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

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

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

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

Hint

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

Practice

1. Why is it useful to have multiple plots in one figure when analyzing data?

easy

A. It automatically cleans the data for errors.

B. It reduces the total number of data points shown.

C. It allows easy comparison of different data sets side by side.

D. It makes the figure file size smaller.

Solution

1. Step 1: Understand the purpose of multiple plots

   Multiple plots in one figure let us see different data sets or views together.

2. Step 2: Compare data side by side

   This side-by-side view helps spot differences or similarities easily.

3. Final Answer:

   It allows easy comparison of different data sets side by side. -> Option C

4. Quick Check:

   Multiple plots = easy comparison [OK]

Hint: Multiple plots show data side by side for easy comparison [OK]

Common Mistakes:

• Thinking multiple plots reduce data points
• Believing multiple plots clean data automatically
• Assuming multiple plots reduce file size

2. Which of the following is the correct way to create a figure with 2 rows and 3 columns of plots using matplotlib?

easy

A. plt.subplots(3, 2)

B. plt.subplots(2, 3)

C. plt.subplot(2, 3)

D. plt.figure(2, 3)

Solution

1. Step 1: Recall the function for multiple plots

   Use plt.subplots(rows, columns) to create a grid of plots.

2. Step 2: Match rows and columns

   For 2 rows and 3 columns, call plt.subplots(2, 3).

3. Final Answer:

   plt.subplots(2, 3) -> Option B

4. Quick Check:

   plt.subplots(rows, cols) = plt.subplots(2, 3) [OK]

Hint: Use plt.subplots(rows, columns) to create grid [OK]

Common Mistakes:

• Confusing plt.subplot with plt.subplots
• Swapping rows and columns
• Using plt.figure for subplots

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
fig, axs = plt.subplots(1, 2)
axs[0].plot([1, 2, 3], [4, 5, 6])
axs[1].plot([1, 2, 3], [6, 5, 4])
plt.show()

medium

A. Two separate plots side by side, each with its own line.

B. A blank figure with no plots.

C. An error because axs is not iterable.

D. A single plot with two lines overlapping.

Solution

1. Step 1: Understand plt.subplots(1, 2)

   This creates 1 row and 2 columns of plots, so axs is an array of two axes.

2. Step 2: Plot on each axis separately

   axs[0] and axs[1] each get their own line plot, so two plots appear side by side.

3. Final Answer:

   Two separate plots side by side, each with its own line. -> Option A

4. Quick Check:

   plt.subplots(1,2) + axs[0], axs[1] plots = two side-by-side plots [OK]

Hint: plt.subplots(1,2) creates two side-by-side plots [OK]

Common Mistakes:

• Thinking axs is a single axis, not an array
• Expecting one plot with two lines
• Assuming code causes error

4. Identify the error in this code that tries to create 2 plots in one figure:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(2)
ax.plot([1, 2, 3], [4, 5, 6])
plt.show()

medium

A. ax is an array but used as a single axis, causing an error.

B. plt.subplots(2) creates only one plot, so ax.plot is invalid.

C. The plot data lists have different lengths causing error.

D. plt.show() is missing and causes no output.

Solution

1. Step 1: Check plt.subplots(2) output

   It creates 2 subplots in one column, so ax is an array of axes.

2. Step 2: Using ax.plot() on array causes error

   ax.plot() expects a single axis, but ax is an array, so this causes an AttributeError.

3. Final Answer:

   ax is an array but used as a single axis, causing an error. -> Option A

4. Quick Check:

   Multiple subplots return array, must index before plot [OK]

Hint: Multiple subplots return array; index before plotting [OK]

Common Mistakes:

• Calling plot on array of axes instead of one axis
• Assuming plt.subplots(2) returns single axis
• Ignoring plt.show() is present

5. You want to compare sales data for 3 products over 4 quarters in one figure. Which approach best uses multiple plots per figure to achieve this?

hard

A. Plot all products on a single plot with overlapping lines and no labels.

B. Use plt.figure() once and call plt.plot() three times without subplots.

C. Create 3 separate figures, each with one product's sales plot.

D. Create a 1x3 grid of plots using plt.subplots(1, 3), plot each product's quarterly sales separately, and label each plot.

Solution

1. Step 1: Understand the goal

   We want to compare 3 products side by side in one figure for clarity.

2. Step 2: Use plt.subplots(1, 3) for side-by-side plots

   This creates 3 plots in one row, perfect for comparing products separately but together.

3. Step 3: Label each plot for clarity

   Clear labels help understand which plot shows which product's sales.

4. Final Answer:

   Create a 1x3 grid of plots using plt.subplots(1, 3), plot each product's quarterly sales separately, and label each plot. -> Option D

5. Quick Check:

   Multiple plots + labels = clear side-by-side comparison [OK]

Hint: Use plt.subplots(1,3) and label each plot for clear comparison [OK]

Common Mistakes:

• Plotting all lines on one plot without labels
• Creating separate figures instead of subplots
• Not labeling plots causing confusion