Generate random integers between 0 and 9

Question

How can I generate random integers between 0 and 9  inclusive  in Python   For example  0  1  2  3  4  5  6  7  8  9

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I thought I d add to these answers with quantumrand  which uses ANU s quantum number generator  Unfortunately this requires an internet connection  but if you re concerned with  quot how random quot  the numbers are then this could be useful  https   pypi org project quantumrand  Example import quantumrand  number   quantumrand randint 0  9   print number   Output  4 The docs have a lot of different examples including dice rolls and a list picker

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While many posts demonstrate how to get one random integer  the original question asks how to generate random integers  plural       How can I generate random integers between 0 and 9  inclusive  in Python    For clarity  here we demonstrate how to get multiple random integers   Given   gt  gt  gt  import random   lo   0 hi   10 size   5   Code  Multiple  Random Integers    A  gt  gt  gt   lo   int random random      hi - lo   for   in range size    5  6  1  3  0        B  gt  gt  gt   random randint lo  hi  for   in range size    9  7  0  7  3        C  gt  gt  gt   random randrange lo  hi  for   in range size    8  3  6  8  7        D  gt  gt  gt  lst   list range lo  hi    gt  gt  gt  random shuffle lst   gt  gt  gt   lst i  for i in range size    6  8  2  5  1        E  gt  gt  gt   random choice range lo  hi   for   in range size    2  1  6  9  5    Sample of Random Integers    F  gt  gt  gt  random choices range lo  hi   k size   3  2  0  8  2        G  gt  gt  gt  random sample range lo  hi   k size   4  5  1  2  3      Details  Some posts demonstrate how to natively generate multiple random integers 1  Here are some options that address the implied question    A  random random returns a random float in the range  0 0  1 0  B  random randint returns a random integer N such that a  lt   N  lt   b C  random randrange alias to randint a  b 1  D  random shuffle shuffles a sequence in place E  random choice returns a random element from the non-empty sequence F  random choices returns k selections from a population  with replacement  Python 3 6   G  random sample returns k unique selections from a population  without replacement  2   See also R  Hettinger s talk on Chunking and Aliasing using examples from the random module   Here is a comparison of some random functions in the Standard Library and Numpy       random                  numpy random                        - ----------------------- ----------------------------------   A  random                  random                              B  randint low  high       randint low  high                   C  randrange low  high     randint low  high                   D  shuffle seq             shuffle seq                         E  choice seq              choice seq                          F  choices seq  k          choice seq  size                    G  sample seq  k           choice seq  size  replace False      You can also quickly convert one of many distributions in Numpy to a sample of random integers 3  Examples   gt  gt  gt  np random normal loc 5  scale 10  size size  astype int  array  17  10   3   1  16     gt  gt  gt  np random poisson lam 1  size size  astype int  array  1  3  0  2  0     gt  gt  gt  np random lognormal mean 0 0  sigma 1 0  size size  astype int  array  1  3  1  5  1     1Namely  John Lawrence Aspden   S T Mohammed   SiddTheKid   user14372   zangw  et al  2 prashanth mentions this module showing one integer  3Demonstrated by  Siddharth Satpathy

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Choose the size of the array  in this example  I have chosen the size to be 20   And then  use the following    import numpy as np    np random randint 10  size  1  20     You can expect to see an output of the following form  different random integers will be returned each time you run it  hence you can expect the integers in the output array to differ from the example given below    array   1  6  1  2  8  6  3  3  2  5  6  5  0  9  5  6  4  5  9  3

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Try this through random shuffle   gt  gt  gt  import random  gt  gt  gt  nums   range 10   gt  gt  gt  random shuffle nums   gt  gt  gt  nums  6  3  5  4  0  1  2  9  8  7

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if you want to use numpy then use the following   import numpy as np print np random randint 0 10

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Generating random integers between 0 and 9   import numpy X   numpy random randint 0  10  size 10  print X    Output    4 8 0 4 9 6 9 9 0 7

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This is more of a mathematical approach but it works 100  of the time   Let s say you want to use random random   function to generate a number between a and b  To achieve this  just do the following   num    b-a  random random     a   Of course  you can generate more numbers

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In case of continuous numbers randint or randrange are probably the best choices but if you have several distinct values in a sequence  i e  a list  you could also use choice    gt  gt  gt  import random  gt  gt  gt  values   list range 10    gt  gt  gt  random choice values  5   choice also works for one item from a not-continuous sample    gt  gt  gt  values    1  2  3  5  7  10   gt  gt  gt  random choice values  7   If you need it  cryptographically strong  there s also a secrets choice in python 3 6 and newer    gt  gt  gt  import secrets  gt  gt  gt  values   list range 10    gt  gt  gt  secrets choice values  2

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OpenTURNS allows to not only simulate the random integers but also to define the associated distribution with the UserDefined defined class    The following simulates 12 outcomes of the distribution   import openturns as ot points     i  for i in range 10   distribution   ot UserDefined points    By default  with equal weights  for i in range 12       x   distribution getRealization       print i x    This prints   0  8  1  7  2  4  3  7  4  3  5  3  6  2  7  9  8  0  9  5  10  9  11  6    The brackets are there becausex is a Point in 1-dimension  It would be easier to generate the 12 outcomes in a single call to getSample   sample   distribution getSample 12    would produce    gt  gt  gt  print sample         v0    0     3     1     9     2     6     3     3     4     2     5     6     6     9     7     5     8     9     9     5    10     3    11     2      More details on this topic are here  http   openturns github io openturns master user manual  generated openturns UserDefined html

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gt  gt  gt  import random  gt  gt  gt  random randrange 10  3  gt  gt  gt  random randrange 10  1   To get a list of ten samples    gt  gt  gt   random randrange 10  for x in range 10    9  0  4  0  5  7  4  3  6  8

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random sample is another that can be used  import random n   1   specify the no  of numbers num   random sample range 10    n  num 0    is the required number

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I had better luck with this for Python 3 6    str Key                                                                                                      str RandomKey                                                                                                for int I in range 128                                                                                             str Key   random choice  0123456789         str RandomKey   str RandomKey   str Key    Just add characters like  ABCD  and  abcd  or      -  lt   to alter the character pool to pull from  change the range to alter the number of characters generated

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Try this   from random import randrange  uniform    randrange gives you an integral value irand   randrange 0  10     uniform gives you a floating-point value frand   uniform 0  10

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The secrets module is new in Python 3 6  This is better than the random module for cryptography or security uses   To randomly print an integer in the inclusive range 0-9   from secrets import randbelow print randbelow 10     For details  see PEP 506

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import random print random randint 0 9       random randint a  b    Return a random integer N such that a  lt   N  lt   b   Docs  https   docs python org 3 1 library random html random randint

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I would try one of the following  1  gt   numpy random randint import numpy as np X1   np random randint low 0  high 10  size  15     print  X1   gt  gt  gt  array  3  0  9  0  5  7  6  9  6  7  9  6  6  9  8    2  gt   numpy random uniform import numpy as np X2   np random uniform low 0  high 10  size  15    astype int   print  X2   gt  gt  gt  array  8  3  6  9  1  0  3  6  3  3  1  2  4  0  4    3  gt   numpy random choice import numpy as np X3   np random choice a 10  size 15    print  X3   gt  gt  gt  array  1  4  0  2  5  2  7  5  0  0  8  4  4  0  9    4  gt   random randrange from random import randrange X4    randrange 10  for i in range 15    print  X4   gt  gt  gt   2  1  4  1  2  8  8  6  4  1  0  5  8  3  5   5  gt   random randint from random import randint X5    randint 0  9  for i in range 0  15    print  X5   gt  gt  gt   6  2  6  9  5  3  2  3  3  4  4  7  4  9  6   Speed    np random uniform and np random randint are much faster   10 times faster  than np random choice  random randrange  random randint    timeit np random randint low 0  high 10  size  15     gt  gt  1 64   s    7 83 ns per loop  mean    std  dev  of 7 runs  1000000 loops each    timeit np random uniform low 0  high 10  size  15    astype int   gt  gt  2 15   s    38 6 ns per loop  mean    std  dev  of 7 runs  100000 loops each    timeit np random choice a 10  size 15    gt  gt  21   s    629 ns per loop  mean    std  dev  of 7 runs  10000 loops each    timeit  randrange 10  for i in range 15    gt  gt  12 9   s    60 4 ns per loop  mean    std  dev  of 7 runs  100000 loops each    timeit  randint 0  9  for i in range 0  15    gt  gt  20   s    386 ns per loop  mean    std  dev  of 7 runs  100000 loops each   Notes   1  gt  np random randint generates random integers over the half-open interval  low  high   2  gt  np random uniform generates uniformly distributed numbers over the half-open interval  low  high   3  gt  np random choice generates a random sample over the half-open interval  low  high  as if the argument a was np arange n   4  gt  random randrange stop  generates a random number from range start  stop  step   5  gt  random randint a  b  returns a random integer N such that a  lt   N  lt   b  6  gt  astype int  casts the numpy array to int data type  7  gt  I have chosen size    15    This will give you a numpy array of length   15

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from random import randint  x    randint 0  9  for p in range 0  10     This generates 10 pseudorandom integers in range 0 to 9 inclusive

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From the documentation page for the random module   Warning  The pseudo-random generators of this module should not be used for security purposes  Use os urandom   or SystemRandom if you require a cryptographically secure pseudo-random number generator   random SystemRandom  which was introduced in Python 2 4  is considered cryptographically secure   It is still available in Python 3 7 1 which is current at time of writing   gt  gt  gt  import string  gt  gt  gt  string digits  0123456789   gt  gt  gt  import random  gt  gt  gt  random SystemRandom   choice string digits   8   gt  gt  gt  random SystemRandom   choice string digits   1   gt  gt  gt  random SystemRandom   choice string digits   8   gt  gt  gt  random SystemRandom   choice string digits   5   Instead of string digits  range could be used per some of the other answers along perhaps with a comprehension   Mix and match according to your needs

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Best way is to use import Random function    import random print random sample range 10   10     or without any library import   n     for i in range 10       n i  i  for p in range 10       print n popitem   1     here the popitems removes and returns an arbitrary value from the dictionary n

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Try  from random import randrange print randrange 10    Docs  https   docs python org 3 library random html random randrange

[python] Generate random integers between 0 and 9

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