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Use pandas to Visualize Salesforce Chatter Data in Python

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

Connecting to Salesforce Chatter Data

Connecting to Salesforce Chatter 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 Chatter uses OAuth 2.0 authentication. To authenticate to Salesforce Chatter via OAuth 2.0, you will need to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL by registering an app with Salesforce Chatter.

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

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

engine = create_engine("salesforcechatter:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:343343&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Salesforce Chatter

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, PostCount FROM Users WHERE SearchTerms = 'Smoked*BBQ'", engine)

Visualize Salesforce Chatter Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Salesforce Chatter Python Connector to start building Python apps and scripts with connectivity to Salesforce Chatter 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("salesforcechatter:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:343343&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, PostCount FROM Users WHERE SearchTerms = 'Smoked*BBQ'", engine)

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