| name | datacommons-client |
| description | Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities. |
| license | Unknown |
| metadata | {"skill-author":"K-Dense Inc."} |
| verified | false |
| lastVerifiedAt | "2026-02-19T05:29:09.098Z" |
| source | builtin |
| trust_score | 100 |
| provenance_sha | c3e82613b35bd254 |
Data Commons Client
Overview
Provides comprehensive access to the Data Commons Python API v2 for querying statistical observations, exploring the knowledge graph, and resolving entity identifiers. Data Commons aggregates data from census bureaus, health organizations, environmental agencies, and other authoritative sources into a unified knowledge graph.
Installation
Install the Data Commons Python client with Pandas support:
uv pip install "datacommons-client[Pandas]"
For basic usage without Pandas:
uv pip install datacommons-client
Core Capabilities
The Data Commons API consists of three main endpoints, each detailed in dedicated reference files:
1. Observation Endpoint - Statistical Data Queries
Query time-series statistical data for entities. See references/observation.md for comprehensive documentation.
Primary use cases:
- Retrieve population, economic, health, or environmental statistics
- Access historical time-series data for trend analysis
- Query data for hierarchies (all counties in a state, all countries in a region)
- Compare statistics across multiple entities
- Filter by data source for consistency
Common patterns:
from datacommons_client import DataCommonsClient
client = DataCommonsClient()
response = client.observation.fetch(
variable_dcids=["Count_Person"],
entity_dcids=["geoId/06"],
date="latest"
)
response = client.observation.fetch(
variable_dcids=["UnemploymentRate_Person"],
entity_dcids=["country/USA"],
date="all"
)
response = client.observation.fetch(
variable_dcids=["MedianIncome_Household"],
entity_expression="geoId/06<-containedInPlace+{typeOf:County}",
date="2020"
)
2. Node Endpoint - Knowledge Graph Exploration
