code: def filter(self, func, dropna=True, *args, **kwargs):  # noqa
          
          if isinstance(func, str):
              wrapper = lambda x: getattr(x, func)(*args, **kwargs)
          else:
              wrapper = lambda x: func(x, *args, **kwargs)
  
          # Interpret np.nan as False.
          def true_and_notna(x, *args, **kwargs):
              b = wrapper(x, *args, **kwargs)
              return b and notna(b)
  
          try:
              indices = [self._get_index(name) for name, group in self
                         if true_and_notna(group)]
          except ValueError:
              raise TypeError("the filter must return a boolean result")
          except TypeError:
              raise TypeError("the filter must return a boolean result")
  
          filtered = self._apply_filter(indices, dropna)
          return filtered
docs: Return a copy of a Series excluding elements from groups that
          do not satisfy the boolean criterion specified by func.
  
          Parameters
          ----------
          func : function
              To apply to each group. Should return True or False.
          dropna : Drop groups that do not pass the filter. True by default;
              if False, groups that evaluate False are filled with NaNs.
  
          Examples
          --------
          >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
          ...                           'foo', 'bar'],
          ...                    'B' : [1, 2, 3, 4, 5, 6],
          ...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
          >>> grouped = df.groupby('A')
          >>> df.groupby('A').B.filter(lambda x: x.mean() > 3.)
          1    2
          3    4
          5    6
          Name: B, dtype: int64
  
          Returns
          -------
          filtered : Series