This post discusses the limitations of the .map_elements() function in Polars for applying custom functions on grouped data, highlighting its inefficiency due to row-by-row processing. It contrasts this with the performance benefits of using .group_by().agg(), though the latter is restricted to simpler functions. The post aims to guide on how to effectively use .group_by().apply() to enable the application of custom functions on grouped data, addressing common pain points in data manipulation with Polars.