Data Commons Client

Overview

Installation

pip install "datacommons-client[Pandas]"

pip install datacommons-client

Core Capabilities

1. Observation Endpoint - Statistical Data Queries

references/observation.md

• 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

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

references/node.md

• 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

# 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

references/resolve.md

• Convert place names to DCIDs for queries
• Resolve coordinates to places
• Map Wikidata IDs to Data Commons entities
• Handle ambiguous entity names

# 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

1. Resolve entities (if starting with names):
   python

   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"]]

2. Discover available variables (optional):
   python

   variables = client.observation.fetch_available_statistical_variables(
       entity_dcids=dcids
   )

3. Query statistical data:
   python

   response = client.observation.fetch(
       variable_dcids=["Count_Person", "UnemploymentRate_Person"],
       entity_dcids=dcids,
       date="latest"
   )

4. Process results:
   python

   # As dictionary
   data = response.to_dict()
   
   # As Pandas DataFrame
   df = response.to_observations_as_records()

Finding Statistical Variables

• 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

# 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

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

• 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/

• No API key required
• Specify custom endpoint:

  client = DataCommonsClient(url="https://custom.datacommons.org")

Reference Documentation

references/

• 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

1. Always start with resolution: Convert names to DCIDs before querying data
2. Use relation expressions for hierarchies: Query all children at once instead of individual queries
3. Check data availability first: Use

   fetch_available_statistical_variables()

   to see what's queryable
4. Leverage Pandas integration: Convert responses to DataFrames for analysis
5. Cache resolutions: If querying the same entities repeatedly, store name→DCID mappings
6. Filter by facet for consistency: Use

   filter_facet_domains

   to ensure data from the same source
7. Read reference docs: Each endpoint has extensive documentation in the

   references/

   directory