# numpy.apply_over_axes

numpy.apply_over_axes(func, a, axes)

  Apply a function repeatedly over multiple axes.

  func is called as res = func(a, axis), where axis is the first element of axes. The result res of the function call must have either the same dimensions as a or one less dimension. If res has one less dimension than a, a dimension is inserted before axis. The call to func is then repeated for each axis in axes, with res as the first argument.

  Parameters
    func function

      This function must take two arguments, func(a, axis).

    a array_like

      Input array.

    axes array_like

      Axes over which func is applied; the elements must be integers.

  Returns
    apply_over_axis ndarray

      The output array. The number of dimensions is the same as a, but the shape can be different. This depends on whether func changes the shape of its output with respect to its input.

  See also

  `apply_along_axis`

    Apply a function to 1-D slices of an array along the given axis.

  Notes

  This function is equivalent to tuple axis arguments to reorderable ufuncs with keepdims=True. Tuple axis arguments to ufuncs have been available since version 1.7.0.

  Examples

      >>> a = np.arange(24).reshape(2,3,4)
      >>> a
      array([[[ 0,  1,  2,  3],
              [ 4,  5,  6,  7],
              [ 8,  9, 10, 11]],
             [[12, 13, 14, 15],
              [16, 17, 18, 19],
              [20, 21, 22, 23]]])

  Sum over axes 0 and 2. The result has same number of dimensions as the original array:

      >>> np.apply_over_axes(np.sum, a, [0,2])
      array([[[ 60],
              [ 92],
              [124]]])

  Tuple axis arguments to ufuncs are equivalent:

      >>> np.sum(a, axis=(0,2), keepdims=True)
      array([[[ 60],
              [ 92],
              [124]]])

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