Seaborn Statistical Plotting

Seaborn Introduction

Seaborn provides statistical visualization built on Matplotlib. It simplifies complex visualizations and follows pandas conventions.

Relational Plots

Scatter plots: sns.scatterplot(data, x, y). Line plots: sns.lineplot(data, x, y). Hue parameter adds grouping.

Relational plots show relationships between variables. They handle pandas DataFrames natively.

Categorical Plots

Bar plots: sns.barplot(data, x, y). Box plots: sns.boxplot(data, x, y). Violin plots: sns.violinplot(data, x, y).

Count plots: sns.countplot(data, x). These visualize distributions across categories.

Distribution Plots

Histograms: sns.histplot(data, x). KDE plots: sns.kdeplot(data, x). Combined: sns.displot(data, x, kde=True).

Pair plots: sns.pairplot(data). This shows relationships across all variable pairs.

Key Takeaways

1. Seaborn simplifies statistical visualizations
2. It integrates with pandas DataFrames
3. Built-in statistical visualizations save development time