Storytelling with visualization sequence in Matplotlib

Introduction

Storytelling with visualization sequence helps you explain data step-by-step. It makes complex information easy to understand by showing it in parts.

When you want to explain a trend over time clearly.

When you need to compare different groups one by one.

When you want to highlight changes in data stepwise.

When teaching data insights to people new to data.

When presenting data in meetings to keep attention.

Syntax

Matplotlib

import matplotlib.pyplot as plt

# Create multiple plots in sequence
plt.figure()
plt.plot(x1, y1)
plt.title('Step 1: First view')
plt.show()

plt.figure()
plt.plot(x2, y2)
plt.title('Step 2: Next detail')
plt.show()

Use plt.show() after each plot to display them one by one.

Each plot can focus on a different part of the story.

Examples

This shows a simple line plot as the first step in a story.

Matplotlib

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 20, 25, 30]

plt.plot(x, y)
plt.title('Basic line plot')
plt.show()

Next, a bar chart can add comparison between groups.

Matplotlib

plt.bar(['A', 'B', 'C'], [5, 7, 3])
plt.title('Bar chart to compare categories')
plt.show()

Finally, a scatter plot can show detailed points.

Matplotlib

plt.scatter([1, 2, 3], [4, 5, 6])
plt.title('Scatter plot for detail')
plt.show()

Sample Program

This program shows three plots one after another. Each plot tells part of the sales story: overall sales by quarter, product sales in the last quarter, and monthly sales trend.

Matplotlib

import matplotlib.pyplot as plt

# Step 1: Show sales over 4 quarters
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
sales = [150, 200, 250, 300]
plt.figure()
plt.plot(quarters, sales, marker='o')
plt.title('Step 1: Sales over quarters')
plt.xlabel('Quarter')
plt.ylabel('Sales')
plt.show()

# Step 2: Show sales by product in Q4
products = ['Product A', 'Product B', 'Product C']
sales_q4 = [120, 80, 100]
plt.figure()
plt.bar(products, sales_q4, color='orange')
plt.title('Step 2: Q4 sales by product')
plt.xlabel('Product')
plt.ylabel('Sales')
plt.show()

# Step 3: Show sales trend with scatter
months = [1, 2, 3, 4, 5, 6]
sales_trend = [100, 150, 200, 250, 300, 350]
plt.figure()
plt.scatter(months, sales_trend, color='green')
plt.title('Step 3: Monthly sales trend')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

Important Notes

Use clear titles and labels to guide the viewer through the story.

Showing plots one by one helps keep focus on each part.

You can save each plot as an image if you want to share the story later.

Summary

Break your data story into small steps using multiple plots.

Use plt.show() to display each plot separately.

Clear titles and labels help your audience follow the story.

Practice

1. What is the main purpose of using multiple plots in a storytelling visualization sequence?

easy

A. To make the plot colors more vibrant

B. To reduce the size of the data

C. To break data into parts and explain it step-by-step

D. To avoid using titles and labels

Solution

1. Step 1: Understand storytelling with visualization

   Storytelling with visualization means showing data in parts to explain it clearly.

2. Step 2: Purpose of multiple plots

   Using multiple plots helps break the data into smaller pieces to tell a clear story step-by-step.

3. Final Answer:

   To break data into parts and explain it step-by-step -> Option C

4. Quick Check:

   Storytelling = breaking data into parts [OK]

Hint: Multiple plots show data in steps for clear explanation [OK]

Common Mistakes:

• Thinking colors are the main reason for multiple plots
• Believing multiple plots reduce data size
• Ignoring the importance of titles and labels

2. Which of the following is the correct way to create two plots side by side using matplotlib?

easy

A. plt.subplot(2, 1, 1) and plt.subplot(2, 1, 3)

B. plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2)

C. plt.subplot(1, 1, 1) and plt.subplot(1, 1, 2)

D. plt.subplot(3, 1, 1) and plt.subplot(3, 1, 2)

Solution

1. Step 1: Understand plt.subplot parameters

   plt.subplot(rows, columns, plot_number) arranges plots in a grid.

2. Step 2: Create two side-by-side plots

   One row and two columns means plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2) for two plots side by side.

3. Final Answer:

   plt.subplot(1, 2, 1) and plt.subplot(1, 2, 2) -> Option B

4. Quick Check:

   One row, two columns = plt.subplot(1, 2, x) [OK]

Hint: Use plt.subplot(1, 2, x) for two side-by-side plots [OK]

Common Mistakes:

• Using wrong plot numbers like 3 in a 2-plot layout
• Mixing rows and columns incorrectly
• Trying to create more plots than grid allows

3. What will be the output arrangement of the following code?

import matplotlib.pyplot as plt
plt.subplot(2, 1, 1)
plt.title('Top Plot')
plt.subplot(2, 1, 2)
plt.title('Bottom Plot')
plt.show()

medium

A. Error because plt.title() is used twice

B. Two plots side by side with titles 'Top Plot' and 'Bottom Plot'

C. One plot with both titles overlapping

D. Two plots stacked vertically with titles 'Top Plot' and 'Bottom Plot'

Solution

1. Step 1: Understand plt.subplot(2, 1, x)

   This creates 2 rows and 1 column, stacking plots vertically.

2. Step 2: Titles assigned to each subplot

   First plot gets 'Top Plot', second gets 'Bottom Plot', shown stacked vertically.

3. Final Answer:

   Two plots stacked vertically with titles 'Top Plot' and 'Bottom Plot' -> Option D

4. Quick Check:

   2 rows, 1 column = vertical stack [OK]

Hint: Rows first, columns second in plt.subplot for layout [OK]

Common Mistakes:

• Thinking plots are side by side with (2,1,x)
• Assuming plt.title() causes error if used twice
• Expecting one plot instead of two

4. Identify the error in this code that tries to create a 2x2 grid of plots:

import matplotlib.pyplot as plt
plt.subplot(2, 2, 1)
plt.plot([1,2,3])
plt.subplot(2, 2, 5)
plt.plot([3,2,1])
plt.show()

medium

A. Using subplot number 5 in a 2x2 grid causes an error

B. plt.plot() cannot be used inside subplot

C. Missing plt.figure() before subplots

D. No error, code runs fine

Solution

1. Step 1: Understand subplot numbering in 2x2 grid

   2 rows and 2 columns means subplot numbers 1 to 4 only.

2. Step 2: Check subplot number 5 usage

   Using subplot(2, 2, 5) is invalid and causes an error.

3. Final Answer:

   Using subplot number 5 in a 2x2 grid causes an error -> Option A

4. Quick Check:

   Max subplot number = rows*columns = 4 [OK]

Hint: Subplot number must be ≤ rowsxcolumns [OK]

Common Mistakes:

• Thinking plt.plot() can't be inside subplot
• Believing plt.figure() is mandatory before subplots
• Ignoring subplot numbering limits

5. You want to tell a story showing sales growth over 3 years with separate plots for each year. Which approach best helps your audience understand the story clearly?

hard

A. Create 3 subplots in one column using plt.subplot(3, 1, x) with clear titles and labels

B. Plot all years on one plot without labels

C. Create 1 subplot and plot only the last year's data

D. Use plt.subplot(1, 3, x) but skip titles and labels

Solution

1. Step 1: Choose subplot layout for storytelling

   Using 3 rows and 1 column (plt.subplot(3, 1, x)) stacks plots vertically, showing each year clearly.

2. Step 2: Importance of titles and labels

   Clear titles and labels help the audience understand each year's data easily.

3. Final Answer:

   Create 3 subplots in one column using plt.subplot(3, 1, x) with clear titles and labels -> Option A

4. Quick Check:

   Separate plots + clear labels = better storytelling [OK]

Hint: Stack plots vertically with titles for clear story [OK]

Common Mistakes:

• Plotting all data in one plot without labels
• Skipping titles and labels reduces clarity
• Showing only last year's data misses story