Use pandas to Visualize Dynamics CRM Data in Python

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Python Connector Libraries for Dynamics CRM Data Connectivity. Integrate Dynamics CRM with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Dynamics CRM enables you use pandas and other modules to analyze and visualize live Dynamics CRM 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 CRM, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Dynamics CRM-connected Python applications and scripts for visualizing Dynamics CRM data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Dynamics CRM 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 CRM data in Python. When you issue complex SQL queries from Dynamics CRM, the driver pushes supported SQL operations, like filters and aggregations, directly to Dynamics CRM and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Dynamics CRM Data

Connecting to Dynamics CRM 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.

The connection string options meet the authentication and connection requirements of different Dynamics CRM instances. To connect to your instance, set the User and Password properties, under the Authentication section, to valid Dynamics CRM user credentials and set the Url to a valid Dynamics CRM server organization root. Additionally, set the CRMVersion property to 'CRM2011+' or 'CRMOnline'. IFD configurations are supported as well; set InternetFacingDeployment to true.

Additionally, you can provide the security token service (STS) or AD FS endpoint in the STSURL property. This value can be retrieved with the GetSTSUrl stored procedure. Office 365 users can connect to the default STS URL by simply setting CRMVersion.

Follow the procedure below to install the required modules and start accessing Dynamics CRM 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 CRM Data in Python

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

engine = create_engine("dynamicscrm:///?User=myuseraccount&Password=mypassword&URL= Version=CRM Online")

Execute SQL to Dynamics CRM

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

df = pandas.read_sql("SELECT FirstName, NumberOfEmployees FROM Account WHERE FirstName = 'Bob'", engine)

Visualize Dynamics CRM Data

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

df.plot(kind="bar", x="FirstName", y="NumberOfEmployees")

Free Trial & More Information

Download a free, 30-day trial of the Dynamics CRM Python Connector to start building Python apps and scripts with connectivity to Dynamics CRM 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("dynamicscrm:///?User=myuseraccount&Password=mypassword&URL= Version=CRM Online")
df = pandas.read_sql("SELECT FirstName, NumberOfEmployees FROM Account WHERE FirstName = 'Bob'", engine)

df.plot(kind="bar", x="FirstName", y="NumberOfEmployees")