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 :
Python
# Python Program illustrating# apply_over_axis() in NumPyimport numpy as geek # Using a 3D arraygeek_array = geek.arange(16).reshape(2, 2, 4)print("geek array :\n", geek_array)# Applying pre-defined sum function over the axis of 3D arrayprint("\nfunc sum : \n ", geek.apply_over_axes(geek.sum, geek_array, [1, 1, 0]))# Applying pre-defined min function over the axis of 3D arrayprint("\nfunc min : \n ", geek.apply_over_axes(geek.min, geek_array, [1, 1, 0])) |
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 :
Python
# Python Program illustrating# apply_over_axis() in NumPyimport numpy as geek # Using a 2D arraygeek_array = geek.arange(16).reshape(4, 4)print("geek array :\n", geek_array)""" ->[[ 0 1 2 3] min : 0 max : 3 sum = 0 + 1 + 2 + 3 -> [ 4 5 6 7] min : 4 max : 7 sum = 4 + 5 + 6 + 7 -> [ 8 9 10 11] min : 8 max : 11 sum = 8 + 9 + 10 + 11 -> [12 13 14 15]] min : 12 max : 15 sum = 12 + 13 + 14 + 15"""# Applying pre-defined min function over the axis of 2D arrayprint("\nApplying func max : \n ", geek.apply_over_axes(geek.max, geek_array, [1, -1]))# Applying pre-defined min function over the axis of 2D arrayprint("\nApplying func min : \n ", geek.apply_over_axes(geek.min, geek_array, [1, -1]))# Applying pre-defined sum function over the axis of 2D arrayprint("\nApplying func sum : \n ", geek.apply_over_axes(geek.sum, geek_array, [1, -1])) |
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()
Python
# Python Program illustrating# equivalent to apply_over_axis()import numpy as geek # Using a 3D arraygeek_array = geek.arange(16).reshape(2, 2, 4)print("geek array :\n", geek_array)# returning sum of all elements as per the axisprint("func : \n", geek.sum(geek_array, axis=(1, 0, 2), keepdims = True)) |
Output :
geek array : [[[ 0 1 2 3] [ 4 5 6 7]] [[ 8 9 10 11] [12 13 14 15]]] func : [[[120]]]
References :
https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.apply_over_axes.html
Note :
These codes won’t run on online IDE’s. Please run them on your systems to explore the working.
This article is contributed by Mohit Gupta_OMG 😀. If you like Lazyroar and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to review-team@geeksforgeeks.org. See your article appearing on the Lazyroar main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
