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Use pandas to Visualize Salesforce Einstein Data in Python

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

Connecting to Salesforce Einstein Data

Connecting to Salesforce Einstein 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 Einstein Analytics uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with Salesforce Einstein Analytics.

See the Getting Started section of the CData data provider documentation for an authentication guide.

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

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

engine = create_engine("sfeinsteinanalytics:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Salesforce Einstein

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

df = pandas.read_sql("SELECT Name, CloseDate FROM Dataset_Opportunity WHERE StageName = 'Closed Won'", engine)

Visualize Salesforce Einstein Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Salesforce Einstein Python Connector to start building Python apps and scripts with connectivity to Salesforce Einstein 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("sfeinsteinanalytics:///?OAuthClientId=MyConsumerKey&OAuthClientSecret=MyConsumerSecret&CallbackURL=http://localhost:portNumber&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, CloseDate FROM Dataset_Opportunity WHERE StageName = 'Closed Won'", engine)

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