Comparing two NumPy arrays for equality element-wise

Question

What is the simplest way to compare two NumPy arrays for equality  where equality is defined as  A   B iff for all indices i  A i      B i     Simply using    gives me a boolean array     gt  gt  gt  numpy array  1 1 1      numpy array  1 1 1    array   True   True   True   dtype bool    Do I have to and the elements of this array to determine if the arrays are equal  or is there a simpler way to compare

User · Answer

Now use np array equal  From documentation   np array equal  1  2    1  2   True np array equal np array  1  2    np array  1  2    True np array equal  1  2    1  2  3   False np array equal  1  2    1  4   False

User · Answer

Usually two arrays will have some small numeric errors   You can use numpy allclose A B   instead of  A  B  all    This returns a bool True False

User · Answer

If you want to check if two arrays have the same shape AND elements you should  use np array equal as it is the method recommended in the documentation      Performance-wise don t expect that any equality check will beat another  as there is not much room to optimize comparing two elements  Just for the sake  i still did some tests     import numpy as np import timeit  A   np zeros  300  300  3   B   np zeros  300  300  3   C   np ones  300  300  3    timeit timeit stmt   A  B  all     setup  from   main   import A  B   number 10  5  timeit timeit stmt  np array equal A  B    setup  from   main   import A  B  np   number 10  5  timeit timeit stmt  np array equiv A  B    setup  from   main   import A  B  np   number 10  5   gt  51 5094  gt  52 555  gt  52 761   So pretty much equal  no need to talk about the speed    The  A  B  all   behaves pretty much as the following code snippet   x    1 2 3  y    1 2 3  print all  x i   y i  for i in range len x      gt  True

User · Answer

A  B  all     test if all values of array  A  B  are True   Note  maybe you also want to test A and B shape  such as A shape    B shape  Special cases and alternatives  from dbaupp s answer and yoavram s comment   It should be noted that    this solution can have a strange behavior in a particular case  if either A or B is empty  and the other one contains a single element  then it return True  For some reason  the comparison A  B returns an empty array  for which the all operator returns True  Another risk is if A and B don t have the same shape and aren t broadcastable  then this approach will raise an error    In conclusion  if you have a doubt about A and B shape or simply want to be safe  use one of the specialized functions   np array equal A B     test if same shape  same elements values np array equiv A B     test if broadcastable shape  same elements values np allclose A B        test if same shape  elements have close enough values

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Let s measure the performance by using the following piece of code   import numpy as np import time  exec time0      exec time1      exec time2       sizeOfArray   5000 numOfIterations   200  for i in xrange numOfIterations        A   np random randint 0 255  sizeOfArray sizeOfArray       B   np random randint 0 255  sizeOfArray sizeOfArray        a   time clock        res    A  B  all       b   time clock       exec time0 append  b - a        a   time clock        res   np array equal A B      b   time clock       exec time1 append  b - a        a   time clock        res   np array equiv A B      b   time clock       exec time2 append  b - a    print  Method   A  B  all             np mean exec time0  print  Method  np array equal A B     np mean exec time1  print  Method  np array equiv A B     np mean exec time2    Output  Method   A  B  all           0 03031857 Method  np array equal A B   0 030025185 Method  np array equiv A B   0 030141515   According to the results above  the numpy methods seem to be faster than the combination of the    operator and the all   method and by comparing the numpy methods the fastest one seems to be the numpy array equal method

User · Answer

The  A  B  all   solution is very neat  but there are some built-in functions for this task  Namely array equal  allclose and array equiv    Although  some quick testing with timeit seems to indicate that the  A  B  all   method is the fastest  which is a little peculiar  given it has to allocate a whole new array

[python] Comparing two NumPy arrays for equality, element-wise

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