dask.array.transpose

dask.array.transpose¶

dask.array.transpose(a, axes=None)[source]¶

Returns an array with axes transposed.

This docstring was copied from numpy.transpose.

Some inconsistencies with the Dask version may exist.

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.atleast2d(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_like: Input array.
axestuple or list of ints, optional: If 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. 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

pndarray: a with its axes permuted. A view is returned whenever possible.

See also

ndarray.transpose: Equivalent method.
moveaxis: Move axes of an array to new positions.
argsort: Return the indices that would sort an array.

Notes

Use transpose(a, argsort(axes)) to invert the transposition of tensors when using the axes keyword argument.

Examples

>>> a = np.array([[1, 2], [3, 4]])  
>>> a  
array([[1, 2],
       [3, 4]])
>>> np.transpose(a)  
array([[1, 3],
       [2, 4]])

>>> a = np.array([1, 2, 3, 4])  
>>> a  
array([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)

dask.array.trace

dask.array.tri