Explanation:
The mapInPandas() function in Databricks is designed to apply a function to each partition of a DataFrame. This approach enables efficient processing of large datasets by breaking them down into smaller, more manageable partitions and applying the function in parallel, thereby optimizing performance.
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In Databricks, what is the main purpose of the mapInPandas() function?
A
Applying a function to grouped data within a DataFrame
B
Applying a function to each partition of a DataFrame
C
Executing multiple models in parallel
D
Applying a function to co-grouped data from two DataFrames
E
None of the above