seaborn

name	seaborn
description	Draws dataset-oriented statistical graphics with seaborn: relplot, displot, catplot, lmplot, hue/size/style encodings, and seaborn.objects Plot layers on tidy pandas frames. Use for grouped estimates, KDE/ECDF, regression bands, or faceted publication figures. Never send Matplotlib Axes/GridSpec/rcParams construction or Altair/Vega chart specs here.
version	1.0.1
license	BSD-3-Clause license
metadata	{"skill-author":"K-Dense Inc."}
risk	unknown
source	community

When to Use

• You need publication-quality statistical graphics directly from tabular datasets.
• You are exploring multivariate relationships, distributions, or grouped comparisons with minimal plotting code.
• You want seaborn's dataset-oriented API and statistical defaults on top of matplotlib.

Prerequisites

• Python environment with seaborn, pandas, and matplotlib installed.
• Windows host (PowerShell) is primary. Ensure your Python environment is activated before running scripts.

Procedure

1. Import Libraries and Load Data Always use well-structured DataFrames with meaningful column names.

   import seaborn as sns
   import matplotlib.pyplot as plt
   import pandas as pd
   
   df = sns.load_dataset('tips')
   

2. Choose Plotting Interface

   • Function Interface (Traditional): Use for quick exploratory analysis and single-purpose visualizations.

     sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
     plt.show()
     

   • Objects Interface (Modern): Use for complex layered visualizations and fine-grained control over transformations.

     from seaborn import objects as so
     (
         so.Plot(data=df, x='total_bill', y='tip')
         .add(so.Dot(), color='day')
         .add(so.Line(), so.PolyFit())
     )
     

3. Select Plot Type by Category

   • Relational: scatterplot(), lineplot(), relplot() (figure-level).
   • Distribution: histplot(), kdeplot(), ecdfplot(), rugplot(), displot(), jointplot(), pairplot().
   • Categorical: stripplot(), swarmplot(), boxplot(), violinplot(), boxenplot(), barplot(), pointplot(), countplot(), catplot().
   • Regression: regplot(), lmplot(), .

Pitfalls

• Figure-Level vs Axes-Level: Figure-level functions (relplot, displot, catplot, lmplot, jointplot, pairplot) manage the entire figure and cannot be placed in existing matplotlib subplots using ax=. Use axes-level functions (scatterplot, histplot, etc.) for custom layouts.
• Data Format: Seaborn prefers long-form (tidy) data where each variable is a column. Wide-form data works for simple cases but limits flexibility. Convert using df.melt(var_name='condition', value_name='measurement').
• Overlapping Labels: Rotate x-ticks and use tight layout: plt.xticks(rotation=45, ha='right'); plt.tight_layout().
• KDE Smoothness: If KDE is too smooth or jagged, adjust bandwidth: sns.kdeplot(data=df, x='x', bw_adjust=0.5).
• Legend Position: Figure-level functions place legends outside. Move using g._legend.set_bbox_to_anchor((0.9, 0.5)).
• Colors Not Distinct: Use sns.set_palette("bright") or specify n_colors in sns.color_palette("husl", n_colors=len(df['category'].unique())).

Verification

1. Check Library Installation:

   python -c "import seaborn; print(seaborn.__version__)"
   

   Expected output: A version number (e.g., 0.12.2).

2. Run Test Plot:

   import seaborn as sns
   import matplotlib.pyplot as plt
   df = sns.load_dataset('tips')
   sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
   plt.show()
   

   Expected output: A scatter plot window displaying total bill vs tip, colored by day.

Official docs

Function names, parameters, and gallery patterns live in seaborn's docs (v0.13), not in this folder:

• Function API: https://seaborn.pydata.org/api.html
• Objects interface (seaborn.objects.Plot): https://seaborn.pydata.org/tutorial/objects_interface.html
• Example gallery: https://seaborn.pydata.org/examples/index.html