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

Use SQLAlchemy ORMs to Access Xero WorkflowMax Data in Python

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

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

Connecting to Xero WorkflowMax Data

Connecting to Xero WorkflowMax 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 to the WorkflowMax API, obtain an APIKey and AccountKey from Xero. This can only be done by contacting Xero support (

After obtaining an API Key and Account Key, set the values in the APIKey and AccountKey connection properties. Once these are set, you are ready to connect.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Xero WorkflowMax 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("xeroworkflowmax:///?APIKey=myApiKey&AccountKey=myAccountKey")

Declare a Mapping Class for Xero WorkflowMax 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 Clients 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 Clients(base): __tablename__ = "Clients" Id = Column(String,primary_key=True) Name = Column(String) ...

Query Xero WorkflowMax 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("xeroworkflowmax:///?APIKey=myApiKey&AccountKey=myAccountKey") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Clients).filter_by(Name="Cynthia"): print("Id: ", instance.Id) print("Name: ", instance.Name) 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

Clients_table = Clients.metadata.tables["Clients"] for instance in session.execute( == "Cynthia")): print("Id: ", instance.Id) print("Name: ", instance.Name) print("---------")

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

Insert Xero WorkflowMax Data

To insert Xero WorkflowMax 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 WorkflowMax.

new_rec = Clients(Id="placeholder", Name="Cynthia") session.add(new_rec) session.commit()

Update Xero WorkflowMax Data

To update Xero WorkflowMax 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 WorkflowMax.

updated_rec = session.query(Clients).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Name = "Cynthia" session.commit()

Delete Xero WorkflowMax Data

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