numpy.apply_along_axis() in Python

Last Updated : 28 Mar, 2022

The numpy.apply_along_axis() function helps us to apply a required function to 1D slices of the given array.
1d_func(ar, *args) : works on 1-D arrays, where ar is 1D slice of arr along axis.

Syntax :

numpy.apply_along_axis(1d_func, axis, array, *args, **kwargs)

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

What *args and **kwargs actually are?

Both of these allow you to pass a variable no. of arguments to the function.
*args : allow to send a non-keyword variable length argument list to the function.

Python

# Python Program illustrating # use of *args args = [3, 8] a = list(range(*args)) print("use of args : \n ", a)

Output :

use of args  :      [3, 4, 5, 6, 7]

**kwargs: allows you to pass keyword variable length of argument to a function. It is used when we want to handle named argument in a function.

Python

# Python Program illustrating  # use of **kwargs  def test_args_kwargs(in1, in2, in3):     print ("in1:", in1)     print ("in2:", in2)     print ("in3:", in3)           kwargs = {"in3": 1, "in2": "No.","in1":"geeks"} test_args_kwargs(**kwargs)

Output :

in1: geeks in2: No. in3: 1

Code 1: Python code explaining the use of numpy.apply_along_axis().

Python

# Python Program illustrating # apply_along_axis() in NumPy  import numpy as geek   # 1D_func is "geek_fun" def geek_fun(a):     # Returning the sum of elements at start index and at last index     # inout array      return (a[0] + a[-1])   arr = geek.array([[1,2,3],                  [4,5,6],                  [7,8,9]])      '''               -> [1,2,3] <-   1 + 7                  [4,5,6]      2 + 8               -> [7,8,9] <-   3 + 9 '''  print("axis=0 : ", geek.apply_along_axis(geek_fun, 0, arr)) print("\n")  '''             |   |                [1,2,3]   1 + 3                [4,5,6]   4 + 6                [7,8,9]   7 + 9                 ^   ^                '''  print("axis=1 : ", geek.apply_along_axis(geek_fun, 1, arr))

Output :

axis=0 :  [ 8 10 12]   axis=1 :  [ 4 10 16]

Code 2: Sorting using apply_along_axis() in NumPy Python

Python

# Python Program illustrating # apply_along_axis() in NumPy  import numpy as geek   geek_array = geek.array([[8,1,7],                          [4,3,9],                          [5,2,6]])  # using pre-defined sorted function as 1D_func print("Sorted as per axis 1 : \n", geek.apply_along_axis(sorted, 1, geek_array))  print("\n")  print("Sorted as per axis 0 : \n", geek.apply_along_axis(sorted, 0, geek_array))

Output :

Sorted as per axis 1 :   [[1 7 8]  [3 4 9]  [2 5 6]]   Sorted as per axis 0 :   [[4 1 6]  [5 2 7]  [8 3 9]]

Note :
These codes won't run on online IDE's. So please, run them on your systems to explore the working.

numpy.apply_along_axis() in Python

Mohit Gupta

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numpy.apply_along_axis() in Python

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