Categorical scatter with jitter in Matplotlib - Mini Project: Build & Apply

Categorical scatter with jitter

📖 Scenario: You work in a small bakery and want to analyze how many cookies of different types are sold each day. You have the sales data for three types of cookies over a week.

🎯 Goal: Create a scatter plot that shows the number of cookies sold for each cookie type. Add jitter to the x-axis positions so the points don't overlap and are easier to see.

📋 What You'll Learn

Create a dictionary called cookie_sales with keys 'Chocolate', 'Vanilla', and 'Strawberry' and values as lists of daily sales numbers.

Create a variable called jitter_strength to control the amount of jitter on the x-axis.

Use a for loop with variables cookie and sales to iterate over cookie_sales.items().

Inside the loop, create a list x_positions with jittered x-axis values for each sale.

Plot the scatter points using plt.scatter(x_positions, sales).

Print the plot with plt.show().

💡 Why This Matters

🌍 Real World

Jittered scatter plots help visualize overlapping data points in categories, useful in sales, biology, and social sciences.

💼 Career

Data analysts and scientists often use jitter plots to explore and present categorical data distributions clearly.

1

Create the cookie sales data

Create a dictionary called cookie_sales with these exact entries: 'Chocolate': [20, 22, 19, 24, 20, 23, 21], 'Vanilla': [15, 17, 14, 16, 18, 15, 17], and 'Strawberry': [10, 12, 11, 13, 12, 14, 13].

Matplotlib

# Create the cookie_sales dictionary with exact values
# Your code here

Need a hint?

Use curly braces {} to create the dictionary and colons : to assign lists to each cookie type.

2

Set the jitter strength

Create a variable called jitter_strength and set it to 0.1 to control the amount of jitter on the x-axis.

Matplotlib

cookie_sales = {
    'Chocolate': [20, 22, 19, 24, 20, 23, 21],
    'Vanilla': [15, 17, 14, 16, 18, 15, 17],
    'Strawberry': [10, 12, 11, 13, 12, 14, 13]
}
# Create jitter_strength variable and set it to 0.1
# Your code here

Need a hint?

Just assign the number 0.1 to the variable jitter_strength.

3

Create jittered x positions and plot scatter points

Import matplotlib.pyplot as plt and random. Use a for loop with variables cookie and sales to iterate over cookie_sales.items(). Inside the loop, create a list x_positions by adding random jitter between -jitter_strength and jitter_strength to the cookie's index. Then plot the scatter points with plt.scatter(x_positions, sales). Use enumerate(cookie_sales) to get the index of each cookie type.

Matplotlib

cookie_sales = {
    'Chocolate': [20, 22, 19, 24, 20, 23, 21],
    'Vanilla': [15, 17, 14, 16, 18, 15, 17],
    'Strawberry': [10, 12, 11, 13, 12, 14, 13]
}
jitter_strength = 0.1

# Import matplotlib.pyplot as plt and random
# Use a for loop with cookie and sales to iterate over cookie_sales.items()
# Inside the loop, create x_positions with jitter using enumerate(cookie_sales)
# Plot scatter points with plt.scatter(x_positions, sales)
# Your code here

Need a hint?

Use enumerate(cookie_sales) to get the index and cookie name. Use a list comprehension to add random jitter to the index for each sale.

4

Show the scatter plot

Add the line plt.show() to display the scatter plot with jittered points.

Matplotlib

import matplotlib.pyplot as plt
import random

cookie_sales = {
    'Chocolate': [20, 22, 19, 24, 20, 23, 21],
    'Vanilla': [15, 17, 14, 16, 18, 15, 17],
    'Strawberry': [10, 12, 11, 13, 12, 14, 13]
}
jitter_strength = 0.1

for index, cookie in enumerate(cookie_sales):
    sales = cookie_sales[cookie]
    x_positions = [index + random.uniform(-jitter_strength, jitter_strength) for _ in sales]
    plt.scatter(x_positions, sales)

# Display the plot
# Your code here

Need a hint?

Use plt.show() to display the plot window.