As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

Welcome to pandas-gbq’s documentation!

The pandas_gbq module provides a wrapper for Google’s BigQuery analytics web service to simplify retrieving results from BigQuery tables using SQL-like queries. Result sets are parsed into a pandas.DataFrame with a shape and data types derived from the source table. Additionally, DataFrames can be inserted into new BigQuery tables or appended to existing tables.

Note: The canonical version of this documentation can always be found on the pandas-gbq site.


To use this module, you will need a valid BigQuery account. Use the BigQuery sandbox to try the service for free.

While BigQuery uses standard SQL syntax, it has some important differences from traditional databases both in functionality, API limitations (size and quantity of queries or uploads), and how Google charges for use of the service. BiqQuery is best for analyzing large sets of data quickly. It is not a direct replacement for a transactional database. Refer to the BigQuery Documentation for details on the service itself.


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