Thanks! This makes sense of course. And yes the operation I am trying to do is rather complicated so I need to rely on a prior selection. Now I would need to optimise this for large arrays and the code does go through these command line many many times. When I have to operate on the two different parts of the array, I guess just using the following is the fastest way (as you indicated) : result = np.empty_like(data) mask = (data == 0) result[mask] = 0.0 result[~mask] = 1.0/data[~mask] But if I only need to do this on one side of the selection, I guess I would just do: result = np.empty_like(data) mask = (data != 0) result[mask] += 1.0 / data[mask] I have tried using three version of "mask = " with the rest of the code being the same: 1- mask = where(data != 0) 2- mask = np.where(data != 0) 3- mask = (data != 0) and it looks like #3 is the fastest, then #2 (20% slower) then #1 (50% slower than #3). I am not sure why, but Is that making sense? Or is there even a faster way (for large data arrays, and complicated operations)? thanks Eric

If your operation doesn't factor like this though then you can always use something more cumbersome like result = np.empty_like(data) mask = (data == 0) result[mask] = 0 result[~mask] = 1.0/data[~mask]

Or in 1.7 this could be written result = np.zeros_like(data) np.divide(1.0, data, where=(data != 0), out=result)

-n