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: |
func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of axis : integer Axis along which arr : ndarray Input array. args : any Additional arguments to kwargs : any Additional named arguments to New in version 1.9.0. |
---|---|
Returns: |
apply_along_axis : ndarray The output array. The shape of |
See also
apply_over_axes
>>> 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 returns a 1D array, 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]])
For a function that returns a higher dimensional array, those dimensions are inserted in place of the axis
dimension.
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]]) >>> np.apply_along_axis(np.diag, -1, b) array([[[1, 0, 0], [0, 2, 0], [0, 0, 3]],
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https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.apply_along_axis.html