datacommons-client

# 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.

## Routing Boundary

Use this skill when the user explicitly asks for Data Commons/datacommons, Data Commons statistical variables, Data Commons entities/DCIDs, or population/economic indicators that fit the Data Commons knowledge graph.

Generic public data, open data, public dataset search, or public download-link collection by itself is not enough to select this skill. Those requests need a clearer Data Commons/statistical-graph signal before this skill becomes the route owner.

## Installation

Install the Data Commons Python client with Pandas support:

```bash
uv pip install "datacommons-client[Pandas]"
```

For basic usage without Pandas:
```bash
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:**
```python
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:**
```python
# 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:**
```python
# 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:

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

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:**
```python
# 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:

```python
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