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 # 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))
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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.
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))
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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.