Seaborn – Statistical Visualization

What is Seaborn and why is it needed

Seaborn — is a powerful data visualization library for Python, built on top of Matplotlib. It provides a high-level interface for creating beautiful and informative statistical graphics. The library was developed to simplify the process of creating visualizations that are often used in data analysis and machine learning.

Key advantages of Seaborn:

Automatic creation of stylish graphs with minimal code Excellent integration with pandas DataFrame Built-in functions for statistical visualization Ease of working with categorical and numerical data Automatic calculation and display of statistical indicators Support for modern color palettes and design styles

History and development of the library

Seaborn was created by Michael Waskom in 2012 as an extension to Matplotlib. The library's name comes from the TV series "The West Wing", where one of the characters had the surname Seaborn. The library is actively developed and regularly updated, adding new features and improving existing capabilities.

Installing and configuring Seaborn

Installation via pip

pip install seaborn

Installation via conda

conda install seaborn

Importing into code

import seaborn as snsimport matplotlib.pyplot as pltimport pandas as pdimport numpy as np

Checking the version

print(sns.__version__)

Key differences between Seaborn and Matplotlib

Architecture and operating principles

Seaborn uses the "grammar of graphics" — a concept where visualization is built from individual components: data, aesthetic elements, geometric objects, and statistical transformations. This allows creating complex visualizations by combining simple elements.

Working with data in Seaborn

Built-in datasets

Seaborn includes many ready-made datasets for learning and experimentation:

# Loading a popular datasetdf = sns.load_dataset("tips")print(df.head())

# Viewing all available datasetsprint(sns.get_dataset_names())

Data preparation

# Example of creating your own datasetimport numpy as npimport pandas as pd

data = {    'x': np.random.randn(100),    'y': np.random.randn(100),    'category': np.random.choice(['A', 'B', 'C'], 100)}df = pd.DataFrame(data)

Main types of graphs in Seaborn

Visualization of distributions

Histograms and distributions

# Modern way to create a histogramsns.histplot(df["total_bill"], bins=20, kde=True)plt.title("Distribution of bill amount")plt.show()

# Density plotsns.kdeplot(df["total_bill"], shade=True)plt.show()

Empirical distribution function