/seaborn

name: seaborn
description: Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
license: BSD-3-Clause license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.8+ and seaborn 0.13.2-compatible dependencies. Install with uv pip install seaborn==0.13.2; use seaborn[stats]==0.13.2 when advanced regression or clustering examples need scipy/statsmodels.
metadata:
  version: "1.3"
  skill-author: K-Dense Inc.

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.

Environment and Installation

Current upstream documentation is for seaborn 0.13.2. Official docs support Python 3.8+ with mandatory NumPy, pandas, and matplotlib dependencies; scipy, statsmodels, and fastcluster are optional for some advanced statistics and clustering workflows.

# Reproducible install for examples in this skill
uv pip install "seaborn==0.13.2"

# Include optional statistical dependencies when needed
uv pip install "seaborn[stats]==0.13.2"

Recommended imports:

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

`sns.load_dataset()` downloads public example data when it is not cached. For private, regulated, or offline work, load local files explicitly with pandas and pass the resulting DataFrame to seaborn.

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. Upstream still describes this interface as experimental and incomplete in 0.13.2, although stable enough for serious use; prefer the function interface for conservative production code unless the compositional API materially simplifies the plot.

**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())
)

Current API Notes

Seaborn 0.12 and 0.13 changed several common plotting patterns:

• Most plotting functions now require keyword arguments for variables. Prefer `sns.scatterplot(data=df, x="x", y="y")` over positional `sns.scatterplot(df["x"], df["y"])`.
• `errorbar` replaces the old `ci` parameter in `lineplot()`, `barplot()`, and `pointplot()`. Regression functions such as `regplot()` and `lmplot()` still use `ci`.
• Categorical plots were rewritten in 0.13. Use `native_scale=True` when numeric or datetime categories should keep their original scale instead of ordinal positions.
• Passing `palette` without assigning `hue` is deprecated for categorical functions. If each category should get its own color, assign a redundant hue such as `hue="day"` and set `legend=False`.
• Prefer renamed parameters: `violinplot(density_norm=..., common_norm=...)` instead of `scale`/`scale_hue`, `boxenplot(width_method=...)` instead of `scale`, and `barplot(err_kws=...)` instead of `errcolor`/`errwidth`.

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')

Plotting Functions, Grids, Palettes, and Patterns

• [references/plotting_functions.md](references/plotting_functions.md): rela