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pythonarraysnumpyreplaceconditional-statements
NLi10Me
NLi10Me

Reputation: 3292

Replace all elements of NumPy array that are greater than some value

I have a 2D NumPy array. How do I replace all values in it greater than a threshold T = 255 with a value x = 255? A slow for-loop based method would be:

# arr = arr.copy()  # Optionally, do not modify original arr.

for i in range(arr.shape[0]):
    for j in range(arr.shape[1]):
        if arr[i, j] > 255:
            arr[i, j] = x

Upvotes: 284

Views: 632257

Answers (8)

askewchan
askewchan

Reputation: 46530

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

Upvotes: 64

dougeemetcalf
dougeemetcalf

Reputation: 71

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')

Upvotes: 7

Hadi Mir
Hadi Mir

Reputation: 5123

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

Upvotes: 4

Shital Shah
Shital Shah

Reputation: 68708

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)

Upvotes: 17

mdml
mdml

Reputation: 22872

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

Upvotes: 472

Mahdi Shahbaba
Mahdi Shahbaba

Reputation: 499

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)]

Upvotes: 9

Amir F
Amir F

Reputation: 2529

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)

Upvotes: 25

lev
lev

Reputation: 2977

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

Upvotes: 15

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