seaborn

# 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: 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. **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()) ) ``` ## 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 - `dodge` - Separate hue levels side-by-side - `orient` - "v" (vertical) or "h" (horizontal) - `kind` - Plot type for catplot: "strip", "swarm", "box", "violin", "bar", "point" ```python # Swarm plot showing all points sns.swarmplot(data=df, x='day', y='total_bill', hue='sex') # Violin plot with split for comparison sns.violinplot(data=df, x='day', y='total_bill', hue='sex', split=True) # Bar plot with error bars sns.barplot(data=df, x='day', y='total_bill', hue='sex', estimator='mean', errorbar='ci') # Faceted categorical plot sns.catplot(data=df, x='day', y='total_bill', col='time', kind='box') ``` ### Regression Plots (Linear Relationships) **Use for:** Visualizing linear regressions and residuals - `regplot()` - Axes-level regression plot with scatter + fit line - `lmplot()` - Figure-level with faceting support - `residplot()` - Residual plot for assessing model fit **Key parameters:** - `x`, `y` - Variables to regress - `order` - Polynomial regression order - `logistic` - Fit logistic regression - `robust` - Use robust regression (less sensitive to outliers) - `ci` - Confidence interval width (default 95) - `scatter_kws`, `line_kws` - Customize scatter and line properties ```python # Simple linear regression sns.regplot(data=df, x='total_bill', y='tip') # Polynomial regression with faceting sns.lmplot(data=df, x='total_bill', y='tip', col='time', order=2, ci=95) # Check residuals sns.residplot(data=df, x='total_bill', y='tip') ``` ### Matrix Plots (Rectangular Data) **Use for:** Visualizing matrices, correlations, and grid-structured data - `heatmap()` - Color-encoded matrix with annotations - `clustermap()` - Hierarchically-clustered heatmap **Key parameters:** - `data` - 2D rectangular dataset (DataFrame or array) - `annot` - Display values in cells - `fmt` - Format string for annotations (e.g., ".2f") - `cmap` - Colormap name - `center` - Value at colormap center (for diverging colormaps) - `vmin`, `vmax` - Color scale limits - `square` - Force square cells - `linewidths` - Gap between cells ```python # Correlation heatmap corr = df.corr() sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm', center=0, square=True) # Clustered heatmap sns.clustermap(data, cmap='viridis', standard_scale=1, figsize=(10, 10)) ``` ## Multi-Plot Grids 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. ```python 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. ```python 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. ```python 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: ### Axes-Level Functions - Plot to a single matplotlib `Axes` object - Integrate easily into complex matplotlib figures - Accept `ax=` parameter for precise placement - Return `Axes` object - Examples: `scatterplot`, `histplot`, `boxplot`, `regplot`, `heatmap` **When to use:** - Building custom multi-plot layouts - Combining different plot types - Need matplotlib-level control - Integrating with existing matplotlib code ```python fig, axes = plt.subplots(2, 2, figsize=(10, 10)) sns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0]) sns.histplot(data=df, x='x', ax=axes[0, 1]) sns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0]) sns.kdeplot(data=df, x='x', y='y', ax=axes[1, 1]) ``` ### Figure-Level Functions - Manage entire figure including all subplots - Built-in faceting via `col` and `row` parameters - Return `FacetGrid`, `JointGrid`, or `PairGrid` objects - Use `height` and `aspect` for sizing (per subplot) - Cannot be placed in existing figure - Examples: `relplot`, `displot`, `catplot`, `lmplot`, `jointplot`, `pairplot` **When to use:** - Faceted visualizations (small multiples) - Quick exploratory analysis - Consistent multi-panel layouts - Don't need to combine with other plot types ```python # Automatic faceting sns.relplot(data=df, x='x', y='y', col='category', row='group', hue='type', height=3, aspect=1.2) ``` ## Data Structure Requirements ### Long-Form Data (Preferred) Each variable is a column, each observation is a row. This "tidy" format provides maximum flexibility: ```python # 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: ```python # 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:** ```python df_long = df.melt(var_name='condition', value_name='measurement') ``` ## Color Palettes Seaborn provides carefully designed color palettes for different data types: ### Qualitative Palettes (Categorical Data) Distinguish categories through hue variation: - `"deep"` - Default, vivid colors - `"muted"` - Softer, less saturated - `"pastel"` - Light, desaturated - `"bright"` - Highly saturated - `"dark"` - Dark values - `"colorblind"` - Safe for color vision deficiency ```python sns.set_palette("colorblind") sns.color_palette("Set2") ``` ### Sequential Palettes (Ordered Data) Show progression from low to high values: - `"rocket"`, `"mako"` - Wide luminance range (good for heatmaps) - `"flare"`, `"crest"` - Restricted luminance (good for points/lines) - `"viridis"`, `"magma"`, `"plasma"` - Matplotlib perceptually uniform ```python