1.7. Visualising Data

We can visualise datasets using a scatter plots. For example, we can visualise the study data we were working with earlier:

Time Spent Studying (hours)	Exam Mark (%)
4.5	60
8	80
1.5	31
3.5	54
5.5	58
3	30
6.5	78

First we need to read the data in from a csv file using pandas, and then we’ll visualise the data using a scatter plot. study.csv

import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv("study.csv")

x = data["Time Spent Studying (hours)"].to_numpy()
y = data["Exam Mark (%)"].to_numpy()

plt.figure(figsize=(4, 4))
plt.scatter(x, y)
plt.xlabel("Amout of Time Spent Studying (hours)")
plt.ylabel("Exam Mark (%)")
plt.xlim([0, 10])
plt.ylim([0, 100])
plt.tight_layout()
plt.savefig("plot.png")

Code Challenge: Visualise Movie Data

We will be using the same movie.csv dataset we were working with earlier.

Instructions

1. Copy and paste in your code from the previous challenge that read 'Budget ($M)' and 'Box Office ($M)' into numpy arrays

2. Create a scatter plot of the data with the following:

• set the figure size to: (4, 4)

• marker size: 5

• alpha: 0.5

• x axis label: Budget ($M)

• y axis label: Box Office ($M)

• x limits: 0 to 240

• y limits: 0 to 1600

Your plot should look like this:

Solution

Solution is locked