Ready to get started?

Learn more about the CData Python Connector for Salesforce Marketing Cloud or download a free trial:

Download Now

Use pandas to Visualize Salesforce Marketing Data in Python

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

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

Connecting to Salesforce Marketing Data

Connecting to Salesforce Marketing 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.

Authenticating to the Salesforce Marketing Cloud APIs

Set the User and Password to your login credentials, or to the credentials for a sandbox user if you are connecting to a sandbox account.

Connecting to the Salesforce Marketing Cloud APIs

By default, the data provider connects to production environments. Set UseSandbox to true to use a Salesforce Marketing Cloud sandbox account.

The default Instance is s7 of the Web Services API; however, if you use a different instance, you can set Instance.

Follow the procedure below to install the required modules and start accessing Salesforce Marketing 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 Salesforce Marketing Data in Python

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

engine = create_engine("sfmarketingcloud:///?User=myUser&Password=myPassword&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Salesforce Marketing

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

df = pandas.read_sql("SELECT Id, Status FROM Subscriber WHERE EmailAddress = 'john.doe@example.com'", engine)

Visualize Salesforce Marketing Data

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

df.plot(kind="bar", x="Id", y="Status")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Salesforce Marketing Python Connector to start building Python apps and scripts with connectivity to Salesforce Marketing 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("sfmarketingcloud:///?User=myUser&Password=myPassword&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Status FROM Subscriber WHERE EmailAddress = 'john.doe@example.com'", engine)

df.plot(kind="bar", x="Id", y="Status")
plt.show()