## Everyone Can Learn Python Scholarship

### 📖 Background

The first "Everyone Can Learn Python" Scholarship from DataCamp is now open for entries.

The challenges below test the Python and SQL skills you gained from Introduction to Python and Introduction to SQL and pair them with your existing problem-solving and creative thinking.

The scholarship is open to people who have completed or are completing their secondary education and are preparing to pursue a degree in computer science or data science. Students preparing for graduate-level computer science or data science degrees are also welcome to apply.

### 💡 Learn more

The following DataCamp courses can help review the skills needed for this challenge:

### ℹ️ Introduction to Data Science Notebooks

You can skip this section if you are already familiar with data science notebooks.

#### Data science notebooks

A *data science notebook* is a document containing text cells (what you're reading now) and code cells. What is unique with a notebook is that it's **interactive**: You can change or add code cells and then **run** a cell by selecting it and then clicking the **Run** button to the right ( **▶**, or Run All on top) or hitting `control + enter`

.

The result will be displayed directly in the notebook.

Try running the Python cell below:

```
# Run this cell to see the result (click on Run on the right, or control+enter)
100 * 1.75 * 20
```

Modify any of the numbers and rerun the cell.

You can add a Markdown, Python, or SQL cell by clicking on the **Add Markdown**, **Add Code**, and **Add SQL** buttons that appear as you move the mouse pointer near the bottom of any cell.

Here at DataCamp, we call our interactive notebook **Workspace**. You can find out more about Workspace here.

### 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
# import visualization libraries
import matplotlib.pyplot as plt
# 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?

### Challenge 1 - Question 1

What is the * median engine size in liters*.

Quickly scanning with `cars.info()`

it was checked that are no missing value.

Let's first play a bit with the data before answering this question. It might be interesting to look the car class and how that relates to the median engine size. (I am not e car expert so I have no clue how the car classes rank). As an exercise we group the data by **Vehicle Class** and determine the median **Engine Size(L)**

```
# print the median engine size by car class
cars.groupby("Vehicle Class")['Engine Size(L)'].mean().plot(kind='bar')
plt.show()
```

```
import seaborn as sns
ax = sns.boxplot(data=cars, x='Vehicle Class',y='Engine Size(L)')
plt.setp(ax.get_xticklabels(), rotation=90)
plt.show()
```

The median engine size in liters