# Numpy Array Conditional Operation Mask?

#### Tags: arrays, numpy, python

Suppose you have an array:

```a = [ 0,1,0] [-1,2,1] [3,-4,2] ```

And lets say you add 20 to everything

``` b = [ 20, 21, 20] [ 19, 22, 21] [ 23, 16, 22] ```

Now lets say I want to add the resulting `b` to the original array `a` but only in cases where `a < 0` i.e at the index `[0,1]` and `[1,2]` where `a = -1, -4` respectively getting the value 0 otherwise. Ultimately leading to a matrix as such:

``` c = [ 0, 0, 0] [ 18, 0, 0] [ 0, 12, 0] ``` ``` 18 = 19 (from b) + -1 (from a) 12 = 16 (from b) + -4 (from a) ```

And assume that I want to be able to extend this to any operation (not just add 20), so that you can’t just filter all values < 20 from matrix `c`. So I want to use matrix `a` as a mask toward matrix c, zeroing the `i, j` where `a[i,j] < 0`.

I’m having a tough time finding a concise example of how to do this in numpy with python. I was hoping you may be able to direct me to the correct implementation of such a method.

What I am struggling to get is this into a mask and only performing operations on the retained values, finally resulting in `c`.

Thanks for the help in advance.

## Answer

Probably something like:

```(a + b)*(a<0)
```

should work unless you have very strong requirements concerning the number of intermediate arrays.

Source: stackoverflow