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 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

If you want a new array result containing a copy of arr whenever arr < 255, and 255 otherwise:

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, @mtitan8's 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 function's 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

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)

You can consider using numpy.putmask:

np.putmask(arr, arr>=T, 255.0)

Here is a performance comparison with the Numpy's builtin indexing:

In [1]: import numpy as np
In [2]: A = np.random.rand(500, 500)

In [3]: timeit np.putmask(A, A>0.5, 5)
1000 loops, best of 3: 1.34 ms per loop

In [4]: timeit A[A > 0.5] = 5
1000 loops, best of 3: 1.82 ms per loop