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

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

Connecting to BigCommerce Data

Connecting to BigCommerce 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.

BigCommerce authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Big Commerce developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL. These three parameters will be set as connection properties to your driver.

Additionally, in order to connect to your BigCommerce Store, you will need your StoreId. To find your Store Id please follow these steps:

1. Log in to your BigCommerce account.
2. From the Home Page, select Advanced Settings > API Accounts.
3. Click Create API Account.
4. A text box named API Path will appear on your screen.
5. Inside you can see a URL of the following structure: https://api.bigcommerce.com/stores/{Store Id}/v3.
6. As demonstrated above, your Store Id will be between the 'stores/' and '/v3' path paramters.
7. Once you have retrieved your Store Id you can either click Cancel or proceed in creating an API Account in case you do not have one already.

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

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

engine = create_engine("bigcommerce:///?OAuthClientId=YourClientId& OAuthClientSecret=YourClientSecret& StoreId='YourStoreID'& CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to BigCommerce

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

df = pandas.read_sql("SELECT FirstName, LastName FROM Customers WHERE FirstName = 'Bob'", engine)

Visualize BigCommerce Data

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

df.plot(kind="bar", x="FirstName", y="LastName")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for BigCommerce to start building Python apps and scripts with connectivity to BigCommerce 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("bigcommerce:///?OAuthClientId=YourClientId& OAuthClientSecret=YourClientSecret& StoreId='YourStoreID'& CallbackURL='http://localhost:33333'InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT FirstName, LastName FROM Customers WHERE FirstName = 'Bob'", engine)

df.plot(kind="bar", x="FirstName", y="LastName")
plt.show()