A concise guide to Seaborn for creating attractive and informative statistical graphics in Python.

Installation

pip install seaborn

Importing Seaborn

import seaborn as sns
import matplotlib.pyplot as plt

1. Relational Plots

Function: sns.relplot()

Creates scatter or line plots to show relationships between variables.

- Example: Scatter Plot

sns.relplot(data=tips, x="total_bill", y="tip", hue="time", kind="scatter")

- Example: Line Plot

sns.relplot(data=tips, x="size", y="tip", kind="line", hue="day", style="time")

Common Attribute for Relational Plots

2. Distribution Plots

Function: sns.displot()

Visualizes distributions with histograms, KDEs, rugs, or ECDFs.

- Histogram Example

sns.displot(data=penguins, x="flipper_length_mm", kind="hist", bins=20)

- KDE Plot Example

sns.displot(data=penguins, x="flipper_length_mm", kind="kde", fill=True)

Common Attribute for Distribution Plots

3. Categorical Plots

Function: sns.catplot()

Visualizes categorical data using multiple plot types.

- Example: Bar Plot

sns.catplot(data=tips, x="day", y="total_bill", kind="bar", hue="sex")

- Example: Violin Plot

sns.catplot(data=tips, x="day", y="total_bill", kind="violin", split=True, hue="sex")

Common Attribute for Categorical Plots

4. Color Palettes (palette)

Custom Palettes

• HUSL Palette: sns.color_palette("husl", n_colors=8)
• CUBEHELIX Palette: sns.color_palette("cubehelix", n_colors=8)
• Custom HEX Colors: ["#4c72b0", "#55a868", "#c44e52"]
• Blend Two Colors: sns.blend_palette(["red", "blue"], n_colors=8)

Style Attributes

Advanced Distribution Attributes

Common Attributes for All Plots