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.]]

The primary source of information on the scipy sparse package is its reference page: docs.scipy.org/doc/scipy/reference/sparse.html. However this not a refined user or beginners guide. Keep in mind that this package is still under development. Matlab's sparse matricies might have better documentation. — hpaulj
– hpaulj, Commented Nov 21, 2013 at 18:19

Jaime · Accepted Answer · 2013-11-21 16:39:03Z

4

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]])

answered Nov 21, 2013 at 16:39

Jaime

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2 Comments

Cupitor Over a year ago

Thanks a lot. Could you please give me also a source for this sparse matrices if you know any.

Jaime Over a year ago

The wikipedia page on sparse matrices is a great starting point on what those three arrays (data, indices and indptr) are. Getting to know them often enables doing things that are (at least currently) beyond the sipy.sparse API in a very quick manner.

Prashant Kumar · Accepted Answer · 2013-11-21 16:32:46Z

4

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.]]

edited Nov 21, 2013 at 16:32

answered Nov 21, 2013 at 16:26

Prashant Kumar

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1 Comment

hpaulj Over a year ago

More compactly: sparse.diags(A.T,[0,1],(4,5)).A

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