IMDLIB Python API Docs | dltHub

IMDLIB is a Python library for handling India Meteorological Department gridded data. It supports downloading, processing, and plotting of meteorological data. The core class IMD handles rain, minimum, and maximum temperature data. The REST API base URL is `` and no HTTP authentication — IMDLIB is a local Python library that downloads IMD binary files directly.

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 IMDLIB data in under 10 minutes.

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What data can I load from IMDLIB?

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

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How do I authenticate with the IMDLIB API?

IMDLIB is a client library; it does not require API tokens or HTTP authentication. Data retrieval is performed by library functions (e.g. get_data) that download files from IMD servers; any network access uses the system network/proxy settings or the proxies parameter passed to get_data.

1. Get your credentials

No credentials are required. IMDLIB downloads IMD binary files directly; if the user needs to access a restricted network, configure system or library proxies via the get_data proxies parameter.

2. Add them to .dlt/secrets.toml

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[sources.imdlib_source]
# no secrets required for IMDLIB

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 IMDLIB 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 imdlib_pipeline.py

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

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Pipeline imdlib_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset imdlib_data
The duckdb destination used duckdb:/imdlib.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 imdlib_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 get_data and open_data from the IMDLIB 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 imdlib_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "get_data", "endpoint": {"path": "imdlib.core.get_data"}},
            {"name": "open_data", "endpoint": {"path": "imdlib.core.open_data"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM imdlib_data.get_data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("imdlib_pipeline").dataset()
data.get_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 IMDLIB 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