Seaborn Technical Guide

Introduction to Seaborn

Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It integrates seamlessly with pandas data structures and offers built-in themes for professional-looking plots.

🎨

Beautiful Defaults

Attractive default styles and color palettes

📈

Statistical Plots

Built-in support for complex visualizations

🔗

Pandas Integration

Works directly with DataFrames

⚡

High-Level API

Less code for complex visualizations

Installation & Setup

Install Seaborn

                    # Install via pip
pip install seaborn

# Install with conda
conda install seaborn
                

Import and Basic Setup

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

# Set the style
sns.set_theme()

# Or set specific style
sns.set_style("whitegrid")
                

💡 Built-in Datasets Seaborn includes several built-in datasets perfect for learning and testing. Access them using sns.load_dataset('dataset_name').

Basic Plotting Concepts

Loading Data

                    # Load built-in dataset
tips = sns.load_dataset("tips")
iris = sns.load_dataset("iris")
titanic = sns.load_dataset("titanic")

# View first few rows
print(tips.head())
                

Figure-Level vs Axes-Level Functions

Understanding Seaborn's Interface

Axes-level: Functions like scatterplot(), lineplot() - plot on a single matplotlib axes
Figure-level: Functions like relplot(), catplot() - create entire figure with multiple subplots

                    # Axes-level function
sns.scatterplot(data=tips, x="total_bill", y="tip")
plt.show()

# Figure-level function
sns.relplot(data=tips, x="total_bill", y="tip", 
            hue="smoker", col="time")
plt.show()
                

Distribution Plots

Histograms and KDE

                    # Histogram
sns.histplot(data=tips, x="total_bill")

# Histogram with KDE overlay
sns.histplot(data=tips, x="total_bill", kde=True)

# KDE plot
sns.kdeplot(data=tips, x="total_bill")

# Multiple distributions
sns.kdeplot(data=tips, x="total_bill", hue="time")

# 2D KDE (bivariate)
sns.kdeplot(data=tips, x="total_bill", y="tip")
                

Distribution Plot (displot)

                    # Figure-level distribution plot
sns.displot(data=tips, x="total_bill", hue="time", 
            kind="kde", fill=True)

# Histogram with facets
sns.displot(data=tips, x="total_bill", col="time", 
            row="sex", kde=True)

# ECDF plot
sns.displot(data=tips, x="total_bill", kind="ecdf")
                

Rug Plot

                    # Add rug plot to show individual observations
sns.kdeplot(data=tips, x="total_bill")
sns.rugplot(data=tips, x="total_bill")
                

Categorical Plots

Box and Violin Plots

                    # Box plot
sns.boxplot(data=tips, x="day", y="total_bill")

# Box plot with hue
sns.boxplot(data=tips, x="day", y="total_bill", hue="smoker")

# Violin plot (shows distribution)
sns.violinplot(data=tips, x="day", y="total_bill")

# Split violin plot
sns.violinplot(data=tips, x="day", y="total_bill", 
               hue="sex", split=True)
                

Strip and Swarm Plots

                    # Strip plot (scattered points)
sns.stripplot(data=tips, x="day", y="total_bill")

# Swarm plot (non-overlapping points)
sns.swarmplot(data=tips, x="day", y="total_bill")

# Combine box plot with swarm plot
sns.boxplot(data=tips, x="day", y="total_bill")
sns.swarmplot(data=tips, x="day", y="total_bill", 
               color="black", alpha=0.5)
                

Bar and Count Plots

                    # Bar plot (shows mean with confidence interval)
sns.barplot(data=tips, x="day", y="total_bill")

# Count plot (counts occurrences)
sns.countplot(data=tips, x="day")

# Count plot with hue
sns.countplot(data=tips, x="day", hue="sex")
                

Point Plot

                    # Point plot (shows mean and CI as points and lines)
sns.pointplot(data=tips, x="day", y="total_bill", hue="sex")
                

Categorical Plot (catplot)

                    # Figure-level categorical plot
sns.catplot(data=tips, x="day", y="total_bill", 
            kind="box", col="time")

# Violin plot with facets
sns.catplot(data=tips, x="day", y="total_bill", 
            kind="violin", hue="sex", col="time")
                

Relational Plots

Scatter Plots

                    # Basic scatter plot
sns.scatterplot(data=tips, x="total_bill", y="tip")

# With hue (color)
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="time")

# With size
sns.scatterplot(data=tips, x="total_bill", y="tip", 
                size="size", hue="day")

# With style (marker)
sns.scatterplot(data=tips, x="total_bill", y="tip", 
                style="smoker", hue="day")
                

Line Plots

                    # Load time series data
flights = sns.load_dataset("flights")

# Basic line plot
sns.lineplot(data=flights, x="year", y="passengers")

# Multiple lines with hue
sns.lineplot(data=flights, x="month", y="passengers", 
             hue="year")

# Aggregated line plot with CI
fmri = sns.load_dataset("fmri")
sns.lineplot(data=fmri, x="timepoint", y="signal", 
             hue="event")
                

Relational Plot (relplot)

                    # Figure-level relational plot
sns.relplot(data=tips, x="total_bill", y="tip", 
            hue="smoker", col="time", row="sex")

# Line plot with facets
sns.relplot(data=flights, x="year", y="passengers", 
            kind="line", col="month", col_wrap=4)
                

Matrix and Heatmaps

Heatmap

                    # Create correlation matrix
corr = tips.corr(numeric_only=True)

# Basic heatmap
sns.heatmap(corr)

# Heatmap with annotations
sns.heatmap(corr, annot=True, fmt=".2f", 
            cmap