Zee - 1 year ago 125

Python Question

I'm struggling to select the specific columns per row of a numpy matrix. I'm after a good way of solving the following issue

Suppose I have the following matrix which I would call X

`[1, 2, 3]`

[4, 5, 6]

[7, 8, 9]

I also have a list of column indexes per every row which I would call Y

`[1, 0, 2]`

I need to get the values

`[2]`

[4]

[9]

Instead of a list with indexes Y I can also produce a matrix with the same shape as X where every column is a bool/(int in the range 0-1 value indicating whether this is the required column.

`[0, 1, 0]`

[1, 0, 0]

[0, 0, 1]

I know this can be done with iterating over the array and selecting the column values I need, however I was wondering if there is a better solution. This will be executed frequently on big arrays of data and that's why i has to run as fast as it can.

Thank you

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

If you've got a boolean array you can do direct selection based on that like so:

```
>>> a = np.array([True, True, True, False, False])
>>> b = np.array([1,2,3,4,5])
>>> b[a]
array([1, 2, 3])
```

To go along with your initial example you could do the following:

```
>>> a = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> b = np.array([[False,True,False],[True,False,False],[False,False,True]])
>>> a[b]
array([2, 4, 9])
```

You can also add in an `arange`

and do direct selection on that, though depending on how you're generating your boolean array and what your code looks like YMMV.

```
>>> a = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> a[np.arange(len(a)), [1,0,2]]
array([2, 4, 9])
```

Hope that helps, let me know if you've got any more questions.

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**