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

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

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

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

Hint

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

Practice

1. What is the main purpose of adding jitter in a categorical scatter plot using matplotlib?

easy

A. To increase the size of the points

B. To change the color of the points randomly

C. To spread overlapping points so they are easier to see

D. To remove outliers from the data

Solution

1. Step 1: Understand what jitter does

   Jitter adds small random noise to the position of points on the categorical axis.

2. Step 2: Effect of jitter on overlapping points

   This noise spreads points that would overlap, making each point visible.

3. Final Answer:

   To spread overlapping points so they are easier to see -> Option C

4. Quick Check:

   Jitter spreads points = A [OK]

Hint: Jitter spreads points to avoid overlap [OK]

Common Mistakes:

• Thinking jitter changes point color
• Assuming jitter increases point size
• Believing jitter removes data points

2. Which of the following matplotlib code snippets correctly adds jitter to a categorical scatter plot on the x-axis?

easy

A. plt.scatter(x + np.random.uniform(-0.1, 0.1, size=len(x)), y)

B. plt.scatter(x * np.random.uniform(-0.1, 0.1, size=len(x)), y)

C. plt.scatter(x - np.random.randint(1, 5, size=len(x)), y)

D. plt.scatter(x / np.random.uniform(0.1, 1, size=len(x)), y)

Solution

1. Step 1: Identify correct jitter addition

   Jitter is added by adding small random noise to the x values.

2. Step 2: Check each option

   plt.scatter(x + np.random.uniform(-0.1, 0.1, size=len(x)), y) adds small uniform noise between -0.1 and 0.1, which is correct jitter. Others multiply, subtract large integers, or divide, which distort data incorrectly.

3. Final Answer:

   plt.scatter(x + np.random.uniform(-0.1, 0.1, size=len(x)), y) -> Option A

4. Quick Check:

   Add small noise = C [OK]

Hint: Add small uniform noise to x for jitter [OK]

Common Mistakes:

• Multiplying x by noise instead of adding
• Subtracting large integers causing big shifts
• Dividing x by noise changing scale

3. What will be the effect of the following code on the scatter plot?

import matplotlib.pyplot as plt
import numpy as np

categories = ['A', 'B', 'C']
values = [5, 7, 6]
x = np.array([0, 1, 2])
jitter = np.random.uniform(-0.2, 0.2, size=3)
plt.scatter(x + jitter, values)
plt.xticks(x, categories)
plt.show()

medium

A. Points will be stacked vertically at each category

B. Plot will show an error due to jitter size mismatch

C. Points will be plotted exactly at integer x positions

D. Points will be spread horizontally around each category label

Solution

1. Step 1: Understand jitter addition

   Jitter adds random noise between -0.2 and 0.2 to x positions, spreading points horizontally.

2. Step 2: Effect on scatter plot

   Points will no longer be exactly at 0,1,2 but slightly shifted, making them spread horizontally around category labels.

3. Final Answer:

   Points will be spread horizontally around each category label -> Option D

4. Quick Check:

   Jitter shifts points horizontally = B [OK]

Hint: Jitter shifts points horizontally near categories [OK]

Common Mistakes:

• Thinking jitter stacks points vertically
• Assuming points stay exactly at integers
• Believing code causes error due to jitter size

4. The following code is intended to add jitter to a categorical scatter plot but does not work as expected. What is the main error?

import matplotlib.pyplot as plt
import numpy as np

x = np.array([0, 1, 2, 0, 1, 2])
y = np.array([5, 6, 7, 5, 6, 7])
jitter = np.random.uniform(-0.1, 0.1)
plt.scatter(x + jitter, y)
plt.xticks([0, 1, 2], ['A', 'B', 'C'])
plt.show()

medium

A. Jitter is a single number, not an array matching x length

B. x and y arrays have different lengths

C. plt.xticks labels do not match x values

D. Random uniform range is too large causing overlap

Solution

1. Step 1: Check jitter variable

   Jitter is created as a single float, not an array matching x length.

2. Step 2: Effect on scatter

   Adding a single float to an array works but adds same jitter to all points, no spread.

3. Final Answer:

   Jitter is a single number, not an array matching x length -> Option A

4. Quick Check:

   Jitter must be array for point-wise noise = A [OK]

Hint: Jitter must be array same size as data [OK]

Common Mistakes:

• Assuming single jitter value adds spread
• Ignoring length mismatch of jitter and x
• Thinking xticks labels cause error

5. You have a dataset with categories 'Red', 'Green', 'Blue' and multiple points per category. You want to create a categorical scatter plot with jitter to visualize all points clearly. Which approach is best to implement jitter correctly in matplotlib?

hard

A. Add jitter by multiplying category integers by random values greater than 1

B. Map categories to integers, add small random noise array to these integers, then plot with category labels on x-axis

C. Use bar plot instead of scatter plot to avoid jitter

D. Plot categories as strings on x-axis and add jitter by changing y values randomly

Solution

1. Step 1: Map categories to integers

   Convert categorical labels to integer positions for plotting on x-axis.

2. Step 2: Add small random noise array to integers

   Add jitter by adding a small random noise array to spread points horizontally.

3. Step 3: Plot with category labels

   Use plt.xticks to label integer positions with original category names.

4. Final Answer:

   Map categories to integers, add small random noise array to these integers, then plot with category labels on x-axis -> Option B

5. Quick Check:

   Integer mapping + noise + labels = D [OK]

Hint: Map categories to numbers, add noise, label ticks [OK]

Common Mistakes:

• Adding jitter to y instead of x
• Using bar plot which doesn't show jitter
• Multiplying integers causing large shifts