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Everyone Can Learn Python Scholarship

1️⃣ Python 🐍 - CO2 Emissions

📖 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]

Analysis

Data Exploration

Before going deep into the analysis, it would be better to check the quality of the data uploaded, as the ouput of this steps could guarantee the credibility of further results.
As a result, the dataframe does not contain null values, as all of the fields do not have null values.

Hidden code

What is the median engine size in liters?

import numpy as np
median_car_engine_size = np.median(cars_engine_sizes)
print(median_car_engine_size)
  1. What is the average fuel consumption for regular gasoline (Fuel Type = X), premium gasoline (Z), ethanol (E), and diesel (D)?
Hidden code
  1. What is the correlation between fuel consumption and CO2 emissions?
cars[['Fuel Consumption Comb (L/100 km)','CO2 Emissions(g/km)']].corr()
Hidden code

**4. Which vehicle class has lower average CO2 emissions, 'SUV - SMALL' or 'MID-SIZE'? **

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