How to Visualize Dynamics 365 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 Dynamics 365-connected Python applications and scripts for visualizing Dynamics 365 data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query Dynamics 365 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 Dynamics 365 Data Integration
CData simplifies access and integration of live Microsoft Dynamics 365 data. Our customers leverage CData connectivity to:
- Read and write data in the full Dynamics 365 ecosystem: Sales, Customer Service, Finance & Operations, Marketing, and more.
- Extend the native features of Dynamics CRM with customizable caching and intelligent query aggregation and separation.
- Authenticate securely with Dynamics 365 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 their Dynamics 365 entities - listing, creating, and removing associations between entities.
CData customers use our Dynamics 365 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 Dynamics 365 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 Dynamics 365 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 "Dynamics 365" from the Add Connection panel
-
Enter the necessary authentication properties to connect to Dynamics 365.
Edition and OrganizationUrl are required connection properties. The Dynamics 365 connector supports connecting to the following editions: CustomerService, FieldService, FinOpsOnline, FinOpsOnPremise, HumanResources, Marketing, ProjectOperations and Sales.
For Dynamics 365 Business Central, use the separate Dynamics 365 Business Central driver.
OrganizationUrl is the URL to your Dynamics 365 organization. For instance, https://orgcb42e1d0.crm.dynamics.com
- 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 Dynamics 365 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, Dynamics3651).
import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai
conn = cdata_connect_ai.connect(
username="[email protected]",
password="<your_pat>",
)
Query Dynamics 365 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 GoalHeadingId, Name "
"FROM [Dynamics3651].[Dynamics365].[GoalHeadings] "
"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 Dynamics 365 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="GoalHeadingId", y="Name") plt.show() conn.close()
More Information and Free Trial
Now you can read live Dynamics 365 data into pandas through the CData Connect AI Python SDK. For more information on connecting to Dynamics 365 (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Dynamics 365 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 GoalHeadingId, Name "
"FROM [Dynamics3651].[Dynamics365].[GoalHeadings] "
"WHERE Name = 'MyAccount'",
conn,
)
df.plot(kind="bar", x="GoalHeadingId", y="Name")
plt.show()
conn.close()