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seaborn skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

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.

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Install the seaborn skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/seaborn ~/.claude/skills/seaborn
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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(densitynorm=..., commonnorm=...) instead of scale/scalehue, boxenplot(widthmethod=...) 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

distribution, categorical, regression, and matrix plots by category.

  • references/plotting_functions.md: relational,

PairGrid, JointGrid, and the figure-level vs axes-level distinction.

  • references/gridsandlevels.md: FacetGrid,

choice (including colorblind-safe options), themes, contexts, and styles.

  • references/palettesandtheming.md: palette

common recipes and what seaborn's errors actually mean.

  • references/patternsandtroubleshooting.md:

interface. references/function_reference.md and references/examples.md: full signatures and more examples.

  • references/objects_interface.md: the seaborn.objects

Best Practices

1. Data Preparation

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

2. Choose the Right Plot Type

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, clustermap Pairwise relationships: pairplot, jointplot

3. Use Figure-Level Functions for Faceting

# 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

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