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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]

    💪 Challenge I

    Help your colleague gain insights on the type of vehicles that have lower CO2 emissions. Include:

    1. What is the median engine size in liters?
    2. What is the average fuel consumption for regular gasoline (Fuel Type = X), premium gasoline (Z), ethanol (E), and diesel (D)?
    3. What is the correlation between fuel consumption and CO2 emissions?
    4. Which vehicle class has lower average CO2 emissions, 'SUV - SMALL' or 'MID-SIZE'?
    5. What are the average CO2 emissions for all vehicles? For vehicles with an engine size of 2.0 liters or smaller?
    6. Any other insights you found during your analysis?

    1. What is the median engine size in liters?

    cars_engine_sizes.mean()

    2. What is the average fuel consumption for regular gasoline (Fuel Type = X), premium gasoline (Z), ethanol (E), and diesel (D)?

    cars['Fuel Type'].value_counts()

    3. What is the correlation between fuel consumption and CO2 emissions?

    It sames like just one vehicle uses the natural gas, and almost the half of vehicles use the regular gasoline.

    Does consuming a lot of fuel result in a lot of CO2 emissions? By taking a look in the correlation plot we'll note that the y values tend to increase as the x values increase. Lets calculate the correlation between this two.

    Before that, remark that the fuel consumption in liters per 100 km, and the tailpipe carbon dioxide emissions in grams per kilometer. So, first we must unify the units.

    #import backages
    import seaborn as sns
    import matplotlib.pyplot as plt
    
    # Create scatterplot of happiness_score vs life_exp with trendline
    sns.lmplot(x='Fuel Consumption Comb (L/100 km)', y='CO2 Emissions(g/km)', data=cars, ci=None)
    
    # Show plot
    plt.show()
    # Convert CO2 emissions per Km to CO2 emissions per 100Km
    cars_co2_emissions_per100km = cars_co2_emissions/100
    cars_co2_emissions_per100km
    
    #Calulate the correlation
    r1 = np.corrcoef(cars_co2_emissions,cars_fuel_consumption)
    r2 = np.corrcoef(cars_co2_emissions_per100km,cars_fuel_consumption)
    
    print(r1)
    print(r2)

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

    Woaw! there is a strong correlation between fuel consumption and CO2 emissions, that's means that large consumption of fuel correspond to large emission of the CO2.

    Let's take a look in the CO2 emissions for each vehicle class.

    print(cars.groupby('Vehicle Class')['CO2 Emissions(g/km)'].mean().sort_values())