Sparse arrays from tuples

Question

I searched the net to find a guide for Scipy sparse matrices and I failed. I would be happy if anybody would share any source for it but now going to question:

I have an array of tuples. I want to change the array of tuples to a sparse matrix where the tuples appear on the main diagonal and diagonal just beside to it as the following example shows it. What is the fancy(efficient) way of doing it?

import numpy as np
A=np.asarray([[1,2],[3,4],[5,6],[7,8]])
B=np.zeros((A.shape[0],A.shape[0]+1))
for i in range(A.shape[0]):
    B[i,i]=A[i,0]
    B[i,i+1]=A[i,1]
print B

Output being:

[[ 1.  2.  0.  0.  0.]
 [ 0.  3.  4.  0.  0.]
 [ 0.  0.  5.  6.  0.]
 [ 0.  0.  0.  7.  8.]]

Answer 1

You can build those really fast as a CSR matrix:

>>> A = np.asarray([[1,2],[3,4],[5,6],[7,8]])
>>> rows = len(A)
>>> cols = rows + 1
>>> data = A.flatten() # we want a copy
>>> indptr = np.arange(0, len(data)+1, 2) # 2 non-zero entries per row
>>> indices = np.repeat(np.arange(cols), [1] + [2] * (cols-2) + [1])
>>> import scipy.sparse as sps
>>> a_sps = sps.csr_matrix((data, indices, indptr), shape=(rows, cols))
>>> a_sps.A
array([[1, 2, 0, 0, 0],
       [0, 3, 4, 0, 0],
       [0, 0, 5, 6, 0],
       [0, 0, 0, 7, 8]])

Answer 2

Try diags from scipy

import numpy as np
import scipy.sparse

A = np.asarray([[1,2],[3,4],[5,6],[7,8]])
B = scipy.sparse.diags([A[:,0], A[:,1]], [0, 1], [4, 5])

When I print B.todense(), it gives me

[[ 1.  2.  0.  0.  0.]
 [ 0.  3.  4.  0.  0.]
 [ 0.  0.  5.  6.  0.]
 [ 0.  0.  0.  7.  8.]]