Data DC Python API Docs | dltHub

Data DC is the District of Columbia's Open Data portal exposing DC government datasets and GIS services via REST APIs (ArcGIS REST map services and web data endpoints). The REST API base URL is https://opendata.dc.gov and Public datasets often require no auth; protected ArcGIS services use token or API key authentication..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Data DC data in under 10 minutes.

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What data can I load from Data DC?

Here are some of the endpoints you can load from Data DC:

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How do I authenticate with the Data DC API?

Many DC map services are public; ArcGIS REST services may require API keys or token-based auth for protected services. Public dataset endpoints on the Open Data site are accessible without credentials; authenticated access (when required) uses provider-specific tokens/keys presented as query parameters or Authorization headers.

1. Get your credentials

1. Visit the DC Open Data portal or the ArcGIS Online/Enterprise site that hosts the service. 2) Sign into the provider portal (ArcGIS Online/ArcGIS Enterprise) or request an API key from the site administrator. 3) Create or copy the API key/token from your account settings and use it as a query parameter or Authorization header per the service docs.

2. Add them to .dlt/secrets.toml

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[sources.data_dc_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Data DC API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python data_dc_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline data_dc_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset data_dc_data
The duckdb destination used duckdb:/data_dc.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline data_dc_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads datasets and arcgis_service from the Data DC API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def data_dc_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://opendata.dc.gov",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "datasets", "endpoint": {"path": "datasets"}},
            {"name": "arcgis_service", "endpoint": {"path": "maps2.dcgis.dc.gov/dcgis/rest/services/{service}/FeatureServer/0/query", "data_selector": "features"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="data_dc_pipeline",
        destination="duckdb",
        dataset_name="data_dc_data",
    )
    load_info = pipeline.run(data_dc_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("data_dc_pipeline").dataset()
sessions_df = data.datasets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM data_dc_data.datasets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("data_dc_pipeline").dataset()
data.datasets.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Data DC data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If a protected ArcGIS REST service requires a token or key, requests without valid credentials return 401/403. Provide api_key as a query param or Authorization header per provider docs.

Pagination and large result sets

Portal dataset lists are paginated. Use the offset/limit (or page/rows) query parameters provided by the dataset API; for ArcGIS FeatureServer use resultOffset/resultRecordCount or resultOffset/resultRecordCount with where=1=1 and orderByFields as needed.

Rate limiting and throttling

Public endpoints may be rate limited; handle 429 responses by backing off and retrying after the Retry-After header.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack