numpy.apply_along_axis(1d_func, axis, array, *args, **kwargs) : 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.
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.
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.
in1: geeks in2: No. in3: 1
Code 1 : Python code explaining the use of numpy.apply_along_axis().
axis=0 : [ 8 10 12] axis=1 : [ 4 10 16]
Code 2 : Sorting using apply_along_axis() in NumPy Python
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]]
These NumPy-Python programs won’t run on onlineID, so run them on your systems to explore them
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