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:
a
is a list or other object that has values we are sampling fromsize
is a number that represents how many values to choosereplace
can be equal toTrue
orFalse
, and determines whether we keep a value ina
after 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 np.random.choice()
:
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)
Output:
# [2, 5]
Instructions
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.
Using the range()
function, create a 12-sided die called die_12
. Use similar logic as die_6
.
Simulate rolling 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!