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Use pandas to Visualize Jira Data in Python

The CData Python Connector for Jira enables you use pandas and other modules to analyze and visualize live Jira 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 Jira, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Jira-connected Python applications and scripts for visualizing Jira data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Jira 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 data in Python. When you issue complex SQL queries from Jira, the driver pushes supported SQL operations, like filters and aggregations, directly to Jira and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Jira Data

Connecting to Jira 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.

To connect to JIRA, provide the User and Password. Additionally, provide the Url; for example, https://yoursitename.atlassian.net.

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

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

engine = create_engine("jira:///?User=admin&Password=123abc&Url=https://yoursitename.atlassian.net")

Execute SQL to Jira

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

df = pandas.read_sql("SELECT Summary, TimeSpent FROM Issues WHERE ReporterDisplayName = 'Bob'", engine)

Visualize Jira Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Jira Python Connector to start building Python apps and scripts with connectivity to Jira 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("jira:///?User=admin&Password=123abc&Url=https://yoursitename.atlassian.net")
df = pandas.read_sql("SELECT Summary, TimeSpent FROM Issues WHERE ReporterDisplayName = 'Bob'", engine)

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