📊 Data Visualization with Seaborn

Seaborn is a powerful Python library for statistical data visualization, built on top of Matplotlib. It simplifies creating complex plots like heatmaps, line plots, scatter plots, and distribution plots.

📋 Key Features of Seaborn

  • Integrated with Pandas: Seamlessly works with dataframes for easy data manipulation.
  • Aesthetic Defaults: Automatically adjusts colors, scales, and styles for better readability.
  • Statistical Annotations: Adds confidence intervals, regression lines, and other statistical elements.

📦 Installation

To use Seaborn, first install it via pip: 

pip install seaborn

Then, import it in your script: 

import seaborn as sns

📈 Common Plot Types

  1. Line Plot 
    sns.lineplot(x="x", y="y", data=df)
  2. Scatter Plot 
    sns.scatterplot(x="x", y="y", hue="category", data=df)
  3. Histogram 
    sns.histplot(data=df, x="value")
  4. Box Plot 
    sns.boxplot(data=df, x="category", y="value")

🧪 Example: Visualizing a Dataset

import seaborn as sns  
sns.set_theme()  
tips = sns.load_dataset("tips")  
sns.relplot(data=tips, x="total_bill", y="tip", col="time", hue="smoker", size="size")
seaborn_code_example

📘 Expand Your Knowledge

For a deeper dive into Seaborn basics, check out our Seaborn Introduction Tutorial. If you're interested in comparing Seaborn with other libraries like Matplotlib, explore this guide.

seaborn_charts

Remember to always clean your data before visualization and use descriptive labels for clarity! 📊