Use SQLAlchemy ORMs to Access Xero Data in Python

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Xero Python Connector

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

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

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

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

Connecting to Xero Data

Connecting to Xero 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.

To connect, set the Schema connection property in addition to any authentication values. Xero offers authentication for private applications, public applications, and partner applications. You will need to set the XeroAppAuthentication property to PUBLIC, PRIVATE, or PARTNER, depending on the type of application configured. To connect from a private application, you will additionally need to set the OAuthAccessToken, OAuthClientId, OAuthClientSecret, CertificateStoreType, CertificateStore, and CertificateStorePassword.

To connect from a public or partner application, you can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL, or you can register an app to obtain your own OAuth values.

See the "Getting Started" chapter of the help documentation for a guide to authenticating to Xero.

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

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

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

Declare a Mapping Class for Xero 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 Items 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 Items(base):
	__tablename__ = "Items"
	Name = Column(String,primary_key=True)
	QuantityOnHand = Column(String)

Query Xero 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("xero:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Items).filter_by(Name="Golf balls - white single"):
	print("Name: ", instance.Name)
	print("QuantityOnHand: ", instance.QuantityOnHand)

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

Using the execute Method

Items_table = Items.metadata.tables["Items"]
for instance in session.execute( == "Golf balls - white single")):
	print("Name: ", instance.Name)
	print("QuantityOnHand: ", instance.QuantityOnHand)

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

Insert Xero Data

To insert Xero 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 Xero.

new_rec = Items(Name="placeholder", Name="Golf balls - white single")

Update Xero Data

To update Xero 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 Xero.

updated_rec = session.query(Items).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "Golf balls - white single"

Delete Xero Data

To delete Xero 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 records (rows).

deleted_rec = session.query(Items).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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