seaborn
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.
npx skills add LeonChaoX/qinyan-academic-skills --skill seaborn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Seaborn is a Python skill that instructs the agent to create statistical visualizations using seaborn, focusing on datasets (with DataFrames) and built-in statistical estimation, enabling multi-panel figures and semantic mappings for variables.
How it works
The skill emphasizes using a dataset-oriented approach with DataFrames, leveraging seaborn interfaces (both function and objects styles) to generate plots such as relational, distribution, categorical, regression, and matrix plots. It demonstrates declarative usage via the objects API, and traditional function calls for common plot types. It includes examples of common plotting functions (e.g., scatterplot, lineplot, histplot, heatmap, pairplot) and figure-level interfaces (relplot, displot, catplot, lmplot, jointplot, pairplot) with parameters like data, x, y, hue, col, row, kind, and others. It also covers grid plots (FacetGrid, PairGrid, JointGrid) and mentions color palettes, theming, and data forms (long vs wide).
When to use it
Use for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code. Best for exploring distributions, relationships, and categorical comparisons with attractive defaults; suitable when you want publication-quality visuals and theme integration with matplotlib.
What it can touch
The skill references using seaborn functions and objects API, with typical touchpoints including imports (import seaborn as sns), loading datasets (sns.load_dataset), and plotting calls like sns.scatterplot, sns.lineplot, sns.relplot, sns.catplot, sns.heatmap, sns.jointplot, sns.pairplot, sns.FacetGrid, sns.PairGrid, sns.JointGrid, and matplotlib integration (plt from matplotlib.pyplot). It also mentions styling and palettes via sns.set_theme, sns.set_style, sns.color_palette, and related theming constructs.
Caveats
No explicit licensing or risk statements beyond the repository license being BSD-3-Clause in the skill metadata. The content shows typical usage patterns and best practices but does not enumerate concrete limitations or warnings beyond the described capabilities and recommended practices.
# 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 prov
- Overview
- Design Philosophy
- Quick Start
- Core Plotting Interfaces
- Function Interface (Traditional)
- Objects Interface (Modern)
- Plotting Functions by Category
- Relational Plots (Relationships Between Variables)
- Distribution Plots (Single and Bivariate Distributions)
- Categorical Plots (Comparisons Across Categories)
- Regression Plots (Linear Relationships)
- Matrix Plots (Rectangular Data)
- Multi-Plot Grids
- FacetGrid
What does the seaborn skill do?
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.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill seaborn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.