Introduction

Here we will learn about some of the abundant uses of randomness. Randomness is useful in games (shuffle a deck, roll a die), but it’s also useful for modeling and simulating a staggering variety of real world processes.

Learning objectives

• You will understand why it is useful to be able to generate pseudo-random numbers or make pseudo-random choices in a computer program.
• You will learn how to use some of the most commonly-used methods from Python’s random module, including
  • random.random() to generate a pseudo-random a floating point number in the interval [0.0, 1.0),
  • random.randint() to generate a pseudo-random integer in a specified interval,
  • random.choice() to make a pseudo-random selection of an item from an iterable object,
  • random.shuffle() to shuffle a list,
  • random.sample() to sample k elements from a population without replacement, and
  • random.gauss() to sample from a normal (Gaussian) distribution given mean and standard deviation.
• You will understand the role of a seed in the generation of pseudo-random numbers, and understand how setting a seed makes predictable the behavior of a program incorporating pseudo-randomness.

Terms introduced

• bell curve
• deterministic
• gambler’s ruin
• Mersenne twister
• Monte Carlo simulation
• normal (Gaussian) distribution
• pseudo-random
• random module
• random walk
• sample
• seed
• shuffle

For mean and standard deviation, see Chapter 14: Data Analysis and Presentation.

Copyright © 2023–2025 Clayton Cafiero

No generative AI was used in producing this material. This was written the old-fashioned way.