📊 Day 21: Stacked Area Chart in Python

 

📊 Day 21: Stacked Area Chart in Python

🔹 What is a Stacked Area Chart?

A Stacked Area Chart displays multiple data series stacked on top of each other.
It shows how individual parts contribute to a total over time.

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🔹 When Should You Use It?

Use a stacked area chart when:

• Showing part-to-whole relationships

• Visualizing cumulative trends

• Comparing contributions of multiple categories

• Tracking how components change over time

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🔹 Example Scenario

Suppose you are analyzing:

• Traffic sources (Organic, Paid, Referral)

• Sales by product categories

• Energy usage by source

A stacked area chart helps you see:

• Overall growth trend

• Individual category contributions

• Shifts in dominance over time

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🔹 Key Idea Behind It

👉 X-axis represents time or categories
👉 Areas are stacked vertically
👉 Top line shows the total

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🔹 Python Code (Stacked Area Chart)

import matplotlib.pyplot as plt 

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
product_a = [30, 40, 45, 50, 60, 70]
product_b = [20, 30, 35, 40, 45, 50]
product_c = [10, 20, 25, 30, 35, 40] 

plt.stackplot(months, product_a, product_b, product_c)

plt.xlabel("Months") 
plt.ylabel("Sales")
plt.title("Stacked Area Chart Example")
plt.legend(["Product A", "Product B", "Product C"]) 

plt.show()

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🔹 Output Explanation

• Each colored area represents one category

• Total height = combined value of all categories

• Shows both individual trends and overall growth

• Easy to compare contributions over time

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🔹 Stacked Area Chart vs Area Chart

Feature	Stacked Area Chart	Area Chart
Series count	Multiple	Single
Purpose	Contribution analysis	Trend analysis
Total view	Yes	No
Complexity	Medium	Simple

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🔹 Key Takeaways

• Stacked area charts show part-to-whole trends

• Best for cumulative time-series data

• Avoid too many categories

• Order of stacking affects readability