A random variable is, in its simplest form, a function. In probability, we often use random variables to represent random events. For example, we could use a random variable to represent the outcome of a die roll: any number between one and six.
Random variables must be numeric, meaning they always take on a number rather than a characteristic or quality. If we want to use a random variable to represent an event with non-numeric outcomes, we can choose numbers to represent those outcomes. For example, we could represent a coin flip as a random variable by assigning “heads” a value of 1 and “tails” a value of 0.
In this lesson, we will use
random.choice(a, size = size, replace = True/False) from the
numpy library to simulate random variables in python. In this method:
ais a list or other object that has values we are sampling from
sizeis a number that represents how many values to choose
replacecan be equal to
False, and determines whether we keep a value in
aafter drawing it (
replace = True) or remove it from the pool (
replace = False).
The following code simulates the outcome of rolling a fair die twice using
import numpy as np # 7 is not included in the range function die_6 = range(1, 7) rolls = np.random.choice(die_6, size = 2, replace = True) print(rolls)
# [2, 5]
Run the given code as-is to simulate rolling a die five times.
Change the value of
num_rolls so that
results_1 has the results of rolling a die ten times.
range() function, create a 12-sided die called
die_12. Use similar logic as
die_12 ten times, and save the rolls as
results_2. Use the
np.random.choice() function to simulate the rolls, and be sure to print out your results!