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Zhongwei Li
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skills/seaborn/SKILL.md
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skills/seaborn/SKILL.md
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---
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name: seaborn
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description: "Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures."
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---
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# Seaborn Statistical Visualization
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## Overview
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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.
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## Design Philosophy
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Seaborn follows these core principles:
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1. **Dataset-oriented**: Work directly with DataFrames and named variables rather than abstract coordinates
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2. **Semantic mapping**: Automatically translate data values into visual properties (colors, sizes, styles)
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3. **Statistical awareness**: Built-in aggregation, error estimation, and confidence intervals
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4. **Aesthetic defaults**: Publication-ready themes and color palettes out of the box
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5. **Matplotlib integration**: Full compatibility with matplotlib customization when needed
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## Quick Start
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```python
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import seaborn as sns
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import matplotlib.pyplot as plt
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import pandas as pd
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# Load example dataset
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df = sns.load_dataset('tips')
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# Create a simple visualization
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sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
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plt.show()
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```
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## Core Plotting Interfaces
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### Function Interface (Traditional)
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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).
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**When to use:**
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- Quick exploratory analysis
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- Single-purpose visualizations
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- When you need a specific plot type
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### Objects Interface (Modern)
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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.
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**When to use:**
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- Complex layered visualizations
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- When you need fine-grained control over transformations
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- Building custom plot types
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- Programmatic plot generation
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```python
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from seaborn import objects as so
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# Declarative syntax
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(
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so.Plot(data=df, x='total_bill', y='tip')
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.add(so.Dot(), color='day')
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.add(so.Line(), so.PolyFit())
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)
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```
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## Plotting Functions by Category
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### Relational Plots (Relationships Between Variables)
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**Use for:** Exploring how two or more variables relate to each other
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- `scatterplot()` - Display individual observations as points
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- `lineplot()` - Show trends and changes (automatically aggregates and computes CI)
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- `relplot()` - Figure-level interface with automatic faceting
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**Key parameters:**
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- `x`, `y` - Primary variables
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- `hue` - Color encoding for additional categorical/continuous variable
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- `size` - Point/line size encoding
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- `style` - Marker/line style encoding
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- `col`, `row` - Facet into multiple subplots (figure-level only)
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```python
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# Scatter with multiple semantic mappings
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sns.scatterplot(data=df, x='total_bill', y='tip',
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hue='time', size='size', style='sex')
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# Line plot with confidence intervals
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sns.lineplot(data=timeseries, x='date', y='value', hue='category')
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# Faceted relational plot
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sns.relplot(data=df, x='total_bill', y='tip',
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col='time', row='sex', hue='smoker', kind='scatter')
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```
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### Distribution Plots (Single and Bivariate Distributions)
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**Use for:** Understanding data spread, shape, and probability density
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- `histplot()` - Bar-based frequency distributions with flexible binning
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- `kdeplot()` - Smooth density estimates using Gaussian kernels
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- `ecdfplot()` - Empirical cumulative distribution (no parameters to tune)
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- `rugplot()` - Individual observation tick marks
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- `displot()` - Figure-level interface for univariate and bivariate distributions
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- `jointplot()` - Bivariate plot with marginal distributions
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- `pairplot()` - Matrix of pairwise relationships across dataset
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**Key parameters:**
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- `x`, `y` - Variables (y optional for univariate)
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- `hue` - Separate distributions by category
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- `stat` - Normalization: "count", "frequency", "probability", "density"
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- `bins` / `binwidth` - Histogram binning control
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- `bw_adjust` - KDE bandwidth multiplier (higher = smoother)
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- `fill` - Fill area under curve
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- `multiple` - How to handle hue: "layer", "stack", "dodge", "fill"
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```python
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# Histogram with density normalization
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sns.histplot(data=df, x='total_bill', hue='time',
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stat='density', multiple='stack')
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# Bivariate KDE with contours
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sns.kdeplot(data=df, x='total_bill', y='tip',
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fill=True, levels=5, thresh=0.1)
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# Joint plot with marginals
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sns.jointplot(data=df, x='total_bill', y='tip',
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kind='scatter', hue='time')
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# Pairwise relationships
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sns.pairplot(data=df, hue='species', corner=True)
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```
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### Categorical Plots (Comparisons Across Categories)
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**Use for:** Comparing distributions or statistics across discrete categories
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**Categorical scatterplots:**
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- `stripplot()` - Points with jitter to show all observations
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- `swarmplot()` - Non-overlapping points (beeswarm algorithm)
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**Distribution comparisons:**
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- `boxplot()` - Quartiles and outliers
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- `violinplot()` - KDE + quartile information
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- `boxenplot()` - Enhanced boxplot for larger datasets
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**Statistical estimates:**
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- `barplot()` - Mean/aggregate with confidence intervals
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- `pointplot()` - Point estimates with connecting lines
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- `countplot()` - Count of observations per category
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**Figure-level:**
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- `catplot()` - Faceted categorical plots (set `kind` parameter)
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**Key parameters:**
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- `x`, `y` - Variables (one typically categorical)
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- `hue` - Additional categorical grouping
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- `order`, `hue_order` - Control category ordering
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- `dodge` - Separate hue levels side-by-side
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- `orient` - "v" (vertical) or "h" (horizontal)
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- `kind` - Plot type for catplot: "strip", "swarm", "box", "violin", "bar", "point"
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```python
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# Swarm plot showing all points
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sns.swarmplot(data=df, x='day', y='total_bill', hue='sex')
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# Violin plot with split for comparison
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sns.violinplot(data=df, x='day', y='total_bill',
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hue='sex', split=True)
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# Bar plot with error bars
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sns.barplot(data=df, x='day', y='total_bill',
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hue='sex', estimator='mean', errorbar='ci')
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# Faceted categorical plot
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sns.catplot(data=df, x='day', y='total_bill',
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col='time', kind='box')
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```
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### Regression Plots (Linear Relationships)
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**Use for:** Visualizing linear regressions and residuals
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- `regplot()` - Axes-level regression plot with scatter + fit line
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- `lmplot()` - Figure-level with faceting support
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- `residplot()` - Residual plot for assessing model fit
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**Key parameters:**
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- `x`, `y` - Variables to regress
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- `order` - Polynomial regression order
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- `logistic` - Fit logistic regression
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- `robust` - Use robust regression (less sensitive to outliers)
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- `ci` - Confidence interval width (default 95)
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- `scatter_kws`, `line_kws` - Customize scatter and line properties
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```python
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# Simple linear regression
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sns.regplot(data=df, x='total_bill', y='tip')
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# Polynomial regression with faceting
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sns.lmplot(data=df, x='total_bill', y='tip',
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col='time', order=2, ci=95)
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# Check residuals
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sns.residplot(data=df, x='total_bill', y='tip')
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```
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### Matrix Plots (Rectangular Data)
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**Use for:** Visualizing matrices, correlations, and grid-structured data
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- `heatmap()` - Color-encoded matrix with annotations
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- `clustermap()` - Hierarchically-clustered heatmap
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**Key parameters:**
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- `data` - 2D rectangular dataset (DataFrame or array)
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- `annot` - Display values in cells
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- `fmt` - Format string for annotations (e.g., ".2f")
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- `cmap` - Colormap name
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- `center` - Value at colormap center (for diverging colormaps)
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- `vmin`, `vmax` - Color scale limits
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- `square` - Force square cells
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- `linewidths` - Gap between cells
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```python
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# Correlation heatmap
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corr = df.corr()
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sns.heatmap(corr, annot=True, fmt='.2f',
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cmap='coolwarm', center=0, square=True)
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# Clustered heatmap
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sns.clustermap(data, cmap='viridis',
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standard_scale=1, figsize=(10, 10))
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```
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## Multi-Plot Grids
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Seaborn provides grid objects for creating complex multi-panel figures:
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### FacetGrid
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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.
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```python
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g = sns.FacetGrid(df, col='time', row='sex', hue='smoker')
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g.map(sns.scatterplot, 'total_bill', 'tip')
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g.add_legend()
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```
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### PairGrid
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Show pairwise relationships between all variables in a dataset.
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```python
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g = sns.PairGrid(df, hue='species')
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g.map_upper(sns.scatterplot)
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g.map_lower(sns.kdeplot)
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g.map_diag(sns.histplot)
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g.add_legend()
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```
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### JointGrid
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Combine bivariate plot with marginal distributions.
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```python
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g = sns.JointGrid(data=df, x='total_bill', y='tip')
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g.plot_joint(sns.scatterplot)
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g.plot_marginals(sns.histplot)
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```
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## Figure-Level vs Axes-Level Functions
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Understanding this distinction is crucial for effective seaborn usage:
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### Axes-Level Functions
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- Plot to a single matplotlib `Axes` object
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- Integrate easily into complex matplotlib figures
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- Accept `ax=` parameter for precise placement
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- Return `Axes` object
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- Examples: `scatterplot`, `histplot`, `boxplot`, `regplot`, `heatmap`
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**When to use:**
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- Building custom multi-plot layouts
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- Combining different plot types
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- Need matplotlib-level control
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- Integrating with existing matplotlib code
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```python
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fig, axes = plt.subplots(2, 2, figsize=(10, 10))
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sns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0])
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sns.histplot(data=df, x='x', ax=axes[0, 1])
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sns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0])
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sns.kdeplot(data=df, x='x', y='y', ax=axes[1, 1])
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```
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### Figure-Level Functions
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- Manage entire figure including all subplots
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- Built-in faceting via `col` and `row` parameters
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- Return `FacetGrid`, `JointGrid`, or `PairGrid` objects
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- Use `height` and `aspect` for sizing (per subplot)
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- Cannot be placed in existing figure
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- Examples: `relplot`, `displot`, `catplot`, `lmplot`, `jointplot`, `pairplot`
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**When to use:**
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- Faceted visualizations (small multiples)
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- Quick exploratory analysis
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- Consistent multi-panel layouts
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- Don't need to combine with other plot types
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```python
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# Automatic faceting
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sns.relplot(data=df, x='x', y='y', col='category', row='group',
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hue='type', height=3, aspect=1.2)
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```
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## Data Structure Requirements
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### Long-Form Data (Preferred)
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Each variable is a column, each observation is a row. This "tidy" format provides maximum flexibility:
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```python
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# Long-form structure
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subject condition measurement
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0 1 control 10.5
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1 1 treatment 12.3
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2 2 control 9.8
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3 2 treatment 13.1
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```
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**Advantages:**
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- Works with all seaborn functions
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- Easy to remap variables to visual properties
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- Supports arbitrary complexity
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- Natural for DataFrame operations
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### Wide-Form Data
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Variables are spread across columns. Useful for simple rectangular data:
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```python
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# Wide-form structure
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control treatment
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0 10.5 12.3
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1 9.8 13.1
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```
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**Use cases:**
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- Simple time series
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- Correlation matrices
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- Heatmaps
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- Quick plots of array data
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**Converting wide to long:**
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```python
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df_long = df.melt(var_name='condition', value_name='measurement')
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```
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## Color Palettes
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Seaborn provides carefully designed color palettes for different data types:
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### Qualitative Palettes (Categorical Data)
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Distinguish categories through hue variation:
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- `"deep"` - Default, vivid colors
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- `"muted"` - Softer, less saturated
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- `"pastel"` - Light, desaturated
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- `"bright"` - Highly saturated
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- `"dark"` - Dark values
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- `"colorblind"` - Safe for color vision deficiency
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```python
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sns.set_palette("colorblind")
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sns.color_palette("Set2")
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```
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### Sequential Palettes (Ordered Data)
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Show progression from low to high values:
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- `"rocket"`, `"mako"` - Wide luminance range (good for heatmaps)
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- `"flare"`, `"crest"` - Restricted luminance (good for points/lines)
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- `"viridis"`, `"magma"`, `"plasma"` - Matplotlib perceptually uniform
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```python
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sns.heatmap(data, cmap='rocket')
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sns.kdeplot(data=df, x='x', y='y', cmap='mako', fill=True)
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```
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### Diverging Palettes (Centered Data)
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Emphasize deviations from a midpoint:
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- `"vlag"` - Blue to red
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- `"icefire"` - Blue to orange
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- `"coolwarm"` - Cool to warm
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- `"Spectral"` - Rainbow diverging
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```python
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sns.heatmap(correlation_matrix, cmap='vlag', center=0)
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```
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### Custom Palettes
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```python
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# Create custom palette
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custom = sns.color_palette("husl", 8)
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# Light to dark gradient
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palette = sns.light_palette("seagreen", as_cmap=True)
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# Diverging palette from hues
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palette = sns.diverging_palette(250, 10, as_cmap=True)
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```
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## Theming and Aesthetics
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### Set Theme
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`set_theme()` controls overall appearance:
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```python
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# Set complete theme
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sns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')
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# Reset to defaults
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sns.set_theme()
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```
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### Styles
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Control background and grid appearance:
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- `"darkgrid"` - Gray background with white grid (default)
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- `"whitegrid"` - White background with gray grid
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- `"dark"` - Gray background, no grid
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- `"white"` - White background, no grid
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- `"ticks"` - White background with axis ticks
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```python
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sns.set_style("whitegrid")
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# Remove spines
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sns.despine(left=False, bottom=False, offset=10, trim=True)
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# Temporary style
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with sns.axes_style("white"):
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sns.scatterplot(data=df, x='x', y='y')
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```
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### Contexts
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Scale elements for different use cases:
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- `"paper"` - Smallest (default)
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- `"notebook"` - Slightly larger
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- `"talk"` - Presentation slides
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- `"poster"` - Large format
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```python
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sns.set_context("talk", font_scale=1.2)
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# Temporary context
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with sns.plotting_context("poster"):
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sns.barplot(data=df, x='category', y='value')
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```
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## Best Practices
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### 1. Data Preparation
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Always use well-structured DataFrames with meaningful column names:
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```python
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# Good: Named columns in DataFrame
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df = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days})
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sns.scatterplot(data=df, x='bill', y='tip', hue='day')
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# Avoid: Unnamed arrays
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sns.scatterplot(x=x_array, y=y_array) # Loses axis labels
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```
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||||
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||||
### 2. Choose the Right Plot Type
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||||
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||||
**Continuous x, continuous y:** `scatterplot`, `lineplot`, `kdeplot`, `regplot`
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||||
**Continuous x, categorical y:** `violinplot`, `boxplot`, `stripplot`, `swarmplot`
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||||
**One continuous variable:** `histplot`, `kdeplot`, `ecdfplot`
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||||
**Correlations/matrices:** `heatmap`, `clustermap`
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||||
**Pairwise relationships:** `pairplot`, `jointplot`
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||||
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||||
### 3. Use Figure-Level Functions for Faceting
|
||||
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||||
```python
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||||
# 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:
|
||||
|
||||
```python
|
||||
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:
|
||||
|
||||
```python
|
||||
# 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:
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
```python
|
||||
# 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
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
```python
|
||||
# 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
|
||||
|
||||
```python
|
||||
# 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:
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
```python
|
||||
plt.xticks(rotation=45, ha='right')
|
||||
plt.tight_layout()
|
||||
```
|
||||
|
||||
### Issue: Figure Too Small
|
||||
|
||||
For figure-level functions:
|
||||
```python
|
||||
sns.relplot(data=df, x='x', y='y', height=6, aspect=1.5)
|
||||
```
|
||||
|
||||
For axes-level functions:
|
||||
```python
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
sns.scatterplot(data=df, x='x', y='y', ax=ax)
|
||||
```
|
||||
|
||||
### Issue: Colors Not Distinct Enough
|
||||
|
||||
```python
|
||||
# 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
|
||||
|
||||
```python
|
||||
# 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.
|
||||
Reference in New Issue
Block a user