numpy.transpose(a, axes=None)
Returns an array with axes transposed.
For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., np.atleast_2d(a).T achieves this, as does a[:,np.newaxis]. For a 2-D array, this is the standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided, then transpose(a).shape==a.shape[::-1].
Parameters:aarray_likeInput array.
axestuple or list of ints, optionalIf specified, it must be a tuple or list which contains a permutation of [0, 1, …, N-1] where N is the number of axes of a. Negative indices can also be used to specify axes. The i-th axis of the returned array will correspond to the axis numbered axes[i] of the input. If not specified, defaults to range(a.ndim)[::-1], which reverses the order of the axes.
Returns:pndarraya with its axes permuted. A view is returned whenever possible.
Notes
Use transpose(a,argsort(axes)) to invert the transposition of tensors when using the axes keyword argument.
Examples
>>> importnumpyasnp>>> a=np.array([[1,2],[3,4]])>>> aarray([[1, 2], [3, 4]])>>> np.transpose(a)array([[1, 3], [2, 4]])>>> a=np.array([1,2,3,4])>>> aarray([1, 2, 3, 4])>>> np.transpose(a)array([1, 2, 3, 4])>>> a=np.ones((1,2,3))>>> np.transpose(a,(1,0,2)).shape(2, 1, 3)>>> a=np.ones((2,3,4,5))>>> np.transpose(a).shape(5, 4, 3, 2)>>> a=np.arange(3*4*5).reshape((3,4,5))>>> np.transpose(a,(-1,0,-2)).shape(5, 3, 4)