Python Libraries Seaborn example

Seaborn: Statistical Data Visualization

Seaborn is a Python data visualization library built on top of Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. It's designed to make exploring and understanding relationships in your data easier.

Key Features & Why it's Useful:

• Attractive Default Styles: Seaborn plots generally look more visually appealing than basic Matplotlib plots, with better color palettes and default settings.
• Statistical Plot Types: It specializes in creating statistical plots like distributions (histograms, KDE plots), categorical plots (box plots, violin plots, bar plots), and relational plots (scatter plots, pair plots).
• Integration with Pandas: Seaborn works seamlessly with Pandas DataFrames, making it easy to visualize data directly from your data analysis workflows.
• Color Palettes: It offers a variety of built-in color palettes that are designed to be visually pleasing and informative.
• Complex Visualizations: Seaborn simplifies the creation of complex visualizations that would be more difficult to achieve with Matplotlib alone (e.g., pair plots showing relationships between multiple variables).
• Built on Matplotlib: Because it's built on Matplotlib, you can still customize Seaborn plots using Matplotlib functions if you need more control.

Simple Example: 

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

# Load a sample dataset
data = sns.load_dataset('iris')

# Create a scatter plot with hue for different species
sns.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=data)
plt.title("Iris Dataset - Sepal Length vs. Sepal Width")
plt.show()