Opendatasoft Python API Docs | dltHub

Opendatasoft Explore API is a REST API providing programmatic access to datasets and catalog features on Opendatasoft-powered portals. The REST API base URL is https://<domain>/api/explore/v2.1 and Requests require an API key passed as a query parameter or HTTP header; OAuth2 is also available for some deployments..

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 Opendatasoft data in under 10 minutes.

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What data can I load from Opendatasoft?

Here are some of the endpoints you can load from Opendatasoft:

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How do I authenticate with the Opendatasoft API?

Authentication is typically done with an API key provided as the apikey query parameter or as the HTTP header X-API-Key. OAuth2 flows are documented for deployments that enabled OAuth2.

1. Get your credentials

1. Log in to your Opendatasoft portal account.
2. Go to your user profile or admin console and find "API keys" or "API access".
3. Create/generate a new API key specifying scopes if prompted.
4. Copy the key and store it securely; use it in requests as ?apikey=YOUR_KEY or header X-API-Key: YOUR_KEY.

2. Add them to .dlt/secrets.toml

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[sources.opendatasoft_source]
apikey = "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 Opendatasoft 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 opendatasoft_pipeline.py

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

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Pipeline opendatasoft_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset opendatasoft_data
The duckdb destination used duckdb:/opendatasoft.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 opendatasoft_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 records and datasets from the Opendatasoft 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 opendatasoft_source(apikey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<domain>/api/explore/v2.1",
            "auth": {
                "type": "api_key",
                "apikey": apikey,
            },
        },
        "resources": [
            {"name": "dataset_records", "endpoint": {"path": "catalog/datasets/{dataset_id}/records", "data_selector": "results"}},
            {"name": "catalog_datasets", "endpoint": {"path": "catalog/datasets", "data_selector": "datasets"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="opendatasoft_pipeline",
        destination="duckdb",
        dataset_name="opendatasoft_data",
    )
    load_info = pipeline.run(opendatasoft_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("opendatasoft_pipeline").dataset()
sessions_df = data.dataset_records.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM opendatasoft_data.dataset_records LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("opendatasoft_pipeline").dataset()
data.dataset_records.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 Opendatasoft 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 you receive 401 Unauthorized, verify your API key is valid and sent either as ?apikey=YOUR_KEY or header X-API-Key. Ensure the key has required scopes and is not expired.

Rate limits and 429 Too Many Requests

The API returns 429 when rate limits are exceeded. Implement exponential backoff and respect portal‑specific rate limits (noted per deployment).

Pagination and incomplete results

The records endpoint is paginated using parameters like limit (or rows) and offset (or start). Responses include total_count and results arrays; use limit/offset to iterate. The endpoint may cap the maximum rows per request; use the exports endpoint for full dataset exports where available.

Common error responses

• 400 Bad Request – invalid query parameters or malformed ODSQL.
• 401 Unauthorized – missing/invalid API key.
• 429 Too Many Requests – rate limit exceeded.
• 500 Internal Server Error – server‑side error; retry with backoff.

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