📊 Day 9: Density Plot in Python

 

📊 Day 9: Density Plot in Python

🔹 What is a Density Plot?

A Density Plot (also called a KDE plot) is a smooth curve that represents the probability density of continuous data.
It shows how data is distributed without using bars or bins like a histogram.

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🔹 When Should You Use It?

Use a density plot when:

• You want a smooth view of data distribution

• Comparing multiple distributions

• You need to identify peaks, spread, and skewness

• Histogram bars feel too noisy or cluttered

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🔹 Example Scenario

Suppose you are analyzing:

• User session durations

• Sensor readings

• Test scores

• Randomly generated values

A density plot helps you understand:

• Where values are most concentrated

• Whether data follows a normal distribution

• How spread out the data is

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🔹 Key Idea Behind It

👉 Uses Kernel Density Estimation (KDE)
👉 Smooths data into a continuous curve
👉 Area under the curve equals 1

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🔹 Python Code (Density Plot)

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

data = np.random.normal(size=1000)

sns.kdeplot(data, fill=True, color="blue", bw_adjust=0.5)

plt.title("Statistical Density Plot (2026)")
plt.xlabel("Value")
plt.ylabel("Density")

plt.show()

🔹 Output Explanation

• X-axis shows data values

• Y-axis shows density

• Highest point = most common values

• Smooth curve highlights overall distribution shape

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🔹 Density Plot vs Histogram

Feature	Density Plot	Histogram
Shape	Smooth curve	Bar-based
Noise	Less	More
Comparison	Easy	Harder
Bins	Not visible	Required

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🔹 Key Takeaways

• Density plots show true distribution shape

• Best for continuous numerical data

• Ideal for comparing multiple datasets

• Cleaner alternative to histograms