numpy.apply_along_axis()

numpy.apply_along_axis(func1d, axis, arr, *args, **kwargs) [source]

Apply a function to 1-D slices along the given axis.

Execute func1d(a, *args) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis.

Parameters:

Parameters:	func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of `arr` along the specified axis. axis : integer Axis along which `arr` is sliced. arr : ndarray Input array. args : any Additional arguments to `func1d`. kwargs: any Additional named arguments to `func1d`. New in version 1.9.0.
Returns:	apply_along_axis : ndarray The output array. The shape of `outarr` is identical to the shape of `arr`, except along the `axis` dimension, where the length of `outarr` is equal to the size of the return value of `func1d`. If `func1d` returns a scalar `outarr` will have one fewer dimensions than `arr`.

func1d : function

This function should accept 1-D arrays. It is applied to 1-D slices of arr along the specified axis.

axis : integer

Axis along which arr is sliced.

arr : ndarray

Input array.

args : any

Additional arguments to func1d.

kwargs: any

Additional named arguments to func1d.

New in version 1.9.0.

Returns:

apply_along_axis : ndarray

The output array. The shape of outarr is identical to the shape of arr, except along the axis dimension, where the length of outarr is equal to the size of the return value of func1d. If func1d returns a scalar outarr will have one fewer dimensions than arr.

Examples

>>> def my_func(a):
...     """Average first and last element of a 1-D array"""
...     return (a[0] + a[-1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([ 4.,  5.,  6.])
>>> np.apply_along_axis(my_func, 1, b)
array([ 2.,  5.,  8.])

For a function that doesn?t return a scalar, the number of dimensions in outarr is the same as arr.

>>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])
>>> np.apply_along_axis(sorted, 1, b)
array([[1, 7, 8],
       [3, 4, 9],
       [2, 5, 6]])

Links:

https://docs.scipy.org/doc/numpy-1.11.0/reference/generated/numpy.apply_along_axis.html

doc_NumPy

2025-01-10 15:47:30

Comments