390e+08 2.645738e+08 1.607778e+08 7.496597e+09 2.023056e+08 5.248825e+06 9.864907e+06 1.059110e+07 1.796723e+07 1.748702e+07
range 3.500000e+05 3.500000e+05 3.500000e+05 3.500000e+05 3.500000e+05 3.500000e+05 4.812000e+03 3.081000e+03 9.530000e+02 1.293000e+03 ... 2.800700e+04 4.707100e+04 3.202300e+04 2.898428e+06 3.999000e+03 7.290000e+02 1.981000e+03 3.231000e+03 4.432000e+03 7.777000e+03
stdev 3.377453e+04 4.102797e+04 4.392996e+04 3.932575e+04 2.741347e+04 2.948443e+04 7.601699e+01 4.940854e+01 2.227745e+01 2.245616e+01 ... 4.051570e+02 8.107703e+02 5.421818e+02 2.302276e+04 4.772473e+02 2.167522e+01 3.882000e+01 4.946218e+01 7.159705e+01 5.888055e+01
q1 3.625000e+04 4.285700e+04 4.785700e+04 4.833300e+04 4.454500e+04 2.625000e+04 0.000000e+00 2.400000e+01 8.000000e+00 8.000000e+00 ... 3.740000e+02 6.930000e+02 3.920000e+02 1.910900e+04 5.520000e+02 7.000000e+00 1.700000e+01 1.500000e+01 3.400000e+01 4.300000e+01
q3 6.228300e+04 7.596200e+04 8.415200e+04 8.030300e+04 6.890600e+04 4.916700e+04 2.600000e+01 5.900000e+01 2.400000e+01 2.500000e+01 ... 6.230000e+02 1.172000e+03 7.150000e+02 3.351600e+04 9.250000e+02 2.400000e+01 4.500000e+01 4.600000e+01 8.000000e+01 7.800000e+01
median 4.937500e+04 5.916700e+04 6.571400e+04 6.375000e+04 5.700000e+04 3.750000e+04 8.000000e+00 4.000000e+01 1.500000e+01 1.600000e+01 ... 4.860000e+02 9.090000e+02 5.350000e+02 2.615000e+04 7.190000e+02 1.500000e+01 3.000000e+01 3.000000e+01 5.600000e+01 5.900000e+01
interquartile_range 2.603300e+04 3.310500e+04 3.629500e+04 3.197000e+04 2.436100e+04 2.291700e+04 2.600000e+01 3.500000e+01 1.600000e+01 1.700000e+01 ... 2.490000e+02 4.790000e+02 3.230000e+02 1.440700e+04 3.730000e+02 1.700000e+01 2.800000e+01 3.100000e+01 4.600000e+01 3.500000e+01

10 rows × 107 columns

Every Dataset instance in the catalog contains other useful metadata:

  • slug: A short ID
  • name and description: Free text attributes
  • country
  • geography: Every dataset is related to a Geography instance
  • category
  • provider
  • data source
  • lang
  • temporal aggregation
  • time coverage
  • update frequency
  • version
  • is_public_data: whether you need a license to use the dataset for enrichment purposes or not
1
dataset.to_dict()
{
  'id': 'carto-do.ags.demographics_sociodemographic_usa_blockgroup_2015_yearly_2019',
  'slug': 'ags_sociodemogr_e92b1637',
  'name': 'Sociodemographic',
  'description': 'Census and ACS sociodemographic data estimated for the current year and data projected to five years. Projected fields are general aggregates (total population, total households, median age, avg income etc.)',
  'country_id': 'usa',
  'geography_id': 'carto-do.ags.geography_usa_blockgroup_2015',
  'geography_name': 'USA Census Block Group',
  'geography_description': None,
  'category_id': 'demographics',
  'category_name': 'Demographics',
  'provider_id': 'ags',
  'provider_name': 'Applied Geographic Solutions',
  'data_source_id': 'sociodemographic',
  'lang': 'eng',
  'temporal_aggregation': 'yearly',
  'time_coverage': '[2019-01-01,2020-01-01)',
  'update_frequency': None,
  'version': '2019',
  'is_public_data': False
}

There’s also some interesting metadata, for each variable in the dataset:

  • id
  • slug: A short ID
  • name and description
  • column_name: Actual column name in the table that contains the data
  • db_type: SQL type in the database
  • dataset_id
  • agg_method: Aggregation method used
  • temporal aggregation and time coverage

Variables are the most important asset in the catalog and when exploring datasets in the Data Observatory catalog it’s very important that you understand clearly what variables are available to enrich your own data.

For each Variable in each dataset, the Data Observatory provides (as it does with datasets) a set of methods and attributes to understand their underlying data.

Some of them are:

  • head and tail methods to get a glimpse of the actual data and start modelling your problem right away.
  • counts, quantiles and a full describe method with stats of the actual values in the dataset, such as: average, stdev, quantiles, min, max, median for each of the variables of the dataset.
  • a histogram plot with the distribution of the values on each variable.

Let’s look at some of that augmented metadata for the variables in the AGS population dataset.

1
2
3
from cartoframes.data.observatory import Variable
variable = Variable.get('POPPY_946f4ed6')
variable

  <Variable.get('POPPY_946f4ed6')> #'Population (2024A)'
1
variable.to_dict()
{'id': 'carto-do.ags.demographics_sociodemographic_usa_blockgroup_2015_yearly_2019.POPPY',
    'slug': 'POPPY_946f4ed6',
    'name': 'POPPY',
    'description': 'Population (2024A)',
    'column_name': 'POPPY',
    'db_type': 'FLOAT',
    'dataset_id': 'carto-do.ags.demographics_sociodemographic_usa_blockgroup_2015_yearly_2019',
    'agg_method': 'SUM',
    'variable_group_id': None,
    'starred': False}

There are also some utility methods to understand the underlying data for each variable:

1
variable.head()
0     0
1     0
2     8
3     0
4     0
5     0
6     4
7     0
8     2
9    59
dtype: int64
1
variable.counts()
all                 217182.000000
null                     0.000000
zero                   303.000000
extreme               9380.000000
distinct              6947.000000
outliers             27571.000000
null_percent             0.000000
zero_percent             0.139514
extreme_percent          0.043190
distinct_percent         3.198700
outliers_percent         0.126949
dtype: float64
1
variable.quantiles()

q1                      867
q3                     1490
median                 1149
interquartile_range     623
dtype: int64
1
variable.histogram()

png

1
variable.describe()
avg                    1.564793e+03
    max                    7.127400e+04
    min                    0.000000e+00
    sum                    3.398448e+08
    range                  7.127400e+04
    stdev                  1.098193e+03
    q1                     8.670000e+02
    q3                     1.490000e+03
    median                 1.149000e+03
    interquartile_range    6.230000e+02
    dtype: float64

Subscribe to a Dataset in the catalog

Once you have explored the catalog and have detected a dataset with the variables you need for your analysis and the right spatial resolution, you have to look at the is_public_data to know if you can simply use it from CARTOframes or if you need to subscribe for a license first.

Subscriptions to datasets allow you to use them from CARTOframes to enrich your own data or to download them. See the enrichment guides for more information about this.

Let’s see the dataset and geography in our previous example:

1
dataset = Dataset.get('ags_sociodemogr_e92b1637')
1
dataset.is_public_data
False
1
2
from cartoframes.data.observatory import Geography
geography = Geography.get(dataset.geography)
1
geography.is_public_data
False

Both dataset and geography are not public data, this means you need a subscription to be able to use them to enrich your own data.

To subscribe to data in the Data Observatory catalog you need a CARTO account with access to the Data Observatory. See the credentials guide for more info on this topic.

1
2
3
from cartoframes.auth import set_default_credentials
set_default_credentials('creds.json')
dataset.subscribe()

png

1
geography.subscribe()

png

Licenses to data in the Data Observatory grant you the right to use the data subscribed for a period of one year. Every dataset or geography you want to use to enrich your own data, as long as they are not public data, require a valid license.

You can check the actual status of your subscriptions directly from the catalog.

1
Catalog().subscriptions()
Datasets: None
Geographies: None

About nested filters in the Catalog instance

Please note that every time you search the catalog you create a new instance of the Catalog class. Alternatively, when applying country, category and geography filters a catalog instance, you can reuse the same instance of the catalog by using the catalog.clean_filters() method.

So for example, if you’ve filtered the catalog this way:

1
2
catalog = Catalog()
catalog.country('usa').category('demographics').datasets
[<Dataset.get('od_acs_181619a3')>,
  <Dataset.get('od_acs_38016c42')>,
  <Dataset.get('od_acs_1f614ee8')>,
  <Dataset.get('od_acs_c6bf32c9')>,
  <Dataset.get('od_acs_91ff81e3')>,
  <Dataset.get('od_acs_13345497')>,
  <Dataset.get('od_acs_87fa66db')>,
  <Dataset.get('od_acs_b98db80e')>,
  <Dataset.get('od_acs_9f4d1f13')>,
  <Dataset.get('od_acs_5b67fbbf')>,
  <Dataset.get('od_acs_29664073')>,
  <Dataset.get('od_acs_4bb9b377')>,
  <Dataset.get('od_acs_9df157a1')>,
  <Dataset.get('od_acs_550657ce')>,
  <Dataset.get('od_tiger_19a6dc83')>,
  <Dataset.get('od_acs_6e4b69f6')>,
  <Dataset.get('od_acs_1a22afad')>,
  <Dataset.get('od_acs_9510981d')>,
  <Dataset.get('od_acs_6d43ed82')>,
  <Dataset.get('od_acs_dc3cfd0f')>,
  <Dataset.get('od_acs_194c5960')>,
  <Dataset.get('od_acs_9a9c93b8')>,
  <Dataset.get('od_acs_7b2649a9')>,
  <Dataset.get('od_acs_478c37b8')>,
  <Dataset.get('od_acs_f98ddfce')>,
  <Dataset.get('od_acs_8b00f653')>,
  <Dataset.get('od_acs_d52a0635')>,
  <Dataset.get('od_acs_1deaa51')>,
  <Dataset.get('od_acs_e0f5ff55')>,
  <Dataset.get('od_acs_52710085')>,
  <Dataset.get('od_acs_b3eac6e8')>,
  <Dataset.get('od_acs_e9e3046f')>,
  <Dataset.get('od_acs_506e3e6a')>,
  <Dataset.get('od_acs_b4cbd26')>,
  <Dataset.get('od_acs_fc07c6c5')>,
  <Dataset.get('od_acs_a1083df8')>,
  <Dataset.get('od_tiger_3336cbf')>,
  <Dataset.get('od_acs_1a09274c')>,
  <Dataset.get('od_tiger_66b9092c')>,
  <Dataset.get('od_acs_db9898c5')>,
  <Dataset.get('od_acs_670c8beb')>,
  <Dataset.get('od_acs_6926adef')>,
  <Dataset.get('mbi_population_678f3375')>,
  <Dataset.get('mbi_retail_spen_e2c1988e')>,
  <Dataset.get('mbi_retail_spen_14142fb4')>,
  <Dataset.get('ags_sociodemogr_e92b1637')>,
  <Dataset.get('ags_consumerspe_fe5d060a')>,
  <Dataset.get('od_acs_e8a7d88d')>,
  <Dataset.get('od_acs_60614ff2')>,
  <Dataset.get('od_acs_f09b24f4')>,
  <Dataset.get('od_acs_1cfa643a')>,
  <Dataset.get('od_acs_c4a00c26')>,
  <Dataset.get('od_acs_c1c86582')>,
  <Dataset.get('od_acs_5b8fdefd')>,
  <Dataset.get('mbi_population_341ee33b')>,
  <Dataset.get('od_spielmansin_5d03106a')>,
  <Dataset.get('mbi_households__109a963')>,
  <Dataset.get('od_acs_c2868f47')>,
  <Dataset.get('od_acs_b581bfd1')>,
  <Dataset.get('od_acs_2d438a42')>,
  <Dataset.get('od_acs_aa92e673')>,
  <Dataset.get('od_acs_1db77442')>,
  <Dataset.get('od_acs_f3eaa128')>,
  <Dataset.get('od_tiger_e5e51d96')>,
  <Dataset.get('od_tiger_41814018')>,
  <Dataset.get('od_tiger_b0608dc7')>,
  <Dataset.get('ags_retailpoten_ddf56a1a')>,
  <Dataset.get('ags_consumerpro_e8344e2e')>,
  <Dataset.get('ags_businesscou_a8310a11')>,
  <Dataset.get('od_acs_5c10acf4')>,
  <Dataset.get('mbi_households__45067b14')>,
  <Dataset.get('od_acs_d28e63ff')>,
  <Dataset.get('ags_sociodemogr_e128078d')>,
  <Dataset.get('ags_crimerisk_9ec89442')>,
  <Dataset.get('od_acs_a9825694')>,
  <Dataset.get('od_tiger_5e55275d')>,
  <Dataset.get('od_acs_a665f9e1')>,
  <Dataset.get('od_acs_5ec6965e')>,
  <Dataset.get('od_acs_f2f40516')>,
  <Dataset.get('od_acs_1209a7e9')>,
  <Dataset.get('od_acs_6c9090b5')>,
  <Dataset.get('od_acs_f9681e48')>,
  <Dataset.get('od_acs_8c8516b')>,
  <Dataset.get('od_acs_59534db1')>,
  <Dataset.get('od_acs_57d06d64')>,
  <Dataset.get('od_acs_6bfd54ac')>,
  <Dataset.get('od_tiger_f9247903')>,
  <Dataset.get('od_acs_abd63a91')>,
  <Dataset.get('mbi_households__981be2e8')>,
  <Dataset.get('od_acs_e1b123b7')>,
  <Dataset.get('od_acs_c31e5f28')>,
  <Dataset.get('od_tiger_476ce2e9')>,
  <Dataset.get('od_tiger_fac69779')>,
  <Dataset.get('od_tiger_384d0b09')>,
  <Dataset.get('od_acs_7c4b8db0')>,
  <Dataset.get('od_acs_eaf66737')>,
  <Dataset.get('od_lodes_b4b9dfac')>,
  <Dataset.get('od_acs_17667f64')>,
  <Dataset.get('od_acs_8c6d324a')>,
  <Dataset.get('od_acs_d60f0d6e')>,
  <Dataset.get('od_tiger_e10059f')>,
  <Dataset.get('od_acs_4f56aa89')>,
  <Dataset.get('od_acs_d9e8a21b')>,
  <Dataset.get('od_acs_c5eb4b5e')>,
  <Dataset.get('od_acs_de856602')>,
  <Dataset.get('od_acs_5978c550')>,
  <Dataset.get('mbi_purchasing__53ab279d')>,
  <Dataset.get('mbi_purchasing__d7fd187')>,
  <Dataset.get('mbi_consumer_sp_54c4abc3')>,
  <Dataset.get('mbi_sociodemogr_b5516832')>,
  <Dataset.get('mbi_households__c943a740')>,
  <Dataset.get('mbi_households__d75b838')>,
  <Dataset.get('mbi_population_d3c82409')>,
  <Dataset.get('mbi_education_53d49ab0')>,
  <Dataset.get('mbi_education_5139bb8a')>,
  <Dataset.get('mbi_education_ecd69207')>,
  <Dataset.get('mbi_consumer_sp_b6a3b235')>,
  <Dataset.get('mbi_consumer_sp_9f31484d')>,
  <Dataset.get('mbi_households__1de12da2')>,
  <Dataset.get('mbi_households__b277b08f')>,
  <Dataset.get('mbi_consumer_pr_8e977645')>,
  <Dataset.get('mbi_retail_spen_ab162703')>,
  <Dataset.get('mbi_retail_spen_c31f0ba0')>,
  <Dataset.get('mbi_retail_cent_eab3bd00')>,
  <Dataset.get('mbi_retail_turn_705247a')>,
  <Dataset.get('mbi_purchasing__31cd621')>,
  <Dataset.get('mbi_purchasing__b27dd930')>,
  <Dataset.get('mbi_consumer_pr_31957ef2')>,
  <Dataset.get('mbi_consumer_pr_55b2234f')>,
  <Dataset.get('mbi_consumer_pr_68d1265a')>,
  <Dataset.get('mbi_population_d88d3bc2')>,
  <Dataset.get('mbi_education_20063878')>,
  <Dataset.get('mbi_retail_cent_55b1b5b7')>,
  <Dataset.get('mbi_sociodemogr_285eaf93')>,
  <Dataset.get('mbi_sociodemogr_bd619b07')>,
  <Dataset.get('mbi_retail_turn_b8072ccd')>,
  <Dataset.get('mbi_sociodemogr_975ca724')>,
  <Dataset.get('mbi_consumer_sp_9a1ba82')>,
  <Dataset.get('mbi_households__be0ba1d4')>
]

Now, if you want to use the financial datasets, you should:

  1. Create a new instance of the catalog: catalog = Catalog()
  2. Call to catalog.clean_filters() over the existing instance.

Although a recommended way to discover data is nesting filters over a Catalog instance, you don’t need to follow the complete hierarchy (country, category, geography) to list the available datasets.

Alternatively, you can just list all the datasets in the US or list all the datasets for the demographics category, and continue exploring the catalog locally with pandas.

Let’s see an example of that, in which we filter public data for the demographics category worldwide:

1
2
df = Catalog().category('demographics').datasets.to_dataframe()
df[df['is_public_data'] == True]
available_in category_id category_name country_id data_source_id description geography_description geography_id geography_name id ... lang name provider_id provider_name slug summary_json temporal_aggregation time_coverage update_frequency version
8 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_pumac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at pumacl... open_data Open Data od_acs_181619a3 None 5yrs [2010-01-01,2014-01-01) None 20102014
9 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at placec... open_data Open Data od_acs_38016c42 None yearly [2017-01-01,2018-01-01) None 2017
10 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_count... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at county... open_data Open Data od_acs_1f614ee8 None 5yrs [2010-01-01,2014-01-01) None 20102014
13 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_pumac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at pumacl... open_data Open Data od_acs_c6bf32c9 None 5yrs [2013-01-01,2018-01-01) None 20132017
14 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_block... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at blockg... open_data Open Data od_acs_91ff81e3 None 5yrs [2010-01-01,2014-01-01) None 20102014
16 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at placec... open_data Open Data od_acs_13345497 None 5yrs [2011-01-01,2015-01-01) None 20112015
17 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at placec... open_data Open Data od_acs_87fa66db None 5yrs [2013-01-01,2018-01-01) None 20132017
20 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_zcta5... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at zcta5c... open_data Open Data od_acs_b98db80e None 5yrs [2011-01-01,2015-01-01) None 20112015
21 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_9f4d1f13 None 5yrs [2010-01-01,2014-01-01) None 20102014
22 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_pumac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at pumacl... open_data Open Data od_acs_5b67fbbf None 5yrs [2006-01-01,2010-01-01) None 20062010
23 None demographics Demographics usa acs None None carto-do-public-data.usa_carto.geography_usa_c... Topologically Integrated Geographic Encoding a... carto-do-public-data.usa_acs.demographics_acs_... ... eng American Community Survey (ACS) data at census... usa_acs USA American Community Survey od_acs_29664073 None 5yrs [2013-01-01,2018-01-01) None 20132017
27 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_cbsac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at cbsacl... open_data Open Data od_acs_4bb9b377 None 5yrs [2010-01-01,2014-01-01) None 20102014
28 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_9df157a1 None yearly [2014-01-01,2015-01-01) None 2014
29 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_count... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at county... open_data Open Data od_acs_550657ce None 5yrs [2011-01-01,2015-01-01) None 20112015
31 None demographics Demographics usa tiger None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.tiger.demographics_tiger_... ... eng Topologically Integrated Geographic Encoding a... open_data Open Data od_tiger_19a6dc83 None yearly [2015-01-01,2016-01-01) None 2015
34 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_6e4b69f6 None yearly [2014-01-01,2015-01-01) None 2014
39 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at placec... open_data Open Data od_acs_1a22afad None 5yrs [2006-01-01,2010-01-01) None 20062010
52 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_cbsac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at cbsacl... open_data Open Data od_acs_9510981d None 5yrs [2013-01-01,2018-01-01) None 20132017
53 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_congr... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at congre... open_data Open Data od_acs_6d43ed82 None 5yrs [2013-01-01,2018-01-01) None 20132017
57 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_dc3cfd0f None 5yrs [2006-01-01,2010-01-01) None 20062010
85 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_194c5960 None yearly [2015-01-01,2016-01-01) None 2015
90 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_9a9c93b8 None yearly [2010-01-01,2011-01-01) None 2010
91 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_7b2649a9 None yearly [2010-01-01,2011-01-01) None 2010
92 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_cbsac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at cbsacl... open_data Open Data od_acs_478c37b8 None yearly [2017-01-01,2018-01-01) None 2017
93 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_congr... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at congre... open_data Open Data od_acs_f98ddfce None 5yrs [2011-01-01,2015-01-01) None 20112015
96 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_pumac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at pumacl... open_data Open Data od_acs_8b00f653 None yearly [2014-01-01,2015-01-01) None 2014
97 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_d52a0635 None 5yrs [2011-01-01,2015-01-01) None 20112015
98 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_cbsac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at cbsacl... open_data Open Data od_acs_1deaa51 None 5yrs [2011-01-01,2015-01-01) None 20112015
99 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_e0f5ff55 None 5yrs [2011-01-01,2015-01-01) None 20112015
100 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_pumac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at pumacl... open_data Open Data od_acs_52710085 None 5yrs [2011-01-01,2015-01-01) None 20112015
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
408 None demographics Demographics usa tiger None None carto-do-public-data.tiger.geography_usa_censu... Topologically Integrated Geographic Encoding a... carto-do-public-data.tiger.demographics_tiger_... ... eng Topologically Integrated Geographic Encoding a... open_data Open Data od_tiger_5e55275d None yearly [2015-01-01,2016-01-01) None 2015
409 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at placec... open_data Open Data od_acs_a665f9e1 None yearly [2010-01-01,2011-01-01) None 2010
410 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_state... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at statec... open_data Open Data od_acs_5ec6965e None 5yrs [2006-01-01,2010-01-01) None 20062010
411 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_congr... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at congre... open_data Open Data od_acs_f2f40516 None yearly [2017-01-01,2018-01-01) None 2017
412 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_pumac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at pumacl... open_data Open Data od_acs_1209a7e9 None yearly [2017-01-01,2018-01-01) None 2017
413 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_congr... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at congre... open_data Open Data od_acs_6c9090b5 None yearly [2010-01-01,2011-01-01) None 2010
414 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_state... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at statec... open_data Open Data od_acs_f9681e48 None yearly [2017-01-01,2018-01-01) None 2017
415 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_cbsac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at cbsacl... open_data Open Data od_acs_8c8516b None 5yrs [2006-01-01,2010-01-01) None 20062010
416 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at placec... open_data Open Data od_acs_59534db1 None 5yrs [2010-01-01,2014-01-01) None 20102014
417 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_state... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at statec... open_data Open Data od_acs_57d06d64 None 5yrs [2011-01-01,2015-01-01) None 20112015
418 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_congr... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at congre... open_data Open Data od_acs_6bfd54ac None yearly [2014-01-01,2015-01-01) None 2014
419 None demographics Demographics usa tiger None None carto-do-public-data.tiger.geography_usa_state... Topologically Integrated Geographic Encoding a... carto-do-public-data.tiger.demographics_tiger_... ... eng Topologically Integrated Geographic Encoding a... open_data Open Data od_tiger_f9247903 None yearly [2015-01-01,2016-01-01) None 2015
420 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_abd63a91 None 5yrs [2010-01-01,2014-01-01) None 20102014
422 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_e1b123b7 None 5yrs [2011-01-01,2015-01-01) None 20112015
423 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_state... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at statec... open_data Open Data od_acs_c31e5f28 None 5yrs [2013-01-01,2018-01-01) None 20132017
432 None demographics Demographics usa tiger None None carto-do-public-data.tiger.geography_usa_block... Topologically Integrated Geographic Encoding a... carto-do-public-data.tiger.demographics_tiger_... ... eng Topologically Integrated Geographic Encoding a... open_data Open Data od_tiger_476ce2e9 None yearly [2015-01-01,2016-01-01) None 2015
433 None demographics Demographics usa tiger None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.tiger.demographics_tiger_... ... eng Topologically Integrated Geographic Encoding a... open_data Open Data od_tiger_fac69779 None yearly [2015-01-01,2016-01-01) None 2015
434 None demographics Demographics usa tiger None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.tiger.demographics_tiger_... ... eng Topologically Integrated Geographic Encoding a... open_data Open Data od_tiger_384d0b09 None yearly [2015-01-01,2016-01-01) None 2015
435 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_7c4b8db0 None yearly [2014-01-01,2015-01-01) None 2014
436 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_schoo... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at school... open_data Open Data od_acs_eaf66737 None yearly [2015-01-01,2016-01-01) None 2015
437 None demographics Demographics usa lodes None None carto-do-public-data.tiger.geography_usa_block... Topologically Integrated Geographic Encoding a... carto-do-public-data.lodes.demographics_lodes_... ... eng LEHD Origin-Destination Employment Statistics ... open_data Open Data od_lodes_b4b9dfac None yearly [2013-01-01,2014-01-01) None 2013
438 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_state... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at statec... open_data Open Data od_acs_17667f64 None yearly [2015-01-01,2016-01-01) None 2015
439 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_pumac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at pumacl... open_data Open Data od_acs_8c6d324a None yearly [2010-01-01,2011-01-01) None 2010
440 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_place... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at placec... open_data Open Data od_acs_d60f0d6e None yearly [2015-01-01,2016-01-01) None 2015
441 None demographics Demographics usa tiger None None carto-do-public-data.tiger.geography_usa_congr... Topologically Integrated Geographic Encoding a... carto-do-public-data.tiger.demographics_tiger_... ... eng Topologically Integrated Geographic Encoding a... open_data Open Data od_tiger_e10059f None yearly [2015-01-01,2016-01-01) None 2015
442 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_block... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at blockg... open_data Open Data od_acs_4f56aa89 None 5yrs [2013-01-01,2018-01-01) None 20132017
443 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_cbsac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at cbsacl... open_data Open Data od_acs_d9e8a21b None yearly [2010-01-01,2011-01-01) None 2010
448 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_count... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at county... open_data Open Data od_acs_c5eb4b5e None yearly [2010-01-01,2011-01-01) None 2010
450 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_cbsac... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at cbsacl... open_data Open Data od_acs_de856602 None yearly [2014-01-01,2015-01-01) None 2014
451 None demographics Demographics usa acs None None carto-do-public-data.tiger.geography_usa_censu... Topologically Integrated Geographic Encoding a... carto-do-public-data.acs.demographics_acs_usa_... ... eng American Community Survey (ACS) data at census... open_data Open Data od_acs_5978c550 None 5yrs [2006-01-01,2010-01-01) None 20062010

92 rows × 21 columns

Conclusion

In this guide we’ve presented how to explore the Data Observatory catalog to identify variables of datasets that we can use to enrich our own data.

We’ve learnt how to:

  • Discover the catalog using nested hierarchical filters.
  • Describe the three main entities in the catalog: Geography, Dataset and their Variables
  • Get a glimpse of the data and stats taken from the actual repository, for a better informed decision on what variables to choose.
  • Subscribe to the chosen datasets to get a license that grants the right to enrich your own data.

We recommend you also check out these resources if you want to know more about the Data Observatory catalog: