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LeonChaoX
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datacommons-client

Resolve place inputs to DCIDs and fetch Data Commons statistical observations.

783|65|Updated Feb 27, 2026
Search Tags:#knowledge graph#time series#pandas#datacommons#entity resolution#data querying#public statistics
One-click install
npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill datacommons-client-leonchaox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: datacommons-client
Source: https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/12-%E7%A7%91%E5%AD%A6%E6%95%B0%E6%8D%AE%E5%BA%93/datacommons-client
Command: npx skills add https://github.com/LeonChaoX/qinyan-academic-skills --skill datacommons-client-leonchaox

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill removes the guesswork and manual lookups involved in finding public statistical indicators and time-series data for specific places by handling Data Commons entity resolution and API querying for you.

Core Features & Use Cases

  • Resolve place names, coordinates, and Wikidata IDs to DCIDs: Start from human input and reliably convert it into the identifiers Data Commons requires.
  • Fetch observations and time series: Retrieve population, economics, health, and environmental statistics for one or many entities (including historical “all” series).
  • Explore the knowledge graph: Discover entity properties, navigate geographic hierarchies, and interpret relationships between statistical entities.
  • Use Pandas-ready outputs: Convert results into record tables for analysis and reporting.
  • Use Cases: Compare unemployment or income across states, get a full historical trend for a country, or pull median income for all counties in a specific state and then map readable names.

Quick Start

Use the datacommons-client skill to query the latest population for California and Texas by resolving their DCIDs and fetching the Count_Person statistical variable.

Frequently Asked Questions about datacommons-client

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

FAQPage Schema
How do I query public statistics for a specific place using Data Commons?▼

You can query public statistics for a specific place by resolving names or coordinates to DCIDs and fetching statistical observations from Data Commons. This retrieves demographic, economic, health, and environmental indicators as Pandas-compatible record tables.

Can I get historical time series data for demographic and economic indicators?▼

Yes, you can retrieve historical time series data for demographic and economic indicators. By querying the full 'all' series from Data Commons observation endpoints, you obtain population and income trends over time for your target entities.

Does Data Commons querying work with Pandas for data analysis?▼

Data Commons querying works with Pandas by providing Pandas-ready outputs. The client converts fetched statistical observations and knowledge-graph properties into record tables for immediate analysis and reporting.

How do I resolve human-readable place names to Data Commons identifiers?▼

You resolve human-readable place names to Data Commons identifiers by using the resolve endpoint. This maps names, coordinates, and Wikidata IDs to the exact DCIDs required for querying statistical observations.

What is needed to configure a Data Commons client for API requests?▼

Configuring a Data Commons client requires a DataCommonsClient with a correct endpoint configuration. You also need an optional API key for authentication to access observation, node, and resolve endpoints for your data queries.

How do I explore the knowledge graph to find geographic hierarchies for entities?▼

To explore the knowledge graph and find geographic hierarchies, you use the node endpoint to discover entity properties and interpret relationships. This allows you to navigate geographic hierarchies between statistical entities.

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