parameters: (groupby, rule, how=None, fill_method=None,
                                 limit=None, kind=None, **kwargs)
return_statement: return _maybe_process_deprecations(r,
                                         how=how,
                                         fill_method=fill_method,
                                         limit=limit)
docstring: Return our appropriate resampler when grouping as well.
docstring_summary: Return our appropriate resampler when grouping as well.
docstring_tokens: Return our appropriate resampler when grouping as well .
function: def get_resampler_for_grouping(groupby, rule, how=None, fill_method=None,
                                 limit=None, kind=None, **kwargs):
      """
      Return our appropriate resampler when grouping as well.
      """
  
      # .resample uses 'on' similar to how .groupby uses 'key'
      kwargs['key'] = kwargs.pop('on', None)
  
      tg = TimeGrouper(freq=rule, **kwargs)
      resampler = tg._get_resampler(groupby.obj, kind=kind)
      r = resampler._get_resampler_for_grouping(groupby=groupby)
      return _maybe_process_deprecations(r,
                                         how=how,
                                         fill_method=fill_method,
                                         limit=limit)
function_tokens: def get_resampler_for_grouping ( groupby , rule , how = None , fill_method = None , limit = None , kind = None , * * kwargs ) : # .resample uses 'on' similar to how .groupby uses 'key' kwargs [ 'key' ] = kwargs . pop ( 'on' , None ) tg = TimeGrouper ( freq = rule , * * kwargs ) resampler = tg . _get_resampler ( groupby . obj , kind = kind ) r = resampler . _get_resampler_for_grouping ( groupby = groupby ) return _maybe_process_deprecations ( r , how = how , fill_method = fill_method , limit = limit )