/seaborn

name: seaborn
description: "Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures."

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())
)

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:** Comparing distributions or statistics across discrete categories

**Categorical scatterplots:**

• `stripplot()` - Points with jitter to show all observations
• `swarmplot()` - Non-overlapping points (beeswarm algorithm)

**Distribution comparisons:**

• `boxplot()` - Quartiles and outliers
• `violinplot()` - KDE + quartile information
• `boxenplot()` - Enhanced boxplot for larger datasets

**Statistical estimates:**

• `barplot()` - Mean/aggregate with confidence intervals
• `pointplot()` - Point estimates with connecting lines
• `countplot()` - Count of observations per category

**Figure-level:**

• `catplot()` - Faceted categorical plots (set `kind` parameter)

**Key parameters:**

• `x`, `y` - Variables (one typically categorical)
• `hue` - Additional categorical grouping
• `order`, `hue_order` - Control category ordering
• `dodge` - Separate hue levels side-by-side
• `orient` - "v" (vertical) or "h" (horizontal)
• `kind` - Plot type for catplot: "strip", "swarm", "box", "violin", "bar", "point"

# Swarm plot showing all points
sns.swarmplot(data=df, x='day', y=