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> Mirror of <https://developer.salesforce.com/docs/data/data-cloud-query-guide/guide/get-started-dc-python.html> (Salesforce, Data 360 Query Guide), read 2026-10-23. Salesforce's own page is the authority; this copy exists because that site refuses non-browser readers.

## Data 360 Python Connector

Use the Data 360 Python Connector to extract and analyze your Data 360 data in Python. The connector enables you to:

*   Query Data 360 data using SQL
*   Work with data in Pandas DataFrames
*   Create visual data models
*   Perform analytical operations
*   Build machine learning and AI models

Install the connector from PyPI:

After successful installation, you’ll see: `Successfully Installed salesforce-cdp-connector-<version>`

Choose one of two authentication methods:

1.   Create an external client app:

    *   Go to **Set up**>**App Manager**>**New External Client App**
    *   Complete the basic information
    *   Enable OAuth settings
    *   Enter your callback URL
    *   Select required OAuth scopes
    *   Save and continue

2.   Get your credentials:

    *   Copy the consumer key (client ID)
    *   Copy the consumer secret

1.   Create an external client app (same steps as Method 1)

2.   Select these OAuth scopes:

    *   refresh_token
    *   api
    *   cdp_query_api
    *   cdp_profile_api

3.   Get your OAuth tokens:

    *   Construct the authorization URL: 
    *   Get the login URL from **Set up**>**My Domain**
    *   Get the callback URL from **Set up**>**App Manager**>**View External Client App**>**Call Back URL**
    *   Open the URL in your browser
    *   Extract the authorization code from the redirect URL
    *   Make a POST request to get tokens: 
    *   Save the access_token and refresh_token from the response

Create a cursor and execute your SQL query:

Choose one of three methods to retrieve your data:

After setting up the Python connector, here are some recommended next steps:

*   Use the connector to query your Data 360 tables
*   Examine the schema of your data model objects
*   Try different SQL queries to understand your data structure

*   Create Pandas DataFrames for data analysis
*   Use Python libraries like matplotlib or seaborn for visualization
*   Perform statistical analysis on your data

*   Connect the connector to your existing Python applications
*   Set up automated data extraction workflows
*   Integrate with your data pipeline tools

*   [Learn about Data 360 Query API](https://developer.salesforce.com/docs/data/data-cloud-query-guide/references/data-cloud-query-api-reference/dc-rest-api-overview.html)
*   [Explore Data 360 data model objects](https://developer.salesforce.com/docs/atlas.en-us.c360a_api.meta/c360a_api/c360a_api_metadata_api.htm)
*   [Understand Data 360 limits and guidelines](https://help.salesforce.com/s/articleView?id=data.c360_a_limits_and_guidelines.htm&type=5)

*   Use connection pooling for better performance
*   Implement proper error handling
*   Follow security best practices for credential management
*   Monitor your API usage and stay within limits

*   Join the [Salesforce Developer Community](https://trailhead.salesforce.com/trailblazer-community/groups/0F94S000000kOWvSAM)
*   Check out [Trailhead modules](https://trailhead.salesforce.com/content/learn/trails/data-cloud) on Data 360
*   Explore [GitHub examples](https://github.com/forcedotcom) for Data 360 integration


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