I think both the fastest and most concise way to do this is to use NumPy’s built-in Fancy indexing. If you have an `ndarray`

named `arr`

, you can replace all elements `>255`

with a value `x`

as follows:

```
arr[arr > 255] = x
```

I ran this on my machine with a 500 x 500 random matrix, replacing all values >0.5 with 5, and it took an average of 7.59ms.

```
In [1]: import numpy as np
In [2]: A = np.random.rand(500, 500)
In [3]: timeit A[A > 0.5] = 5
100 loops, best of 3: 7.59 ms per loop
```