Seaborn - Distribution Plot

🔹 Overview

There are multiple ways to display the distribution of a feature. This note covers three key methods:

Rugplot – Simple but not ideal for large datasets.
Histograms – Shows distribution using bins.
Kernel Density Estimation (KDE) – Smooths the distribution.

🗂 Data Source

Dataset used: dm_office_sales.csv
Alternative: Generate random data using numpy (np.random.randint() or np.random.normal()).

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
 
df = pd.read_csv("dm_office_sales.csv")
df.head()
df.info()

📌 1. Rugplot 🏷️

A rugplot places a small tick on the x-axis for each data point.

sns.rugplot(x='salary', data=df)
sns.rugplot(x='salary', data=df, height=0.5)

🔹 Limitations: Not useful for large datasets as it becomes cluttered.

📌 2. Histograms 📊

A histogram groups data into bins and counts occurrences.

sns.displot(data=df, x='salary', kde=True)  # Histogram + KDE
sns.displot(data=df, x='salary')  # Only histogram
sns.histplot(data=df, x='salary')  # Alternative method

🎛 Adjusting Bin Size

More bins = more granularity.
Fewer bins = smoother distribution.

sns.histplot(data=df, x='salary', bins=10)
sns.histplot(data=df, x='salary', bins=100)

🎨 Styling Histograms

🌫️ Grid Styles

Change the background style:

sns.set(style='darkgrid')  # Options: darkgrid, whitegrid, dark, white, ticks
sns.histplot(data=df, x='salary', bins=100)

🎨 Custom Styling

You can modify colors, edge styles, and linewidth.

sns.displot(data=df, x='salary', bins=20, kde=False,
            color='red', edgecolor='black', lw=4, ls='--')

📌 3. Kernel Density Estimation (KDE) 📈

KDE provides a smoothed estimate of the probability density function (PDF).

sns.kdeplot(data=df, x='salary')

🔹 Example with Random Data

np.random.seed(42)
sample_ages = np.random.randint(0, 100, 200)
sample_ages = pd.DataFrame(sample_ages, columns=["age"])
 
sns.kdeplot(data=sample_ages, x='age')

✂ Cutting Off KDE (If Data Has Limits)

To prevent KDE from estimating outside known values (e.g., negative ages):

sns.kdeplot(data=sample_ages, x='age', clip=[0, 100])

📏 Bandwidth Adjustment

Bandwidth controls the smoothness of the KDE.
Smaller values = more sensitivity (narrow peaks).
Larger values = smoother curve.

sns.kdeplot(data=sample_ages, x='age', bw_adjust=0.1)
sns.kdeplot(data=sample_ages, x='age', bw_adjust=0.5)
sns.kdeplot(data=sample_ages, x='age', bw_adjust=1)

🌈 Advanced Styling

Enable shading and custom colors:

sns.kdeplot(data=sample_ages, x='age', bw_adjust=0.5, shade=True, color='red')

📌 4. 2D KDE Plots 🌍

Compare two continuous features using a 2D KDE plot.

random_data = pd.DataFrame(np.random.normal(0,1,size=(100,2)), columns=['x', 'y'])
sns.kdeplot(data=random_data, x='x', y='y')

🏆 Summary

Plot Type	Description	Best Use Case
Rugplot	Marks each data point on x-axis	Small datasets
Histogram	Groups data into bins	General distribution analysis
KDE	Smooth distribution curve	Understanding probability density

🪴LYC