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Data Commons aggregates data from census bureaus, health organizations, environmental agencies, and other authoritative sources into a unified knowledge graph.\n\n## Installation\n\nInstall the Data Commons Python client with Pandas support:\n\n```bash\nuv pip install \"datacommons-client[Pandas]\"\n```\n\nFor basic usage without Pandas:\n```bash\nuv pip install datacommons-client\n```\n\n## Core Capabilities\n\nThe Data Commons API consists of three main endpoints, each detailed in dedicated reference files:\n\n### 1. Observation Endpoint - Statistical Data Queries\n\nQuery time-series statistical data for entities. See `references\u002Fobservation.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Retrieve population, economic, health, or environmental statistics\n- Access historical time-series data for trend analysis\n- Query data for hierarchies (all counties in a state, all countries in a region)\n- Compare statistics across multiple entities\n- Filter by data source for consistency\n\n**Common patterns:**\n```python\nfrom datacommons_client import DataCommonsClient\n\nclient = DataCommonsClient()\n\n# Get latest population data\nresponse = client.observation.fetch(\n    variable_dcids=[\"Count_Person\"],\n    entity_dcids=[\"geoId\u002F06\"],  # California\n    date=\"latest\"\n)\n\n# Get time series\nresponse = client.observation.fetch(\n    variable_dcids=[\"UnemploymentRate_Person\"],\n    entity_dcids=[\"country\u002FUSA\"],\n    date=\"all\"\n)\n\n# Query by hierarchy\nresponse = client.observation.fetch(\n    variable_dcids=[\"MedianIncome_Household\"],\n    entity_expression=\"geoId\u002F06\u003C-containedInPlace+{typeOf:County}\",\n    date=\"2020\"\n)\n```\n\n### 2. Node Endpoint - Knowledge Graph Exploration\n\nExplore entity relationships and properties within the knowledge graph. See `references\u002Fnode.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Discover available properties for entities\n- Navigate geographic hierarchies (parent\u002Fchild relationships)\n- Retrieve entity names and metadata\n- Explore connections between entities\n- List all entity types in the graph\n\n**Common patterns:**\n```python\n# Discover properties\nlabels = client.node.fetch_property_labels(\n    node_dcids=[\"geoId\u002F06\"],\n    out=True\n)\n\n# Navigate hierarchy\nchildren = client.node.fetch_place_children(\n    node_dcids=[\"country\u002FUSA\"]\n)\n\n# Get entity names\nnames = client.node.fetch_entity_names(\n    node_dcids=[\"geoId\u002F06\", \"geoId\u002F48\"]\n)\n```\n\n### 3. Resolve Endpoint - Entity Identification\n\nTranslate entity names, coordinates, or external IDs into Data Commons IDs (DCIDs). See `references\u002Fresolve.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Convert place names to DCIDs for queries\n- Resolve coordinates to places\n- Map Wikidata IDs to Data Commons entities\n- Handle ambiguous entity names\n\n**Common patterns:**\n```python\n# Resolve by name\nresponse = client.resolve.fetch_dcids_by_name(\n    names=[\"California\", \"Texas\"],\n    entity_type=\"State\"\n)\n\n# Resolve by coordinates\ndcid = client.resolve.fetch_dcid_by_coordinates(\n    latitude=37.7749,\n    longitude=-122.4194\n)\n\n# Resolve Wikidata IDs\nresponse = client.resolve.fetch_dcids_by_wikidata_id(\n    wikidata_ids=[\"Q30\", \"Q99\"]\n)\n```\n\n## Typical Workflow\n\nMost Data Commons queries follow this pattern:\n\n1. **Resolve entities** (if starting with names):\n   ```python\n   resolve_response = client.resolve.fetch_dcids_by_name(\n       names=[\"California\", \"Texas\"]\n   )\n   dcids = [r[\"candidates\"][0][\"dcid\"]\n            for r in resolve_response.to_dict().values()\n            if r[\"candidates\"]]\n   ```\n\n2. **Discover available variables** (optional):\n   ```python\n   variables = client.observation.fetch_available_statistical_variables(\n       entity_dcids=dcids\n   )\n   ```\n\n3. **Query statistical data**:\n   ```python\n   response = client.observation.fetch(\n       variable_dcids=[\"Count_Person\", \"UnemploymentRate_Person\"],\n       entity_dcids=dcids,\n       date=\"latest\"\n   )\n   ```\n\n4. **Process results**:\n   ```python\n   # As dictionary\n   data = response.to_dict()\n\n   # As Pandas DataFrame\n   df = response.to_observations_as_records()\n   ```\n\n## Finding Statistical Variables\n\nStatistical variables use specific naming patterns in Data Commons:\n\n**Common variable patterns:**\n- `Count_Person` - Total population\n- `Count_Person_Female` - Female population\n- `UnemploymentRate_Person` - Unemployment rate\n- `Median_Income_Household` - Median household income\n- `Count_Death` - Death count\n- `Median_Age_Person` - Median age\n\n**Discovery methods:**\n```python\n# Check what variables are available for an entity\navailable = client.observation.fetch_available_statistical_variables(\n    entity_dcids=[\"geoId\u002F06\"]\n)\n\n# Or explore via the web interface\n# https:\u002F\u002Fdatacommons.org\u002Ftools\u002Fstatvar\n```\n\n## Working with Pandas\n\nAll observation responses integrate with Pandas:\n\n```python\nresponse = client.observation.fetch(\n    variable_dcids=[\"Count_Person\"],\n    entity_dcids=[\"geoId\u002F06\", \"geoId\u002F48\"],\n    date=\"all\"\n)\n\n# Convert to DataFrame\ndf = response.to_observations_as_records()\n# Columns: date, entity, variable, value\n\n# Reshape for analysis\npivot = df.pivot_table(\n    values='value',\n    index='date',\n    columns='entity'\n)\n```\n\n## API Authentication\n\n**For datacommons.org (default):**\n- An API key is required\n- Set via environment variable: `export DC_API_KEY=\"your_key\"`\n- Or pass when initializing: `client = DataCommonsClient(api_key=\"your_key\")`\n- Request keys at: https:\u002F\u002Fapikeys.datacommons.org\u002F\n\n**For custom Data Commons instances:**\n- No API key required\n- Specify custom endpoint: `client = DataCommonsClient(url=\"https:\u002F\u002Fcustom.datacommons.org\")`\n\n## Reference Documentation\n\nComprehensive documentation for each endpoint is available in the `references\u002F` directory:\n\n- **`references\u002Fobservation.md`**: Complete Observation API documentation with all methods, parameters, response formats, and common use cases\n- **`references\u002Fnode.md`**: Complete Node API documentation for graph exploration, property queries, and hierarchy navigation\n- **`references\u002Fresolve.md`**: Complete Resolve API documentation for entity identification and DCID resolution\n- **`references\u002Fgetting_started.md`**: Quickstart guide with end-to-end examples and common patterns\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.datacommons.org\u002Fapi\u002Fpython\u002Fv2\u002F\n- **Statistical Variable Explorer**: https:\u002F\u002Fdatacommons.org\u002Ftools\u002Fstatvar\n- **Data Commons Browser**: https:\u002F\u002Fdatacommons.org\u002Fbrowser\u002F\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Fdatacommonsorg\u002Fapi-python\n\n## Tips for Effective Use\n\n1. **Always start with resolution**: Convert names to DCIDs before querying data\n2. **Use relation expressions for hierarchies**: Query all children at once instead of individual queries\n3. **Check data availability first**: Use `fetch_available_statistical_variables()` to see what's queryable\n4. **Leverage Pandas integration**: Convert responses to DataFrames for analysis\n5. **Cache resolutions**: If querying the same entities repeatedly, store name→DCID mappings\n6. **Filter by facet for consistency**: Use `filter_facet_domains` to ensure data from the same source\n7. 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