Day one Python API Docs | dltHub

One Call API is a consolidated weather API providing current weather, minute/hourly/daily forecasts, historical data, daily aggregations, weather overview and government alerts for a specified geographic coordinate. The REST API base URL is https://api.openweathermap.org/data/3.0 and all requests require an api key passed as appid (or X-Api-Key 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 Day one data in under 10 minutes.

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What data can I load from Day one?

Here are some of the endpoints you can load from Day one:

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How do I authenticate with the Day one API?

Authentication is by OpenWeather API key. Include it as query parameter appid={API key} or set header X-Api-Key: {API key}.

1. Get your credentials

1. Create an account at https://home.openweathermap.org/users/sign_up. 2) Sign in to your account. 3) Open the API keys page https://home.openweathermap.org/api_keys. 4) Create or copy your API key; use it in requests as appid or X-Api-Key.

2. Add them to .dlt/secrets.toml

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[sources.day_one_source]
api_key = "your_openweather_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 Day one 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 day_one_pipeline.py

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

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Pipeline day_one_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset day_one_data
The duckdb destination used duckdb:/day_one.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 day_one_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 onecall and onecall/timemachine from the Day one 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 day_one_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.openweathermap.org/data/3.0",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "onecall", "endpoint": {"path": "onecall"}},
            {"name": "onecall_timemachine", "endpoint": {"path": "onecall/timemachine"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="day_one_pipeline",
        destination="duckdb",
        dataset_name="day_one_data",
    )
    load_info = pipeline.run(day_one_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("day_one_pipeline").dataset()
sessions_df = data.onecall.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM day_one_data.onecall LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("day_one_pipeline").dataset()
data.onecall.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 Day one 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 responses stating "Invalid API key" or "Invalid appid", verify your API key on https://home.openweathermap.org/api_keys and supply it as appid query parameter or X-Api-Key header. Ensure there are no extra characters or newline in the key.

Rate limits and billing

One Call API 3.0 has free tier limits (e.g., 1,000 calls/day) and enforced limits per subscription. Exceeding limits returns HTTP 429 Too Many Requests. Check your subscription and usage on your OpenWeather account; consider reducing call frequency, caching results, or upgrading your plan.

Missing fields / conditional fields

Some fields (rain, snow, minutely) are omitted if not applicable for the location/time. Always check for the presence of keys (e.g., 'alerts', 'minutely') before dereferencing.

Pagination and historical data notes

Responses are single-call and not paginated. For historical hourly data prior to One Call API 3.0 conventions, use the timemachine endpoint per timestamp; to get hourly history for a day you must request each hour/timestamp as required by the documentation.

Common API error structure

Errors return non-2xx codes with JSON describing the error. Typical codes:

• 401 Unauthorized: invalid or missing API key
• 429 Too Many Requests: quota exceeded
• 400 Bad Request: missing required parameters (lat, lon, dt)
• 403 Forbidden: plan restrictions or access denied

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