I’m doing a bunch of linear algebra on large matrices over and over millions of times in a while loop using numpy, at the beginning of each iteration I need the arrays to be all zeros.

Is it most efficient to reuse the existing arrays by setting all the elements to zero, ie: `array[:, :, :] = 0`

or to create a new array of all zeros, ie: `array = np.zeros((a, b, c))`

I would think that setting the elements to zero is best, but I don’t know.

### >Solution :

Setting a new array seems 1000 times faster on 10M cells

### new array

```
%%timeit
a = np.zeros((1000,10000))
```

Output:

```
20.2 µs ± 1.56 µs per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```

### filling existing array

```
%%timeit
a[:,:] = 0
```

Output:

```
19.4 ms ± 1.77 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)
```