seaborn - Agent Skills

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:

Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates

Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles)

Statistical awareness: Built-in aggregation, error estimation, and confidence intervals

Aesthetic defaults: Publication-ready themes and color palettes out of the box

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='total_bill', hue='sex')
Violin plot with split for comparison

sns.violinplot(data=df, x='day', y='total_bill',
               hue='sex', split=True)
Bar plot with error bars

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

sns.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 regression
sns.regplot(data=df, x='total_bill', y='tip')
Polynomial regression with faceting

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

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

sns.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 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
Correlations/matrices: heatmap, clustermap
Pairwise relationships: pairplot, jointplot

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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