seaborn

What this skill does

# 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.

```bash
# 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:

```python
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

```python
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

```python
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`.

## 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)

```python
# 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"

```python
# 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
- `native_scale` - Preserve numeric/datetime scale on the categorical axis
- `log_scale` - Apply log scaling without dropping down to matplotlib
- `formatter` - Control categorical tick labels
- `dodge`, `gap` - Separate hue levels side-by-side and space dodged elements
- `orient` - "x"/"y" or "v"/"h" to specify the categorical axis
- `legend` - True/False or "auto", "bri