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Use pandas to Visualize QuickBooks Data in Python

The CData Python Connector for QuickBooks enables you use pandas and other modules to analyze and visualize live QuickBooks 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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build QuickBooks-connected Python applications and scripts for visualizing QuickBooks data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to QuickBooks 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 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.

Follow the procedure below to install the required modules and start accessing QuickBooks 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 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.

engine = create_engine("quickbooks:///?URL=http://remotehost:8166&User=admin&Password=admin123")

Execute SQL to QuickBooks

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

df = pandas.read_sql("SELECT Name, CustomerBalance FROM Customers WHERE Type = 'Commercial'", engine)

Visualize QuickBooks Data

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

df.plot(kind="bar", x="Name", y="CustomerBalance")
plt.show()

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 pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("quickbooks:///?URL=http://remotehost:8166&User=admin&Password=admin123")
df = pandas.read_sql("SELECT Name, CustomerBalance FROM Customers WHERE Type = 'Commercial'", engine)

df.plot(kind="bar", x="Name", y="CustomerBalance")
plt.show()