datacommons-client

You can query time-series data from Data Commons using a Python client that retrieves observations programmatically, enabling analysis of geography, demographics, economy, and health datasets without manual API calls.

DCID resolution maps place names or coordinates to unique Data Commons identifiers, allowing you to navigate the knowledge graph and retrieve contextual statistical observations for specific entities.

Yes, the Python client integrates with Pandas, allowing you to fetch Data Commons observations and load them directly into DataFrames for time-series analysis and cross-entity variable comparisons.

You resolve place names to DCIDs by using the Resolve endpoint, which translates names or coordinates into identifiers for navigating the Data Commons knowledge graph and retrieving related statistical variables.

Yes, the client supports coordinate-based entity resolution through the Resolve endpoint, enabling you to map geographic coordinates directly to Data Commons identifiers for localized statistical data queries.

The best way to compare variables across entities is using the Observation and Node endpoints, which let you inspect and retrieve statistical data for multiple resolved DCIDs and analyze differences within the Pandas integration.