Google Sheets Python API Docs | dltHub

Google Sheets API is a RESTful interface for reading and modifying Google Sheets spreadsheet data. The REST API base URL is https://sheets.googleapis.com/v4/ and all requests require a Bearer token.

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 Google Sheets data in under 10 minutes.

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What data can I load from Google Sheets?

Here are some of the endpoints you can load from Google Sheets:

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How do I authenticate with the Google Sheets API?

Requests require an 'Authorization: Bearer <access_token>' header. The access token is obtained via the OAuth 2.0 flow or service account credentials.

1. Get your credentials

To obtain credentials for the Google Sheets API, go to the Google Cloud Console and navigate to APIs & Services > Credentials. For most data pipelines (including dlt), a Service Account is recommended: 1. Click Create credentials > Service account. 2. Provide a name and description, then click Create and Continue. 3. Assign a role (usually 'Editor' or 'Viewer' for the project) and click Done. 4. In the Service accounts list, click on your new account, then go to the Keys tab. 5. Click Add key > Create new key, select JSON, and download the resulting file. This file contains the 'project_id', 'client_email', and 'private_key' needed for authentication. If you are using OAuth instead, create an OAuth 2.0 Client ID for a 'Web application' or 'Desktop app' and download the JSON file to extract 'client_id' and 'client_secret'.

2. Add them to .dlt/secrets.toml

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[sources.google_sheets_source]
credentials = "REPLACE_ME"

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 harness:

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dlthub 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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dlthub 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 Google Sheets 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 google_sheets_pipeline.py

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

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Pipeline google_sheets_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_sheets_data
The duckdb destination used duckdb:/google_sheets.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 google_sheets_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 spreadsheets.get and spreadsheets.values.get from the Google Sheets 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 google_sheets_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://sheets.googleapis.com/v4/",
            "auth": {"type": "bearer", "token": credentials},
        },
        "resources": [
            {"name": "values", "endpoint": {"path": "spreadsheets/{spreadsheetId}/values/{range}", "data_selector": "values"}},
            {"name": "batch_get_values", "endpoint": {"path": "spreadsheets/{spreadsheetId}/values:batchGet", "data_selector": "valueRanges"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="google_sheets_pipeline",
        destination="duckdb",
        dataset_name="google_sheets_data",
    )
    load_info = pipeline.run(google_sheets_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("google_sheets_pipeline").dataset()
sessions_df = data.spreadsheets.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM google_sheets_data.spreadsheets LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_sheets_pipeline").dataset()
data.spreadsheets.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 Google Sheets 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 harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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