numpy.apply_over_axes() in Python

The numpy.apply_over_axes()applies a function repeatedly over multiple axes in an array.

Syntax : 

numpy.apply_over_axes(func, array, axes)

Parameters :  

1d_func : the required function to perform over 1D array. It can only be applied in 
 1D slices of input array and that too along a particular axis. 
axis : required axis along which we want input array to be sliced
array : Input array to work on 
*args : Additional arguments to 1D_function 
**kwargs : Additional arguments to 1D_function 

Return :  

The output array. Shape of the output array can be different depending on whether func 
changes the shape of its output with respect to its input.

Code 1 :  

Output : 

geek array :
 [[[ 0 1 2 3]
 [ 4 5 6 7]]

 [[ 8 9 10 11]
 [12 13 14 15]]]

func sum : 
 [[[24 28 32 36]]]

func min : 
 [[[0 1 2 3]]]

Code 2 :  

Output : 

geek array :
 [[ 0 1 2 3]
 [ 4 5 6 7]
 [ 8 9 10 11]
 [12 13 14 15]]

Applying func max : 
 [[ 3]
 [ 7]
 [11]
 [15]]

Applying func min : 
 [[ 0]
 [ 4]
 [ 8]
 [12]]

Applying func sum : 
 [[ 6]
 [22]
 [38]
 [54]]

Code 3 : Equivalent to Code 2 without using numpy.apply_over_axis() 

Output : 

geek array :
 [[[ 0 1 2 3]
 [ 4 5 6 7]]

 [[ 8 9 10 11]
 [12 13 14 15]]]
func : 
 [[[120]]]

Note : 
These codes won’t run on online IDE’s. Please run them on your systems to explore the working.