Answer: By using the groupBy method followed by the apply method on a DataFrame

A grouped map Pandas UDF in PySpark enables the application of a function to each group within a DataFrame, akin to the pandas `apply` function but designed for scalability with Spark. This function processes pandas DataFrames grouped by specified columns, facilitating complex operations beyond standard Spark DataFrame transformations and aggregations. The correct method involves: 1. Grouping the DataFrame with the `groupBy` method to partition the data. 2. Applying the Pandas UDF to each group using the `apply` method. This approach allows for custom operations on each data group, harnessing pandas' power within Spark's distributed environment. - **Option A** is incorrect as the `apply` method on a DataFrame column is more aligned with pandas and not directly related to grouped map Pandas UDFs in PySpark. - **Option B** describes using `groupBy` followed by `agg`, which is suitable for aggregations but not for applying grouped map Pandas UDFs. - **Option D** mentions the `applyInPandas` method, which is a general method for various Pandas UDFs but not specifically for grouped map operations in this context.

Author: LeetQuiz Editorial Team