4.4 Random-Number Generation

  • Can introduce the element of chance via the Python Standard Library’s random module.

Rolling a Six-Sided Die

  • Product 10 random integers in the range 1–6 to simulate rolling a six-sided die:
In [1]:
import random
In [2]:
for roll in range(10):
    print(random.randrange(1, 7), end=' ')

6 2 3 5 6 5 1 1 3 2
  • randrange function generates an integer from the first argument value up to, but not including, the second argument value.
  • Different values are displayed if you re-execute the loop.
In [3]:
for roll in range(10):
    print(random.randrange(1, 7), end=' ')

3 3 4 2 6 2 4 3 5 6
  • Can guarantee reproducibility of a random sequence with the random module’s seed function.

Rolling a Six-Sided Die 6,000,000 Times

  • If randrange truly produces integers at random, every number in its range has an equal probability (or chance or likelihood) of being returned each time we call it.
  • Roll a die 6,000,000 times.
  • Each die face should occur approximately 1,000,000 times.
  • We used Python’s underscore (_) digit separator to make the value 6000000 more readable. 
### Rolling a Six-Sided Die 6,000,000 Times

# fig04_01.py
"""Roll a six-sided die 6,000,000 times."""
import random

# face frequency counters
frequency1 = 0
frequency2 = 0
frequency3 = 0
frequency4 = 0
frequency5 = 0
frequency6 = 0

# 6,000,000 die rolls
for roll in range(6_000_000):  # note underscore separators
    face = random.randrange(1, 7)

    # increment appropriate face counter
    if face == 1:
        frequency1 += 1
    elif face == 2:
        frequency2 += 1
    elif face == 3:
        frequency3 += 1
    elif face == 4:
        frequency4 += 1
    elif face == 5:
        frequency5 += 1
    elif face == 6:
        frequency6 += 1

print(f'Face{"Frequency":>13}')
print(f'{1:>4}{frequency1:>13}')
print(f'{2:>4}{frequency2:>13}')
print(f'{3:>4}{frequency3:>13}')
print(f'{4:>4}{frequency4:>13}')
print(f'{5:>4}{frequency5:>13}')
print(f'{6:>4}{frequency6:>13}')
In [4]:
run fig04_01.py

Face    Frequency
   1      1000562
   2       999042
   3       999988
   4      1000966
   5       999281
   6      1000161

Seeding the Random-Number Generator for Reproducibility

  • Function randrange generates pseudorandom numbers.
  • Numbers appear to be random, because each time you start a new interactive session or execute a script that uses the random module’s functions, Python internally uses a different seed value.
  • When you’re debugging logic errors in programs that use randomly generated data, it can be helpful to use the same sequence of random numbers.
  • To do this, use the random module’s seed function to seed the random-number generator:
In [5]:
random.seed(32)
In [6]:
for roll in range(10):
    print(random.randrange(1, 7), end=' ')

1 2 2 3 6 2 4 1 6 1
In [7]:
for roll in range(10):
    print(random.randrange(1, 7), end=' ')

1 3 5 3 1 5 6 4 3 5
In [8]:
random.seed(32)
In [9]:
for roll in range(10):
    print(random.randrange(1, 7), end=' ')

1 2 2 3 6 2 4 1 6 1

©1992–2020 by Pearson Education, Inc. All Rights Reserved. This content is based on Chapter 4 of the book Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud.

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