datacommons-client

To query Data Commons observations, use a Python client to resolve entity DCIDs and fetch time-series data across demographic and economic variables. The client supports observation, node, and resolve endpoints, returning results directly for analysis.

Yes, you can resolve place names, coordinates, or Wikidata IDs to Data Commons DCIDs programmatically. The resolve endpoint handles this mapping, enabling subsequent observation queries and knowledge graph exploration for the identified entities.

Yes, the Data Commons Python client supports optional Pandas integration for data processing. It converts observation queries and time-series retrieval results into Pandas dataframes, allowing you to process and analyze demographic, economic, health, and environmental data directly.

The best way to explore the Data Commons knowledge graph is using the node endpoint to query property relationships and hierarchies. This allows you to traverse entities and fetch observations across multiple entities and time ranges within the graph structure.

Yes, you need an API key to fetch statistics from datacommons.org. The Python client includes API key handling to authenticate requests when resolving DCIDs and retrieving observation data across various statistical domains.

Resolving a DCID fails when the entity name, coordinate, or Wikidata ID does not match a valid entry in the Data Commons knowledge graph. Ensure the input matches recognized place names or identifiers before attempting to fetch observations.