Pandas GBQ Python API Docs | dltHub

Pandas GBQ is a Python library that provides utilities to read from and write pandas DataFrames to Google BigQuery, acting as a wrapper around the Google BigQuery APIs and Python BigQuery/BigQuery Storage clients. The REST API base URL is https://www.googleapis.com/bigquery/v2 and All access is authenticated with Google credentials (OAuth2 service account or user credentials / Application Default Credentials)..

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 Pandas GBQ data in under 10 minutes.

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What data can I load from Pandas GBQ?

Here are some of the endpoints you can load from Pandas GBQ:

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How do I authenticate with the Pandas GBQ API?

pandas-gbq uses google-auth Credentials objects (user OAuth2 flow or service account credentials). You can pass credentials explicitly to read_gbq/to_gbq via the credentials parameter or rely on Application Default Credentials (set GOOGLE_APPLICATION_CREDENTIALS). The library may also launch a local webserver for user OAuth flows when running interactive authentication.

1. Get your credentials

1. In Google Cloud Console enable the BigQuery API for your project. 2) Create a service account (IAM & admin → Service Accounts), grant BigQuery roles (e.g., BigQuery User, BigQuery Data Viewer). 3) Create and download a JSON key for the service account. 4) Locally set GOOGLE_APPLICATION_CREDENTIALS=/path/to/key.json or load the JSON and create google.oauth2.service_account.Credentials.from_service_account_file(...) and pass that object into pandas_gbq.read_gbq(..., credentials=creds).

2. Add them to .dlt/secrets.toml

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[sources.pandas_gbq_source]
credentials_path = "/path/to/service_account.json"

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 Pandas GBQ 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 pandas_gbq_pipeline.py

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

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Pipeline pandas_gbq_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pandas_gbq_data
The duckdb destination used duckdb:/pandas_gbq.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 pandas_gbq_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 tables and jobs_getQueryResults from the Pandas GBQ 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 pandas_gbq_source(credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.googleapis.com/bigquery/v2",
            "auth": {
                "type": "oauth2",
                "credentials": credentials,
            },
        },
        "resources": [
            {"name": "tables", "endpoint": {"path": "projects/{projectId}/datasets/{datasetId}/tables", "data_selector": "tables"}},
            {"name": "jobs_getQueryResults", "endpoint": {"path": "projects/{projectId}/queries/{jobId}", "data_selector": "rows"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pandas_gbq_pipeline",
        destination="duckdb",
        dataset_name="pandas_gbq_data",
    )
    load_info = pipeline.run(pandas_gbq_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("pandas_gbq_pipeline").dataset()
sessions_df = data.tables.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pandas_gbq_data.tables LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pandas_gbq_pipeline").dataset()
data.tables.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 Pandas GBQ 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 credentials are missing or invalid you'll receive 401 Unauthorized or 403 Forbidden with messages about insufficient authentication or permissions. Ensure the BigQuery API is enabled, the service account has BigQuery roles, and the credentials used are valid (set GOOGLE_APPLICATION_CREDENTIALS or pass google.auth credentials into pandas_gbq.read_gbq).

Quotas and rate limits

BigQuery enforces API quotas and rate limits (requests per minute, concurrent queries). Exceeding quotas returns 429 or 403 errors with quotaExceeded or rateLimitExceeded messages. Implement retry/backoff and ensure appropriate project quota.

Pagination and large result sets

Many endpoints return pageToken / pageToken parameters (e.g., tabledata.list, jobs.getQueryResults). Results are returned in pages; check the response pageToken and use it to fetch subsequent pages. For query results, jobs.getQueryResults returns totalRows and rows[]. Use BigQuery Storage API (use_bqstorage_api option) for faster large-volume downloads.

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