How to Visualize Microsoft Dataverse Data in Python with pandas via CData Connect AI
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas and Matplotlib modules, you can build Microsoft Dataverse-connected Python applications and scripts for visualizing Microsoft Dataverse data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query Microsoft Dataverse data and visualize the results.
The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.
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 Microsoft Entra ID, 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
Connect to Microsoft Dataverse in Connect AI
CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.
- Log into Connect AI, click Sources, and then click Add Connection
- Select "Microsoft Dataverse" from the Add Connection panel
-
Enter the necessary authentication properties to connect to Microsoft Dataverse.
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.
- Click Save & Test
- Navigate to the Permissions tab and update the user-based permissions.
Generate a Personal Access Token (PAT)
The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.
- Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
- On the Settings page, go to the Access Tokens section and click Create PAT.
- Give the PAT a name and click Create.
- The PAT is only visible at creation, so copy it and store it securely.
Install Required Modules
Install the SDK (with the pandas extra) and Matplotlib using the pip utility:
pip install "cdata-connect-ai[full]" pip install matplotlib
Visualize Microsoft Dataverse Data in Python
Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, CDS1).
import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai
conn = cdata_connect_ai.connect(
username="[email protected]",
password="<your_pat>",
)
Query Microsoft Dataverse with pandas
Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.
df = pandas.read_sql(
"SELECT AccountId, Name "
"FROM [CDS1].[CDS].[Accounts] "
"WHERE Name = 'MyAccount'",
conn,
)
Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.
Visualize Microsoft Dataverse Data
With the query results stored in a DataFrame, use the plot function to build a chart. The show method displays the chart in a new window.
df.plot(kind="bar", x="AccountId", y="Name") plt.show() conn.close()
More Information and Free Trial
Now you can read live Microsoft Dataverse data into pandas through the CData Connect AI Python SDK. For more information on connecting to Microsoft Dataverse (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Microsoft Dataverse data in Python.
Full Source Code
import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai
conn = cdata_connect_ai.connect(
username="[email protected]",
password="<your_pat>",
)
df = pandas.read_sql(
"SELECT AccountId, Name "
"FROM [CDS1].[CDS].[Accounts] "
"WHERE Name = 'MyAccount'",
conn,
)
df.plot(kind="bar", x="AccountId", y="Name")
plt.show()
conn.close()