The error you are seeing when using mapInPandas in serverless compute with Environment version 2 is due to an incompatibility in the environment's supported Spark features. Specifically, Environment version 2 on serverless compute does not support mapInPandas, which triggers the Py4JException indicating that the method does not exist on the JVM side of Spark in your environment.

Why This Happens

• Serverless Environment Restrictions
  The version of Spark or configuration for serverless pools (especially with certain environments like Databricks Runtime 11.x or higher) may not expose certain Apache Spark features, including mapInPandas, for security and resource isolation reasons.

• Method Not Available
  The error message means the Spark JVM backend does not recognize or export the mapInPandas method for remote invocation. It's not your code—it's the compute environment not supporting direct Python UDFs with this Spark construct.

What You Can Do

• Switch to Standard Compute
  If possible, use a non-serverless, standard compute cluster or an environment version (like Databricks Runtime 9.x or below) where mapInPandas is supported.

• Use Supported APIs
  In environments where mapInPandas is not supported, use alternatives such as:

  • applyInPandas (on newer versions/environments that support only some Pandas UDFs)

  • Explicit Spark SQL or DataFrame operations

• Environment Upgrade/Change
  Check if a newer version of the serverless environment, or a configuration update, supports this method since feature support evolves frequently in managed Spark environments.