Open Data Communities Python API Docs | dltHub

The Open Data Communities API provides access to Energy Performance Certificates data, including domestic and non-domestic certificates, via authenticated HTTP GET requests. The main endpoints include informational, domestic, and non-domestic APIs. The API requires an authorization token for access. The REST API base URL is https://epc.opendatacommunities.org/api/v1 and all requests require HTTP Basic auth using an API key encoded as Basic (sent in the Authorization header).

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 Open Data Communities data in under 10 minutes.

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What data can I load from Open Data Communities?

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

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How do I authenticate with the Open Data Communities API?

The API uses HTTP Basic authentication: include an Authorization header with value "Basic ". Also set Accept to the requested response format (application/json or text/csv).

1. Get your credentials

1. Register or sign in at Open Data Communities (opendatacommunities.org).
2. Navigate to the API keys / developer section.
3. Create or view your API key.
4. Use the provided key as the Basic token (the docs show using the token directly as the Basic header value). Encode if required by your client (docs examples use 'Basic ').

2. Add them to .dlt/secrets.toml

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[sources.open_data_communities_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 Open Data Communities 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 open_data_communities_pipeline.py

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

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Pipeline open_data_communities_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset open_data_communities_data
The duckdb destination used duckdb:/open_data_communities.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 open_data_communities_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 domestic_search and non_domestic_search from the Open Data Communities 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 open_data_communities_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://epc.opendatacommunities.org/api/v1",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "domestic_search", "endpoint": {"path": "domestic/search", "data_selector": "results"}},
            {"name": "non_domestic_search", "endpoint": {"path": "non-domestic/search", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="open_data_communities_pipeline",
        destination="duckdb",
        dataset_name="open_data_communities_data",
    )
    load_info = pipeline.run(open_data_communities_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("open_data_communities_pipeline").dataset()
sessions_df = data.domestic_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM open_data_communities_data.domestic_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("open_data_communities_pipeline").dataset()
data.domestic_search.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 Open Data Communities 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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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