# Combine by-integer and by-boolean numpy slicing

I’m looking for a way to combine two index arrays `b` and `i` (one of type boolean, one of type integer) to slice another array `x`.

``````x = np.array([5.5, 6.6, 3.3, 7.7, 8.8])
i = np.array([1, 4])
b = np.array([True, True, False, False, False])
``````

The resulting array should be `x == [6.6]` because it’s indexed by `i` (`i`) and by the value in `b`.

In other words, I’m looking for a way to express `x[i & b]` with `i` being an integer array. I know how to convert boolean index arrays to integer index arrays (`np.where(b)`), but that would merely shift the problem to combining two integer index arrays, which I also don’t have a solution for.

Obviously subsequent slicing doesn’t work (i.e. `x[i][b]` or vice versa), because the dimensionality changes after each separate slicing.

Any help would be appreciated.

### >Solution :

One `O(n)` method is to convert the index array `i` to a boolean array and then take the `&`:

``````b_i = np.zeros_like(b)
b_i[i] = True

output = x[b_i & b]
``````