RSK World - Seaborn Statistical Visualization Guide Documentation | Python | Seaborn | Jupyter Notebook | Statistical Data Visualization | Correlation | Distribution | Regression | RSK World

Quick Start Guide | Get Started in 3 Steps

🚀 Get Started with Seaborn in 3 Simple Steps

Step 1: Install

pip install -r requirements.txt

Step 2: Launch

jupyter notebook

Step 3: Learn

Open 01_distribution_plots.ipynb and start learning!

Table of Contents | Navigation Guide

Overview Features Installation Usage Examples Project Structure Troubleshooting

Overview | What is Seaborn Statistical Visualization Guide?

📚 About This Guide

The Seaborn Statistical Visualization Guide is a comprehensive educational resource for mastering statistical data visualization with Seaborn. Perfect for beginners and intermediate users who want to learn distribution plots, correlation analysis, categorical plots, regression analysis, advanced statistical visualizations, and statistical testing.

✨ What You'll Learn:

Comprehensive Jupyter Notebooks with 8+ sections covering all aspects of Seaborn
Distribution Plots - Histograms, KDE plots, rug plots, distribution comparisons
Correlation Heatmaps - Correlation matrices, heatmaps, cluster maps, masked heatmaps
Categorical Plots - Bar plots, count plots, box plots, violin plots, swarm plots
Regression Plots - Linear regression, polynomial regression, robust regression, residual plots
Advanced Visualizations - Pair plots, joint plots, faceted grids
Advanced Statistical Analysis - Q-Q plots, ECDF plots, statistical tests, annotations
Styling and Themes - Multiple styles, color palettes, contexts, custom styling
Matrix Plots - Clustermap, matrix plot variations, multi-variable visualizations

📦 Includes: Comprehensive Jupyter notebooks with 8+ sections, practical examples, Python scripts, and comprehensive documentation.

Screenshots | Project Preview

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Seaborn Statistical Visualization Guide - Python Seaborn - Statistical Data Visualization - Correlation - Distribution - Regression - Data Science - RSK World

Core Features | What's Included

Distribution Plots

Histograms
KDE Plots
Rug Plots
Distribution Comparisons
Multiple Histograms
KDE by Category

Correlation Heatmaps

Correlation Matrices
Annotated Heatmaps
Cluster Maps
Masked Heatmaps
Color Palettes
Strong Correlations

Categorical Plots

Bar Plots
Count Plots
Box Plots
Violin Plots
Swarm Plots
Strip Plots

Regression Plots

Linear Regression
Polynomial Regression
Robust Regression
Residual Plots
Log Scale Regression

Advanced Visualizations

Pair Plots
Joint Plots
Faceted Grids
Relational Plots
Categorical Facets

Statistical Analysis

Q-Q Plots
ECDF Plots
Statistical Tests
Test Annotations
Summary Tables

Advanced Features | Advanced Operations

Export Formats

PNG, PDF, SVG export
EPS, JPG support
High-resolution output
Publication quality

Custom Colormaps

Built-in colormaps
Custom colormaps
Color schemes
Colorbar customization

GridSpec Layouts

Complex layouts
Custom arrangements
Multi-panel figures
Flexible spacing

Style Sheets

Multiple built-in styles
Custom style creation
Theme application
Consistent styling

Complete Feature List | All Features Overview

Feature	Description	Use Case
Distribution Plots	Comprehensive implementation of statistical distribution plots including histograms, KDE plots, rug plots, and distribution comparisons	Visualize data distributions effectively, understand data spread, compare distributions across categories
Correlation Heatmaps	Correlation analysis with correlation matrices, heatmaps, cluster maps, and masked heatmaps for relationship visualization	Visualize relationships between variables, identify correlations, understand data patterns
Categorical Plots	Bar plots, count plots, box plots, violin plots, and swarm plots for categorical data visualization and comparison	Compare categories effectively, visualize categorical relationships, display frequency distributions
Regression Plots	Linear regression, polynomial regression, robust regression, and residual plots for relationship analysis	Analyze relationships between variables, perform regression analysis, visualize model fits
Advanced Visualizations	Pair plots, joint plots, and faceted grids for multi-variable visualization and complex relationship exploration	Explore complex relationships, visualize multiple variables, create comprehensive dashboards
Advanced Statistical Analysis	Q-Q plots, ECDF plots, statistical tests (t-tests, ANOVA), and statistical annotations for data validation	Validate data statistically, test hypotheses, perform statistical comparisons
Styling and Themes	Multiple built-in styles, color palettes, contexts, and custom styling options for professional visualizations	Customize visualization appearance, apply professional themes, create consistent styling
Matrix Plots	Clustermap and matrix plot variations for multi-variable analysis and hierarchical clustering visualization	Visualize relationships across multiple dimensions, perform cluster analysis, create matrix visualizations
Export Formats	Export figures in multiple formats including PNG, PDF, SVG, EPS, and JPG for various use cases	Export for presentations, publications, web use, and print media
Comprehensive Jupyter Notebooks	Interactive learning with comprehensive notebooks with 8+ sections covering all aspects of Seaborn statistical visualization	Learn Seaborn step-by-step, practice with examples, understand concepts through hands-on exercises
Python Source Code	Complete Python modules for distribution plots, correlation analysis, categorical plots, regression analysis, and statistical tests	Run examples directly, understand implementation details, customize for your needs
Practical Examples	Hands-on examples with real datasets, comprehensive code comments, and step-by-step explanations	Learn by doing, understand best practices, apply to your own projects

Technologies | Tech Stack

This Seaborn Statistical Visualization Guide project is built using modern Python and statistical data visualization technologies. The core implementation uses Python 3.x as the programming language, Seaborn >= 0.12.0 for statistical visualization, Matplotlib >= 3.6.0 for plotting foundation, NumPy >= 1.23.0 for numerical computing, Pandas >= 1.5.0 for data manipulation and analysis, Jupyter >= 1.0.0 for interactive learning and data exploration, SciPy >= 1.9.0 for statistical functions, and Statsmodels >= 0.14.0 for statistical modeling. The Seaborn guide features comprehensive Jupyter notebooks with 8+ sections covering distribution plots, correlation heatmaps, categorical plots, regression plots, advanced visualizations, statistical analysis, styling, and matrix plots. Advanced features include distribution plots (Histograms, KDE plots, Rug plots, Distribution comparisons), correlation heatmaps (Correlation matrices, Heatmaps, Cluster maps, Masked heatmaps), categorical plots (Bar plots, Count plots, Box plots, Violin plots, Swarm plots), regression plots (Linear regression, Polynomial regression, Robust regression, Residual plots), advanced visualizations (Pair plots, Joint plots, Faceted grids), advanced statistical analysis (Q-Q plots, ECDF plots, Statistical tests, Annotations), styling and themes (Multiple styles, Color palettes, Contexts, Custom styling), and matrix plots (Clustermap, Matrix plot variations, Multi-variable visualizations).

The project uses Python as the core programming language and Seaborn for statistical data visualization. It supports comprehensive statistical visualization through comprehensive Jupyter notebooks with step-by-step examples and practical exercises, distribution plots for visualizing data distributions, correlation heatmaps for relationship analysis, categorical plots for category comparison, regression plots for relationship modeling, advanced visualizations for multi-variable exploration, advanced statistical analysis for data validation, styling and themes with multiple styles and customization options, matrix plots for multi-variable analysis, and comprehensive documentation including README, release notes, and detailed notebook descriptions. The project includes comprehensive Jupyter notebooks with 8+ sections for interactive learning, practical examples in the notebooks, Python scripts with examples, and requirements file for easy dependency installation.

Python 3.x Seaborn 0.12+ Pandas 1.5+ Jupyter Notebook Statistical Visualization Correlation Analysis Distribution Plots Statistical Tests Regression Plots Data Science

Installation & Setup | Getting Started

Installation

Version: v1.0.0 (January 2025)

Install all required dependencies for the Seaborn Statistical Visualization Guide project:

# Install all requirements
pip install -r requirements.txt

# Required packages:
# - seaborn>=0.12.0
# - matplotlib>=3.6.0
# - numpy>=1.23.0
# - pandas>=1.5.0
# - jupyter>=1.0.0
# - scipy>=1.9.0
# - statsmodels>=0.14.0

# Verify installation
python -c "import seaborn; import pandas; import jupyter; print('Installation successful!')"

# Start Jupyter Notebook
jupyter notebook

Running Jupyter Notebooks

Start Jupyter Notebook to learn Seaborn statistical visualization:

# Start Jupyter Notebook
jupyter notebook

# Or use JupyterLab
jupyter lab

# Open the notebooks in order:
# 01_distribution_plots.ipynb - Distribution plots (histograms, KDE, rug plots)
# 02_correlation_heatmaps.ipynb - Correlation matrices and heatmaps
# 03_categorical_plots.ipynb - Categorical plots (bar, count, box, violin, swarm)
# 04_regression_plots.ipynb - Regression plots (linear, polynomial, robust)
# 05_advanced_visualizations.ipynb - Advanced visualizations (pair, joint, faceted)
# 06_advanced_statistical_analysis.ipynb - Statistical analysis (Q-Q, ECDF, tests)
# 07_styling_and_themes.ipynb - Styling and themes
# 08_matrix_plots.ipynb - Matrix plots and multi-variable visualizations

Running Example Scripts

Run Python example scripts to see Seaborn statistical visualization operations:

# Example usage in Python:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Create sample data
df = pd.DataFrame({
    'value': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'category': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']
})

# Create a distribution plot
sns.histplot(data=df, x='value', kde=True)
plt.title('Distribution Plot')
plt.show()

# Create a correlation heatmap
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

# Save the figure
plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Project Structure | File Organization

                seaborn-statistical/

                ├── README.md                          # Main documentation

                ├── PROJECT_IMAGE_INFO.md              # Image information

                ├── requirements.txt                   # Python dependencies

                ├── generate_images.py                 # Image generation script

                ├── index.html                         # Demo webpage

                ├── LICENSE                           # License file

                ├── .gitignore                         # Git ignore rules

                │

                ├── notebooks/                         # Jupyter notebooks

                │   ├── 01_distribution_plots.ipynb   # Distribution plots tutorial

                │   ├── 02_correlation_heatmaps.ipynb # Correlation heatmaps tutorial

                │   ├── 03_categorical_plots.ipynb   # Categorical plots tutorial

                │   ├── 04_regression_plots.ipynb     # Regression plots tutorial

                │   ├── 05_advanced_visualizations.ipynb # Advanced visualizations

                │   ├── 06_advanced_statistical_analysis.ipynb # Statistical analysis

                │   ├── 07_styling_and_themes.ipynb   # Styling and themes

                │   ├── 08_matrix_plots.ipynb         # Matrix plots

                │   └── complete_guide.ipynb          # Complete guide

                │

                ├── images/                            # Generated visualization images

                │   └── (generated visualization outputs)

                │

                └── data/

                    └── sample_data.csv

Configuration | Settings & Options

Seaborn Statistical Visualization Configuration

Version: v1.0.0 (January 2025)

Configure Seaborn settings and statistical visualization options:

# Seaborn Statistical Visualization Configuration

# 1. Import Required Libraries
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# 2. Configure Seaborn Style
sns.set_style('darkgrid')  # Use a style (darkgrid, whitegrid, dark, white, ticks)
sns.set_context('notebook')  # Set context (paper, notebook, talk, poster)
sns.set_palette('husl')  # Set color palette

# 3. Create Sample Data
df = pd.DataFrame({
    'value': np.random.randn(100),
    'category': np.random.choice(['A', 'B', 'C'], 100)
})

# 4. Create a Distribution Plot
sns.histplot(data=df, x='value', kde=True, hue='category')
plt.title('Distribution Plot')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

# 5. Save Figure
plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Configuration Tips:

STYLES: Use sns.set_style() to apply built-in styles (darkgrid, whitegrid, dark, white, ticks)
CONTEXTS: Use sns.set_context() for different contexts (paper, notebook, talk, poster)
COLOR PALETTES: Use sns.set_palette() to set color palettes (husl, Set2, viridis, coolwarm)
FIGURE SIZE: Set figure size using figsize parameter or plt.rcParams['figure.figsize'] for consistent sizing
RESOLUTION: Use dpi=300 for high-resolution output suitable for publications
EXPORT FORMATS: Save figures in PNG, PDF, SVG, EPS, or JPG formats depending on your needs

Data Format & Customization

Seaborn supports NumPy arrays, Pandas DataFrames, CSV, Excel, and JSON files. Customize plots with styles, color palettes, contexts, and layouts:

# Load data and customize plots
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('data.csv')

# Apply style and customize
sns.set_style('darkgrid')
sns.set_context('notebook')
sns.set_palette('husl')
plt.rcParams['figure.figsize'] = (10, 6)

# Create correlation heatmap
corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Heatmap', fontsize=16)
plt.tight_layout()
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.show()

Architecture | System Design

Seaborn Statistical Visualization Guide Architecture

1. Jupyter Notebook Platform:

Built on Jupyter Notebook for interactive learning and data exploration
Uses Seaborn library for statistical data visualization
Comprehensive notebooks with 8+ sections covering all Seaborn topics
Interactive code execution with immediate visual results
Markdown cells for explanations and documentation
Export capabilities (HTML, PDF) and sharing via Jupyter Notebook Viewer

2. Visualization Pipeline:

Practical examples and exercises in the notebooks for hands-on learning
Python code examples demonstrating distribution plots, correlation heatmaps, categorical plots, regression plots, and statistical analysis
Data loading from CSV, built-in datasets, and various formats
Data preparation including NumPy arrays and Pandas DataFrames
Statistical plot creation, customization, styling, and export operations
Figure export utilities for saving plots in multiple formats (PNG, PDF, SVG)

3. Learning Components:

Comprehensive Jupyter notebooks with 8+ sections and step-by-step examples
Distribution plots (Histograms, KDE plots, Rug plots, Distribution comparisons)
Correlation heatmaps (Correlation matrices, Heatmaps, Cluster maps, Masked heatmaps)
Categorical plots (Bar plots, Count plots, Box plots, Violin plots, Swarm plots)
Regression plots (Linear regression, Polynomial regression, Robust regression, Residual plots)
Advanced visualizations (Pair plots, Joint plots, Faceted grids)
Advanced statistical analysis (Q-Q plots, ECDF plots, Statistical tests, Annotations)
Styling and themes (Multiple styles, Color palettes, Contexts, Custom styling)
Matrix plots (Clustermap, Matrix plot variations, Multi-variable visualizations)

Usage Examples | How to Use

Creating Basic Seaborn Plots

How to perform different types of statistical visualization operations with Seaborn:

# Basic Seaborn Statistical Visualization Operations:

# 1. Import Libraries:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# 2. Create Sample Data:
df = pd.DataFrame({
    'value': np.random.randn(1000),
    'category': np.random.choice(['A', 'B', 'C'], 1000),
    'x': np.random.randn(1000),
    'y': np.random.randn(1000)
})

# 3. Create Distribution Plots:
# Histogram with KDE
sns.histplot(data=df, x='value', kde=True, hue='category')
plt.title('Distribution Plot')
plt.show()

# KDE Plot
sns.kdeplot(data=df, x='value', hue='category')
plt.title('KDE Plot')
plt.show()

# 4. Correlation Heatmap:
corr = df[['value', 'x', 'y']].corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Heatmap')
plt.show()

# 5. Categorical Plots:
# Bar Plot
sns.barplot(data=df, x='category', y='value')
plt.title('Bar Plot')
plt.show()

# Box Plot
sns.boxplot(data=df, x='category', y='value')
plt.title('Box Plot')
plt.show()

# Violin Plot
sns.violinplot(data=df, x='category', y='value')
plt.title('Violin Plot')
plt.show()

# 6. Save Figures:
plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Using Advanced Seaborn Features

Perform advanced Seaborn operations with regression plots, pair plots, statistical analysis, and more:

# Advanced Seaborn Features:

# 1. Regression Plots:
sns.regplot(data=df, x='x', y='y')
plt.title('Linear Regression Plot')
plt.show()

# Polynomial Regression
sns.regplot(data=df, x='x', y='y', order=2)
plt.title('Polynomial Regression Plot')
plt.show()

# 2. Pair Plot:
sns.pairplot(df, hue='category')
plt.title('Pair Plot')
plt.show()

# 3. Joint Plot:
sns.jointplot(data=df, x='x', y='y', kind='scatter')
plt.title('Joint Plot')
plt.show()

# 4. Faceted Grids:
g = sns.FacetGrid(df, col='category')
g.map(sns.histplot, 'value')
plt.show()

# 5. Cluster Map:
corr = df[['value', 'x', 'y']].corr()
sns.clustermap(corr, annot=True, cmap='coolwarm')
plt.title('Cluster Map')
plt.show()

# 6. Statistical Tests (with scipy):
from scipy import stats

# Q-Q Plot
stats.probplot(df['value'], dist="norm", plot=plt)
plt.title('Q-Q Plot')
plt.show()

# 7. Custom Styling:
sns.set_style('darkgrid')
sns.set_context('notebook')
sns.set_palette('husl')
plt.figure(figsize=(10, 6))
sns.histplot(data=df, x='value', kde=True)
plt.title('Styled Plot', fontsize=16, fontweight='bold')
plt.show()

# 8. Save High-Resolution:
plt.savefig('plot.png', dpi=300, bbox_inches='tight', 
            facecolor='white', edgecolor='none')

Exporting Figures

Save figures in multiple formats for different use cases:

# Export figures in different formats
plt.savefig('plot.png', dpi=300, bbox_inches='tight')  # PNG
plt.savefig('plot.pdf', dpi=300, bbox_inches='tight')  # PDF
plt.savefig('plot.svg', dpi=300, bbox_inches='tight')  # SVG
plt.savefig('plot.eps', dpi=300, bbox_inches='tight')  # EPS
plt.savefig('plot.jpg', dpi=300, bbox_inches='tight')  # JPG

Complete Workflow | Step-by-Step Tutorial

Step-by-Step Seaborn Statistical Visualization Guide Setup

Step 1: Install Dependencies

# Install all required packages
pip install -r requirements.txt

# Required packages:
# - seaborn>=0.12.0
# - matplotlib>=3.6.0
# - numpy>=1.23.0
# - pandas>=1.5.0
# - jupyter>=1.0.0
# - scipy>=1.9.0
# - statsmodels>=0.14.0

# Verify installation
python -c "import seaborn; import pandas; import jupyter; print('Installation successful!')"

# Start Jupyter Notebook
jupyter notebook

Step 2: Open Jupyter Notebooks

Start Jupyter: jupyter notebook
Open notebooks in order: 01_distribution_plots.ipynb → 08_matrix_plots.ipynb
Run cells step-by-step and follow along with examples
Experiment with code modifications

Data Formats | Supported File Types

Data Format Requirements

The Seaborn Statistical Visualization guide works with data for statistical visualization in various formats:

Supported formats: CSV, Excel (.xlsx, .xls), JSON, Pandas DataFrames, NumPy arrays, Python lists
Data types: Numerical data (int, float), time series data, categorical data for grouping
Data shapes: 1D arrays for single series, 2D arrays for multiple series, DataFrames for structured data
Automatic data type inference when loading from CSV or Excel files
Support for loading from files, creating synthetic data with NumPy, and using built-in datasets
Efficient handling of large datasets with Pandas and NumPy for visualization

Data Loading Examples

Examples of loading data for visualization:

# Data loading examples for visualization:

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

# 1. Load from CSV
df = pd.read_csv('data.csv')
x = df['x_column']
y = df['y_column']

# 2. Create NumPy arrays
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 6, 8, 10])

# 3. Generate synthetic data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# 4. Load from Excel
df = pd.read_excel('data.xlsx')
x = df.iloc[:, 0]
y = df.iloc[:, 1]

# 5. Load from JSON
import json
with open('data.json', 'r') as f:
    data = json.load(f)
df = pd.DataFrame(data)
x = df['x']
y = df['y']

# View data:
print(df.shape)
print(df.head())
print(df.describe())

# Create a plot
plt.figure(figsize=(10, 6))
plt.plot(x, y)
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Data Visualization')
plt.show()

Using Your Own Data

Load data from CSV, Excel, JSON, or create NumPy arrays:

# Load and visualize your data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# From CSV
df = pd.read_csv('data.csv')
x = df['x_column']
y = df['y_column']

# Or create arrays
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create plot
plt.figure(figsize=(10, 6))
plt.plot(x, y, 'b-', linewidth=2, label='Data')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Data Visualization')
plt.legend()
plt.grid(True)
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.show()

Troubleshooting & Best Practices | Common Issues | Performance Optimization | Best Practices

Common Issues

Import Errors: Install dependencies: pip install -r requirements.txt. Verify Python 3.8+
Plot Not Displaying: Use plt.show() or %matplotlib inline in Jupyter
Figure Size: Set using figsize parameter or plt.rcParams['figure.figsize']
Export Errors: Check file path and permissions. Supported formats: PNG, PDF, SVG, EPS, JPG
Memory Issues: Close figures with plt.close() after saving
Style Sheets: Use plt.style.available to see available styles
Subplot Layout: Use plt.tight_layout() to adjust spacing
High Resolution: Use dpi=300 and bbox_inches='tight' for publications

Performance Optimization Tips

Data Size: Downsample large datasets for faster plotting
Figure Management: Close figures with plt.close() to free memory
Export: Use DPI 72 for web, 300 for print. Choose vector formats (PDF, SVG) for scalability
Notebook Performance: Use %matplotlib inline in Jupyter. Clear output regularly

Best Practices

Data Quality: Clean and format data before plotting. Handle missing values
Labels: Always add clear axis labels, titles, and legends
Color Schemes: Use colorblind-friendly schemes. Apply consistent colors across plots
Export: Use PNG for web, PDF for publications, SVG for scalability. High DPI (300) for print
Styling: Apply consistent styling. Use style sheets for uniformity
Figure Management: Close figures after saving. Clear plots in notebooks regularly

Use Cases and Applications

Data Exploration: Understand patterns, distributions, and relationships
Publication Figures: High-quality figures for academic papers and reports
Time Series: Visualize trends and temporal patterns
Statistical Analysis: Histograms, box plots, violin plots
Scientific Visualization: 3D plots, contour plots, surface plots
Dashboards: Multi-panel figures for data presentation

System Requirements

Minimum: Python 3.8+, Jupyter 1.0.0+, 4 GB RAM, 2 CPU cores

Recommended: Python 3.9+, Latest Jupyter, 8 GB RAM, 4 CPU cores

Performance: Small datasets (1K-10K points) render in <1s. Large datasets (100K+) may take 3-10s. Consider downsampling for very large datasets.

Contact Information | Support | Get Help | Contact RSK World

Get in Touch

Developer: Molla Samser
Designer & Tester: Rima Khatun

rskworld.in

help@rskworld.in support@rskworld.in

+91 93305 39277

Frequently Asked Questions (FAQ) | Seaborn Statistical Visualization Guide FAQ | Common Questions

Seaborn Statistical Visualization Guide is a comprehensive educational resource for mastering statistical data visualization with Seaborn. It includes comprehensive Jupyter notebooks with 8+ sections covering distribution plots (histograms, KDE, rug plots), correlation heatmaps (correlation matrices, heatmaps, cluster maps), categorical plots (bar, count, box, violin, swarm), regression plots (linear, polynomial, robust), advanced visualizations (pair plots, joint plots, faceted grids), advanced statistical analysis (Q-Q plots, ECDF, statistical tests), styling and themes, and matrix plots. Features include comprehensive statistical plotting techniques, correlation analysis, distribution visualization, regression analysis, statistical testing, and high-resolution export formats. Perfect for mastering statistical data visualization in Python for exploratory data analysis, statistical insights, and data science projects.

Install all required dependencies using: pip install -r requirements.txt. The project requires Python 3.x, Seaborn >= 0.12.0, Matplotlib >= 3.6.0, NumPy >= 1.23.0, Pandas >= 1.5.0, Jupyter >= 1.0.0, SciPy >= 1.9.0, and Statsmodels >= 0.14.0. Then open Jupyter Notebook using: jupyter notebook. Start with the first notebook: 01_distribution_plots.ipynb to begin learning Seaborn statistical visualization.

The project includes comprehensive Jupyter notebooks with 8+ sections covering Distribution Plots (histograms, KDE, rug plots, distribution comparisons), Correlation Heatmaps (correlation matrices, heatmaps, cluster maps, masked heatmaps), Categorical Plots (bar, count, box, violin, swarm), Regression Plots (linear, polynomial, robust, residual), Advanced Visualizations (pair plots, joint plots, faceted grids), Advanced Statistical Analysis (Q-Q plots, ECDF, statistical tests, annotations), Styling and Themes, and Matrix Plots. Advanced features include multiple built-in styles, color palettes, contexts, custom styling, statistical test annotations, high-resolution output (300 DPI), multiple export formats (PNG, PDF, SVG), and multi-panel statistical dashboards.

Yes, the project supports exporting figures in multiple formats including PNG, PDF, SVG, EPS, and JPG. All export operations are demonstrated in the notebooks with practical examples. You can export high-resolution figures suitable for publications and presentations.

The project is built with Python 3.x (programming language), Matplotlib >= 3.7.0 (plotting library), NumPy >= 1.24.0 (numerical computing), Pandas >= 2.0.0 (data analysis), Jupyter >= 1.0.0 (interactive learning environment), and Seaborn >= 0.12.0 (statistical visualization).

Yes, Seaborn Statistical Visualization Guide includes comprehensive practical examples in the Jupyter notebooks with 8+ sections. The notebooks contain hands-on exercises covering distribution plots, correlation heatmaps, categorical plots, regression plots, advanced visualizations, statistical analysis, styling, and matrix plots. You can practice with the provided examples or use your own data.

Yes, Seaborn Statistical Visualization Guide is completely free and open source. You can download the source code from GitHub and use it for personal, academic, or commercial projects. The project includes comprehensive documentation, Jupyter notebooks with 8+ sections, and Python scripts with examples.

License | Open Source License | Project License

This project is for educational purposes only. See LICENSE file for more details.

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