Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
npxskills add davila7/claude-code-templates--skill seabornLoading…
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.
Seaborn follows these core principles:
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()
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:
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:
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())
)
Use for: Exploring how two or more variables relate to each other
scatterplot() - Display individual observations as pointslineplot() - Show trends and changes (automatically aggregates and computes CI)relplot() - Figure-level interface with automatic facetingKey parameters:
x, y - Primary variableshue - Color encoding for additional categorical/continuous variablesize - Point/line size encodingstyle - Marker/line style encodingcol, row - Facet into multiple subplots (figure-level only)# 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')
Use for: Understanding data spread, shape, and probability density
histplot() - Bar-based frequency distributions with flexible binningkdeplot() - Smooth density estimates using Gaussian kernelsecdfplot() - Empirical cumulative distribution (no parameters to tune)rugplot() - Individual observation tick marksdisplot() - Figure-level interface for univariate and bivariate distributionsjointplot() - Bivariate plot with marginal distributionspairplot() - Matrix of pairwise relationships across datasetKey parameters:
x, y - Variables (y optional for univariate)hue - Separate distributions by categorystat - Normalization: "count", "frequency", "probability", "density"bins / binwidth - Histogram binning controlbw_adjust - KDE bandwidth multiplier (higher = smoother)fill - Fill area under curvemultiple - How to handle hue: "layer", "stack", "dodge", "fill"# 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)
Use for: Comparing distributions or statistics across discrete categories
Categorical scatterplots:
stripplot() - Points with jitter to show all observationsswarmplot() - Non-overlapping points (beeswarm algorithm)Distribution comparisons:
boxplot() - Quartiles and outliersviolinplot() - KDE + quartile informationboxenplot() - Enhanced boxplot for larger datasetsStatistical estimates:
barplot() - Mean/aggregate with confidence intervalspointplot() - Point estimates with connecting linescountplot() - Count of observations per categoryFigure-level:
catplot() - Faceted categorical plots (set kind parameter)Key parameters:
x, y - Variables (one typically categorical)hue - Additional categorical groupingorder, hue_order - Control category orderingdodge - Separate hue levels side-by-sideorient - "v" (vertical) or "h" (horizontal)kind - Plot type for catplot: "strip", "swarm", "box", "violin", "bar", "point"# 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')
Use for: Visualizing linear regressions and residuals
regplot() - Axes-level regression plot with scatter + fit linelmplot() - Figure-level with faceting supportresidplot() - Residual plot for assessing model fitKey parameters:
x, y - Variables to regressorder - Polynomial regression orderlogistic - Fit logistic regressionrobust - Use robust regression (less sensitive to outliers)ci - Confidence interval width (default 95)scatter_kws, line_kws - Customize scatter and line properties# 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')
Use for: Visualizing matrices, correlations, and grid-structured data
heatmap() - Color-encoded matrix with annotationsclustermap() - Hierarchically-clustered heatmapKey parameters:
data - 2D rectangular dataset (DataFrame or array)annot - Display values in cellsfmt - Format string for annotations (e.g., ".2f")cmap - Colormap namecenter - Value at colormap center (for diverging colormaps)vmin, vmax - Color scale limitssquare - Force square cellslinewidths - Gap between cells# 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))
Seaborn provides grid objects for creating complex multi-panel figures:
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.
g = sns.FacetGrid(df, col='time', row='sex', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
Show pairwise relationships between all variables in a dataset.
g = sns.PairGrid(df, hue='species')
g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.map_diag(sns.histplot)
g.add_legend()
Combine bivariate plot with marginal distributions.
g = sns.JointGrid(data=df, x='total_bill', y='tip')
g.plot_joint(sns.scatterplot)
g.plot_marginals(sns.histplot)
Understanding this distinction is crucial for effective seaborn usage:
Axes objectax= parameter for precise placementAxes objectscatterplot, histplot, boxplot, regplot, heatmapWhen to use:
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])
col and row parametersFacetGrid, JointGrid, or PairGrid objectsheight and aspect for sizing (per subplot)relplot, displot, catplot, lmplot, , When to use:
# Automatic faceting
sns.relplot(data=df, x='x', y='y', col='category', row='group',
hue='type', height=3, aspect=1.2)
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:
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:
Converting wide to long:
df_long = df.melt(var_name='condition', value_name='measurement')
Seaborn provides carefully designed color palettes for different data types:
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 deficiencysns.set_palette("colorblind")
sns.color_palette("Set2")
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 uniformsns.heatmap(data, cmap='rocket')
sns.kdeplot(data=df, x='x', y='y', cmap='mako', fill=True)
Emphasize deviations from a midpoint:
"vlag" - Blue to red"icefire" - Blue to orange"coolwarm" - Cool to warm"Spectral" - Rainbow divergingsns.heatmap(correlation_matrix, cmap='vlag', center=0)
# Create custom palette
custom = sns.color_palette("husl", 8)
# Light to dark gradient
palette = sns.light_palette("seagreen", as_cmap=True)
# Diverging palette from hues
palette = sns.diverging_palette(250, 10, as_cmap=True)
set_theme() controls overall appearance:
# Set complete theme
sns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')
# Reset to defaults
sns.set_theme()
Control background and grid appearance:
"darkgrid" - Gray background with white grid (default)"whitegrid" - White background with gray grid"dark" - Gray background, no grid"white" - White background, no grid"ticks" - White background with axis tickssns.set_style("whitegrid")
# Remove spines
sns.despine(left=False, bottom=False, offset=10, trim=True)
# Temporary style
with sns.axes_style("white"):
sns.scatterplot(data=df, x='x', y='y')
Scale elements for different use cases:
"paper" - Smallest (default)"notebook" - Slightly larger"talk" - Presentation slides"poster" - Large formatsns.set_context("talk", font_scale=1.2)
# Temporary context
with sns.plotting_context("poster"):
sns.barplot(data=df, x='category', y='value')
Always use well-structured DataFrames with meaningful column names:
# Good: Named columns in DataFrame
df = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days})
sns.scatterplot(data=df, x='bill', y='tip', hue='day')
# Avoid: Unnamed arrays
sns.scatterplot(x=x_array, y=y_array) # Loses axis labels
Continuous x, continuous y: scatterplot, lineplot, kdeplot, regplot
Continuous x, categorical y: violinplot, boxplot, stripplot, swarmplot
One continuous variable: histplot, kdeplot, ecdfplot
Correlations/matrices: heatmap,
,
# 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
Use hue, size, and style to encode additional dimensions:
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
Many functions compute statistics automatically. Understand and customize:
# 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
Seaborn integrates seamlessly with matplotlib for fine-tuning:
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()
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
# 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)
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')
# 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()
# 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)')
Figure-level functions place legends outside by default. To move inside:
g = sns.relplot(data=df, x='x', y='y', hue='category')
g._legend.set_bbox_to_anchor((0.9, 0.5)) # Adjust position
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
For figure-level functions:
sns.relplot(data=df, x='x', y='y', height=6, aspect=1.5)
For axes-level functions:
fig, ax = plt.subplots(figsize=(10, 6))
sns.scatterplot(data=df, x='x', y='y', ax=ax)
# 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)
# 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
This skill includes reference materials for deeper exploration:
function_reference.md - Comprehensive listing of all seaborn functions with parameters and examplesobjects_interface.md - Detailed guide to the modern seaborn.objects APIexamples.md - Common use cases and code patterns for different analysis scenariosLoad reference files as needed for detailed function signatures, advanced parameters, or specific examples.
jointplotpairplotclustermappairplotjointplot
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).