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   gt   T              result x  y    255    What is the most concise and pythonic way to do this  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

User · Answer

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)

User · Answer

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  gt 255 with a value x as follows   arr arr  gt  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  gt  0 5    5 100 loops  best of 3  7 59 ms per loop

User · Answer

Since you actually want a different array which is arr where arr  lt  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   s per loop  In  293     timeit           c   np copy a            c a gt 255    255            10000 loops  best of 3  86 6   s 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   s per loop  In  329     timeit           a   np random randint 0  300   100 100             a a gt 255    255            100000 loops  best of 3  356   s 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 gt 255    255 a a lt 0    0

User · Answer

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  x000D   x000D  import numpy as np  my arr   np arange 0 21    creates an array my arr my arr  gt  10    0   modifies the value x000D   x000D   x000D    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    x000D   x000D  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  gt  10    0  x000D   x000D   x000D

User · Answer

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

User · Answer

You can also use  amp      and or  for more flexibility    values between 5 and 10  A  A gt 5  amp  A lt 10    values greater than 10 or smaller than 5  A  A lt 5   A gt 10

User · Answer

You can consider using numpy putmask   np putmask arr  arr gt  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 gt 0 5  5  1000 loops  best of 3  1 34 ms per loop  In  4   timeit A A  gt  0 5    5 1000 loops  best of 3  1 82 ms per loop

[python] Replace all elements of Python NumPy Array that are greater than some value

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