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

How to Visualize Azure DevOps Data in Python with pandas

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

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

Connecting to Azure DevOps Data

Connecting to Azure DevOps 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 connect to your Azure DevOps account by providing the Organization and PersonalAccessToken.

Obtaining a Personal Access Token

A PersonalAccessToken is necessary for account authentication.

To generate one, log in to your Azure DevOps Organization account and navigate to Profile -> Personal Access Tokens -> New Token. The generated token will be displayed.

If you wish to authenticate to Azure DevOps using OAuth refer to the online Help documentation for an authentication guide.

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

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

engine = create_engine("azuredevops:///?AuthScheme=Basic&Organization=MyAzureDevOpsOrganization&ProjectId=MyProjectId&PersonalAccessToken=MyPAT&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Azure DevOps

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, BuildNumber FROM Builds WHERE Reason = 'Manual'", engine)

Visualize Azure DevOps Data

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

df.plot(kind="bar", x="Id", y="BuildNumber")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Azure DevOps to start building Python apps and scripts with connectivity to Azure DevOps 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("azuredevops:///?AuthScheme=Basic&Organization=MyAzureDevOpsOrganization&ProjectId=MyProjectId&PersonalAccessToken=MyPAT&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, BuildNumber FROM Builds WHERE Reason = 'Manual'", engine)

df.plot(kind="bar", x="Id", y="BuildNumber")