Learn_Seaborn

Welcome to the Learn_Seaborn repository! This repo is dedicated to providing helpful resources, tutorials, and examples for using the Seaborn library in Python.

Table of Contents

• Learn_Seaborn
  • Table of Contents
  • Roadmap
  • Introduction
  • Installation
  • Usage
    • Basic Plot
    • Categorical Plot
    • Matrix Plot
    • Multi-plot Grids
  • Features

Roadmap

Introduction

Seaborn is a Python visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics. It is built on top of Matplotlib and is closely integrated with pandas data structures.

This repository aims to help users of all skill levels to better understand and utilize the Seaborn library through comprehensive guides, code snippets, and example projects.

Installation

To install Seaborn, you can use pip, the Python package installer. Ensure you have Python installed, then run:

pip install seaborn

For more detailed installation instructions, please refer to the official Seaborn installation guide.

Usage

Here are some basic examples to get you started with Seaborn:

Basic Plot

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset('iris')
sns.scatterplot(x='sepal_length', y='sepal_width', data=data)
plt.title('Basic Scatter Plot')
plt.show()

Categorical Plot

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset('tips')
sns.catplot(x='day', y='total_bill', hue='sex', kind='bar', data=data)
plt.title('Categorical Plot')
plt.show()

Matrix Plot

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset('flights')
data = data.pivot('month', 'year', 'passengers')
sns.heatmap(data, annot=True, fmt='d', cmap='YlGnBu')
plt.title('Heatmap')
plt.show()

Multi-plot Grids

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset('iris')
g = sns.PairGrid(data, hue='species')
g = g.map_diag(plt.hist)
g = g.map_offdiag(plt.scatter)
g = g.add_legend()
plt.title('Pair Grid')
plt.show()

For more examples and detailed tutorials, please refer to the official Seaborn documentation.

Features

• High-level interface for drawing attractive statistical graphics
• Built on top of Matplotlib and closely integrated with pandas
• Themes for styling matplotlib graphics
• Functions for visualizing univariate and bivariate data
• Tools for fitting and visualizing linear regression models
• Functions for visualizing matrices and data frames
• Utilities for creating complex visualizations

⚖ ➤ License

This project is licensed under the MIT License. See LICENSE for details.

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