seaborn-statistical-visualizationlisted

# Seaborn — Statistical Visualization ## Overview Seaborn is a Python visualization library for creating publication-quality statistical graphics with minimal code. It works directly with pandas DataFrames, provides automatic statistical estimation (means, CIs, KDE), and offers attractive default themes. Built on matplotlib for full customization access. ## When to Use - Creating distribution plots (histograms, KDE, violin plots, box plots) for data exploration - Visualizing relationships between variables with automatic trend fitting and confidence intervals - Comparing distributions across categorical groups (treatment vs control, tissue types) - Generating correlation heatmaps and clustered heatmaps - Quick exploratory data analysis with `pairplot` for all pairwise relationships - Multi-panel figures with automatic faceting by categorical variables - For **interactive plots** with hover/zoom, use plotly instead - For **low-level figure control** or custom layouts, use matplotlib directly ## Prerequisites ```bash pip install seaborn matplotlib pandas ``` ## Quick Start ```python import seaborn as sns import matplotlib.pyplot as plt import pandas as pd df = sns.load_dataset("tips") sns.scatterplot(data=df, x="total_bill", y="tip", hue="day", style="time") plt.title("Tips by Day and Time") plt.tight_layout() plt.savefig("scatter.png", dpi=150) print("Saved scatter.png") ``` ## Core API ### 1. Distribution Plots Visualize univariate and bivariate distributions. ``