What is Data Commons?

Data Commons is an open knowledge graph that aggregates data from authoritative global sources like the US Census, CDC, and World Bank into a unified, queryable schema.

Can I search for data using geographic coordinates?

Yes, the Resolve endpoint within the skill allows you to fetch DCIDs by providing latitude and longitude coordinates.

Do I need an API key to use this skill?

Yes, a free API key from datacommons.org is required for the default instance, which can be configured via the DC_API_KEY environment variable.

Can this skill export data to Pandas?

Yes, the skill includes built-in support for Pandas, allowing you to convert statistical observation responses directly into DataFrames using the 'to_observations_as_records' method.

How do I find specific variable names like 'Population'?

You can use the 'fetch_available_statistical_variables' method within the skill or use the Data Commons web-based Variable Explorer to find specific DCIDs like 'Count_Person'.

Data Commons Client

Name: Data Commons Client
Author: ovachiever

byovachiever

•

Data Science & ML

Accesses and analyzes global public statistical data from the Data Commons knowledge graph for research and development.

About

The Data Commons Client skill empowers Claude to programmatically query millions of public data points across demographics, economics, health, and environmental sectors. By interfacing with the Data Commons API v2, it enables users to resolve entity identifiers, navigate complex geographic hierarchies, and retrieve historical time-series observations directly into Pandas DataFrames. This skill is an essential tool for data scientists and developers who require authoritative, aggregated statistics from sources like the US Census, World Bank, and NOAA without the overhead of manual data scraping or cleaning.

Key Features

Query time-series statistical observations for global entities
5 GitHub stars
Native Pandas integration for immediate data analysis and visualization
Navigate geographic hierarchies to compare data across states, counties, or regions
Resolve place names, coordinates, and Wikidata IDs to Data Commons DCIDs
Explore entity relationships and properties within a unified knowledge graph

Use Cases

Comparing economic indicators like GDP and unemployment across multiple countries or regions
Correlating environmental data with public health statistics to identify regional trends
Analyzing historical demographic shifts and population trends for urban planning research

About

Key Features

Query time-series statistical observations for global entities
5 GitHub stars
Native Pandas integration for immediate data analysis and visualization
Navigate geographic hierarchies to compare data across states, counties, or regions
Resolve place names, coordinates, and Wikidata IDs to Data Commons DCIDs
Explore entity relationships and properties within a unified knowledge graph

Use Cases

Comparing economic indicators like GDP and unemployment across multiple countries or regions
Correlating environmental data with public health statistics to identify regional trends
Analyzing historical demographic shifts and population trends for urban planning research