How to Visualize Google Analytics Data in Python with pandas

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 Analytics, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Google Analytics-connected Python applications and scripts for visualizing Google Analytics data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Google Analytics data, execute queries, and visualize the results.

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

Connecting to Google Analytics Data

Connecting to Google Analytics 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 on 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, set Profile to the profile you want to connect to. This can be set to either the Id or website URL for the Profile. If not specified, the first Profile returned will be used.

Follow the procedure below to install the required modules and start accessing Google Analytics 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 Google Analytics Data in Python

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

engine = create_engine("googleanalytics:///?Profile=MyProfile&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Google Analytics

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

df = pandas.read_sql("SELECT Browser, Sessions FROM Traffic WHERE Transactions = '0'", engine)

Visualize Google Analytics Data

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

df.plot(kind="bar", x="Browser", y="Sessions")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Google Analytics to start building Python apps and scripts with connectivity to Google Analytics 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("googleanalytics:///?Profile=MyProfile&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Browser, Sessions FROM Traffic WHERE Transactions = '0'", engine)

df.plot(kind="bar", x="Browser", y="Sessions")
plt.show()