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KDE Plot

  • This module teaches how to create KDE plots in Seaborn to visualize smooth probability density curves. You will learn to compare multiple categories and apply shaded KDE for better data distribution analysis in Python.

  • What is a KDE Plot?

    A KDE Plot (Kernel Density Estimation Plot) is used to:

    ✔ Show probability density of numerical data
    ✔ Display smooth distribution curve
    ✔ Understand data shape
    ✔ Identify skewness & peaks

    It is a smooth version of histogram.


    Probability Density Curve

    Theory

    Unlike histogram (which shows frequency), KDE shows:

    Probability density

    Important:

    • Y-axis → Density (not count)

    • Total area under curve = 1

    It estimates how data is distributed across values.

    Example Code

KDE Plot of Total Bill

This visualization uses a Kernel Density Estimation (KDE) plot to show the smooth distribution of total bill amounts. 

import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
sns.kdeplot(x="total_bill", data=tips)
plt.title("KDE Plot of Total Bill")
plt.show()
Lesson image

  • Output Explanation

    • X-axis → Total Bill

    • Y-axis → Density

    • Curve peak → Most common values

    • Long tail → Skewed data

    If curve leans right → Right-skewed distribution.
    If symmetric → Normal distribution.


    Smooth Distribution

    Why Smooth?

    Histogram:

    • Bars

    • Sharp edges

    KDE:

    • Smooth continuous curve

    • Better visualization of shape

    It uses a mathematical technique called Kernel Density Estimation to smooth data.

    Example — Tip Distribution

Smooth Distribution of Tips (KDE Plot)

This visualization uses a Kernel Density Estimation (KDE) plot to display a smooth distribution of tip amounts. 

sns.kdeplot(x="tip", data=tips)
plt.title("Smooth Distribution of Tips")
plt.show()

  • What You Observe:

    ✔ Peaks → Concentration of values
    ✔ Width → Spread of data
    ✔ Skewness → Direction of tail


    Comparing Multiple Categories

    Why Compare?

    To see:

    • Which group has higher values

    • Difference in distribution

    • Spread comparison

    Example — Compare by Gender

Total Bill Distribution by Gender (KDE Plot)

This visualization uses a Kernel Density Estimation (KDE) plot to compare the smooth distribution of total bill amounts between male and female customers. 

sns.kdeplot(x="total_bill",
           hue="sex",
           data=tips)
plt.title("Total Bill Distribution by Gender")
plt.show()
Lesson image

  • Output Explanation

    • Two curves → Male & Female

    • Compare:

      • Peak height

      • Curve spread

      • Skewness

      • Overlapping area

    If one curve shifts right → That group spends more.


    Shaded KDE (Filled Curve)

    Why Use Fill?

    • Better visual clarity

    • More presentation-friendly

    • Easy comparison

    Example — Filled KDE

Shaded KDE Plot of Total Bill

This visualization uses a Kernel Density Estimation (KDE) plot with shading to show the smooth distribution of total bill amounts. 

sns.kdeplot(x="total_bill",
           data=tips,
           fill=True)
plt.title("Shaded KDE Plot")
plt.show()
Lesson image

  • Example — Shaded with Hue

Shaded KDE Plot by Gender

This visualization uses a Kernel Density Estimation (KDE) plot with shading to compare the distribution of total bill amounts between male and female customers. 

sns.kdeplot(x="total_bill",
           hue="sex",
           data=tips,
           fill=True)
plt.title("Shaded KDE by Gender")
plt.show()
Lesson image
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