How to Visualize QuickBooks POS Data in Python with pandas

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build QuickBooks POS-connected Python applications and scripts for visualizing QuickBooks POS data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to QuickBooks POS data, execute queries, and visualize the results.

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.

Follow the procedure below to install the required modules and start accessing QuickBooks POS through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize QuickBooks POS Data in Python

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

engine = create_engine("quickbookspos:///?")

Execute SQL to QuickBooks POS

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT ListId, AccountLimit FROM Customers WHERE LastName = 'Cook'", engine)

Visualize QuickBooks POS Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the QuickBooks POS data. The show method displays the chart in a new window.

df.plot(kind="bar", x="ListId", y="AccountLimit")
plt.show()

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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("quickbookspos:///?")
df = pandas.read_sql("SELECT ListId, AccountLimit FROM Customers WHERE LastName = 'Cook'", engine)

df.plot(kind="bar", x="ListId", y="AccountLimit")
plt.show()