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

Use SQLAlchemy ORMs to Access Google Analytics Data in Python

The CData Python Connector for Google Analytics enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Google Analytics 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 Analytics and the SQLAlchemy toolkit, you can build Google Analytics-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Google Analytics data to query Google Analytics data.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model 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.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

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

Declare a Mapping Class for Google Analytics 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 Traffic 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 Traffic(base): __tablename__ = "Traffic" Browser = Column(String,primary_key=True) Sessions = Column(String) ...

Query Google Analytics 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("googleanalytics:///?Profile=MyProfile&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Traffic).filter_by(Transactions="0"): print("Browser: ", instance.Browser) print("Sessions: ", instance.Sessions) 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

Traffic_table = Traffic.metadata.tables["Traffic"] for instance in session.execute( == "0")): print("Browser: ", instance.Browser) print("Sessions: ", instance.Sessions) print("---------")

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

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