📊 Day 5: Stacked Bar Chart in Python 

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🔍 What is a Stacked Bar Chart?

A stacked bar chart displays bars on top of each other instead of side by side.

Each bar represents:

• The total value of a category

• The contribution of each sub-category within that total

This makes it easy to see both:
✔ Overall totals
✔ Individual contributions

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✅ When Should You Use a Stacked Bar Chart?

Use a stacked bar chart when:

• You want to show part-to-whole relationships

• Total value and composition both matter

• Comparing how components change across categories

Real-world examples:

• Product-wise sales per year

• Department-wise expenses

• Male vs female population by year

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📊 Example Dataset

Let’s visualize yearly sales of two products:

Year	Product A	Product B
2022	50	40
2023	70	60
2024	90	75

Each bar shows total yearly sales, while colors show Product A and Product B contributions.

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🧠 Python Code: Stacked Bar Chart Using Matplotlib

import matplotlib.pyplot as plt
import numpy as np

years = ['2022', '2023', '2024'] 
product_a = [50, 70, 90]
product_b = [40, 60, 75]

x = np.arange(len(years))

plt.bar(x, product_a, label='Product A')
plt.bar(x, product_b, bottom=product_a, label='Product B')
 
plt.xlabel('Year')
plt.ylabel('Sales')
plt.title('Yearly Sales (Stacked Bar Chart)')
plt.xticks(x, years) 
plt.legend()

plt.show()

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🧩 Code Explanation (Simple)

• plt.bar(x, product_a) → creates the base bars

• bottom=product_a → stacks Product B on top of Product A

• legend() → identifies each product

• Total bar height = Product A + Product B

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📊 Stacked Bar Chart vs Grouped Bar Chart

Stacked Bar Chart	Grouped Bar Chart

Shows composition

Shows comparison

Highlights totals

Highlights differences

Parts stacked

Bars side by side

🔑 Key Takeaways

✔ Best for showing total + breakdown
✔ Useful for composition analysis
✔ Easy to understand trends in parts