Observations Dataframe
In addition to the Observation endpoint, the client provides direct access to a special property method, observations_dataframe which provides the same functionality, but returns results as Pandas DataFrames.
Note: To use this feature, you must have installed the
Pandasmodule. See Install the Python Data Commons V2 API for details.
observations_dataframe
Fetches observations for specified variables, dates, and entities, by DCID or entity type.
Signature
observations_dataframe(variable_dcids, date, entity_dcids, entity_type, parent_entity, property_filters)
Input parameters
| Name | Type | Description |
|---|---|---|
| variable_dcids |
string or list of strings | One or more DCIDs of the statistical variables to query. |
| date |
string or string literal | The date (and time) for which the observations are being requested. Allowed values are: - "latest": return the latest observations. One observation is returned for each specified entity and variable, for each provenance of the data. - A string in ISO-8601 format that specifies the date and time used by the target variable; for example, 2020 or 2010-12.- "all": Get all observations for the specified variables and entities. |
| entity_dcids |
string or list of strings or string literal | By default this is set to "all", in which case you must use the entity_type parameter, to limit the results to a given type. To limit to specific entities, set this to one or more DCIDs of the entities to query. |
| entity_type | string | The DCID of the type of the entities to query; for example, Country or Region. Required when entity_dcids is set to "all" (the default); invalid otherwise. |
| parent_entity | string | The DCID of the parent entities to query; for example, africa for African countries, or Earth for all countries. Required when entity_dcids is set to "all" (the default); invalid otherwise. |
| property_filters |
dict mapping a string to a string or list of strings | The observation properties by which to filter the results, where the key is the observation property, such as measurementMethod, unit, or observationPeriod, and the value is the list of values to filter by. |
Examples
Example 1: Get all observations for a single variable and entity
This example retrieves the count of men in the state of Arkansas over all data history.
Request:
client.observations_dataframe(variable_dcids="Count_Person_Male", date="all", entity_dcids="geoId/05")
Response:
(truncated)
date entity entity_name variable ... measurementMethod observationPeriod provenanceUrl unit
0 2011 geoId/05 Arkansas Count_Person_Male ... CensusACS5yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
1 2012 geoId/05 Arkansas Count_Person_Male ... CensusACS5yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
2 2013 geoId/05 Arkansas Count_Person_Male ... CensusACS5yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
3 2014 geoId/05 Arkansas Count_Person_Male ... CensusACS5yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
4 2015 geoId/05 Arkansas Count_Person_Male ... CensusACS5yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
.. ... ... ... ... ... ... ... ... ...
191 2015 geoId/05 Arkansas Count_Person_Male ... CensusACS1yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
192 2016 geoId/05 Arkansas Count_Person_Male ... CensusACS1yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
193 2017 geoId/05 Arkansas Count_Person_Male ... CensusACS1yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
194 2018 geoId/05 Arkansas Count_Person_Male ... CensusACS1yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
195 2019 geoId/05 Arkansas Count_Person_Male ... CensusACS1yrSurvey None https://www.census.gov/programs-surveys/acs/da... None
[196 rows x 12 columns]
Example 2: Get all observations for a single variable and multiple entities
This example compares the historic populations of Sudan and South Sudan.
Request:
client.observations_dataframe(variable_dcids="Count_Person", date="all", entity_dcids=["country/SSD","country/SDN"])
Response:
(truncated)
date entity entity_name variable ... measurementMethod observationPeriod provenanceUrl unit
0 1960 country/SSD South Sudan Count_Person ... None P1Y https://datacatalog.worldbank.org/dataset/worl... None
1 1961 country/SSD South Sudan Count_Person ... None P1Y https://datacatalog.worldbank.org/dataset/worl... None
2 1962 country/SSD South Sudan Count_Person ... None P1Y https://datacatalog.worldbank.org/dataset/worl... None
3 1963 country/SSD South Sudan Count_Person ... None P1Y https://datacatalog.worldbank.org/dataset/worl... None
4 1964 country/SSD South Sudan Count_Person ... None P1Y https://datacatalog.worldbank.org/dataset/worl... None
.. ... ... ... ... ... ... ... ... ...
165 2016 country/SDN Sudan Count_Person ... WorldBankSubnationalPopulationEstimate P1Y https://databank.worldbank.org/source/subnatio... None
166 2024 country/SDN Sudan Count_Person ... Wikipedia None https://www.wikipedia.org None
167 2008 country/SDN Sudan Count_Person ... WikidataPopulation None https://www.wikidata.org/wiki/Wikidata:Main_Page None
168 2015 country/SDN Sudan Count_Person ... WikidataPopulation None https://www.wikidata.org/wiki/Wikidata:Main_Page None
169 2017 country/SDN Sudan Count_Person ... WikidataPopulation None https://www.wikidata.org/wiki/Wikidata:Main_Page None
[170 rows x 12 columns]
Example 3: Get all observations for multiple variables and multiple entities
This example compares the historic populations, median ages, and unemployment rates of the US, California, and Santa Clara County.
Request:
client.observations_dataframe(variable_dcids=["Count_Person", "Median_Age_Person", "UnemploymentRate_Person"], date="all", entity_dcids=["country/USA", "geoId/06", "geoId/06085"])
Response:
(truncated)
date entity entity_name variable ... measurementMethod observationPeriod provenanceUrl unit
0 1900 geoId/06 California Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
1 1901 geoId/06 California Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
2 1902 geoId/06 California Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
3 1903 geoId/06 California Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
4 1904 geoId/06 California Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
... ... ... ... ... ... ... ... ... ...
4151 2014 geoId/06085 Santa Clara County UnemploymentRate_Person ... None None https://www.atsdr.cdc.gov/placeandhealth/svi/d... None
4152 2016 geoId/06085 Santa Clara County UnemploymentRate_Person ... None None https://www.atsdr.cdc.gov/placeandhealth/svi/d... None
4153 2018 geoId/06085 Santa Clara County UnemploymentRate_Person ... None None https://www.atsdr.cdc.gov/placeandhealth/svi/d... None
4154 2020 geoId/06085 Santa Clara County UnemploymentRate_Person ... None None https://www.atsdr.cdc.gov/placeandhealth/svi/d... None
4155 2022 geoId/06085 Santa Clara County UnemploymentRate_Person ... None None https://www.atsdr.cdc.gov/placeandhealth/svi/d... None
[4156 rows x 12 columns]
Example 4: Get latest observations for a single variable and multiple entities, limited by type and parent
Ths example gets all observatons for the proportion of population below the international poverty line for all countries in Africa.
Request:
client.observations_dataframe(variable_dcids="sdg/SI_POV_DAY1", date="latest", entity_type="Country", parent_entity="africa")
Response:
date entity entity_name variable ... measurementMethod observationPeriod provenanceUrl unit
0 2012 country/COD Congo [DRC] sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
1 2016 country/SWZ Eswatini sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
2 2018 country/GIN Guinea sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
3 2018 country/GNB Guinea-Bissau sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
34 2016 country/SSD South Sudan sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
35 2016 country/LBR Liberia sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
36 2014 country/CMR Cameroon sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
37 2019 country/EGY Egypt sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
38 2018 country/SYC Seychelles sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
39 2015 country/NAM Namibia sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
40 2018 country/BEN Benin sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
41 2008 country/CAF Central African Republic sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
42 2019 country/ZWE Zimbabwe sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
43 2017 country/STP São Tomé and Príncipe sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
44 2018 country/TCD Chad sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
45 2014 country/MRT Mauritania sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
46 2020 country/GMB Gambia sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
47 2013 country/BDI Burundi sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
48 2017 country/MUS Mauritius sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
49 2014 country/COM Comoros sdg/SI_POV_DAY1 ... SDG_G_G None https://unstats.un.org/sdgs/dataportal SDG_PERCENT
[50 rows x 12 columns]
Example 5: Get all observations for a single variable and entity, with a property filter
This example gets all observations for the populaton of the U.S., and uses a property filter to limit the results to datasets that use an observation period of P1Y.
Request:
client.observations_dataframe(variable_dcids=["Count_Person"], date="all", entity_dcids=["country/USA"], property_filters={"observationPeriod": ["P1Y"]})
Response:
(truncated)
date entity entity_name variable ... measurementMethod observationPeriod provenanceUrl unit
0 1900 country/USA United States of America Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
1 1901 country/USA United States of America Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
2 1902 country/USA United States of America Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
3 1903 country/USA United States of America Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
4 1904 country/USA United States of America Count_Person ... CensusPEPSurvey P1Y https://www2.census.gov/programs-surveys/popes... None
.. ... ... ... ... ... ... ... ... ...
252 2019 country/USA United States of America Count_Person ... OECDRegionalStatistics P1Y https://data-explorer.oecd.org/vis?fs[0]=Topic... None
253 2020 country/USA United States of America Count_Person ... OECDRegionalStatistics P1Y https://data-explorer.oecd.org/vis?fs[0]=Topic... None
254 2021 country/USA United States of America Count_Person ... OECDRegionalStatistics P1Y https://data-explorer.oecd.org/vis?fs[0]=Topic... None
255 2022 country/USA United States of America Count_Person ... OECDRegionalStatistics P1Y https://data-explorer.oecd.org/vis?fs[0]=Topic... None
256 2023 country/USA United States of America Count_Person ... OECDRegionalStatistics P1Y https://data-explorer.oecd.org/vis?fs[0]=Topic... None
[257 rows x 12 columns]
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