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

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

Connecting to Stripe Data

Connecting to Stripe 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.

Use the OAuth authentication standard to connect to Stripe. To authenticate using OAuth, register an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

engine = create_engine("stripe:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH")

Execute SQL to Stripe

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

df = pandas.read_sql("SELECT Email, Discount FROM Customers WHERE Delinquent = 'False'", engine)

Visualize Stripe Data

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

df.plot(kind="bar", x="Email", y="Discount")
plt.show()

👁 Stripe data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Stripe to start building Python apps and scripts with connectivity to Stripe data. Reach out to our Support Team if you have any questions.

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Full Source Code

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

engine = create_engine("stripe:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:33333&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT Email, Discount FROM Customers WHERE Delinquent = 'False'", engine)

df.plot(kind="bar", x="Email", y="Discount")
plt.show()