seaborn

Seaborn Statistical Visualization

Overview

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Design Philosophy

Seaborn follows these core principles:

1. Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates
2. Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles)
3. Statistical awareness: Built-in aggregation, error estimation, and confidence intervals
4. Aesthetic defaults: Publication-ready themes and color palettes out of the box
5. Matplotlib integration: Full compatibility with matplotlib customization when needed

Quick Start

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Load example dataset
df = sns.load_dataset('tips')

# Create a simple visualization
sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
plt.show()

Core Plotting Interfaces

Function Interface (Traditional)

The function interface provides specialized plotting functions organized by visualization type. Each category has axes-level functions (plot to single axes) and figure-level functions (manage entire figure with faceting).

When to use:

• Quick exploratory analysis
• Single-purpose visualizations
• When you need a specific plot type

Objects Interface (Modern)

The seaborn.objects interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales.

When to use:

• Complex layered visualizations
• When you need fine-grained control over transformations
• Building custom plot types
• Programmatic plot generation

from seaborn import objects as so

# Declarative syntax
(
    so.Plot(data=df, x='total_bill', y='tip')
    .add(so.Dot(), color='day')
    .add(so.Line(), so.PolyFit())
)