📊 Day 15: Heatmap in Python

 

📊 Day 15: Heatmap in Python

🔹 What is a Heatmap?

A Heatmap is a data visualization technique that represents values using color intensity.
Higher values are shown with darker/brighter colors, while lower values use lighter colors.

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🔹 When Should You Use It?

Use a heatmap when:

• Visualizing patterns in large datasets

• Showing correlations between variables

• Comparing values across two dimensions

• Quickly spotting highs, lows, and trends

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🔹 Example Scenario

Suppose you are analyzing:

• Correlation between features in a dataset

• Website activity by day vs hour

• Sales by region vs product

A heatmap instantly highlights:

• Strong correlations

• Hotspots and cold zones

• Hidden patterns

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🔹 Key Idea Behind It

👉 Data values → color scale
👉 Color intensity → magnitude
👉 Great for pattern recognition

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🔹 Python Code (Heatmap)

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd 

data = np.random.rand(5, 5)
df = pd.DataFrame(data, columns=list("ABCDE"), index=list("ABCDE"))

sns.heatmap(df, annot=True) 

plt.title("Heatmap Example") 
plt.show()

🔹 Output Explanation

• Each cell represents a value

• Color shows how high or low the value is

• Numbers inside cells make interpretation easier

• Darker color = higher intensity

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🔹 Heatmap vs Scatter Plot

Feature	Heatmap	Scatter Plot
Data size	Large	Small–Medium
Pattern detection	Excellent	Limited
Dimensions	2D matrix	2 variables
Use case	Correlation & intensity	Relationship

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🔹 Key Takeaways

• Heatmaps turn numbers into patterns

• Excellent for correlation analysis

• Easy to interpret visually

• Widely used in EDA & ML workflows