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Seaborn:

1. Introduction to Seaborn

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

Key Features of Seaborn

  • Built-in themes for better-looking plots
  • Works well with Pandas DataFrames
  • Simplifies the creation of complex visualizations
  • Provides powerful tools for statistical data visualization
  • Allows customization and integration with Matplotlib

2. Installation and Setup

Installing Seaborn

To install Seaborn, use:

pip install seaborn

Or, if using Jupyter Notebook:

!pip install seaborn

Importing Required Libraries

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

3. Seaborn Built-in Datasets

Seaborn provides several built-in datasets. You can load and view these datasets using:

tips = sns.load_dataset("tips")
print(tips.head())

Common Built-in Datasets

  • tips – Restaurant tipping data
  • iris – Measurements of iris flowers
  • penguins – Penguin species data
  • titanic – Titanic passenger survival data

4. Basic Seaborn Visualizations

Scatter Plot

sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.show()

Line Plot

sns.lineplot(x="size", y="total_bill", data=tips)
plt.show()

Histogram

sns.histplot(tips["total_bill"], bins=20, kde=True)
plt.show()

Box Plot

sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Violin Plot

sns.violinplot(x="day", y="total_bill", data=tips, hue="sex", split=True)
plt.show()

5. Advanced Seaborn Visualizations

Pair Plot

sns.pairplot(tips, hue="sex")
plt.show()

Heatmap (Correlation Matrix)

corr_matrix = tips.corr()
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", linewidths=0.5)
plt.show()

Catplot

sns.catplot(x="day", y="total_bill", hue="sex", kind="bar", data=tips)
plt.show()

Regression Plot

sns.lmplot(x="total_bill", y="tip", data=tips)
plt.show()

6. Customizing Seaborn Plots

Changing Themes

sns.set_theme(style="darkgrid")
sns.scatterplot(x="total_bill", y="tip", data=tips)
plt.show()

Available Themes

  • "darkgrid"
  • "whitegrid"
  • "dark"
  • "white"
  • "ticks"

Changing Color Palettes

sns.set_palette("pastel")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Other Color Palettes

  • "deep"
  • "muted"
  • "bright"
  • "colorblind"

7. Combining Seaborn with Matplotlib

plt.figure(figsize=(8, 6))
sns.barplot(x="day", y="total_bill", data=tips)
plt.title("Total Bill Amount by Day")
plt.xlabel("Day of the Week")
plt.ylabel("Total Bill ($)")
plt.show()

8. Saving Seaborn Plots

sns.boxplot(x="day", y="total_bill", data=tips)
plt.savefig("boxplot.png", dpi=300)

9. Conclusion

Seaborn is a powerful data visualization tool that simplifies statistical analysis. It works well with Pandas and Matplotlib, making it an excellent choice for data analysis and storytelling. With its ease of use and customization options, Seaborn is a must-learn library for any data scientist or analyst.