📊 Day 18: Hexbin Plot in Python

 

📊 Day 18: Hexbin Plot in Python

🔹 What is a Hexbin Plot?

A Hexbin Plot is a 2D visualization that groups data points into hexagonal bins.
Color intensity represents the number of points inside each hexagon.

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🔹 When Should You Use It?

Use a hexbin plot when:

• You have large scatter datasets

• Points overlap too much in a scatter plot

• You want to see data density patterns

• Analyzing two continuous variables

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🔹 Example Scenario

Suppose you are analyzing:

• Website clicks vs session time

• GPS location data

• Large sensor datasets

A hexbin plot helps you:

• Identify dense regions

• Spot clusters clearly

• Reduce overplotting

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🔹 Key Idea Behind It

👉 Space is divided into hexagonal bins
👉 Color shows density
👉 Hexagons reduce visual bias vs squares

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🔹 Python Code (Hexbin Plot)

import matplotlib.pyplot as plt
import numpy as np 
x = np.random.randn(10000)
y = np.random.randn(10000)

plt.hexbin(x, y, gridsize=30, cmap='Blues')
plt.colorbar(label='Count') 

plt.xlabel("X Values")
plt.ylabel("Y Values")
plt.title("Hexbin Plot Example") 

plt.show()

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🔹 Output Explanation

• Each hexagon represents a region

• Darker hexagons = more data points

• Shows true density structure

• Eliminates clutter from overlapping dots

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🔹 Hexbin Plot vs Scatter Plot

Feature	Hexbin Plot	Scatter Plot
Data size	Large	Small–Medium
Overplotting	No	Yes
Density view	Clear	Hard
Performance	Better	Slower

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🔹 Key Takeaways

• Hexbin plots are ideal for big datasets

• Reveal hidden density patterns

• Cleaner than scatter plots

• Widely used in data science & geospatial analysis