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

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

Connecting to Gumroad Data

Connecting to Gumroad 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.

Using OAuth Authentication

To authenticate to Gumroad and connect to your own data or to allow other users to connect to their data, you can use the OAuth 2.0 standard. This is the recommended authentication method.

First you need to register an OAuth application with Gumroad. You can create an OAuth application by visiting your Gumroad account settings at https://app.gumroad.com/settings/advanced and navigating to the Applications section.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• InitiateOAuth: Set this to GETANDREFRESH. The CData API Profile for Gumroad will automatically walk through the OAuth process in order to obtain the access token.
• OAuthClientID: Set this to the client_id that is specified in your app settings.
• OAuthClientSecret: Set this to the client_secret that is specified in your app settings.
• CallbackURL: Set this to the Redirect URI you specified in your app settings.

Example connection string

Profile=C:\profiles\Gumroad.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;

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

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

engine = create_engine("api:///?Profile=C:\profiles\Gumroad.apip&AuthScheme=OAuth&InitiateOAuth=GETANDREFRESH&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")

Execute SQL to Gumroad

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

df = pandas.read_sql("SELECT ,  FROM CustomFields WHERE ProductId = 'prod_abc123xyz'", engine)

Visualize Gumroad Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Gumroad 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("api:///?Profile=C:\profiles\Gumroad.apip&AuthScheme=OAuth&InitiateOAuth=GETANDREFRESH&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
df = pandas.read_sql("SELECT ,  FROM CustomFields WHERE ProductId = 'prod_abc123xyz'", engine)

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