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Use pandas to Visualize Dynamics 365 Business Central Data in Python

The CData Python Connector for Dynamics 365 Business Central enables you use pandas and other modules to analyze and visualize live Dynamics 365 Business Central 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 Dynamics 365 Business Central, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Dynamics 365 Business Central-connected Python applications and scripts for visualizing Dynamics 365 Business Central data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Dynamics 365 Business Central data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Dynamics 365 Business Central data in Python. When you issue complex SQL queries from Dynamics 365 Business Central, the driver pushes supported SQL operations, like filters and aggregations, directly to Dynamics 365 Business Central and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Dynamics 365 Business Central Data

Connecting to Dynamics 365 Business Central 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.

To authenticate to Dynamics 365 Business Central, you must provide the User and AccessKey properties.

To obtain the User and AccessKey values, navigate to the Users page in Dynamics 365 Business Central and then click on Edit. The User Name and Web Service Access Key values are what you will enter as the User and AccessKey connection string properties. Note that the User Name is not your email address. It is a shortened user name.

To connect to data, specify OrganizationUrl. If you have multiple companies in your organization, you must also specify the Company to indicate which company you would like to connect to. Company does not need to be specified if you have only one company.

Follow the procedure below to install the required modules and start accessing Dynamics 365 Business Central 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 Dynamics 365 Business Central Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Dynamics 365 Business Central data.

engine = create_engine("d365businesscentral:///?OrganizationUrl=https://myaccount.financials.dynamics.com/")

Execute SQL to Dynamics 365 Business Central

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 Dynamics 365 Business Central Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Dynamics 365 Business Central 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 Dynamics 365 Business Central Python Connector to start building Python apps and scripts with connectivity to Dynamics 365 Business Central 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("d365businesscentral:///?OrganizationUrl=https://myaccount.financials.dynamics.com/")
df = pandas.read_sql("SELECT accountid, Name FROM Accounts WHERE Name = 'MyAccount'", engine)

df.plot(kind="bar", x="accountid", y="Name")
plt.show()