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.