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Use SQLAlchemy ORMs to Access Google Sheets Data in Python

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

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

Connecting to Google Sheets Data

Connecting to Google Sheets 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.

You can connect to a spreadsheet by providing authentication to Google and then setting the Spreadsheet connection property to the name or feed link of the spreadsheet. If you want to view a list of information about the spreadsheets in your Google Drive, execute a query to the Spreadsheets view after you authenticate.

ClientLogin (username/password authentication) has been officially deprecated since April 20, 2012 and is now no longer available. Instead, use the OAuth 2.0 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.

See the Getting Started chapter in the help documentation to connect to Google Sheets from different types of accounts: Google accounts, Google Apps accounts, and accounts using two-step verification.

Follow the procedure below to install SQLAlchemy and start accessing Google Sheets 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 Sheets Data in Python

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

engine = create_engine("googlesheets///?Spreadsheet=MySheet&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Google Sheets 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 Orders 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 Orders(base):
	__tablename__ = "Orders"
	Shipcountry = Column(String,primary_key=True)
	OrderPrice = Column(String)
	...

Query Google Sheets 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("googlesheets///?Spreadsheet=MySheet&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCity="Madrid"):
	print("Shipcountry: ", instance.Shipcountry)
	print("OrderPrice: ", instance.OrderPrice)
	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

Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCity == "Madrid")):
	print("Shipcountry: ", instance.Shipcountry)
	print("OrderPrice: ", instance.OrderPrice)
	print("---------")

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

Insert Google Sheets Data

To insert Google Sheets data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Google Sheets.

new_rec = Orders(Shipcountry="placeholder", ShipCity="Madrid")
session.add(new_rec)
session.commit()

Update Google Sheets Data

To update Google Sheets data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Google Sheets.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCity = "Madrid"
session.commit()

Delete Google Sheets Data

To delete Google Sheets data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided recoreds (rows).

deleted_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

Download a free, 30-day trial of the Google Sheets Python Connector to start building Python apps and scripts with connectivity to Google Sheets data. Reach out to our Support Team if you have any questions.