# Replace all elements of Python NumPy Array that are greater than some value

## Replace all elements of Python NumPy Array that are greater than some value

I think both the fastest and most concise way to do this is to use NumPys 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
```

Since you actually want a different array which is `arr`

where `arr < 255`

, and `255`

otherwise, this can be done simply:

```
result = np.minimum(arr, 255)
```

More generally, for a lower and/or upper bound:

```
result = np.clip(arr, 0, 255)
```

If you just want to access the values over 255, or something more complicated, @mtitan8s answer is more general, but `np.clip`

and `np.minimum`

(or `np.maximum`

) are nicer and much faster for your case:

```
In [292]: timeit np.minimum(a, 255)
100000 loops, best of 3: 19.6 µs per loop
In [293]: %%timeit
.....: c = np.copy(a)
.....: c[a>255] = 255
.....:
10000 loops, best of 3: 86.6 µs per loop
```

If you want to do it in-place (i.e., modify `arr`

instead of creating `result`

) you can use the `out`

parameter of `np.minimum`

:

```
np.minimum(arr, 255, out=arr)
```

or

```
np.clip(arr, 0, 255, arr)
```

(the `out=`

name is optional since the arguments in the same order as the functions definition.)

For in-place modification, the boolean indexing speeds up a lot (without having to make and then modify the copy separately), but is still not as fast as `minimum`

:

```
In [328]: %%timeit
.....: a = np.random.randint(0, 300, (100,100))
.....: np.minimum(a, 255, a)
.....:
100000 loops, best of 3: 303 µs per loop
In [329]: %%timeit
.....: a = np.random.randint(0, 300, (100,100))
.....: a[a>255] = 255
.....:
100000 loops, best of 3: 356 µs per loop
```

For comparison, if you wanted to restrict your values with a minimum as well as a maximum, without `clip`

you would have to do this twice, with something like

```
np.minimum(a, 255, a)
np.maximum(a, 0, a)
```

or,

```
a[a>255] = 255
a[a<0] = 0
```

#### Replace all elements of Python NumPy Array that are greater than some value

I think you can achieve this the quickest by using the `where`

function:

For example looking for items greater than 0.2 in a numpy array and replacing those with 0:

```
import numpy as np
nums = np.random.rand(4,3)
print np.where(nums > 0.2, 0, nums)
```