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Use SQLAlchemy ORMs to Access QuickBooks Online Data in Python

The CData Python Connector for QuickBooks Online enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of QuickBooks Online data.

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

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

Connecting to QuickBooks Online Data

Connecting to QuickBooks Online 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.

QuickBooks Online uses the OAuth authentication standard. OAuth requires the authenticating user to log in through the browser. To authenticate using OAuth, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app with Intuit. Additionally, if you want to connect to sandbox data, set UseSandbox to true.

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 QuickBooks Online 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 QuickBooks Online Data in Python

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

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

Declare a Mapping Class for QuickBooks Online 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 Customers 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 Customers(base):
	__tablename__ = "Customers"
	DisplayName = Column(String,primary_key=True)
	Balance = Column(String)
	...

Query QuickBooks Online 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("quickbooksonline///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(FullyQualifiedName="Cook, Brian"):
	print("DisplayName: ", instance.DisplayName)
	print("Balance: ", instance.Balance)
	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

Customers_table = Customers.metadata.tables["Customers"]
for instance in session.execute(Customers_table.select().where(Customers_table.c.FullyQualifiedName == "Cook, Brian")):
	print("DisplayName: ", instance.DisplayName)
	print("Balance: ", instance.Balance)
	print("---------")

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

Insert QuickBooks Online Data

To insert QuickBooks Online 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 QuickBooks Online.

new_rec = Customers(DisplayName="placeholder", FullyQualifiedName="Cook, Brian")
session.add(new_rec)
session.commit()

Update QuickBooks Online Data

To update QuickBooks Online 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 QuickBooks Online.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.FullyQualifiedName = "Cook, Brian"
session.commit()

Delete QuickBooks Online Data

To delete QuickBooks Online 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(Customers).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 QuickBooks Online Python Connector to start building Python apps and scripts with connectivity to QuickBooks Online data. Reach out to our Support Team if you have any questions.