alterlab-datacommons

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.

Verified against datacommons-client 2.x (the current major). This is the V2 client (package datacommons_client), not the legacy datacommons (V1) package — the two have different APIs; do not mix them.

Installation

Install the Data Commons V2 client with Pandas support (extra is lowercase pandas):

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()

# Get latest population data
response = client.observation.fetch(
    variable_dcids=["Count_Person"],
    entity_dcids=["geoId/06"],  # California
    date="latest"
)

# Get time series
response = client.observation.fetch(
    variable_dcids=["UnemploymentRate_Person"],
    entity_dcids=["country/USA"],
    date="all"
)

# Query by hierarchy
response = client.observation.fetch(
    variable_dcids=["Median_Income_Household"],
    entity_expression="geoId/06<-containedInPlace+{typeOf:County}",
    date="2020"
)