I have a numpy 1D array of numbers representing columns e.g: `[0,0,2,1]`

And a matrix e.g:

```
[[1,1,1],
[1,1,1],
[1,1,1],
[1,1,1]]
```

Now I a want to change the values in the matrix to 0 where the column index is bigger than the value given in the 1D array:

```
[[1,0,0],
[1,0,0],
[1,1,1],
[1,1,0]]
```

How can I achieve this? I think I need a condition based on the index, not on the value

Explanation:

The first row in the matrix has indices [0,0 ; 0,1 ; 0,2] where the second index is the column. For indices 0,0 ; 0,1 and 0,2 the value 0 is given. 1 and 2 are bigger than 0. Thus only 0,0 is not changed to zero.

### >Solution :

Assuming `a`

the 2D array and `v`

the 1D vector, you can create a mask of the same size and use `numpy.where`

:

```
x,y = a.shape
np.where(np.tile(np.arange(y), (x,1)) <= v[:,None], a, 0)
```

Input:

```
a = np.array([[1,1,1],
[1,1,1],
[1,1,1],
[1,1,1]])
v = np.array([0,0,2,1])
```

Output:

```
array([[1, 0, 0],
[1, 0, 0],
[1, 1, 1],
[1, 1, 0]])
```

Intermediates:

```
>>> np.tile(np.arange(y), (x,1))
[[0 1 2]
[0 1 2]
[0 1 2]
[0 1 2]]
>>> np.tile(np.arange(y), (x,1)) <= v[:,None]
[[ True False False]
[ True False False]
[ True True True]
[ True True False]]
```