# Fix Python – NumPy selecting specific column index per row by using a list of indexes

## Question

I’m struggling to select the specific columns per row of a NumPy matrix.

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, this will be executed frequently on big arrays of data and that’s why it has to run as fast as it can.

I was thus wondering if there is a better solution?

Now we will see solution for issue: NumPy selecting specific column index per row by using a list of indexes

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])
``````

This question is answered By – Slater Victoroff