Use pandas to Visualize Zendesk Data in Python

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Zendesk Python Connector

Python Connector Libraries for Zendesk Data Connectivity. Integrate Zendesk with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Zendesk enables you use pandas and other modules to analyze and visualize live Zendesk 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 Zendesk, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Zendesk-connected Python applications and scripts for visualizing Zendesk data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Zendesk data, execute queries, and visualize the results.

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

Connecting to Zendesk Data

Connecting to Zendesk 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.

Connecting to Zendesk

To connect, set the URL and provide authentication. The URL is your Zendesk Support URL: https://{subdomain}.zendesk.com.

Authenticating to Zendesk

You can authenticate using the Basic or OAuth methods.

Using Basic Authentication

To use Basic authentication, specify your email address and password or your email address and an API token. Set User to your email address and follow the steps below to provide the Password or ApiToken.

  • Enable password access in the Zendesk Support admin interface at Admin > Channels > API.
  • Manage API tokens in the Zendesk Support Admin interface at Admin > Channels > API. More than one token can be active at the same time. Deleting a token deactivates it permanently.

Using OAuth Authentication

See the Getting Started guide in the CData driver documentation for an authentication guide.

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

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

engine = create_engine("zendesk:///?URL=https://subdomain.zendesk.com&User=my@email.com&Password=test123&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Zendesk

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

df = pandas.read_sql("SELECT Id, Subject FROM Tickets WHERE Industry = 'Floppy Disks'", engine)

Visualize Zendesk Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Zendesk Python Connector to start building Python apps and scripts with connectivity to Zendesk 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("zendesk:///?URL=https://subdomain.zendesk.com&User=my@email.com&Password=test123&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Subject FROM Tickets WHERE Industry = 'Floppy Disks'", engine)

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