numpy.ndarray.transpose() function returns a view of the array with axes transposed.
For a 1-D array this has no effect, as a transposed vector is simply the same vector. For a 2-D array, this is a standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted. If axes are not provided and arr.shape = (i[0], i[1], … i[n-2], i[n-1]), then arr.transpose().shape = (i[n-1], i[n-2], … i[1], i[0]).
Syntax : numpy.ndarray.transpose(*axes)
Parameters :
axes : [None, tuple of ints, or n ints] None or no argument: reverses the order of the axes.
tuple of ints: i in the j-th place in the tuple means arr’s i-th axis becomes arr.transpose()’s j-th axis.
n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)Return : [ndarray] View of arr, with axes suitably permuted.
Code #1 :
# Python program explaining # numpy.ndarray.transpose() function # importing numpy as geek import numpy as geek arr = geek.array([[5, 6], [7, 8]]) gfg = arr.transpose() print( gfg ) |
Output :
[[5 7] [6 8]]
Code #2 :
# Python program explaining # numpy.ndarray.transpose() function # importing numpy as geek import numpy as geek arr = geek.array([[5, 6], [7, 8]]) gfg = arr.transpose((1, 0)) print( gfg ) |
Output :
[[5 7] [6 8]]
