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 googleapis.dev pandas-gbq site.

Note

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

Also, consider using BigQuery DataFrames (bit.ly/bigframes-intro) to process large results with pandas compatible APIs with transparent SQL pushdown to BigQuery engine. This provides an opportunity to save on costs and improve performance.

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.

Contents:

• Installation
  • Pip
  • Install from Source
  • Dependencies
• Introduction
  • Authenticating to BigQuery
  • Reading data from BigQuery
  • Writing data to BigQuery
• Authentication
  • Default Authentication Methods
  • Authenticating with a Service Account
  • Authenticating with a User Account
  • Authenticating from Highly Constrained Development Environments
• Reading Tables
  • Querying with legacy SQL syntax
  • Inferring the DataFrame’s dtypes
  • Improving download performance
  • Advanced configuration
• Writing Tables
  • Writing to an Existing Table
  • Inferring the Table Schema
  • Troubleshooting Errors
• API Reference
• Contributing to pandas-gbq
  • Where to start?
  • Bug reports and enhancement requests
  • Working with the code
  • Contributing to the code base
  • Contributing your changes to pandas-gbq
• Changelog
  • 0.24.0 (2024-10-14)
  • 0.23.2 (2024-09-20)
  • 0.23.1 (2024-06-07)
  • 0.23.0 (2024-05-20)
  • 0.22.0 (2024-03-05)
  • 0.21.0 (2024-01-25)
  • 0.20.0 (2023-12-10)
  • 0.19.2 (2023-05-10)
  • 0.19.1 (2023-01-25)
  • 0.19.0 (2023-01-11)
  • 0.18.1 (2022-11-28)
  • 0.18.0 (2022-11-19)
  • 0.17.9 (2022-09-27)
  • 0.17.8 (2022-08-09)
  • 0.17.7 (2022-07-11)
  • 0.17.6 (2022-06-03)
  • 0.17.5 (2022-05-09)
  • 0.17.4 (2022-03-14)
  • 0.17.3 (2022-03-05)
  • 0.17.2 (2022-03-02)
  • 0.17.1 (2022-02-24)
  • 0.17.0 (2022-01-19)
  • 0.16.0 (2021-11-08)
  • 0.15.0 / 2021-03-30
  • 0.14.1 / 2020-11-10
  • 0.14.0 / 2020-10-05
  • 0.13.3 / 2020-09-30
  • 0.13.2 / 2020-05-14
  • 0.13.1 / 2020-02-13
  • 0.13.0 / 2019-12-12
  • 0.12.0 / 2019-11-25
  • 0.11.0 / 2019-07-29
  • 0.10.0 / 2019-04-05
  • 0.9.0 / 2019-01-11
  • 0.8.0 / 2018-11-12
  • 0.7.0 / 2018-10-19
  • 0.6.1 / 2018-09-11
  • 0.6.0 / 2018-08-15
  • 0.5.0 / 2018-06-15
  • 0.4.1 / 2018-04-05
  • 0.4.0 / 2018-04-03
  • 0.3.1 / 2018-02-13
  • 0.3.0 / 2018-01-03
  • 0.2.1 / 2017-11-27
  • 0.2.0 / 2017-07-24
  • 0.1.6 / 2017-05-03
  • 0.1.4 / 2017-03-17
  • 0.1.3 / 2017-03-04
  • 0.1.2 / 2017-02-23
• Privacy
  • Google account and user data
  • Policies for application authors
• Sign in to BigQuery

Indices and tables

• Index

• Module Index

• Search Page