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

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

Connecting to Jira Service Management Data

Connecting to Jira Service Management 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.

You can establish a connection to any Jira Service Desk Cloud account or Server instance.

Connecting with a Cloud Account

To connect to a Cloud account, you'll first need to retrieve an APIToken. To generate one, log in to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

Supply the following to connect to data:

• User: Set this to the username of the authenticating user.
• APIToken: Set this to the API token found previously.

Connecting with a Service Account

To authenticate with a service account, supply the following connection properties:

• User: Set this to the username of the authenticating user.
• Password: Set this to the password of the authenticating user.
• URL: Set this to the URL associated with your JIRA Service Desk endpoint. For example, https://yoursitename.atlassian.net.

Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.

Accessing Custom Fields

By default, the connector only surfaces system fields. To access the custom fields for Issues, set IncludeCustomFields.

Follow the procedure below to install the required modules and start accessing Jira Service Management 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 Jira Service Management Data in Python

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

engine = create_engine("jiraservicedesk:///?ApiKey=myApiKey&User=MyUser&InitiateOAuth=GETANDREFRESH")

Execute SQL to Jira Service Management

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

df = pandas.read_sql("SELECT RequestId, ReporterName FROM Requests WHERE CurrentStatus = 'Open'", engine)

Visualize Jira Service Management Data

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

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

👁 Jira Service Management data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Jira Service Management to start building Python apps and scripts with connectivity to Jira Service Management 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("jiraservicedesk:///?ApiKey=myApiKey&User=MyUser&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT RequestId, ReporterName FROM Requests WHERE CurrentStatus = 'Open'", engine)

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