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

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How to use SQLAlchemy ORM to access QuickBooks Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of QuickBooks 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 and the SQLAlchemy toolkit, you can build QuickBooks-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to QuickBooks data to query, update, delete, and insert QuickBooks data.

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

Connecting to QuickBooks Data

Connecting to QuickBooks 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.

When you are connecting to a local QuickBooks instance, you do not need to set any connection properties.

Requests are made to QuickBooks through the Remote Connector. The Remote Connector runs on the same machine as QuickBooks and accepts connections through a lightweight, embedded Web server. The server supports SSL/TLS, enabling users to connect securely from remote machines.

The first time you connect, you will need to authorize the Remote Connector with QuickBooks. See the "Getting Started" chapter of the help documentation for a guide.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with QuickBooks 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("quickbooks:///?URL=http://remotehost:8166&User=admin&Password=admin123")

Declare a Mapping Class for QuickBooks 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" Name = Column(String,primary_key=True) CustomerBalance = Column(String) ...

Query QuickBooks 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("quickbooks:///?URL=http://remotehost:8166&User=admin&Password=admin123") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customers).filter_by(Type="Commercial"): print("Name: ", instance.Name) print("CustomerBalance: ", instance.CustomerBalance) 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.Type == "Commercial")): print("Name: ", instance.Name) print("CustomerBalance: ", instance.CustomerBalance) print("---------")

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

Insert QuickBooks Data

To insert QuickBooks 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.

new_rec = Customers(Name="placeholder", Type="Commercial") session.add(new_rec) session.commit()

Update QuickBooks Data

To update QuickBooks 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.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Type = "Commercial" session.commit()

Delete QuickBooks Data

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