Why multiple plots per figure matter in Matplotlib - The Real Reasons

The Big Idea

What if you could see all your data stories at once, without flipping pages?

The Scenario

Imagine you have several sets of data to compare, like sales numbers for different products over time. You try to draw each chart separately and then flip between them to understand the story.

The Problem

This manual way is slow and confusing. You lose the big picture because you must remember details from one chart while looking at another. It's easy to miss patterns or make mistakes when switching back and forth.

The Solution

Using multiple plots in one figure lets you see all data side by side. This clear layout helps you spot trends and differences quickly without flipping around. It saves time and reduces errors.

Before vs After

✗ Before

plt.plot(data1)
plt.show()
plt.plot(data2)
plt.show()

✓ After

fig, axs = plt.subplots(2)
axs[0].plot(data1)
axs[1].plot(data2)
plt.show()

What It Enables

It enables quick, clear comparison of multiple data sets in one view, making insights easier and faster to find.

Real Life Example

A marketing team compares website visits, sales, and ad clicks side by side to decide where to invest next.

Key Takeaways

Manual separate plots slow down understanding.

Multiple plots per figure show data side by side.

This approach makes spotting patterns easier and faster.

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