How to Visualize Salesforce Data Cloud Data in Python with pandas



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

Connecting to Salesforce Data Cloud Data

Connecting to Salesforce Data Cloud 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 Data Cloud supports authentication via the OAuth standard.

OAuth

Set AuthScheme to OAuth.

Desktop Applications

CData provides an embedded OAuth application that simplifies authentication at the desktop.

You can also authenticate from the desktop via a custom OAuth application, which you configure and register at the Salesforce Data Cloud console. For further information, see Creating a Custom OAuth App in the Help documentation.

Before you connect, set these properties:

  • InitiateOAuth: GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
  • OAuthClientId (custom applications only): The Client ID assigned when you registered your custom OAuth application.
  • OAuthClientSecret (custom applications only): The Client Secret assigned when you registered your custom OAuth application.

When you connect, the driver opens Salesforce Data Cloud's OAuth endpoint in your default browser. Log in and grant permissions to the application.

The driver then completes the OAuth process as follows:

  • Extracts the access token from the callback URL.
  • Obtains a new access token when the old one expires.
  • Saves OAuth values in OAuthSettingsLocation so that they persist across connections.
  • For other OAuth methods, including Web Applications and Headless Machines, refer to the Help documentation.

    Follow the procedure below to install the required modules and start accessing Salesforce Data Cloud 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 Cloud 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 Cloud data.

    engine = create_engine("salesforcedatacloud:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
    

    Execute SQL to Salesforce Data Cloud

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

    df = pandas.read_sql("SELECT [Account ID], [Account Name] FROM Account WHERE EmployeeCount = '250'", engine)
    

    Visualize Salesforce Data Cloud Data

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

    df.plot(kind="bar", x="[Account ID]", y="[Account Name]")
    plt.show()

    Free Trial & More Information

    Download a free, 30-day trial of the CData Python Connector for Salesforce Data Cloud to start building Python apps and scripts with connectivity to Salesforce Data Cloud 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("salesforcedatacloud:///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
    df = pandas.read_sql("SELECT [Account ID], [Account Name] FROM Account WHERE EmployeeCount = '250'", engine)
    
    df.plot(kind="bar", x="[Account ID]", y="[Account Name]")
    plt.show()
    

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