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
```