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

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

Connecting to WooCommerce Data

Connecting to WooCommerce 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.

WooCommerce supports the following authentication methods: one-legged OAuth1.0 Authentication and standard OAuth2.0 Authentication.

Connecting using one-legged OAuth 1.0 Authentication

Specify the following properties (NOTE: the below credentials are generated from WooCommerce settings page and should not be confused with the credentials generated by using WordPress OAuth2.0 plugin):

• ConsumerKey
• ConsumerSecret

Connecting using WordPress OAuth 2.0 Authentication

After having configured the plugin, you may connect to WooCommerce by providing the following connection properties:

• OAuthClientId
• OAuthClientSecret
• CallbackURL
• InitiateOAuth - Set this to either GETANDREFRESH or REFRESH

In either case, you will need to set the Url property to the URL of the WooCommerce instance.

Follow the procedure below to install the required modules and start accessing WooCommerce 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 WooCommerce Data in Python

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

engine = create_engine("woocommerce:///?Url=https://example.com/& ConsumerKey=ck_ec52c76185c088ecaa3145287c8acba55a6f59ad& ConsumerSecret=cs_9fde14bf57126156701a7563fc87575713c355e5& InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to WooCommerce

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

df = pandas.read_sql("SELECT ParentId, Total FROM Orders WHERE ParentId = '3'", engine)

Visualize WooCommerce Data

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

df.plot(kind="bar", x="ParentId", y="Total")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for WooCommerce to start building Python apps and scripts with connectivity to WooCommerce 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("woocommerce:///?Url=https://example.com/& ConsumerKey=ck_ec52c76185c088ecaa3145287c8acba55a6f59ad& ConsumerSecret=cs_9fde14bf57126156701a7563fc87575713c355e5& InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT ParentId, Total FROM Orders WHERE ParentId = '3'", engine)

df.plot(kind="bar", x="ParentId", y="Total")
plt.show()