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

How to Visualize Microsoft Dataverse Data in Python with pandas

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

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

Connecting to Microsoft Dataverse Data

Connecting to Microsoft Dataverse 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 without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.

  • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
  • OrganizationUrl: Set this to the organization URL you are connecting to, such as
  • Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.

When you connect the Common Data Service OAuth endpoint opens in your default browser. Log in and grant permissions. The OAuth process completes automatically.

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

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

engine = create_engine("cds:///?OrganizationUrl=")

Execute SQL to Microsoft Dataverse

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

df = pandas.read_sql("SELECT AccountId, Name FROM Accounts WHERE Name = 'MyAccount'", engine)

Visualize Microsoft Dataverse Data

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

df.plot(kind="bar", x="AccountId", y="Name")

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Microsoft Dataverse to start building Python apps and scripts with connectivity to Microsoft Dataverse 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("cds:///?OrganizationUrl=")
df = pandas.read_sql("SELECT AccountId, Name FROM Accounts WHERE Name = 'MyAccount'", engine)

df.plot(kind="bar", x="AccountId", y="Name")