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:
- 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=["MedianIncome_Household"], entity_expression="geoId/06<-containedInPlace+{typeOf:County}", date="2020" )
- Node Endpoint - Knowledge Graph Exploration
Explore entity relationships and properties within the knowledge graph. See references/node.md for comprehensive documentation.
Primary use cases:
Discover available properties for entities Navigate geographic hierarchies (parent/child relationships) Retrieve entity names and metadata Explore connections between entities List all entity types in the graph
Common patterns:
Discover properties
labels = client.node.fetch_property_labels( node_dcids=["geoId/06"], out=True )
Navigate hierarchy
children = client.node.fetch_place_children( node_dcids=["country/USA"] )
Get entity names
names = client.node.fetch_entity_names( node_dcids=["geoId/06", "geoId/48"] )
- Resolve Endpoint - Entity Identification
Translate entity names, coordinates, or external IDs into Data Commons IDs (DCIDs). See references/resolve.md for comprehensive documentation.
Primary use cases:
Convert place names to DCIDs for queries Resolve coordinates to places Map Wikidata IDs to Data Commons entities Handle ambiguous entity names
Common patterns:
Resolve by name
response = client.resolve.fetch_dcids_by_name( names=["California", "Texas"], entity_type="State" )
Resolve by coordinates
dcid = client.resolve.fetch_dcid_by_coordinates( latitude=37.7749, longitude=-122.4194 )
Resolve Wikidata IDs
response = client.resolve.fetch_dcids_by_wikidata_id( wikidata_ids=["Q30", "Q99"] )
Typical Workflow
Most Data Commons queries follow this pattern:
Resolve entities (if starting with names):
resolve_response = client.resolve.fetch_dcids_by_name( names=["California", "Texas"] ) dcids = [r["candidates"][0]["dcid"] for r in resolve_response.to_dict().values() if r["candidates"]]
Discover available variables (optional):
variables = client.observation.fetch_available_statistical_variables( entity_dcids=dcids )
Query statistical data:
response = client.observation.fetch( variable_dcids=["Count_Person", "UnemploymentRate_Person"], entity_dcids=dcids, date="latest" )
Process results:
As dictionary
data = response.to_dict()
As Pandas DataFrame
df = response.to_observations_as_records()
Finding Statistical Variables
Statistical variables use specific naming patterns in Data Commons:
Common variable patterns:
Count_Person - Total population Count_Person_Female - Female population UnemploymentRate_Person - Unemployment rate Median_Income_Household - Median household income Count_Death - Death count Median_Age_Person - Median age
Discovery methods:
Check what variables are available for an entity
available = client.observation.fetch_available_statistical_variables( entity_dcids=["geoId/06"] )
Or explore via the web interface
https://datacommons.org/tools/statvar
Working with Pandas
All observation responses integrate with Pandas:
response = client.observation.fetch( variable_dcids=["Count_Person"], entity_dcids=["geoId/06", "geoId/48"], date="all" )
Convert to DataFrame
df = response.to_observations_as_records()
Columns: date, entity, variable, value
Reshape for analysis
pivot = df.pivot_table( values='value', index='date', columns='entity' )
API Authentication
For datacommons.org (default):
An API key is required Set via environment variable: export DC_API_KEY="your_key" Or pass when initializing: client = DataCommonsClient(api_key="your_key") Request keys at: https://apikeys.datacommons.org/
For custom Data Commons instances:
No API key required Specify custom endpoint: client = DataCommonsClient(url="https://custom.datacommons.org") Reference Documentation
Comprehensive documentation for each endpoint is available in the references/ directory:
references/observation.md: Complete Observation API documentation with all methods, parameters, response formats, and common use cases references/node.md: Complete Node API documentation for graph exploration, property queries, and hierarchy navigation references/resolve.md: Complete Resolve API documentation for entity identification and DCID resolution references/getting_started.md: Quickstart guide with end-to-end examples and common patterns Additional Resources Official Documentation: https://docs.datacommons.org/api/python/v2/ Statistical Variable Explorer: https://datacommons.org/tools/statvar Data Commons Browser: https://datacommons.org/browser/ GitHub Repository: https://github.com/datacommonsorg/api-python Tips for Effective Use Always start with resolution: Convert names to DCIDs before querying data Use relation expressions for hierarchies: Query all children at once instead of individual queries Check data availability first: Use fetch_available_statistical_variables() to see what's queryable Leverage Pandas integration: Convert responses to DataFrames for analysis Cache resolutions: If querying the same entities repeatedly, store name→DCID mappings Filter by facet for consistency: Use filter_facet_domains to ensure data from the same source Read reference docs: Each endpoint has extensive documentation in the references/ directory