Python Client for Google BigQuery

Querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Google BigQuery solves this problem by enabling super-fast, SQL queries against append-mostly tables, using the processing power of Google’s infrastructure.

Related Libraries

For higher-level data analysis and DataFrame operations, consider using one of the following connector libraries:

Library	Description	Documentation
BigQuery DataFrames (bigframes)	Flexible pandas-like and scikit-learn-like API powered by BigQuery engine for large-scale data analysis and ML.	BigFrames Docs
pandas-gbq	Convenient integration allowing pandas DataFrames to load from and write to BigQuery.	pandas-gbq Docs

Quick Start

In order to use this library, you first need to go through the following steps:

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.10

Unsupported Python Versions

Python <= 3.9.

The last version of this library compatible with Python 2.7 and 3.5 is google-cloud-bigquery==1.28.0.

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-bigquery

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-bigquery

Example Usage

Perform a query

from google.cloud import bigquery

client = bigquery.Client()

# Perform a query.
QUERY = (
    'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '
    'WHERE state = "TX" '
    'LIMIT 100')
query_job = client.query(QUERY)  # API request
rows = query_job.result()  # Waits for query to finish

for row in rows:
    print(row.name)

Instrumenting With OpenTelemetry

This application uses OpenTelemetry to output tracing data from API calls to BigQuery. To enable OpenTelemetry tracing in the BigQuery client the following PyPI packages need to be installed:

pip install google-cloud-bigquery[opentelemetry] opentelemetry-exporter-gcp-trace

After installation, OpenTelemetry can be used in the BigQuery client and in BigQuery jobs. First, however, an exporter must be specified for where the trace data will be outputted to. An example of this can be found here:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.cloud_trace import CloudTraceSpanExporter
tracer_provider = TracerProvider()
tracer_provider = BatchSpanProcessor(CloudTraceSpanExporter())
trace.set_tracer_provider(TracerProvider())

In this example all tracing data will be published to the Google Cloud Trace console. For more information on OpenTelemetry, please consult the OpenTelemetry documentation.

Release files for google-cloud-bigquery 3.45.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for google-cloud-bigquery 3.45.2
File	Size	Uploaded
google_cloud_bigquery-3.45.2.tar.gz	529.3 kB	Sep 17, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for google-cloud-bigquery 3.45.2
File	Interpreter	ABI	Platform	Reset
google_cloud_bigquery-3.45.2-py3-none-any.whl 267.8 kB Sep 17, 2026	Python 3	none	any	Details

Total release size: 797.1 kB

Release files / google_cloud_bigquery-3.45.2.tar.gz

Download URL	google_cloud_bigquery-3.45.2.tar.gz
Size	529.3 kB
Tags	Source
SHA-256 checksum How to use checksums	`9342158e1a413c66b44b676a42c79cb8d1750da3971b957b267c9327749b4723`
BLAKE2b-256 checksum How to use checksums	`47d28a1d3b94ebab86003661b5264882f2f2d9596b33c77a5c994f3761f4e0ef`
Upload date	Sep 17, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.2.0 CPython/3.11.2`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: Google Cloud
Publishing commit: /
Publisher account: google-cloud-sdk-py@oss-exit-gate-prod.iam.gserviceaccount.com

Workflow

Publishing configuration: /blob//

Artifact

Subject: google_cloud_bigquery-3.45.2.tar.gz
SHA-256 checksum: 9342158e1a413c66b44b676a42c79cb8d1750da3971b957b267c9327749b4723 Verifying attestations

Release files / google_cloud_bigquery-3.45.2-py3-none-any.whl

Download URL	google_cloud_bigquery-3.45.2-py3-none-any.whl
Size	267.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`6920be2d7199810e2472be58204d7787dcf3cf677a21e682d5c636bae1b082ea`
BLAKE2b-256 checksum How to use checksums	`2f64fc977715bb54d78e4863d705eb6013241ade24247f1373a2be20e05cf6e3`
Upload date	Sep 17, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.2.0 CPython/3.11.2`

Provenance

Identity

Publishing platform: Google Cloud
Publishing commit: /
Publisher account: google-cloud-sdk-py@oss-exit-gate-prod.iam.gserviceaccount.com

Workflow

Publishing configuration: /blob//

Artifact

Subject: google_cloud_bigquery-3.45.2-py3-none-any.whl
SHA-256 checksum: 6920be2d7199810e2472be58204d7787dcf3cf677a21e682d5c636bae1b082ea Verifying attestations