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.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Instruções da origem · Visualização somente leitura
name
seaborn
description
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.
type
skill
created
2026-02-27T00:00:00.000Z
domain
ai-ml
category
ml-data-science
risk
unknown
source
community
tags
["skill","ai-ml","ml-data-science","seaborn"]
Seaborn Statistical Visualization
When to Use
You need publication-quality statistical graphics directly from tabular datasets.
You are exploring multivariate relationships, distributions, or grouped comparisons with minimal plotting code.
You want seaborn's dataset-oriented API and statistical defaults on top of matplotlib.
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)
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:
# 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)
# 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.
