Everyone Can Learn Python Scholarship
1️⃣ Python 🐍 - CO2 Emissions
Now let's now move on to the competition and challenge.
📖 Background
You volunteer for a public policy advocacy organization in Canada, and your colleague asked you to help her draft recommendations for guidelines on CO2 emissions rules.
After researching emissions data for a wide range of Canadian vehicles, she would like you to investigate which vehicles produce lower emissions.
💾 The data I
You have access to seven years of CO2 emissions data for Canadian vehicles (source):
- "Make" - The company that manufactures the vehicle.
- "Model" - The vehicle's model.
- "Vehicle Class" - Vehicle class by utility, capacity, and weight.
- "Engine Size(L)" - The engine's displacement in liters.
- "Cylinders" - The number of cylinders.
- "Transmission" - The transmission type: A = Automatic, AM = Automatic Manual, AS = Automatic with select shift, AV = Continuously variable, M = Manual, 3 - 10 = the number of gears.
- "Fuel Type" - The fuel type: X = Regular gasoline, Z = Premium gasoline, D = Diesel, E = Ethanol (E85), N = natural gas.
- "Fuel Consumption Comb (L/100 km)" - Combined city/highway (55%/45%) fuel consumption in liters per 100 km (L/100 km).
- "CO2 Emissions(g/km)" - The tailpipe carbon dioxide emissions in grams per kilometer for combined city and highway driving.
The data comes from the Government of Canada's open data website.
# Import the pandas and numpy packages
import pandas as pd
import numpy as np
# Load the data
cars = pd.read_csv('data/co2_emissions_canada.csv')
# create numpy arrays
cars_makes = cars['Make'].to_numpy()
cars_models = cars['Model'].to_numpy()
cars_classes = cars['Vehicle Class'].to_numpy()
cars_engine_sizes = cars['Engine Size(L)'].to_numpy()
cars_cylinders = cars['Cylinders'].to_numpy()
cars_transmissions = cars['Transmission'].to_numpy()
cars_fuel_types = cars['Fuel Type'].to_numpy()
cars_fuel_consumption = cars['Fuel Consumption Comb (L/100 km)'].to_numpy()
cars_co2_emissions = cars['CO2 Emissions(g/km)'].to_numpy()
# Preview the dataframe
cars
# Look at the first ten items in the CO2 emissions array
cars_co2_emissions[:10]
💪 Challenge I
Help your colleague gain insights on the type of vehicles that have lower CO2 emissions. Include:
- What is the median engine size in liters?
- What is the average fuel consumption for regular gasoline (Fuel Type = X), premium gasoline (Z), ethanol (E), and diesel (D)?
- What is the correlation between fuel consumption and CO2 emissions?
- Which vehicle class has lower average CO2 emissions, 'SUV - SMALL' or 'MID-SIZE'?
- What are the average CO2 emissions for all vehicles? For vehicles with an engine size of 2.0 liters or smaller?
- Any other insights you found during your analysis?
#1
cars['Engine Size(L)'].mean()
The median engine size in liters is 3.16
#2
zcars =cars.loc[cars['Fuel Type']=='Z']
zcars['Engine Size(L)'].mean()
ecars =cars.loc[cars['Fuel Type']=='E']
ecars['Engine Size(L)'].mean()
dcars =cars.loc[cars['Fuel Type']=='D']
dcars['Engine Size(L)'].mean()
#3
import numpy as np
cars['CO2 Emissions(g/km)'].corr(cars['Fuel Consumption Comb (L/100 km)'])
The correlation between fuel consumption and CO2 emissions is 0.918. That means that there is a high correlation between them, so a higher fuel consumption will mean high CO2 emissions
#4
suvcars=cars.loc[cars['Vehicle Class']=='SUV - SMALL']
suvcars['CO2 Emissions(g/km)'].mean()
midcars=cars.loc[cars['Vehicle Class']=='MID-SIZE']
midcars['CO2 Emissions(g/km)'].mean()