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seaborn

スター17

フォーク29

更新日2026年4月22日 12:55

Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.

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lucifertrj

lucifertrj/skills-based-app

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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:

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

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

Load dataset

df = sns.load_dataset('tips')

================= BASIC PLOTS =================

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

sns.lineplot(data=df, x='total_bill', y='tip') plt.show()

================= OBJECTS INTERFACE =================

from seaborn import objects as so ( so.Plot(df, x='total_bill', y='tip') .add(so.Dot(), color='day') .add(so.Line(), so.PolyFit()) )

================= RELATIONAL PLOTS =================

sns.scatterplot(data=df, x='total_bill', y='tip', hue='time') sns.scatterplot(data=df, x='total_bill', y='tip', style='sex') sns.lineplot(data=df, x='size', y='total_bill') sns.relplot(data=df, x='total_bill', y='tip', col='time', hue='sex') plt.show()

================= DISTRIBUTION PLOTS =================

sns.histplot(data=df, x='total_bill', bins=20) sns.histplot(data=df, x='total_bill', hue='time', kde=True) sns.kdeplot(data=df, x='total_bill', fill=True) sns.ecdfplot(data=df, x='total_bill') sns.displot(data=df, x='total_bill', col='time') plt.show()

================= CATEGORICAL PLOTS =================

sns.boxplot(data=df, x='day', y='total_bill') sns.violinplot(data=df, x='day', y='total_bill') sns.stripplot(data=df, x='day', y='total_bill', jitter=True) sns.swarmplot(data=df, x='day', y='total_bill') sns.barplot(data=df, x='day', y='total_bill') sns.countplot(data=df, x='day') sns.catplot(data=df, x='day', y='total_bill', kind='box') plt.show()

================= REGRESSION PLOTS =================

sns.regplot(data=df, x='total_bill', y='tip') sns.lmplot(data=df, x='total_bill', y='tip', col='time') sns.residplot(data=df, x='total_bill', y='tip') plt.show()

================= MATRIX PLOTS =================

corr = df.corr(numeric_only=True) sns.heatmap(corr, annot=True, cmap='coolwarm') sns.clustermap(corr) plt.show()

================= MULTI-PLOT GRIDS =================

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

sns.pairplot(df) sns.jointplot(data=df, x='total_bill', y='tip', kind='scatter') plt.show()

================= AXES VS FIGURE =================

fig, ax = plt.subplots() sns.scatterplot(data=df, x='total_bill', y='tip', ax=ax)

sns.relplot(data=df, x='total_bill', y='tip', col='time') plt.show()

================= DATA TRANSFORMATION =================

df_long = df.melt() df_pivot = df.pivot_table(values='total_bill', index='day', columns='time')

================= COLOR PALETTES =================

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

================= THEMES =================

sns.set_theme(style='whitegrid') sns.set_theme(style='darkgrid') sns.set_context("talk")

================= CUSTOMIZATION =================

ax = sns.scatterplot(data=df, x='total_bill', y='tip', hue='sex') ax.set(title='Custom Title', xlabel='Bill', ylabel='Tip') plt.axhline(y=5, linestyle='--') plt.axvline(x=20, linestyle='--') plt.show()

================= GROUPED ANALYSIS =================

grouped = df.groupby('day')['total_bill'].mean() print(grouped)

================= ADVANCED EXAMPLES =================

sns.scatterplot(data=df, x='total_bill', y='tip', size='size', hue='sex') sns.lineplot(data=df, x='size', y='tip', hue='time') sns.boxenplot(data=df, x='day', y='total_bill') sns.violinplot(data=df, x='day', y='total_bill', hue='sex', split=True) plt.show()

================= TIME SERIES SIMULATION =================

dates = pd.date_range(start='2023-01-01', periods=50) values = np.random.randn(50).cumsum()

ts_df = pd.DataFrame({'date': dates, 'value': values}) sns.lineplot(data=ts_df, x='date', y='value') plt.xticks(rotation=45) plt.show()

================= MULTI-PANEL =================

fig, axes = plt.subplots(2, 2, figsize=(10, 8))

sns.scatterplot(data=df, x='total_bill', y='tip', ax=axes[0, 0]) sns.histplot(data=df, x='total_bill', ax=axes[0, 1]) sns.boxplot(data=df, x='day', y='total_bill', ax=axes[1, 0]) sns.kdeplot(data=df, x='total_bill', ax=axes[1, 1])

plt.tight_layout() plt.show()

================= SAVING FIGURES =================

plt.figure() sns.scatterplot(data=df, x='total_bill', y='tip') plt.savefig('plot.png', dpi=300)

================= EDA =================

sns.pairplot(df) sns.heatmap(df.corr(numeric_only=True), annot=True) plt.show()

================= PUBLICATION STYLE =================

sns.set_theme(style='ticks') sns.catplot(data=df, x='day', y='total_bill', kind='box') plt.show()

================= TROUBLESHOOTING =================

plt.figure(figsize=(10, 6)) sns.scatterplot(data=df, x='total_bill', y='tip') plt.xticks(rotation=45)

sns.set_palette("bright") sns.kdeplot(data=df, x='total_bill', bw_adjust=0.5)

plt.show()

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Seaborn Statistical Visualization

Overview

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

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

================= CATEGORICAL PLOTS =================

================= MULTI-PANEL =================

fig, axes = plt.subplots(2, 2, figsize=(10, 8))

plt.tight_layout() plt.show()

name	seaborn
description	Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.