Lab 5: Simulations
Welcome to lab 5! This week, we will go over iteration and simulations, and introduce the concept of randomness. All of this material is covered in Chapter 9
(https://www.inferentialthinking.com/chapters/09/randomness.html) and Chapter 10 (https://www.inferentialthinking.com/chapters/10/sampling-and-empirical-
distributions.html) of the textbook.
The data used in this lab will contain salary data and statistics for basketball players from the 2014-2015 NBA season. This data was collected from sports analytic
sites basketball-reference (http://www.basketball-reference.com) and spotrac (http://www.spotrac.com).
First, set up the tests and imports by running the cell below.
In [ ]: # Run this cell, but please don't change it.
# These lines import the Numpy and Datascience modules.
import numpy as np
from datascience import *
# These lines do some fancy plotting magic
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
# Don't change this cell; just run it.
from client.api.notebook import Notebook
ok = Notebook('lab05.ok')
_ = ok.auth(inline=True)
1. Nachos and Conditionals ¶
In Python, Boolean values can either be True or False . We get Boolean values when using comparison operators, among which are < (less than), > (greater
than), and == (equal to). For a complete list, refer to Booleans and Comparison (https://www.inferentialthinking.com/chapters/09/randomness.html#Booleans-and-
Comparison) at the start of Chapter 9.
Run the cell below to see an example of a comparison operator in action.
In [ ]: 3 > 1 + 1
We can even assign the result of a comparison operation to a variable.
In [ ]: result = == 5
result
Arrays are compatible with comparison operators. The output is an array of boolean values.
In [ ]: make_array(1, 5, 7, 8, 3, -1) > 3
Waiting on the dining table just for you is a hot bowl of nachos! Let's say that whenever you take a nacho, it will have cheese, salsa, both, or neither (just a plain
tortilla chip).
Using the function call np.random.choice(array_name) , let's simulate taking nachos from the bowl at random. Start by running the cell below several times,
and observe how the results change.
In [ ]: nachos = make_array('cheese', 'salsa', 'both', 'neither')
np.random.choice(nachos)
Question 1. Assume we took ten nachos at random, and stored the results in an array called ten_nachos as done below. Find the number of nachos with only
cheese using code (do not hardcode the answer).
Hint: Our solution involves a comparison operator and the np.count_nonzero method.
, In [ ]: ten_nachos = make_array('neither', 'cheese', 'both', 'both', 'cheese', 'salsa', 'both', 'neither', 'cheese', 'bo
th')
number_cheese = np.count_nonzero(ten_nachos == 'cheese') #SOLUTION
number_cheese
In [ ]: _ = ok.grade('q1_1')
Conditional Statements
A conditional statement is made up of many lines that allow Python to choose from different alternatives based on whether some condition is true.
Here is a basic example.
def sign(x):
if x > 0:
return 'Positive'
How the function works is if the input x is greater than 0 , we get the string 'Positive' back.
If we want to test multiple conditions at once, we use the following general format.
if <if expression>:
<if body>
elif <elif expression 0>:
<elif body 0>
elif <elif expression 1>:
<elif body 1>
...
else:
<else body>
Only one of the bodies will ever be executed. Each if and elif expression is evaluated and considered in order, starting at the top. As soon as a true value is
found, the corresponding body is executed, and the rest of the expression is skipped. If none of the if or elif expressions are true, then the else body is
executed. For more examples and explanation, refer to Section 9.1 (https://www.inferentialthinking.com/chapters/09/1/conditional-statements.html).
Question 2. Complete the following conditional statement so that the string 'More please' is assigned to say_please if the number of nachos with cheese in
ten_nachos is less than 5 .
Hint: You should not have to directly reference the variable ten_nachos .
In [ ]: say_please = '?'
if ...:
say_please = 'More please'
say_please
In [ ]: say_please = '?'
if number_cheese < 5:
say_please = 'More please'
say_please
In [ ]: _ = ok.grade('q1_2')
Question 3. Write a function called nacho_reaction that returns a string based on the type of nacho passed in as an argument. From top to bottom, the
conditions should correspond to: 'cheese' , 'salsa' , 'both' , 'neither' .
In [ ]: def nacho_reaction(nacho):
if ...:
return 'Cheesy!'
# next condition should return 'Spicy!'
...
# next condition should return 'Wow!'
...
# next condition should return 'Meh.'
...
spicy_nacho = nacho_reaction('salsa')
spicy_nacho
Welcome to lab 5! This week, we will go over iteration and simulations, and introduce the concept of randomness. All of this material is covered in Chapter 9
(https://www.inferentialthinking.com/chapters/09/randomness.html) and Chapter 10 (https://www.inferentialthinking.com/chapters/10/sampling-and-empirical-
distributions.html) of the textbook.
The data used in this lab will contain salary data and statistics for basketball players from the 2014-2015 NBA season. This data was collected from sports analytic
sites basketball-reference (http://www.basketball-reference.com) and spotrac (http://www.spotrac.com).
First, set up the tests and imports by running the cell below.
In [ ]: # Run this cell, but please don't change it.
# These lines import the Numpy and Datascience modules.
import numpy as np
from datascience import *
# These lines do some fancy plotting magic
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
# Don't change this cell; just run it.
from client.api.notebook import Notebook
ok = Notebook('lab05.ok')
_ = ok.auth(inline=True)
1. Nachos and Conditionals ¶
In Python, Boolean values can either be True or False . We get Boolean values when using comparison operators, among which are < (less than), > (greater
than), and == (equal to). For a complete list, refer to Booleans and Comparison (https://www.inferentialthinking.com/chapters/09/randomness.html#Booleans-and-
Comparison) at the start of Chapter 9.
Run the cell below to see an example of a comparison operator in action.
In [ ]: 3 > 1 + 1
We can even assign the result of a comparison operation to a variable.
In [ ]: result = == 5
result
Arrays are compatible with comparison operators. The output is an array of boolean values.
In [ ]: make_array(1, 5, 7, 8, 3, -1) > 3
Waiting on the dining table just for you is a hot bowl of nachos! Let's say that whenever you take a nacho, it will have cheese, salsa, both, or neither (just a plain
tortilla chip).
Using the function call np.random.choice(array_name) , let's simulate taking nachos from the bowl at random. Start by running the cell below several times,
and observe how the results change.
In [ ]: nachos = make_array('cheese', 'salsa', 'both', 'neither')
np.random.choice(nachos)
Question 1. Assume we took ten nachos at random, and stored the results in an array called ten_nachos as done below. Find the number of nachos with only
cheese using code (do not hardcode the answer).
Hint: Our solution involves a comparison operator and the np.count_nonzero method.
, In [ ]: ten_nachos = make_array('neither', 'cheese', 'both', 'both', 'cheese', 'salsa', 'both', 'neither', 'cheese', 'bo
th')
number_cheese = np.count_nonzero(ten_nachos == 'cheese') #SOLUTION
number_cheese
In [ ]: _ = ok.grade('q1_1')
Conditional Statements
A conditional statement is made up of many lines that allow Python to choose from different alternatives based on whether some condition is true.
Here is a basic example.
def sign(x):
if x > 0:
return 'Positive'
How the function works is if the input x is greater than 0 , we get the string 'Positive' back.
If we want to test multiple conditions at once, we use the following general format.
if <if expression>:
<if body>
elif <elif expression 0>:
<elif body 0>
elif <elif expression 1>:
<elif body 1>
...
else:
<else body>
Only one of the bodies will ever be executed. Each if and elif expression is evaluated and considered in order, starting at the top. As soon as a true value is
found, the corresponding body is executed, and the rest of the expression is skipped. If none of the if or elif expressions are true, then the else body is
executed. For more examples and explanation, refer to Section 9.1 (https://www.inferentialthinking.com/chapters/09/1/conditional-statements.html).
Question 2. Complete the following conditional statement so that the string 'More please' is assigned to say_please if the number of nachos with cheese in
ten_nachos is less than 5 .
Hint: You should not have to directly reference the variable ten_nachos .
In [ ]: say_please = '?'
if ...:
say_please = 'More please'
say_please
In [ ]: say_please = '?'
if number_cheese < 5:
say_please = 'More please'
say_please
In [ ]: _ = ok.grade('q1_2')
Question 3. Write a function called nacho_reaction that returns a string based on the type of nacho passed in as an argument. From top to bottom, the
conditions should correspond to: 'cheese' , 'salsa' , 'both' , 'neither' .
In [ ]: def nacho_reaction(nacho):
if ...:
return 'Cheesy!'
# next condition should return 'Spicy!'
...
# next condition should return 'Wow!'
...
# next condition should return 'Meh.'
...
spicy_nacho = nacho_reaction('salsa')
spicy_nacho
