numpy.apply_along_axis() in Python
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 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 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 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 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.
