xorbits.numpy.diag(v, k=0, sparse=None, gpu=None, chunk_size=None)[source]#

Extract a diagonal or construct a diagonal array.

See the more detailed documentation for numpy.diagonal if you use this function to extract a diagonal and wish to write to the resulting array; whether it returns a copy or a view depends on what version of numpy you are using.

  • v (array_like) – If v is a 2-D array, return a copy of its k-th diagonal. If v is a 1-D array, return a 2-D array with v on the k-th diagonal.

  • k (int, optional) – Diagonal in question. The default is 0. Use k>0 for diagonals above the main diagonal, and k<0 for diagonals below the main diagonal.


out – The extracted diagonal or constructed diagonal array.

Return type


See also


Return specified diagonals.


Create a 2-D array with the flattened input as a diagonal.


Sum along diagonals.


Upper triangle of an array.


Lower triangle of an array.


>>> x = np.arange(9).reshape((3,3))  
>>> x  
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])
>>> np.diag(x)  
array([0, 4, 8])
>>> np.diag(x, k=1)  
array([1, 5])
>>> np.diag(x, k=-1)  
array([3, 7])
>>> np.diag(np.diag(x))  
array([[0, 0, 0],
       [0, 4, 0],
       [0, 0, 8]])
sparse: bool, optional

Create sparse tensor if True, False as default

gpubool, optional

Allocate the tensor on GPU if True, False as default

chunk_sizeint or tuple of int or tuple of ints, optional

Desired chunk size on each dimension

This docstring was copied from numpy.