How can Python randomly shuffle a list of 100,000 items?











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I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.




NB.




  • Fairness trumps efficiency or speed.


  • I have access to /dev/urandom.


  • The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.







python random shuffle







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  • 1




    What have you tried so far?
    – BernardL
    Nov 22 at 16:38










  • Can you use NumPy?
    – Nils Werner
    Nov 22 at 16:38










  • @NilsWerner I can, but won't that be subject to the 2080 limit?
    – Paul Uszak
    Nov 22 at 16:40










  • No, of course not!
    – Nils Werner
    Nov 22 at 16:40















up vote
-1
down vote

favorite












I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.




NB.




  • Fairness trumps efficiency or speed.


  • I have access to /dev/urandom.


  • The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.







python random shuffle







share|improve this question




















  • 1




    What have you tried so far?
    – BernardL
    Nov 22 at 16:38










  • Can you use NumPy?
    – Nils Werner
    Nov 22 at 16:38










  • @NilsWerner I can, but won't that be subject to the 2080 limit?
    – Paul Uszak
    Nov 22 at 16:40










  • No, of course not!
    – Nils Werner
    Nov 22 at 16:40













up vote
-1
down vote

favorite









up vote
-1
down vote

favorite











I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.




NB.




  • Fairness trumps efficiency or speed.


  • I have access to /dev/urandom.


  • The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.







python random shuffle







share|improve this question















I'm trying to perform some statistical analysis on long sequences of numbers. That requires a randomised shuffle of the list. The tests are sensitive, so fairness and randomness is very important. The list is 100,000 integers, but I would like to try 1 million.




NB.




  • Fairness trumps efficiency or speed.


  • I have access to /dev/urandom.


  • The USA's NIST laboratory does it using C++ within their entropy measurement suite, SP800-90B, EntropyAssessment. They sort sequences of 1 million bytes. It's @ https://github.com/usnistgov/SP800-90B_EntropyAssessment.







python random shuffle




python random shuffle






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share|improve this question













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share|improve this question








edited Nov 22 at 17:05

























asked Nov 22 at 16:36









Paul Uszak

209414




209414








  • 1




    What have you tried so far?
    – BernardL
    Nov 22 at 16:38










  • Can you use NumPy?
    – Nils Werner
    Nov 22 at 16:38










  • @NilsWerner I can, but won't that be subject to the 2080 limit?
    – Paul Uszak
    Nov 22 at 16:40










  • No, of course not!
    – Nils Werner
    Nov 22 at 16:40














  • 1




    What have you tried so far?
    – BernardL
    Nov 22 at 16:38










  • Can you use NumPy?
    – Nils Werner
    Nov 22 at 16:38










  • @NilsWerner I can, but won't that be subject to the 2080 limit?
    – Paul Uszak
    Nov 22 at 16:40










  • No, of course not!
    – Nils Werner
    Nov 22 at 16:40








1




1




What have you tried so far?
– BernardL
Nov 22 at 16:38




What have you tried so far?
– BernardL
Nov 22 at 16:38












Can you use NumPy?
– Nils Werner
Nov 22 at 16:38




Can you use NumPy?
– Nils Werner
Nov 22 at 16:38












@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40




@NilsWerner I can, but won't that be subject to the 2080 limit?
– Paul Uszak
Nov 22 at 16:40












No, of course not!
– Nils Werner
Nov 22 at 16:40




No, of course not!
– Nils Werner
Nov 22 at 16:40












2 Answers
2






active

oldest

votes

















up vote
0
down vote



accepted










You can easily shuffle millions of numbers in NumPy:



import numpy as np



data = np.arange(1e6)

%timeit np.random.shuffle(data)

# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)





share|improve this answer























  • I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
    – Paul Uszak
    Nov 22 at 17:05


















up vote
0
down vote













Have you tried using numpy's shuffle?



https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html



or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html






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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes








    up vote
    0
    down vote



    accepted










    You can easily shuffle millions of numbers in NumPy:



    import numpy as np
    

    
data = np.arange(1e6)
    
%timeit np.random.shuffle(data)
    
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)





    share|improve this answer























    • I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
      – Paul Uszak
      Nov 22 at 17:05















    up vote
    0
    down vote



    accepted










    You can easily shuffle millions of numbers in NumPy:



    import numpy as np
    

    
data = np.arange(1e6)
    
%timeit np.random.shuffle(data)
    
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)





    share|improve this answer























    • I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
      – Paul Uszak
      Nov 22 at 17:05













    up vote
    0
    down vote



    accepted







    up vote
    0
    down vote



    accepted






    You can easily shuffle millions of numbers in NumPy:



    import numpy as np
    

    
data = np.arange(1e6)
    
%timeit np.random.shuffle(data)
    
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)





    share|improve this answer














    You can easily shuffle millions of numbers in NumPy:



    import numpy as np
    

    
data = np.arange(1e6)
    
%timeit np.random.shuffle(data)
    
# 32.7 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)






    share|improve this answer














    share|improve this answer



    share|improve this answer








    edited Nov 22 at 17:09

























    answered Nov 22 at 16:41









    Nils Werner

    17.2k13859




    17.2k13859












    • I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
      – Paul Uszak
      Nov 22 at 17:05


















    • I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
      – Paul Uszak
      Nov 22 at 17:05
















    I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
    – Paul Uszak
    Nov 22 at 17:05




    I misunderstood what the "Maximal" link was actually telling me. Of course you're right, of course :-)
    – Paul Uszak
    Nov 22 at 17:05












    up vote
    0
    down vote













    Have you tried using numpy's shuffle?



    https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html



    or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html






    share|improve this answer

























      up vote
      0
      down vote













      Have you tried using numpy's shuffle?



      https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html



      or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html






      share|improve this answer























        up vote
        0
        down vote










        up vote
        0
        down vote









        Have you tried using numpy's shuffle?



        https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html



        or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html






        share|improve this answer












        Have you tried using numpy's shuffle?



        https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.random.shuffle.html



        or permutation if you don't want to do this inplace: https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.random.permutation.html







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 22 at 16:39









        Dan

        36.7k95199




        36.7k95199






























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