Use SQLAlchemy ORMs to Access 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 to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Google Ads data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Google Ads and the SQLAlchemy toolkit, you can build Google Ads-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Google Ads data to query Google Ads data.

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 CData Connector 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 SQLAlchemy and start accessing Google Ads through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model 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")

Declare a Mapping Class for Google Ads Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the CampaignPerformance table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class CampaignPerformance(base):
	__tablename__ = "CampaignPerformance"
	Device = Column(String,primary_key=True)
	Clicks = Column(String)
	...

Query Google Ads Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("googleads:///?DeveloperToken=MyDeveloperToken&ClientCustomerId=MyClientCustomerId&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(CampaignPerformance).filter_by(Device="'Mobile devices with full browsers'"):
	print("Device: ", instance.Device)
	print("Clicks: ", instance.Clicks)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

CampaignPerformance_table = CampaignPerformance.metadata.tables["CampaignPerformance"]
for instance in session.execute(CampaignPerformance_table.select().where(CampaignPerformance_table.c.Device == "'Mobile devices with full browsers'")):
	print("Device: ", instance.Device)
	print("Clicks: ", instance.Clicks)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

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.