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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"

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='total_bill', hue='sex')

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 regression

  sns.regplot(data=df, x='total_bill', y='tip')

  Polynomial regression with faceting

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

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, ,

When to use:

• Faceted visualizations (small multiples)

• Quick exploratory analysis

• Consistent multi-panel layouts

• Don't need to combine with other plot types

  Automatic faceting

  sns.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  measurement
0        1    control         10.5
1        1  treatment         12.3
2        2    control          9.8
3        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  treatment
0     10.5       12.3
1      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 palette
custom = sns.color_palette("husl", 8)

# Light to dark gradient
palette = sns.light_palette("seagreen", as_cmap=True)

# Diverging palette from hues
palette = sns.diverging_palette(250, 10, as_cmap=True)

Theming and Aesthetics

Set Theme

set_theme() controls overall appearance:

# Set complete theme
sns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')

# Reset to defaults
sns.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 spines

  sns.despine(left=False, bottom=False, offset=10, trim=True)

  Temporary style

  with 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 context

  with 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 DataFrame
df = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days})
sns.scatterplot(data=df, x='bill', y='tip', hue='day')

# Avoid: Unnamed arrays
sns.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 , ,

3. Use Figure-Level Functions for Faceting

# Instead of manual subplot creation
sns.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 default
sns.lineplot(data=df, x='time', y='value',
             errorbar='sd')  # Use standard deviation instead

# Barplot computes mean by default
sns.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 relationships
sns.pairplot(data=df, hue='target', corner=True)

# Distribution exploration
sns.displot(data=df, x='variable', hue='group',
            kind='kde', fill=True, col='category')

# Correlation analysis
corr = 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 seaborn
fig, 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 CI
sns.lineplot(data=timeseries, x='date', y='measurement',
             hue='sensor', style='location', errorbar='sd')

# For more control
g = 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 palette
sns.set_palette("bright")

# Or specify number of colors
palette = 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 bandwidth
sns.kdeplot(data=df, x='x', bw_adjust=0.5)  # Less smooth
sns.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.

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