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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)wherefunc1doperates on 1-D arrays andais a 1-D slice ofarralongaxis.Parameters: func1d : function
This function should accept 1-D arrays. It is applied to 1-D slices of
arralong the specified axis.axis : integer
Axis along which
arris 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
outarris identical to the shape ofarr, except along theaxisdimension, where the length ofoutarris equal to the size of the return value offunc1d. Iffunc1dreturns a scalaroutarrwill have one fewer dimensions thanarr.See also
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apply_over_axes - Apply a function repeatedly over multiple axes.
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
outarris the same asarr.>>> 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]]) -
numpy.apply_along_axis()
2025-01-10 15:47:30
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