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Get the Report →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).
About Microsoft Dataverse Data Integration
CData provides the easiest way to access and integrate live data from Microsoft Dataverse (formerly the Common Data Service). Customers use CData connectivity to:
- Access both Dataverse Entities and Dataverse system tables to work with exactly the data they need.
- Authenticate securely with Microsoft Dataverse in a variety of ways, including Azure Active Directory, Azure Managed Service Identity credentials, and Azure Service Principal using either a client secret or a certificate.
- Use SQL stored procedures to manage Microsoft Dataverse entities - listing, creating, and removing associations between entities.
CData customers use our Dataverse connectivity solutions for a variety of reasons, whether they're looking to replicate their data into a data warehouse (alongside other data sources)or analyze live Dataverse data from their preferred data tools inside the Microsoft ecosystem (Power BI, Excel, etc.) or with external tools (Tableau, Looker, etc.).
Getting Started
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 https://myorganization.crm.dynamics.com.
- 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=https://myaccount.crm.dynamics.com/InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
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") plt.show()

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=https://myaccount.crm.dynamics.com/InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") df = pandas.read_sql("SELECT AccountId, Name FROM Accounts WHERE Name = 'MyAccount'", engine) df.plot(kind="bar", x="AccountId", y="Name") plt.show()