Extract, Transform, and Load QuickBooks POS Data in Python

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 POS and the petl framework, you can build QuickBooks POS-connected applications and pipelines for extracting, transforming, and loading QuickBooks POS data. This article shows how to connect to QuickBooks POS with the CData Python Connector and use petl and pandas to extract, transform, and load QuickBooks POS data.

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

Connecting to QuickBooks POS Data

Connecting to QuickBooks POS 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 POS through the Remote Connector. The Remote Connector runs on the same machine as QuickBooks POS 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 POS. See the "Getting Started" chapter of the help documentation for a guide.

After installing the CData QuickBooks POS Connector, follow the procedure below to install the other required modules and start accessing QuickBooks POS 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 POS 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.quickbookspos as mod

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

cnxn = mod.connect("")

Create a SQL Statement to Query QuickBooks POS

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

sql = "SELECT ListId, AccountLimit FROM Customers WHERE LastName = 'Cook'"

Extract, Transform, and Load the QuickBooks POS Data

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

Loading QuickBooks POS Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to QuickBooks POS

table1 = [ ['ListId','AccountLimit'], ['NewListId1','NewAccountLimit1'], ['NewListId2','NewAccountLimit2'], ['NewListId3','NewAccountLimit3'] ]

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

With the CData Python Connector for QuickBooks POS, you can work with QuickBooks POS 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 CData Python Connector for QuickBooks POS to start building Python apps and scripts with connectivity to QuickBooks POS 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.quickbookspos as mod

cnxn = mod.connect("")

sql = "SELECT ListId, AccountLimit FROM Customers WHERE LastName = 'Cook'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['ListId','AccountLimit'], ['NewListId1','NewAccountLimit1'], ['NewListId2','NewAccountLimit2'], ['NewListId3','NewAccountLimit3'] ]

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