An exploration of Python's random module for the curious programmer, this talk will give a little background in statistics and pseudorandom number generation, explain the properties of python's choice of pseudorandom generator and explore through visualizations the different distributions provided by the module.

1. Non Sequitur
An exploration of Python's random module
Jair Trejo, EuroPython 2014

2. Jair Trejo
Director of Operations at Vinco Orbis

8. Random sample

9. Simulation

10. 552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048
552433147702632768969101985048

11. 14159265358979323846
26433832795028841971
69399375105820974944
59230781640628620899
86280348253421170679

12. 4.77
Counter({
'9': 14,
'2': 12,
'8': 12,
'3': 11,
'4': 10,
'6': 9,
'1': 8,
'0': 8,
'5': 8,
'7': 8})

16. 14159265358979323846
26433832795028841971
69399375105820974944
59230781640628620899
86280348253421170679

17. 3.1415926535897932384
626433832795028841971
693993751058209749445
923078164062862089986
280348253421170679

23. 7638, 3390, 4921, 2162, 6742, 4545, 6570, 1649, 7192,
7248, 5335, 4622, 3628, 1623, 6341, 2082, 3347, 2024,
965, 3122, 7468, 7710, 4441, 7224, 1861, 4633, 4646,
5853, 2576, 6357, 4114, 9249, 5440, 5936, 2360, 5696,
4444, 7491, 1150, 3225, 4006, 480, 3040, 2416, 8370, 569,
2376, 6453, 6412, 1137, 2927, 5673, 1829, 3452, 9163,
9605, 2560, 5536, 6472, 8867, 6236, 8876, 7833, 3558,
6593, 4676, 8649, 8052, 8347, 6724, 2121, 4986, 8601,
9772, 4919, 1965, 8612, 1665, 7722, 6292, 5892, 7156,
2083, 3388, 4785, 8962, 3174, 742, 5056, 5631, 7081,
1405, 9740, 8676, 2729, 4474, 166, 2755, 5900, 8100

26. 7, 23, 41, 59, 89

36. Python’s random module source code

39. import random

40. r = random.Random(seed)
r.random()

41. r.randrange(max)
r.random() * max

42. r.randrange(start, stop)
start + r.randrange(stop - start)

43. r.randrange(start, stop, step)

44. r.random()
r.randrange(start, stop, step)
r.randint(a, b)

45. r.choice(a_sequence)

46. r.sample(population, how_many)

47. r.shuffle(a_list)

48. sorted(a_list, key=lambda e: r.random())

54. r.triangular
r.gammavariate
r.betavariate
r.paretovariate
r.weibullvariate

56. import random
!
random.random()

57. random.WichmannHill
random.SystemRandom
randomdotorg.RandomDotOrg

58. Conclusions
• The definition of randomness is more a philosophical than a mathematical
problem.
• But we can use mathematical definitions that are useful for our purposes.
• If we need sequences that are deterministic, but behave as if random, we can
use pseudo-random number generation.
• If we need numbers that are completely unpredictable, we need sources of
entropy like input devices, noise measurements or other external sources.
• For most of our random number needs, python provides more than adequate
capabilities.

59. References

60. 10 PRINT CHR$(205.5+RND(1)); : GOTO 10
By Nick Montfort, Patsy Baudoin, John Bell, Ian Bogost,
Jeremy Douglass, Mark C. Marino, Michael Mateas,
Casey Reas, Mark Sample and Noah Vawter

61. The art of computer programming, Volume 2
Seminumerical Algorithms

62. On randomness in cryptography!
http://blog.cloudflare.com/why-randomness-matters
On random number generators testing!
http://www.fourmilab.ch/hotbits/statistical_testing/stattest.html
On the possible NSA backdoor into RSA’s random number generator!
http://arstechnica.com/security/2014/01/how-the-nsa-may-have-put-a-backdoor
rsas-cryptography-a-technical-primer/

63. Thank you!