Seaborn Statistical Visualization

Overview

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Built on matplotlib, it provides a dataset-oriented interface for multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Design Philosophy

1. Dataset-oriented: Work directly with DataFrames and named variables
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

Quick Start

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

df = sns.load_dataset('tips')
sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
plt.show()

Plotting Interfaces

Function Interface (Traditional)

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).

Objects Interface (Modern)

The seaborn.objects interface provides a declarative, composable API similar to ggplot2:

from seaborn import objects as so

(
    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

Use for: Exploring relationships between two or more variables.

• scatterplot() -- Individual observations as points
• lineplot() -- Trends with automatic aggregation and CI
• relplot() -- Figure-level with automatic faceting

Key parameters: x, y, hue, size, style, col, row

sns.scatterplot(data=df, x='total_bill', y='tip',
                hue='time', size='size', style='sex')

sns.lineplot(data=timeseries, x='date', y='value', hue='category')

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

Distribution Plots

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

• histplot() -- Bar-based frequency distributions
• kdeplot() -- Smooth density estimates (Gaussian kernels)
• ecdfplot() -- Empirical cumulative distribution
• rugplot() -- Individual observation tick marks
• displot() -- Figure-level distribution interface
• jointplot() -- Bivariate plot with marginal distributions
• pairplot() -- Matrix of pairwise relationships

Key parameters: stat (count, frequency, probability, density), bins/binwidth, bw_adjust, fill, multiple (layer, stack, dodge, fill)

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

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

sns.jointplot(data=df, x='total_bill', y='tip', kind='scatter', hue='time')

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

Categorical Plots

Use for: Comparing distributions or statistics across discrete categories.

Categorical scatterplots:

• stripplot() -- Points with jitter
• 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() (set kind parameter)

sns.swarmplot(data=df, x='day', y='total_bill', hue='sex')

sns.violinplot(data=df, x='day', y='total_bill', hue='sex', split=True)

sns.barplot(data=df, x='day', y='total_bill',
            hue='sex', estimator='mean', errorbar='ci')

sns.catplot(data=df, x='day', y='total_bill', col='time', kind='box')

Regression Plots

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

sns.regplot(data=df, x='total_bill', y='tip')
sns.lmplot(data=df, x='total_bill', y='tip', col='time', order=2, ci=95)

Matrix Plots

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

corr = df.corr()
sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', center=0, square=True)

sns.clustermap(data, cmap='viridis', standard_scale=1, figsize=(10, 10))

Figure-Level vs Axes-Level Functions

Axes-Level Functions

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

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
• Examples: relplot, displot, catplot, lmplot, jointplot, pairplot

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

Multi-Plot Grids

FacetGrid

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

PairGrid

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

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

Data Structure Requirements

Long-Form Data (Preferred)

Each variable is a column, each observation is a row ("tidy" format):

   subject  condition  measurement
0        1    control         10.5
1        1  treatment         12.3

Wide-Form Data

Variables spread across columns. Useful for simple rectangular data.

Converting wide to long:

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

Color Palettes

Qualitative (Categorical Data)

• "deep" -- Default, vivid
• "muted" -- Softer
• "colorblind" -- Color vision safe
• "bright", "dark", "pastel"

Sequential (Ordered Data)

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

Diverging (Centered Data)

• "vlag" -- Blue to red
• "icefire" -- Blue to orange
• "coolwarm", "Spectral"

sns.set_palette("colorblind")
custom = sns.color_palette("husl", 8)
palette = sns.light_palette("seagreen", as_cmap=True)
palette = sns.diverging_palette(250, 10, as_cmap=True)

Theming and Aesthetics

Styles

• "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

Contexts (Scaling)

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

sns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')
sns.set_context("talk", font_scale=1.2)
sns.despine(left=False, bottom=False, offset=10, trim=True)

Best Practices

1. Data Preparation

Always use well-structured DataFrames with meaningful column names:

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

2. Choose the Right Plot Type

3. Leverage Semantic Mappings

sns.scatterplot(data=df, x='x', y='y',
                hue='category', size='importance', style='type')

4. Control Statistical Estimation

sns.lineplot(data=df, x='time', y='value', errorbar='sd')
sns.barplot(data=df, x='category', y='value',
            estimator='median', errorbar=('ci', 95))

5. Combine with Matplotlib

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()

6. 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

Troubleshooting