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Python Connector Libraries for Google BigQuery Data Connectivity. Integrate Google BigQuery with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to Visualize BigQuery Data in Python with pandas

Use pandas and other modules to analyze and visualize live BigQuery data in Python.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Google BigQuery, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build BigQuery-connected Python applications and scripts for visualizing BigQuery data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to BigQuery data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live BigQuery data in Python. When you issue complex SQL queries from BigQuery, the driver pushes supported SQL operations, like filters and aggregations, directly to BigQuery and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to BigQuery Data

Connecting to BigQuery data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, you will need to register an application to obtain the OAuth JWT values.

In addition to the OAuth values, you will need to specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

Follow the procedure below to install the required modules and start accessing BigQuery through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize BigQuery Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with BigQuery data.

engine = create_engine("googlebigquery:///?DataSetId=MyDataSetId&ProjectId=MyProjectId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to BigQuery

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

Visualize BigQuery Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the BigQuery data. The show method displays the chart in a new window.

df.plot(kind="bar", x="OrderName", y="Freight")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Google BigQuery to start building Python apps and scripts with connectivity to BigQuery data. Reach out to our Support Team if you have any questions.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("googlebigquery:///?DataSetId=MyDataSetId&ProjectId=MyProjectId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

df.plot(kind="bar", x="OrderName", y="Freight")