code: def naive_grouped_rowwise_apply(data,
                                  group_labels,
                                  func,
                                  func_args=(),
                                  out=None):
      
      if out is None:
          out = np.empty_like(data)
  
      for (row, label_row, out_row) in zip(data, group_labels, out):
          for label in np.unique(label_row):
              locs = (label_row == label)
              out_row[locs] = func(row[locs], *func_args)
      return out
docs: Simple implementation of grouped row-wise function application.
  
      Parameters
      ----------
      data : ndarray[ndim=2]
          Input array over which to apply a grouped function.
      group_labels : ndarray[ndim=2, dtype=int64]
          Labels to use to bucket inputs from array.
          Should be the same shape as array.
      func : function[ndarray[ndim=1]] -> function[ndarray[ndim=1]]
          Function to apply to pieces of each row in array.
      func_args : tuple
          Additional positional arguments to provide to each row in array.
      out : ndarray, optional
          Array into which to write output.  If not supplied, a new array of the
          same shape as ``data`` is allocated and returned.
  
      Examples
      --------
      >>> data = np.array([[1., 2., 3.],
      ...                  [2., 3., 4.],
      ...                  [5., 6., 7.]])
      >>> labels = np.array([[0, 0, 1],
      ...                    [0, 1, 0],
      ...                    [1, 0, 2]])
      >>> naive_grouped_rowwise_apply(data, labels, lambda row: row - row.min())
      array([[ 0.,  1.,  0.],
             [ 0.,  0.,  2.],
             [ 0.,  0.,  0.]])
      >>> naive_grouped_rowwise_apply(data, labels, lambda row: row / row.sum())
      array([[ 0.33333333,  0.66666667,  1.        ],
             [ 0.33333333,  1.        ,  0.66666667],
             [ 1.        ,  1.        ,  1.        ]])