datacommons-client

Query Data Commons using the Observations endpoint to retrieve statistical data by specifying variables, entities, and date modes. The datacommons-client Python API handles API calls and returns results that you can convert to a Pandas DataFrame for analysis of demographic, economic, health, or environmental observations.

Yes, the Resolve endpoint converts place names, coordinates, and Wikidata identifiers into Data Commons entity identifiers (DCIDs). Once resolved, you can use these identifiers to query observations and explore the knowledge graph across geographic hierarchies and related entities.

Use the datacommons-client to query observations across multiple time periods with the Observations endpoint, then pivot results into a Pandas DataFrame. This workflow supports time-series analysis and comparative queries across demographic, economic, health, and environmental datasets without manual data wrangling.

Yes, the datacommons-client supports native Pandas integration, allowing you to convert API responses directly into DataFrames. This enables seamless data pivoting, filtering, and analysis workflows within the Pandas ecosystem.

Use the Node endpoint with relation expressions to navigate the knowledge graph and retrieve connected entities and their properties. This supports exploration of place hierarchies, demographic associations, and cross-domain relationships across the knowledge graph.

The datacommons-client supports facet filtering to narrow observations by specific dimensions and date modes—including latest observations or all historical data. Filter results before conversion to Pandas to optimize analysis scope and performance.