Use pandas to Visualize Salesforce Data in Python

Ready to get started?

Download a free trial:

Download Now

Learn more:

Salesforce Python Connector

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

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

Connecting to Salesforce Data

Connecting to Salesforce 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.

There are several authentication methods available for connecting to Salesforce: Login, OAuth, and SSO. The Login method requires you to have the username, password, and security token of the user.

If you do not have access to the username and password or do not wish to require them, you can use OAuth authentication.

SSO (single sign-on) can be used by setting the SSOProperties, SSOLoginUrl, and TokenUrl connection properties, which allow you to authenticate to an identity provider. See the "Getting Started" chapter in the help documentation for more information.

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

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

engine = create_engine("salesforce:///?User=username&Password=password&SecurityToken=Your_Security_Token")

Execute SQL to Salesforce

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

df = pandas.read_sql("SELECT Industry, AnnualRevenue FROM Account WHERE Name = 'GenePoint'", engine)

Visualize Salesforce Data

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

df.plot(kind="bar", x="Industry", y="AnnualRevenue")

Free Trial & More Information

Download a free, 30-day trial of the Salesforce Python Connector to start building Python apps and scripts with connectivity to Salesforce 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("salesforce:///?User=username&Password=password&SecurityToken=Your_Security_Token")
df = pandas.read_sql("SELECT Industry, AnnualRevenue FROM Account WHERE Name = 'GenePoint'", engine)

df.plot(kind="bar", x="Industry", y="AnnualRevenue")