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" : "Lazyroar" } test_args_kwargs( * * kwargs) |
Output :
in1: Lazyroar 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.
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