Electricity Maps Python API Docs | dltHub

Electricity Maps API is your gateway to real-time, historical and forecasted electricity data worldwide. The REST API base URL is https://api.electricitymaps.com/v3 and All requests except for /zones require an API key for 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 Electricity Maps data in under 10 minutes.

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What data can I load from Electricity Maps?

Here are some of the endpoints you can load from Electricity Maps:

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How do I authenticate with the Electricity Maps API?

Authentication requires an API key to be included as an 'auth-token' header in the request, or alternatively, Basic Auth can be used.

1. Get your credentials

To obtain API credentials, start by getting a test token from the Electricity Maps Developer Hub Playground. For a full API key, you would typically register and generate one through your account dashboard on the Electricity Maps platform.

2. Add them to .dlt/secrets.toml

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[sources.electricity_maps_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 Electricity Maps 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 electricity_maps_pipeline.py

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

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Pipeline electricity_maps_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset electricity_maps_data
The duckdb destination used duckdb:/electricity_maps.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 electricity_maps_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 zones and latest from the Electricity Maps 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 electricity_maps_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.electricitymaps.com/v3",
            "auth": {
                "type": "api_key",
                "auth-token": api_key,
            },
        },
        "resources": [
            {"name": "zones", "endpoint": {"path": "zones"}},
            {"name": "latest", "endpoint": {"path": "latest"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="electricity_maps_pipeline",
        destination="duckdb",
        dataset_name="electricity_maps_data",
    )
    load_info = pipeline.run(electricity_maps_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("electricity_maps_pipeline").dataset()
sessions_df = data.zones.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM electricity_maps_data.zones LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("electricity_maps_pipeline").dataset()
data.zones.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 Electricity Maps 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 encounter a 401 Unauthorized error, it typically indicates that your API key is either missing or invalid. Ensure that your auth-token header is correctly set with a valid API key for all requests except to the /zones endpoint.

Rate Limits

The documentation does not exhaustively detail specific rate limits for the API. Users should be aware that rate limits may apply and monitor their usage to avoid unexpected interruptions.

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