When I execute the following code I get a spares matrix:
import numpy as np
from scipy.sparse import csr_matrix
row = np.array([0, 0, 1, 2, 2, 2])
col = np.array([0, 2, 2, 0, 1, 2])
data = np.array([1, 2, 3, 4, 5, 6])
sp = csr_matrix((data, (row, col)), shape=(3, 3))
print(sp)
(0, 0) 1
(0, 2) 2
(1, 2) 3
(2, 0) 4
(2, 1) 5
(2, 2) 6
I want to add another column to this sparse matrix so the output is:
(0, 0) 1
(0, 2) 2
(0, 3) 7
(1, 2) 3
(1, 3) 7
(2, 0) 4
(2, 1) 5
(2, 2) 6
(2, 3) 6
Basically I want to add another column that has the values 7,7,7.
The sparse.hstack
used in @Paul Panzer's
link is the simplest.
In [760]: sparse.hstack((sp,np.array([7,7,7])[:,None])).A
Out[760]:
array([[1, 0, 2, 7],
[0, 0, 3, 7],
[4, 5, 6, 7]], dtype=int32)
sparse.hstack
is not complicated; it just calls bmat([blocks])
.
sparse.bmat
gets the coo
attributes of all the blocks, joins them with the appropriate offself, and builds a new coo_matrix
.
In this case it joins
In [771]: print(sp)
(0, 0) 1
(0, 2) 2
(1, 2) 3
(2, 0) 4
(2, 1) 5
(2, 2) 6
In [772]: print(sparse.coo_matrix(np.array([7,7,7])[:,None]))
(0, 0) 7
(1, 0) 7
(2, 0) 7
while changing the column numbers of the last to 3
.
In [761]: print(sparse.hstack((sp,np.array([7,7,7])[:,None])))
(0, 0) 1
(0, 2) 2
(1, 2) 3
(2, 0) 4
(2, 1) 5
(2, 2) 6
(0, 3) 7
(1, 3) 7
(2, 3) 7