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 snsimport matplotlib.pyplot as pltimport pandas as pd
# Load example datasetdf = sns.load_dataset('tips')
# Create a simple visualizationsns.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 mappingssns.scatterplot(data=df, x='total_bill', y='tip',                hue='time', size='size', style='sex')
# Line plot with confidence intervalssns.lineplot(data=timeseries, x='date', y='value', hue='category')
# Faceted relational plotsns.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 normalizationsns.histplot(data=df, x='total_bill', hue='time',             stat='density', multiple='stack')
# Bivariate KDE with contourssns.kdeplot(data=df, x='total_bill', y='tip',            fill=True, levels=5, thresh=0.1)
# Joint plot with marginalssns.jointplot(data=df, x='total_bill', y='tip',              kind='scatter', hue='time')
# Pairwise relationshipssns.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 pointssns.swarmplot(data=df, x='day', y='total_bill', hue='sex')
# Violin plot with split for comparisonsns.violinplot(data=df, x='day', y='total_bill',               hue='sex', split=True)
# Bar plot with error barssns.barplot(data=df, x='day', y='total_bill',            hue='sex', estimator='mean', errorbar='ci')
# Faceted categorical plotsns.catplot(data=df, x='day', y='total_bill',            col='time', kind='box')

Regression Plots (Linear Relationships)

Use for: Visualizing linear regressions and residuals

• regplot() - Axes-level regression plot with scatter + fit line
• lmplot() - Figure-level with faceting support
• residplot() - Residual plot for assessing model fit

Key parameters:

• x, y - Variables to regress
• order - Polynomial regression order
• logistic - Fit logistic regression
• robust - Use robust regression (less sensitive to outliers)
• ci - Confidence interval width (default 95)
• scatter_kws, line_kws - Customize scatter and line properties

# Simple linear regressionsns.regplot(data=df, x='total_bill', y='tip')
# Polynomial regression with facetingsns.lmplot(data=df, x='total_bill', y='tip',           col='time', order=2, ci=95)
# Check residualssns.residplot(data=df, x='total_bill', y='tip')

Matrix Plots (Rectangular Data)

Use for: Visualizing matrices, correlations, and grid-structured data

• heatmap() - Color-encoded matrix with annotations
• clustermap() - Hierarchically-clustered heatmap

Key parameters:

• data - 2D rectangular dataset (DataFrame or array)
• annot - Display values in cells
• fmt - Format string for annotations (e.g., ".2f")
• cmap - Colormap name
• center - Value at colormap center (for diverging colormaps)
• vmin, vmax - Color scale limits
• square - Force square cells
• linewidths - Gap between cells

# Correlation heatmapcorr = df.corr()sns.heatmap(corr, annot=True, fmt='.2f',            cmap='coolwarm', center=0, square=True)
# Clustered heatmapsns.clustermap(data, cmap='viridis',               standard_scale=1, figsize=(10, 10))

Multi-Plot Grids

Seaborn provides grid objects for creating complex multi-panel figures:

FacetGrid

Create subplots based on categorical variables. Most useful when called through figure-level functions (relplot, displot, catplot), but can be used directly for custom plots.

g = sns.FacetGrid(df, col='time', row='sex', hue='smoker')g.map(sns.scatterplot, 'total_bill', 'tip')g.add_legend()

PairGrid

Show pairwise relationships between all variables in a dataset.

g = sns.PairGrid(df, hue='species')g.map_upper(sns.scatterplot)g.map_lower(sns.kdeplot)g.map_diag(sns.histplot)g.add_legend()

JointGrid

Combine bivariate plot with marginal distributions.

g = sns.JointGrid(data=df, x='total_bill', y='tip')g.plot_joint(sns.scatterplot)g.plot_marginals(sns.histplot)

Figure-Level vs Axes-Level Functions

Understanding this distinction is crucial for effective seaborn usage:

Axes-Level Functions

• Plot to a single matplotlib Axes object
• Integrate easily into complex matplotlib figures
• Accept ax= parameter for precise placement
• Return Axes object
• Examples: scatterplot, histplot, boxplot, regplot, heatmap

When to use:

• Building custom multi-plot layouts
• Combining different plot types
• Need matplotlib-level control
• Integrating with existing matplotlib code

fig, axes = plt.subplots(2, 2, figsize=(10, 10))sns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0])sns.histplot(data=df, x='x', ax=axes[0, 1])sns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0])sns.kdeplot(data=df, x='x', y='y', ax=axes[1, 1])

Figure-Level Functions

• Manage entire figure including all subplots
• Built-in faceting via col and row parameters
• Return FacetGrid, JointGrid, or PairGrid objects
• Use height and aspect for sizing (per subplot)
• Cannot be placed in existing figure
• Examples: relplot, displot, catplot, lmplot, jointplot, pairplot

When to use:

• Faceted visualizations (small multiples)
• Quick exploratory analysis
• Consistent multi-panel layouts
• Don't need to combine with other plot types

# Automatic facetingsns.relplot(data=df, x='x', y='y', col='category', row='group',            hue='type', height=3, aspect=1.2)

Data Structure Requirements

Long-Form Data (Preferred)

Each variable is a column, each observation is a row. This "tidy" format provides maximum flexibility:

# Long-form structure   subject  condition  measurement0        1    control         10.51        1  treatment         12.32        2    control          9.83        2  treatment         13.1

Advantages:

• Works with all seaborn functions
• Easy to remap variables to visual properties
• Supports arbitrary complexity
• Natural for DataFrame operations

Wide-Form Data

Variables are spread across columns. Useful for simple rectangular data:

# Wide-form structure   control  treatment0     10.5       12.31      9.8       13.1

Use cases:

• Simple time series
• Correlation matrices
• Heatmaps
• Quick plots of array data

Converting wide to long:

df_long = df.melt(var_name='condition', value_name='measurement')

Color Palettes

Seaborn provides carefully designed color palettes for different data types:

Qualitative Palettes (Categorical Data)

Distinguish categories through hue variation:

• "deep" - Default, vivid colors
• "muted" - Softer, less saturated
• "pastel" - Light, desaturated
• "bright" - Highly saturated
• "dark" - Dark values
• "colorblind" - Safe for color vision deficiency

sns.set_palette("colorblind")sns.color_palette("Set2")

Sequential Palettes (Ordered Data)

Show progression from low to high values:

• "rocket", "mako" - Wide luminance range (good for heatmaps)
• "flare", "crest" - Restricted luminance (good for points/lines)
• "viridis", "magma", "plasma" - Matplotlib perceptually uniform

sns.heatmap(data, cmap='rocket')sns.kdeplot(data=df, x='x', y='y', cmap='mako', fill=True)

Diverging Palettes (Centered Data)

Emphasize deviations from a midpoint:

• "vlag" - Blue to red
• "icefire" - Blue to orange
• "coolwarm" - Cool to warm
• "Spectral" - Rainbow diverging

sns.heatmap(correlation_matrix, cmap='vlag', center=0)

Custom Palettes

# Create custom palettecustom = sns.color_palette("husl", 8)
# Light to dark gradientpalette = sns.light_palette("seagreen", as_cmap=True)
# Diverging palette from huespalette = sns.diverging_palette(250, 10, as_cmap=True)

Theming and Aesthetics

Set Theme

set_theme() controls overall appearance:

# Set complete themesns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')
# Reset to defaultssns.set_theme()

Styles

Control background and grid appearance:

• "darkgrid" - Gray background with white grid (default)
• "whitegrid" - White background with gray grid
• "dark" - Gray background, no grid
• "white" - White background, no grid
• "ticks" - White background with axis ticks

sns.set_style("whitegrid")
# Remove spinessns.despine(left=False, bottom=False, offset=10, trim=True)
# Temporary stylewith sns.axes_style("white"):    sns.scatterplot(data=df, x='x', y='y')

Contexts

Scale elements for different use cases:

• "paper" - Smallest (default)
• "notebook" - Slightly larger
• "talk" - Presentation slides
• "poster" - Large format

sns.set_context("talk", font_scale=1.2)
# Temporary contextwith sns.plotting_context("poster"):    sns.barplot(data=df, x='category', y='value')

Best Practices

1. Data Preparation

Always use well-structured DataFrames with meaningful column names:

# Good: Named columns in DataFramedf = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days})sns.scatterplot(data=df, x='bill', y='tip', hue='day')
# Avoid: Unnamed arrayssns.scatterplot(x=x_array, y=y_array)  # Loses axis labels

2. Choose the Right Plot Type

Continuous x, continuous y: scatterplot, lineplot, kdeplot, regplot Continuous x, categorical y: violinplot, boxplot, stripplot, swarmplot One continuous variable: histplot, kdeplot, ecdfplot Correlations/matrices: heatmap, clustermap Pairwise relationships: pairplot, jointplot

3. Use Figure-Level Functions for Faceting

# Instead of manual subplot creationsns.relplot(data=df, x='x', y='y', col='category', col_wrap=3)
# Not: Creating subplots manually for simple faceting

4. Leverage Semantic Mappings

Use hue, size, and style to encode additional dimensions:

sns.scatterplot(data=df, x='x', y='y',                hue='category',      # Color by category                size='importance',    # Size by continuous variable                style='type')         # Marker style by type

5. Control Statistical Estimation

Many functions compute statistics automatically. Understand and customize:

# Lineplot computes mean and 95% CI by defaultsns.lineplot(data=df, x='time', y='value',             errorbar='sd')  # Use standard deviation instead
# Barplot computes mean by defaultsns.barplot(data=df, x='category', y='value',            estimator='median',  # Use median instead            errorbar=('ci', 95))  # Bootstrapped CI

6. Combine with Matplotlib

Seaborn integrates seamlessly with matplotlib for fine-tuning:

ax = sns.scatterplot(data=df, x='x', y='y')ax.set(xlabel='Custom X Label', ylabel='Custom Y Label',       title='Custom Title')ax.axhline(y=0, color='r', linestyle='--')plt.tight_layout()

7. Save High-Quality Figures

fig = sns.relplot(data=df, x='x', y='y', col='group')fig.savefig('figure.png', dpi=300, bbox_inches='tight')fig.savefig('figure.pdf')  # Vector format for publications

Common Patterns

Exploratory Data Analysis

# Quick overview of all relationshipssns.pairplot(data=df, hue='target', corner=True)
# Distribution explorationsns.displot(data=df, x='variable', hue='group',            kind='kde', fill=True, col='category')
# Correlation analysiscorr = df.corr()sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)

Publication-Quality Figures

sns.set_theme(style='ticks', context='paper', font_scale=1.1)
g = sns.catplot(data=df, x='treatment', y='response',                col='cell_line', kind='box', height=3, aspect=1.2)g.set_axis_labels('Treatment Condition', 'Response (μM)')g.set_titles('{col_name}')sns.despine(trim=True)
g.savefig('figure.pdf', dpi=300, bbox_inches='tight')

Complex Multi-Panel Figures

# Using matplotlib subplots with seabornfig, axes = plt.subplots(2, 2, figsize=(12, 10))
sns.scatterplot(data=df, x='x1', y='y', hue='group', ax=axes[0, 0])sns.histplot(data=df, x='x1', hue='group', ax=axes[0, 1])sns.violinplot(data=df, x='group', y='y', ax=axes[1, 0])sns.heatmap(df.pivot_table(values='y', index='x1', columns='x2'),            ax=axes[1, 1], cmap='viridis')
plt.tight_layout()

Time Series with Confidence Bands

# Lineplot automatically aggregates and shows CIsns.lineplot(data=timeseries, x='date', y='measurement',             hue='sensor', style='location', errorbar='sd')
# For more controlg = sns.relplot(data=timeseries, x='date', y='measurement',                col='location', hue='sensor', kind='line',                height=4, aspect=1.5, errorbar=('ci', 95))g.set_axis_labels('Date', 'Measurement (units)')

Troubleshooting

Issue: Legend Outside Plot Area

Figure-level functions place legends outside by default. To move inside:

g = sns.relplot(data=df, x='x', y='y', hue='category')g._legend.set_bbox_to_anchor((0.9, 0.5))  # Adjust position

Issue: Overlapping Labels

plt.xticks(rotation=45, ha='right')plt.tight_layout()

Issue: Figure Too Small

For figure-level functions:

sns.relplot(data=df, x='x', y='y', height=6, aspect=1.5)

For axes-level functions:

fig, ax = plt.subplots(figsize=(10, 6))sns.scatterplot(data=df, x='x', y='y', ax=ax)

Issue: Colors Not Distinct Enough

# Use a different palettesns.set_palette("bright")
# Or specify number of colorspalette = sns.color_palette("husl", n_colors=len(df['category'].unique()))sns.scatterplot(data=df, x='x', y='y', hue='category', palette=palette)

Issue: KDE Too Smooth or Jagged

# Adjust bandwidthsns.kdeplot(data=df, x='x', bw_adjust=0.5)  # Less smoothsns.kdeplot(data=df, x='x', bw_adjust=2)    # More smooth

Resources

This skill includes reference materials for deeper exploration:

references/

• function_reference.md - Comprehensive listing of all seaborn functions with parameters and examples
• objects_interface.md - Detailed guide to the modern seaborn.objects API
• examples.md - Common use cases and code patterns for different analysis scenarios

Load reference files as needed for detailed function signatures, advanced parameters, or specific examples.