Most efficient way to reverse a numpy array

Question

Believe it or not  after profiling my current code  the repetitive operation of numpy array reversion ate a giant chunk of the running time  What I have right now is the common view-based method   reversed arr   arr   -1    Is there any other way to do it more efficiently  or is it just an illusion from my obsession with unrealistic numpy performance

User · Answer

Because this seems to not be marked as answered yet... The Answer of Thomas Arildsen should be the proper one: just use

np.flipud(your_array)

if it is a 1d array (column array).

With matrizes do

fliplr(matrix)

if you want to reverse rows and flipud(matrix) if you want to flip columns. No need for making your 1d column array a 2dimensional row array (matrix with one None layer) and then flipping it.

User · Answer

Expanding on what others have said I will give a short example   If you have a 1D array       gt  gt  gt  import numpy as np  gt  gt  gt  x   np arange 4    array  0  1  2  3    gt  gt  gt  x   -1    returns a view Out 1    array  3  2  1  0     But if you are working with a 2D array       gt  gt  gt  x   np arange 10  reshape 2  5   gt  gt  gt  x Out 2   array   0  1  2  3  4           5  6  7  8  9      gt  gt  gt  x   -1    returns a view  Out 3   array   5  6  7  8  9                   0  1  2  3  4      This does not actually reverse the Matrix   Should use np flip to actually reverse the elements   gt  gt  gt  np flip x  Out 4   array   9  8  7  6  5                   4  3  2  1  0      If you want to print the elements of a matrix one-by-one use flat along with flip   gt  gt  gt  for el in np flip x  flat   gt  gt  gt      print el  end        9 8 7 6 5 4 3 2 1 0

User · Answer

I will expand on the earlier answer about np fliplr    Here is some code that demonstrates constructing a 1d array  transforming it into a 2d array  flipping it  then converting back into a 1d array  time clock   will be used to keep time  which is presented in terms of seconds   import time import numpy as np  start   time clock   x   np array range 3    transform to 2d x   np atleast 2d x   flip array x   np fliplr x   take first  and only  element x   x 0   print x end   time clock   print end-start   With print statement uncommented    2 1 0  0 00203907123594   With print statement commented out   5 59799927506e-05   So  in terms of efficiency  I think that s decent  For those of you that love to do it in one line  here is that form   np fliplr np atleast 2d np array range 3     0

User · Answer

When you create reversed arr you are creating a view into the original array   You can then change the original array  and the view will update to reflect the changes   Are you re-creating the view more often than you need to   You should be able to do something like this   arr   np array some sequence  reversed arr   arr   -1   do something arr  look at reversed arr  do something else arr  look at reversed arr    I m not a numpy expert  but this seems like it would be the fastest way to do things in numpy   If this is what you are already doing  I don t think you can improve on it   P S  Great discussion of numpy views here   View onto a numpy array

User · Answer

np fliplr   flips the array left to right    Note that for 1d arrays  you need to trick it a bit    arr1d   np array some sequence  reversed arr   np fliplr  arr1d   0

User · Answer

As mentioned above  a   -1  really only creates a view  so it s a constant-time operation  and as such doesn t take longer as the array grows   If you need the array to be contiguous  for example because you re performing many vector operations with it   ascontiguousarray is about as fast as flipud fliplr    Code to generate the plot  import numpy import perfplot   perfplot show      setup lambda n  numpy random randint 0  1000  n       kernels           lambda a  a   -1           lambda a  numpy ascontiguousarray a   -1            lambda a  numpy fliplr  a   0              labels   quot a   -1  quot    quot ascontiguousarray a   -1   quot    quot fliplr quot        n range  2    k for k in range 25        xlabel  quot len a  quot

User · Answer

In order to have it working with negative numbers and a long list you can do the following   b   numpy flipud numpy array a split   float     Where flipud is for 1d arra

[python] Most efficient way to reverse a numpy array

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