Seaborn — Statistical Plots

Overview

Seaborn is a Python library for statistical data visualization built on top of matplotlib. It works directly with pandas DataFrames, automatically handles grouping by categorical variables, computes confidence intervals and kernel density estimates, and produces attractive publication-ready figures with minimal configuration. Seaborn separates axes-level functions (embeddable in custom layouts) from figure-level functions (with built-in faceting), enabling both quick exploratory analysis and structured multi-panel figures.

When to Use

• Comparing gene expression, protein abundance, or measurement distributions across experimental conditions (treatment vs. control, cell lines, time points)
• Generating grouped box plots, violin plots, or strip plots to show both summary statistics and individual data points simultaneously
• Visualizing pairwise correlations in multi-gene or multi-feature datasets as annotated heatmaps
• Plotting regression fits with confidence bands between continuous variables (e.g., cell viability vs. drug concentration)
• Faceting a single plot type across multiple sample subsets, tissue types, or experimental batches in one call
• Rapid exploratory analysis of a new dataset using pairplot to survey all pairwise relationships at once
• Use matplotlib directly when you need pixel-level control over figure elements, complex mixed-type layouts, or non-statistical custom plots
• Use plotly when the output must be interactive (hover tooltips, zoom, pan) or embedded in a web application

Prerequisites