Why multiple plots per figure matter in Matplotlib - See It in Action

Why Multiple Plots Per Figure Matter

📖 Scenario: Imagine you are a data analyst working for a small bakery. You want to compare the sales of different types of bread over a week. Instead of making separate charts for each bread type, you want to put all sales charts in one figure to see the differences side by side easily.

🎯 Goal: You will create a figure with multiple plots (subplots) using matplotlib. This will help you compare sales data for different bread types in one place.

📋 What You'll Learn

Create a dictionary called sales_data with bread types as keys and a list of 7 daily sales numbers as values.

Create a variable called days that holds a list of strings representing the days of the week.

Use matplotlib.pyplot.subplots() to create a figure with 3 subplots arranged in one row.

Plot the sales data for each bread type on its own subplot with appropriate titles.

Display the figure with all subplots visible.

💡 Why This Matters

🌍 Real World

Data analysts often need to compare multiple related datasets side by side to find patterns or differences quickly.

💼 Career

Creating multiple plots per figure is a common skill for data visualization roles, helping communicate insights clearly to teams and clients.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Sourdough': [20, 22, 19, 24, 25, 23, 21], 'Baguette': [15, 17, 14, 16, 18, 17, 16], and 'Rye': [10, 12, 11, 13, 14, 12, 11].

Matplotlib

# Create the sales_data dictionary with exact bread types and sales lists
# Your code here

Need a hint?

Use curly braces {} to create a dictionary. Each bread type is a key with a list of 7 numbers as its value.

2

Create the days list

Create a list called days with these exact strings: 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'.

Matplotlib

sales_data = {
    'Sourdough': [20, 22, 19, 24, 25, 23, 21],
    'Baguette': [15, 17, 14, 16, 18, 17, 16],
    'Rye': [10, 12, 11, 13, 14, 12, 11]
}
# Create the days list with exact day names
# Your code here

Need a hint?

Use square brackets [] to create a list of day names as strings.

3

Create subplots and plot sales data

Import matplotlib.pyplot as plt. Use plt.subplots() to create a figure and 3 subplots in one row. Use a for loop with variables ax, (bread, sales) to plot each bread's sales on its subplot. Set the title of each subplot to the bread type.

Matplotlib

sales_data = {
    'Sourdough': [20, 22, 19, 24, 25, 23, 21],
    'Baguette': [15, 17, 14, 16, 18, 17, 16],
    'Rye': [10, 12, 11, 13, 14, 12, 11]
}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

# Import matplotlib.pyplot as plt
# Create figure and 3 subplots in one row
# Use a for loop with ax, (bread, sales) to plot sales on each subplot
# Set each subplot title to bread
# Your code here

Need a hint?

Use plt.subplots(1, 3) to create one row with three plots. Use zip(axes, sales_data.items()) to loop through axes and data together.

4

Display the figure with all subplots

Use plt.tight_layout() to adjust spacing and then plt.show() to display the figure with all three subplots.

Matplotlib

import matplotlib.pyplot as plt

sales_data = {
    'Sourdough': [20, 22, 19, 24, 25, 23, 21],
    'Baguette': [15, 17, 14, 16, 18, 17, 16],
    'Rye': [10, 12, 11, 13, 14, 12, 11]
}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

fig, axes = plt.subplots(1, 3, figsize=(12, 4))

for ax, (bread, sales) in zip(axes, sales_data.items()):
    ax.plot(days, sales, marker='o')
    ax.set_title(bread)
    ax.set_xlabel('Day')
    ax.set_ylabel('Sales')

# Adjust layout and show the figure
# Your code here

Need a hint?

Use plt.tight_layout() before plt.show() to make sure the plots do not overlap.