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

## Question:

I have a 2D NumPy array and would like to replace all values in it greater than or equal to a threshold T with 255.0. To my knowledge, the most fundamental way would be:

``````shape = arr.shape
result = np.zeros(shape)
for x in range(0, shape[0]):
for y in range(0, shape[1]):
if arr[x, y] >= T:
result[x, y] = 255
``````
1. What is the most concise and pythonic way to do this?

2. Is there a faster (possibly less concise and/or less pythonic) way to do this?

This will be part of a window/level adjustment subroutine for MRI scans of the human head. The 2D numpy array is the image pixel data.

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
``````

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, @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
``````

``````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
``````

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)
``````

Another way is to use `np.place` which does in-place replacement and works with multidimentional arrays:

``````import numpy as np

# create 2x3 array with numbers 0..5
arr = np.arange(6).reshape(2, 3)

# replace 0 with -10
np.place(arr, arr == 0, -10)
``````

You can also use `&`, `|` (and/or) for more flexibility:

values between 5 and 10: `A[(A>5)&(A<10)]`

values greater than 10 or smaller than 5: `A[(A<5)|(A>10)]`

Lets us assume you have a `numpy` array that has contains the value from 0 all the way up to 20 and you want to replace numbers greater than 10 with 0

``````import numpy as np

my_arr = np.arange(0,21) # creates an array
my_arr[my_arr > 10] = 0 # modifies the value
``````

Note this will however modify the original array to avoid overwriting the original array try using `arr.copy()` to create a new detached copy of the original array and modify that instead.

``````import numpy as np

my_arr = np.arange(0,21)
my_arr_copy = my_arr.copy() # creates copy of the orignal array

my_arr_copy[my_arr_copy > 10] = 0
``````

np.where() works great!

``````np.where(arr > 255, 255, arr)
``````

example:

``````FF = np.array([[0, 0],
[1, 0],
[0, 1],
[1, 1]])
np.where(FF == 1, '+', '-')
Out[]:
array([['-', '-'],
['+', '-'],
['-', '+'],
['+', '+']], dtype='<U1')
``````
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