Climatiq Python API Docs | dltHub

Climatiq is an API platform that converts activity data into scientifically validated CO2e emission estimates. The REST API base URL is https://api.climatiq.io and all requests require a Bearer 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 Climatiq data in under 10 minutes.

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What data can I load from Climatiq?

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

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How do I authenticate with the Climatiq API?

Every request must include an Authorization header with a Bearer token containing your Climatiq API key (Authorization: Bearer <CLIMATIQ_API_KEY>). Requests must use HTTPS.

1. Get your credentials

1. Sign in to the Climatiq dashboard at https://www.climatiq.io and open your project. 2) Navigate to the API / Authentication or API keys section (Dashboard → API keys). 3) Create a new API key if none exists. 4) Copy the key and store it securely; use it in the Authorization header as Bearer .

2. Add them to .dlt/secrets.toml

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[sources.climatiq_energy_source]
api_key = "your_climatiq_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 Climatiq 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 climatiq_energy_pipeline.py

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

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Pipeline climatiq_energy_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset climatiq_energy_data
The duckdb destination used duckdb:/climatiq_energy.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 climatiq_energy_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 search and estimate from the Climatiq 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 climatiq_energy_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.climatiq.io",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "data/v1/search", "data_selector": "results"}},
            {"name": "classifications", "endpoint": {"path": "data/v1/classifications", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="climatiq_energy_pipeline",
        destination="duckdb",
        dataset_name="climatiq_energy_data",
    )
    load_info = pipeline.run(climatiq_energy_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("climatiq_energy_pipeline").dataset()
sessions_df = data.search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM climatiq_energy_data.search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("climatiq_energy_pipeline").dataset()
data.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 Climatiq 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 or 403 Forbidden, verify the Authorization header is present and formatted as: Authorization: Bearer <YOUR_API_KEY>. Ensure the key is active in your project dashboard and you are using HTTPS.

Rate limiting and queued requests

The API may queue requests if too many concurrent requests are made. Avoid more than ~10 concurrent requests to prevent queueing and slower responses. Implement retries with backoff for slow/queued responses.

Pagination

Search and other list endpoints are paginated and include current_page and last_page (and return the list under the "results" key). Use the page query parameter to retrieve subsequent pages.

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