CIMIS Python API Docs | dltHub

CIMIS is a REST API providing California irrigation-related weather and reference evapotranspiration (ETo) data from the CIMIS Weather Station Network (WSN) and Spatial CIMIS System (SCS). The REST API base URL is https://et.water.ca.gov/api and All requests require an application key supplied as the appKey query parameter..

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 CIMIS data in under 10 minutes.

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What data can I load from CIMIS?

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

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How do I authenticate with the CIMIS API?

Authentication uses a per‑user application key (appKey) passed as the required query parameter appKey on every request; set Accept: application/json to receive JSON responses.

1. Get your credentials

1. Visit the CIMIS Web API site (https://et.water.ca.gov/).
2. Register for an account or request an application key via the 'Register' or API key request link on the site.
3. After registration, copy the issued appKey value; this key is used as the appKey query parameter in all API calls. No username/password is required for the requests themselves.

2. Add them to .dlt/secrets.toml

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[sources.cimis_data_source]
app_key = "YOUR_APP_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 CIMIS 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 cimis_data_pipeline.py

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

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Pipeline cimis_data_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset cimis_data_data
The duckdb destination used duckdb:/cimis_data.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 cimis_data_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 data and station from the CIMIS 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 cimis_data_source(app_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://et.water.ca.gov/api",
            "auth": {
                "type": "api_key",
                "app_key": app_key,
            },
        },
        "resources": [
            {"name": "data", "endpoint": {"path": "api/data", "data_selector": "Data.Providers[].Records"}},
            {"name": "station", "endpoint": {"path": "api/station", "data_selector": "Stations"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cimis_data_pipeline",
        destination="duckdb",
        dataset_name="cimis_data_data",
    )
    load_info = pipeline.run(cimis_data_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("cimis_data_pipeline").dataset()
sessions_df = data.data.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM cimis_data_data.data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("cimis_data_pipeline").dataset()
data.data.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 CIMIS 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 ERR1006 (HTTP 403) the appKey parameter is invalid. Verify you are sending appKey=YOUR_APP_KEY and that the key is active.

Unsupported targets / not found

ERR1019 (STATION NOT FOUND) and ERR1031 (UNSUPPORTED ZIP CODE) return 404. Verify targets parameter values (station numbers, zip codes, coordinates or addresses) and that you are not mixing target types in a single request.

Date and parameter validation

ERR1010/ERR1011/ERR1012 indicate invalid date ranges (future dates, dates before 1982-06-07, or startDate > endDate). ERR1032 indicates invalid unitOfMeasure; valid values are 'E' or 'M'. ERR1035 indicates an invalid dataItems value.

Data volume & hourly SCS limitations

ERR2112 (DATA VOLUME VIOLATION) 400 when request exceeds data limits. SCS does not support hourly data; including hourly dataItems with coordinate/zip SCS targets may produce ERR2007 or 400 errors. Use prioritizeSCS parameter (Y/N) for zip code requests to control provider selection.

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