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Extract, Transform, and Load QuickBooks Data in Python

The CData Python Connector for QuickBooks enables you to create ETL applications and pipelines for QuickBooks data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for QuickBooks and the petl framework, you can build QuickBooks-connected applications and pipelines for extracting, transforming, and loading QuickBooks data. This article shows how to connect to QuickBooks with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.

After installing the CData QuickBooks Connector, follow the procedure below to install the other required modules and start accessing QuickBooks through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for QuickBooks Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.quickbooks as mod

You can now connect with a connection string. Use the connect function for the CData QuickBooks Connector to create a connection for working with QuickBooks data.

cnxn = mod.connect("URL=http://remotehost:8166;User=admin;Password=admin123;")

Create a SQL Statement to Query QuickBooks

Use SQL to create a statement for querying QuickBooks. In this article, we read data from the Customers entity.

sql = "SELECT Name, CustomerBalance FROM Customers WHERE Type = 'Commercial'"

Extract, Transform, and Load the QuickBooks Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the QuickBooks data. In this example, we extract QuickBooks data, sort the data by the CustomerBalance column, and load the data into a CSV file.

Loading QuickBooks Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'CustomerBalance')

etl.tocsv(table2,'customers_data.csv')

In the following example, we add new rows to the Customers table.

Adding New Rows to QuickBooks

table1 = [ ['Name','CustomerBalance'], ['NewName1','NewCustomerBalance1'], ['NewName2','NewCustomerBalance2'], ['NewName3','NewCustomerBalance3'] ]

etl.appenddb(table1, cnxn, 'Customers')

With the CData Python Connector for QuickBooks, you can work with QuickBooks data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

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



Full Source Code


import petl as etl
import pandas as pd
import cdata.quickbooks as mod

cnxn = mod.connect("URL=http://remotehost:8166;User=admin;Password=admin123;")

sql = "SELECT Name, CustomerBalance FROM Customers WHERE Type = 'Commercial'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'CustomerBalance')

etl.tocsv(table2,'customers_data.csv')

table3 = [ ['Name','CustomerBalance'], ['NewName1','NewCustomerBalance1'], ['NewName2','NewCustomerBalance2'], ['NewName3','NewCustomerBalance3'] ]

etl.appenddb(table3, cnxn, 'Customers')