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Manage BigQuery DataFrames sessions and I/OThis document explains how to manage sessions and perform input and output (I/O) operations when you use BigQuery DataFrames. You will learn how to create and use sessions, work with in-memory data, and read from and write to files and BigQuery tables.
BigQuery sessionsBigQuery DataFrames uses a local session object internally to manage metadata. Each DataFrame and Series object connects to a session, each session connects to a location, and each query in a session runs in the location where you created the session. Use the following code sample to manually create a session and use it for loading data:
You can't combine data from multiple session instances, even if you initialize them with the same settings. The following code sample shows that trying to combine data from different session instances causes an error:
Global sessionBigQuery DataFrames provides a default global session that you can access with the bigframes.pandas.get_global_session() method. In Colab, you must provide a project ID for the bigframes.pandas.options.bigquery.project attribute before you use it. You can also set a location with the bigframes.pandas.options.bigquery.location attribute, which defaults to the US multi-region.
The following code sample shows how to set options for the global session:
To reset the global session's location or project, close the current session by running the bigframes.pandas.close_session() method.
Many BigQuery DataFrames built-in functions use the global session by default. The following code sample shows how built-in functions use the global session:
In-memory dataYou can create Dataframes and Series objects with built-in Python or NumPy data structures, similar to how you create objects with pandas. Use the following code sample to create an object:
To convert pandas objects to DataFrames objects using the read_pandas() method or constructors, use the following code sample:
To use the to_pandas() method to load BigQuery DataFrames data into your memory, use the following code sample:
dry_run parameter
Loading a large amount of data can take a lot of time and resources. To see how much data is being processed, use the dry_run=True parameter in the to_pandas() call. Use the following code sample to perform a dry run:
You can read data from compatible files into a BigQuery DataFrames. These files can be on your local machine or in Cloud Storage. Use the following code sample to read data from a CSV file:
To save your BigQuery DataFrames to local files or Cloud Storage files using the to_csv method, use the following code sample:
To create BigQuery DataFrames using BigQuery table references and the bigframes.pandas.read_gbq function, use the following code sample:
To use a SQL string with the read_gbq() function to read data into BigQuery DataFrames, use the following code sample:
read_gbq(), read_gbq_table(), or read_gbq_query() function, and you haven't set the bigframes.pandas.options.bigquery.location attribute before the function call, then BigQuery DataFrames automatically sets the bigframes.pandas.options.bigquery.location attribute to the table's location. For information on how to manually specify the location, see Global session.
To save your DataFrame object to a BigQuery table, use the to_gbq() method of your DataFrame object. The following code sample shows how to do that:
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-08-07 UTC.
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