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Introduction to Seaborn

Seaborn is a statistical data visualization library built on top of Matplotlib. It integrates closely with Pandas DataFrames and provides a high-level interface for drawing attractive, informative, and modern statistical graphics.

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Why use Seaborn?

  • Beautiful default themes and color palettes.
  • Simplifies complex plots (like heatmaps, distribution plots, and linear regressions) into a single line of code.
  • Automatically handles Pandas DataFrames.

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Creating a Seaborn Plot

Here’s how to create a simple scatter plot with a regression line: 
import seaborn as sns
import matplotlib.pyplot as plt

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

# Create a scatter plot with regression line
sns.lmplot(data=tips, x="total_bill", y="tip", hue="smoker")

# Add a title and show
plt.title("Tips vs Total Bill by Smoker Status")
plt.show()

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Key Plot Types

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1. Distribution Plots

Visualize how your data is distributed: 
sns.histplot(data=tips, x="total_bill", kde=True)

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2. Categorical Plots

Compare different categories (like box plots or bar plots): 
sns.boxplot(data=tips, x="day", y="total_bill")

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3. Correlation Heatmaps

Perfect for finding relationships between numerical columns: 
# Select only numerical columns
numerical_tips = tips.select_dtypes(include=["number"])
sns.heatmap(numerical_tips.corr(), annot=True, cmap="coolwarm")

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Summary

By combining NumPy, Pandas, Matplotlib, and Seaborn, you have a full stack of tools to load, process, analyze, and visualize data in Python.