datacommons-client

Query time-series data and resolve entities via the Data Commons Python API v2.

1|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/yf8578/clawomics --skill datacommons-client-yf8578
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: datacommons-client
Source: https://github.com/yf8578/clawomics/tree/main/skills/datacommons-client
Command: npx skills add https://github.com/yf8578/clawomics --skill datacommons-client-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides programmatic access to a vast repository of public statistical data from global sources, enabling users to easily query and analyze demographic, economic, health, and environmental information.

Core Features & Use Cases

  • Statistical Data Queries: Retrieve time-series data for population, GDP, unemployment, disease prevalence, and more.
  • Knowledge Graph Exploration: Discover entity relationships, properties, and navigate geographic hierarchies.
  • Entity Resolution: Translate place names, coordinates, or external IDs into Data Commons IDs (DCIDs) for querying.
  • Use Case: Analyze the correlation between median income and unemployment rates across different US states over the past decade.

Quick Start

Use the datacommons-client skill to fetch the latest population count for California and Texas.

Frequently Asked Questions about datacommons-client

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I access global public statistical data programmatically?

You can access global public statistical data via the Data Commons Python API v2, which facilitates querying time-series observations and exploring the knowledge graph for demographic, economic, health, and environmental datasets.

How do I query demographic and economic time-series data using Data Commons?

Query demographic and economic time-series data by using the API client to retrieve specific statistical observations, resolving place names or external IDs into Data Commons IDs for targeted geographic searches.

Can I use Pandas to manipulate public statistical data retrieved from Data Commons?

Yes, the Data Commons API client supports Pandas integration, allowing you to directly manipulate and analyze retrieved time-series observations and knowledge graph data within Pandas DataFrames.

What's the best way to resolve place names or coordinates into Data Commons identifiers?

Use the entity resolution feature to translate place names, coordinates, or external IDs into Data Commons IDs (DCIDs), enabling precise queries within the public statistical knowledge graph.

How does the Data Commons knowledge graph help explore geographic hierarchies?

The Data Commons knowledge graph allows you to discover entity relationships and properties, enabling navigation across geographic hierarchies to contextualize public statistical data.

What types of public datasets can I analyze with this API client?

You can analyze public datasets covering demographics, economic indicators like GDP and unemployment, health metrics such as disease prevalence, and environmental information using the Data Commons API.