Question

Using Python: 

cities = pd.DataFrame(
    [[2463431, 2878.52], [1392609, 5110.21], [5928040, 5905.71]],
 
    columns=['population', 'area'],
    index=pd.Index(['Vancouver','Calgary','Toronto'], name='city')
)

Do the following:

• Estimate the density for each city
• rank the cities from 1 to 3 with 1 is the city with the highest density and 3 is the lowest
• Display an updated data frame with the ranking and density information upended as new columns

Answer 1

# The code for the required problem is given below with screenshots of code

# for indentation and output and if you feel any problem then feel free to ask

# importing pandas module with name pd

import pandas as pd

# data frame named cities

cities = pd.DataFrame(

[[2463431, 2878.52], [1392609, 5110.21], [5928040, 5905.71]],

columns=['population', 'area'],

index=pd.Index(['Vancouver','Calgary','Toronto'], name='city')

)

# empty list named density to store the calculted densities

density=[]

# calculating the densities and storing them into the list

for i in range(3):

density.append( cities.population[i]/cities.area[i])

# Appending the density column into the data frame

cities['density']=density

# appending the rank column into the data frame according

# the rank function with ascending parameter is used

# for achieving the ranking as required

cities['rank'] = cities['density'].rank(ascending=False)

# printing the updated data frame

print(cities)

OUTPUT:-