datacommons-client
GitHub提供Data Commons Python API v2客户端，用于查询全球公共统计数据、探索知识图谱及解析实体标识。支持人口、经济、健康等数据的时序查询、层级导航及名称到DCID的转换。
Trigger Scenarios
Install
npx skills add synthetic-sciences/openscience --skill datacommons-client -g -y
SKILL.md
Frontmatter
{
"name": "datacommons-client",
"license": "Unknown",
"category": "databases",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"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."
}
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()
# 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"
)
2. 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"]
)
3. 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 populationCount_Person_Female- Female populationUnemploymentRate_Person- Unemployment rateMedian_Income_Household- Median household incomeCount_Death- Death countMedian_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 casesreferences/node.md: Complete Node API documentation for graph exploration, property queries, and hierarchy navigationreferences/resolve.md: Complete Resolve API documentation for entity identification and DCID resolutionreferences/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_domainsto ensure data from the same source - Read reference docs: Each endpoint has extensive documentation in the
references/directory
Version History
- e9844a4 Current 2026-07-11 17:24
