Seaborn Statistical Visualization

When to Use

• You need publication-quality statistical graphics directly from tabular datasets.
• You are exploring multivariate relationships, distributions, or grouped comparisons with minimal plotting code.
• You want seaborn's dataset-oriented API and statistical defaults on top of matplotlib.

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())
)

Plotting Functions by Category

Relational Plots (Relationships Between Variables)

Use for: Exploring how two or more variables relate to each other

• scatterplot() - Display individual observations as points
• lineplot() - Show trends and changes (automatically aggregates and computes CI)
• relplot() - Figure-level interface with automatic faceting

Key parameters: - x, y - Primary variables - hue - Color encoding for additional categorical/continuous variable - size - Point/line size encoding - style - Marker/line style encoding - col, row - Facet into multiple subplots (figure-level only)

# Scatter with multiple semantic mappings
sns.scatterplot(data=df, x='total_bill', y='tip',
                hue='time', size='size', style='sex')

# Line plot with confidence intervals
sns.lineplot(data=timeseries, x='date', y='value', hue='category')

# Faceted relational plot
sns.relplot(data=df, x='total_bill', y='tip',
            col='time', row='sex', hue='smoker', kind='scatter')

Distribution Plots (Single and Bivariate Distributions)

Use for: Understanding data spread, shape, and probability density

• histplot() - Bar-based frequency distributions with flexible binning
• kdeplot() - Smooth density estimates using Gaussian kernels
• ecdfplot() - Empirical cumulative distribution (no parameters to tune)
• rugplot() - Individual observation tick marks
• displot() - Figure-level interface for univariate and bivariate distributions
• jointplot() - Bivariate plot with marginal distributions
• pairplot() - Matrix of pairwise relationships across dataset

Key parameters: - x, y - Variables (y optional for univariate) - hue - Separate distributions by category - stat - Normalization: "count", "frequency", "probability", "density" - bins / binwidth - Histogram binning control - bw_adjust - KDE bandwidth multiplier (higher = smoother) - fill - Fill area under curve - multiple - How to handle hue: "layer", "stack", "dodge", "fill"

# Histogram with density normalization
sns.histplot(data=df, x='total_bill', hue='time',
             stat='density', multiple='stack')

# Bivariate KDE with contours
sns.kdeplot(data=df, x='total_bill', y='tip',
            fill=True, levels=5, thresh=0.1)

# Joint plot with marginals
sns.jointplot(data=df, x='total_bill', y='tip',
              kind='scatter', hue='time')

# Pairwise relationships
sns.pairplot(data=df, hue='species', corner=True)

Categorical Plots (Comparisons Across Categories)

**Use for