seaborn

Use sns.boxplot or sns.violinplot with a DataFrame, passing a categorical variable to x and a continuous variable to y. Add hue for subgroup comparison and split=True on violin plots to compare two groups side by side.

Seaborn is built on matplotlib and provides dataset-oriented functions with automatic statistical aggregation, confidence intervals, and attractive defaults. Use seaborn for statistical exploration and matplotlib for low-level custom control; they integrate freely.

Axes-level functions like scatterplot and boxplot draw onto a single matplotlib Axes and accept an ax parameter. Figure-level functions like relplot, displot, and catplot manage entire figures with built-in faceting via col and row parameters.

Compute the correlation matrix with df.corr(), then call sns.heatmap with annot=True, fmt='.2f', a diverging colormap like coolwarm, and center=0. Use a mask on the upper triangle to avoid duplicated values.

The KDE bandwidth controls smoothness. Adjust it with the bw_adjust parameter: values below 1 produce a more jagged curve, values above 1 produce a smoother curve. The default bandwidth follows the scott method.

Swarmplot positions points without overlap using a beeswarm algorithm, ideal for small datasets. For more than about 1000 points it becomes slow and crowded, so use stripplot with jitter instead.