And Operator¶
In [1]:
b1 = True and True
b2 = True and False
b3 = False and False
b4 = False and False
print(b1)
print(b2)
print(b3)
print(b4)
Or Operator¶
In [2]:
b5 = True or True
b6 = True or False
b7 = False or False
b8 = False or False
print(b5)
print(b6)
print(b7)
print(b8)
Not Operator¶
In [3]:
print(not True)
print(not False)
Example¶
In [4]:
import random
In [5]:
random.seed(1)
rlist = random.sample(range(1,21), 10)
print(rlist)
In [6]:
for i in range(0, len(rlist)):
if ( (rlist[i] > 5) and (rlist[i] < 15) ):
print(rlist[i], "is between.")
print()
for i in range(0, len(rlist)):
if ( (rlist[i] <= 5) or (rlist[i] >= 15) ):
print(rlist[i], "is not between.")
print()
for i in range(0, len(rlist)):
if ( not((rlist[i] > 5) and (rlist[i] < 15)) ):
print(rlist[i], "is not between.")
The & and | Symbols¶
In [7]:
print(True & True)
print(True & False)
print(False & True)
print(False & False)
In [8]:
print(True | True)
print(True | False)
print(False | True)
print(False | False)
In [9]:
n = 5
bool1 = (n > 9 or n < 7)
bool2 = (n > 9 | n < 7)
print(bool1)
print(bool2)
In [10]:
print(9 | 5)
Example: The Titanic Dataset¶
In this example, we will explore a dataset containing information about the 887 passengers on the first (and last) voyage of the RMS Titanic.
The following two code cells loads two packages needed for this example.
In [11]:
import pandas as pd
import matplotlib.pyplot as plt
The next cell reads the data from the titanic.csv file into our Python session and creates five lists, each with 887 elements:
name- Contains the names of the titanic passengers.sex- Contains the sex of each of the passengers. Each element is either the string'male'or the string'female'.age- Contains the age of each of the passengers, as an integer.pclass- Contains the ticket class of each of the passengers. Each element is an integer 1, 2, or 3. Lower numbers indicate a higher class.surv- Explains whether each of the passengers lived or died in the disaster. Each element is either a 1, indicating that the passenger survived, or a 0, indicating that the passenger died.
Note that any particular index position refers to the same passenger in all three list. In other words, the passenger whose name is in name[37] has their sex recorded in sex[37], has their age recorded in age[37], and so on.
In [12]:
df = pd.read_csv('titanic.csv')
name = list(df['Name'])
sex = list(df['Sex'])
age = list(df['Age'])
pclass = list(df['Pclass'])
surv = list(df['Survived'])
The following cell will print the record for a randomly selected passenger each time it is ran.
In [13]:
n = random.choice(range(0, len(name)))
print("Name:", name[n])
print("Sex:", sex[n])
print("Age:", age[n])
print("Class:", pclass[n])
print("Survived:", surv[n])
Survival Rates by Sex¶
In [14]:
female_survived = 0
female_died = 0
male_survived = 0
male_died = 0
for i in range(0, len(sex)):
if((sex[i] == "female") and (surv[i] == 1)):
female_survived += 1
if((sex[i] == "female") and (surv[i] == 0)):
female_died += 1
if((sex[i] == "male") and (surv[i] == 1)):
male_survived += 1
if((sex[i] == "male") and (surv[i] == 0)):
male_died += 1
female_total = female_survived + female_died
male_total = male_survived + male_died
print("Total Female Passengers:", female_total)
print("Female Passengers who Survived:", female_survived)
print("Female Passengers who Died:", female_died)
print()
print("Total Male Passengers:", male_total)
print("Male Passengers who Survived:", male_survived)
print("Male Passengers who Died:", male_died)
In [15]:
female_survival_rate = round(female_survived / female_total, 4)
male_survival_rate = round(male_survived / male_total, 4)
print("The proportion of females who survived the Titanic voyage is equal to " + str(female_survival_rate) + ".")
print("The proportion of males who survived the Titanic voyage is equal to " + str(male_survival_rate) + ".")
In [16]:
survival_rates = [female_survival_rate, male_survival_rate]
death_rates = [1-female_survival_rate,1- male_survival_rate]
plt.bar(range(2),survival_rates, label='Survived', color='cornflowerblue')
plt.bar(range(2),death_rates, label='Died', bottom=survival_rates, color='Salmon')
plt.legend(loc="center left", bbox_to_anchor=(1.03,0.5))
plt.xticks(range(2), ['Female', 'Male'])
plt.ylabel('Proportion')
plt.title('Survival Rate by Sex')
plt.show()
Survival Rates by Passenger Class¶
In [17]:
c1_survived = 0
c2_survived = 0
c3_survived = 0
c1_total = 0
c2_total = 0
c3_total = 0
for i in range(0, len(pclass)):
if(pclass[i] == 1):
c1_total += 1
if(surv[i] == 1):
c1_survived += 1
elif(pclass[i] == 2):
c2_total += 1
if(surv[i] == 1):
c2_survived += 1
else:
c3_total += 1
if(surv[i] == 1):
c3_survived +=1
c1_survival_rate = round(c1_survived / c1_total, 4)
c2_survival_rate = round(c2_survived / c2_total, 4)
c3_survival_rate = round(c3_survived / c3_total, 4)
print("The proportion of 1st class passengers who survived the Titanic voyage is equal to " + str(c1_survival_rate) + ".")
print("The proportion of 2nd class passengers who survived the Titanic voyage is equal to " + str(c2_survival_rate) + ".")
print("The proportion of 3rd class passengers who survived the Titanic voyage is equal to " + str(c3_survival_rate) + ".")
In [18]:
survival_rates_by_class = [c1_survival_rate, c2_survival_rate, c3_survival_rate]
death_rates_by_class = [1-c1_survival_rate, 1-c2_survival_rate, 1-c3_survival_rate]
plt.bar(range(3),survival_rates_by_class, label='Survived', color='cornflowerblue')
plt.bar(range(3),death_rates_by_class, label='Died', bottom=survival_rates_by_class, color='Salmon')
plt.legend(loc="center left", bbox_to_anchor=(1.03,0.5))
plt.xticks(range(3), ['Class 1', 'Class 2', 'Class 3'])
plt.ylabel('Proportion')
plt.title('Survival Rate by Class')
plt.show()
