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

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

Connecting to Confluence Data

Connecting to Confluence 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.

Obtaining an API Token

An API token is necessary for account authentication. To generate one, login to your Atlassian account and navigate to API tokens > Create API token. The generated token will be displayed.

Connect Using a Confluence Cloud Account

To connect to a Cloud account, provide the following (Note: Password has been deprecated for connecting to a Cloud Account and is now used only to connect to a Server Instance.):

• User: The user which will be used to authenticate with the Confluence server.
• APIToken: The API Token associated with the currently authenticated user.
• Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

Connect Using a Confluence Server Instance

To connect to a Server instance, provide the following:

• User: The user which will be used to authenticate with the Confluence instance.
• Password: The password which will be used to authenticate with the Confluence server.
• Url: The URL associated with your JIRA endpoint. For example, https://yoursitename.atlassian.net.

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

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

engine = create_engine("confluence:///?User=admin&APIToken=myApiToken&Url=https://yoursitename.atlassian.net&Timezone=America/New_York")

Execute SQL to Confluence

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

df = pandas.read_sql("SELECT Key, Name FROM Pages WHERE Id = '10000'", engine)

Visualize Confluence Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Confluence to start building Python apps and scripts with connectivity to Confluence 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("confluence:///?User=admin&APIToken=myApiToken&Url=https://yoursitename.atlassian.net&Timezone=America/New_York")
df = pandas.read_sql("SELECT Key, Name FROM Pages WHERE Id = '10000'", engine)

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