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

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

Connecting to GitHub Data

Connecting to GitHub 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.

GitHub uses the OAuth 2 authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Getting Started chapter of the CData help documentation for an authentication guide.

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

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

engine = create_engine("github:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to GitHub

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, Email FROM Users WHERE UserLogin = 'mojombo'", engine)

Visualize GitHub Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for GitHub to start building Python apps and scripts with connectivity to GitHub 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("github:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, Email FROM Users WHERE UserLogin = 'mojombo'", engine)

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