How to Visualize BigQuery Data in Python with pandas via CData Connect AI

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

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

Getting Started

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Connect to BigQuery in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "BigQuery" from the Add Connection panel

3. BigQuery uses OAuth to authenticate. Click "Sign in" to authenticate with BigQuery.
4. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize BigQuery Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, GoogleBigQuery1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Query BigQuery with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT OrderName, Freight "
    "FROM [GoogleBigQuery1].[GoogleBigQuery].[Orders] "
    "WHERE ShipCity = 'New York'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize BigQuery Data

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

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

conn.close()

More Information and Free Trial

Now you can read live BigQuery data into pandas through the CData Connect AI Python SDK. For more information on connecting to BigQuery (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live BigQuery data in Python.

Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pandas.read_sql(
    "SELECT OrderName, Freight "
    "FROM [GoogleBigQuery1].[GoogleBigQuery].[Orders] "
    "WHERE ShipCity = 'New York'",
    conn,
)

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

conn.close()