Use pandas to Visualize Google Ads Data in Python

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Google AdWords Python Connector

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



The CData Python Connector for Google Ads enables you use pandas and other modules to analyze and visualize live Google Ads 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 Ads, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Google Ads-connected Python applications and scripts for visualizing Google Ads data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Google Ads 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 Ads data in Python. When you issue complex SQL queries from Google Ads, the driver pushes supported SQL operations, like filters and aggregations, directly to Google Ads and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Google Ads Data

Connecting to Google Ads 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, specify the DeveloperToken and ClientCustomerId.

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 Google Ads 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 Ads Data in Python

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

engine = create_engine("googleads:///?DeveloperToken=MyDeveloperToken&ClientCustomerId=MyClientCustomerId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Google Ads

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

df = pandas.read_sql("SELECT Device, Clicks FROM CampaignPerformance WHERE Device = ''Mobile devices with full browsers''", engine)

Visualize Google Ads Data

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

df.plot(kind="bar", x="Device", y="Clicks")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Google Ads Python Connector to start building Python apps and scripts with connectivity to Google Ads 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("googleads:///?DeveloperToken=MyDeveloperToken&ClientCustomerId=MyClientCustomerId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Device, Clicks FROM CampaignPerformance WHERE Device = ''Mobile devices with full browsers''", engine)

df.plot(kind="bar", x="Device", y="Clicks")
plt.show()