US Census Data Python API Docs | dltHub

The US Census Data API is a REST API providing programmatic access to raw statistical data from multiple Census Bureau datasets (e.g., ACS, Population Estimates) and related geographic services (TIGERweb GeoServices). The REST API base URL is https://api.census.gov/data and Requests use an API key passed as a query parameter; an API key is required for higher-volume use..

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 US Census Data data in under 10 minutes.

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What data can I load from US Census Data?

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

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How do I authenticate with the US Census Data API?

The Census Data API accepts an API key appended to requests as &key=YOUR_KEY (query-string). Many endpoints work without a key for low-volume use (up to ~500 requests/day per IP); register and include the key to raise limits.

1. Get your credentials

1. Visit the Census Developers site: https://www.census.gov/developers/. 2) Click "Request an API Key" (or go to https://www.census.gov/data/developers/api-key.html). 3) Complete the short form with your contact info. 4) You will receive the key by email; include it in calls as &key=YOUR_KEY.

2. Add them to .dlt/secrets.toml

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[sources.us_census_data_source]
api_key = "your_census_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 US Census Data 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 us_census_data_pipeline.py

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

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Pipeline us_census_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset us_census_data_data
The duckdb destination used duckdb:/us_census_data.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 us_census_data_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 acs/acs5 and pep/population from the US Census Data 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 us_census_data_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.census.gov/data",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "dataset_query", "endpoint": {"path": "data/{year}/{dataset}"}},
            {"name": "tiger_mapservice_query", "endpoint": {"path": "tigerweb/arcgis/rest/services/TIGERweb/MapServer/{layer}/query", "data_selector": "features"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="us_census_data_pipeline",
        destination="duckdb",
        dataset_name="us_census_data_data",
    )
    load_info = pipeline.run(us_census_data_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("us_census_data_pipeline").dataset()
sessions_df = data.dataset_query.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM us_census_data_data.dataset_query LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("us_census_data_pipeline").dataset()
data.dataset_query.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 US Census Data 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 requests return errors or unexpectedly low rate limits, ensure you appended &key=YOUR_KEY to the query string (or remove if testing low-volume). The Census key is case-sensitive and must be included as a query parameter.

Rate limits and daily quotas

Unauthenticated requests are limited (~500 queries per IP per day). Register for an API key to exceed this. If you hit quota‑like behavior, switch to using a registered key; also confirm corporate proxies aren’t collapsing many users onto one IP.

Response format & selector quirks

Most Census data endpoints (api.census.gov/data/…) return results as a top‑level JSON array of arrays where the first sub‑array is column names and subsequent arrays are records — there is no wrapping key (data selector is empty). TIGERweb GeoServices returns an object with a features array (data selector = features).

Common HTTP errors

400 Bad Request — malformed query (bad get= or missing required predicates). 401/403 — invalid or unauthorized API key (ensure key is correct and not expired). 429 or apparent quota errors — rate limits or exceeded daily query allowance; register and use key. 500 series — server errors; retry with backoff and contact census.data@census.gov if persistent.

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