numpy.apply_over_axes — NumPy v2.2 Manual (original) (raw)

numpy.apply_over_axes(func, a, axes)[source]#

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:

funcfunction

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

aarray_like

Input array.

axesarray_like

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

Returns:

apply_over_axisndarray

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

import numpy as np 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]]])