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Use pandas to Visualize Salesforce Pardot Data in Python

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

Connecting to Salesforce Pardot Data

Connecting to Salesforce Pardot 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.

Salesforce Pardot supports connecting through API Version, Username, Password and User Key.

  • ApiVersion: The Salesforce Pardot API version which the provided account can access. Defaults to 4.
  • User: The Username of the Salesforce Pardot account.
  • Password: The Password of the Salesforce Pardot account.
  • UserKey: The unique User Key for the Salesforce Pardot account. This key does not expire.
  • IsDemoAccount (optional): Set to TRUE to connect to a demo account.

Accessing the Pardot User Key

The User Key of the current account may be accessed by going to Settings -> My Profile, under the API User Key row.

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

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

engine = create_engine("salesforcepardot:///?ApiVersion=4&User=YourUsername&Password=YourPassword&UserKey=YourUserKey")

Execute SQL to Salesforce Pardot

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, Email FROM Prospects WHERE ProspectAccountId = '703'", engine)

Visualize Salesforce Pardot Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Salesforce Pardot Python Connector to start building Python apps and scripts with connectivity to Salesforce Pardot 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("salesforcepardot:///?ApiVersion=4&User=YourUsername&Password=YourPassword&UserKey=YourUserKey")
df = pandas.read_sql("SELECT Id, Email FROM Prospects WHERE ProspectAccountId = '703'", engine)

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