Seaborn Complete Notes

What is Seaborn?

Seaborn is a Python data visualization library built on top of Matplotlib. It helps to:

Create beautiful statistical plots
Reduce plotting code
Improve plot styling automatically
Visualize complex datasets easily

Used in:

Data Science
Machine Learning
Data Analysis

Installing Seaborn

Installation

pip install seaborn

Importing Libraries

import seaborn as sns
import pandas as pd
import numpy as np

Explanation

sns → seaborn alias
pd → pandas
np → numpy

Loading Dataset in Seaborn

Shows available built-in datasets.

sns.get_dataset_names()

Output

List of datasets like:

['penguins', 'tips', 'iris', 'diamonds', ...]

Loads built-in dataset.

penguins = sns.load_dataset('penguins')

penguins.head()

Output

species island bill_length_mm ...

Explanation

Loads penguins dataset into DataFrame.

`value_counts()`

Counts category occurrences.

penguins['species'].value_counts()

Output

Adelie       152
Gentoo       124
Chinstrap     68

Explanation

Counts penguins species frequency.

Scatter Plot

Used to visualize relationship between two numerical variables.

sns.scatterplot(
    data=penguins,
    x='flipper_length_mm',
    y='body_mass_g',
    hue='island'
)

Explanation

x → x-axis variable
y → y-axis variable
hue → color grouping

Output

Scatter plot grouped by island colors.

Styling in Seaborn

Changes plot background style.

sns.set_style('whitegrid')

Available Styles

white
dark
whitegrid
darkgrid
ticks

Removes plot borders/spines.

sns.despine(left=True)

Explanation

Removes left spine.

Controls scaling of plot elements.

sns.set_context('talk')

Context Types

Context	Usage
paper	Small plots
notebook	Default
talk	Presentation
poster	Large displays

Palette

`palette`

Controls color theme.

sns.scatterplot(
    data=penguins,
    x='flipper_length_mm',
    y='body_mass_g',
    hue='island',
    palette='Dark2'
)

Explanation

Uses Dark2 color palette.

Scatter Plot with Style and Alpha

sns.scatterplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='island',
    style='sex',
    alpha=0.5
)

Explanation

style → marker style changes
alpha → transparency

Strip Plot

Shows distribution of categorical data.

sns.stripplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='island'
)

Output

Categorical scatter-like plot.

`dodge=True`

Separates hue categories.

sns.stripplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='island',
    dodge=True
)

`jitter=True`

Adds random spacing.

sns.stripplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='island',
    dodge=True,
    jitter=True
)

Explanation

Avoids overlapping points.

Swarm Plot

Automatically prevents overlap.

sns.swarmplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='island'
)

Output

Bee swarm arrangement of points.

Histogram

Shows data distribution.

sns.histplot(
    data=penguins,
    x='body_mass_g',
    hue='sex',
    multiple='stack'
)

Explanation

multiple='stack' → stacked histogram

Regression Plot

Adds regression trend line.

sns.regplot(
    data=penguins,
    x='body_mass_g',
    y='flipper_length_mm',
    color='green'
)

Explanation

Shows linear relationship between variables.

Line Plot

Shows continuous trends.

sns.lineplot(
    data=penguins,
    x='body_mass_g',
    y='flipper_length_mm',
    hue='island',
    style='sex'
)

Explanation

Different colors → islands
Different styles → sex

Joint Plot

Combines scatter plot + distributions.

sns.jointplot(
    data=penguins,
    x='body_mass_g',
    y='flipper_length_mm',
    kind='scatter'
)

Output

Central scatter plot with side histograms.

KDE Joint Plot

sns.jointplot(
    data=penguins,
    x='body_mass_g',
    y='flipper_length_mm',
    hue='sex',
    kind='kde'
)

Explanation

Uses density estimation instead of scatter points.

Bar Plot

Shows average values by category.

sns.barplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='sex',
    palette=['red','blue']
)

Explanation

Compares mean body mass.

Count Plot

Counts categorical occurrences.

sns.countplot(
    data=penguins,
    x='species'
)

Output

Bar chart of species counts.

Box Plot

Shows:

median
quartiles
outliers

sns.boxplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='sex'
)

Output

Distribution comparison across species.

Violin Plot

Combines boxplot + density plot.

sns.violinplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='sex'
)

Explanation

Width shows density of values.

Split Violin Plot

sns.violinplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='sex',
    split=True
)

Explanation

Male and female shown in one violin.

Inner Quartiles

sns.violinplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    hue='sex',
    split=True,
    inner='quartile'
)

Explanation

Shows quartile lines inside violin.

Swarm + Violin Combined

sns.violinplot(data=penguins,
               x='species',
               y='body_mass_g')

sns.swarmplot(
    data=penguins,
    x='species',
    y='body_mass_g',
    color='black',
    size=3
)

Explanation

Combines density + individual points.

KDE Plot

Smooth probability density curve.

sns.kdeplot(
    data=penguins,
    x='body_mass_g',
    hue='species',
    fill=True
)

Explanation

Smooth histogram alternative
fill=True fills area

Heatmap

Displays matrix with colors.

columns = [
    "bill_length_mm",
    "bill_depth_mm",
    "flipper_length_mm",
    "body_mass_g"
]

penguins_corr = penguins[columns].corr()

sns.heatmap(
    data=penguins_corr,
    annot=True,
    vmin=-0.2
)

Explanation

corr() → correlation matrix
annot=True → show values
vmin → minimum color scale

Output

Correlation heatmap.

Rug Plot

Shows individual data points as ticks.

sns.rugplot(
    data=penguins,
    x='body_mass_g',
    hue='species',
    palette='Set2'
)

Output

Small tick marks along axis.

Pair Plot

Creates pairwise plots automatically.

sns.pairplot(
    data=penguins,
    hue='species'
)

Output

Grid of scatter plots and histograms.

Pair Plot with Histogram

sns.pairplot(
    data=penguins,
    hue='species',
    diag_kind='hist'
)

Explanation

Diagonal uses histograms instead of KDE.

Pair Grid

Custom subplot grid.

g = sns.PairGrid(
    data=penguins,
    hue='sex',
    palette='Set2'
)

g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.map_diag(sns.histplot)

g.add_legend()

Explanation

map_upper() → upper triangle plots
map_lower() → lower triangle plots
map_diag() → diagonal plots

Output

Fully customized pairwise visualization grid.

Seaborn Plot Summary

Plot	Purpose
scatterplot	Relationship between variables
stripplot	Categorical spread
swarmplot	Non-overlapping stripplot
histplot	Distribution
regplot	Regression trend
lineplot	Continuous trends
jointplot	Combined distributions
barplot	Average comparison
countplot	Frequency count
boxplot	Outlier detection
violinplot	Density + boxplot
kdeplot	Smooth distribution
heatmap	Correlation matrix
rugplot	Individual data ticks
pairplot	Automatic pair relationships
PairGrid	Custom pairwise plots

Important Seaborn Functions

Function	Purpose
`set_style()`	Plot style
`set_context()`	Scaling
`despine()`	Remove borders
`scatterplot()`	Scatter plot
`histplot()`	Histogram
`regplot()`	Regression line
`lineplot()`	Line graph
`barplot()`	Bar chart
`countplot()`	Count categories
`boxplot()`	Quartiles & outliers
`violinplot()`	Density visualization
`heatmap()`	Matrix heatmap
`pairplot()`	Pairwise analysis

Seaborn Helps

Seaborn helps to:

Create attractive statistical plots
Analyze distributions
Detect patterns
Understand correlations
Visualize categorical and numerical data easily

Advantages:

Less code
Better styling
Easy integration with Pandas
Built on Matplotlib

​Seaborn Complete Notes

​What is Seaborn?

​Installing Seaborn

​Installation

​Importing Libraries

​Explanation

​Loading Dataset in Seaborn

​sns.get_dataset_names()

​Output

​sns.load_dataset()

​Output

​Explanation

​value_counts()

​Output

​Explanation

​Scatter Plot

​sns.scatterplot()

​Explanation

​Output

​Styling in Seaborn

​sns.set_style()

​Available Styles

​sns.despine()

​Explanation

​sns.set_context()

​Context Types

​Palette

​palette

​Explanation

​Scatter Plot with Style and Alpha

​Explanation

​Strip Plot

​sns.stripplot()

​Output

​dodge=True

​jitter=True

​Explanation

​Swarm Plot

​sns.swarmplot()

​Output

​Histogram

​sns.histplot()

​Explanation

​Regression Plot

​sns.regplot()

​Explanation

​Line Plot

​sns.lineplot()

​Explanation

​Joint Plot

​sns.jointplot()

​Output

​KDE Joint Plot

​Explanation

​Bar Plot

​sns.barplot()

​Explanation

​Count Plot

​sns.countplot()

​Output

​Box Plot

​sns.boxplot()

​Output

​Violin Plot

​sns.violinplot()

​Explanation

​Split Violin Plot

​Explanation

​Inner Quartiles

​Explanation

​Swarm + Violin Combined

​Explanation

​KDE Plot

​sns.kdeplot()

​Explanation

​Heatmap

​sns.heatmap()

​Explanation

​Output

​Rug Plot

Seaborn Complete Notes

What is Seaborn?

Installing Seaborn

Installation

Importing Libraries

Explanation

Loading Dataset in Seaborn

`sns.get_dataset_names()`

Output

`sns.load_dataset()`

Output

Explanation

`value_counts()`

Output

Explanation

Scatter Plot

`sns.scatterplot()`

Explanation

Output

Styling in Seaborn

`sns.set_style()`

Available Styles

`sns.despine()`

Explanation

`sns.set_context()`

Context Types

Palette

`palette`

Explanation

Scatter Plot with Style and Alpha

Explanation

Strip Plot

`sns.stripplot()`

Output

`dodge=True`

`jitter=True`

Explanation

Swarm Plot

`sns.swarmplot()`

Output

Histogram

`sns.histplot()`

Explanation

Regression Plot

`sns.regplot()`

Explanation

Line Plot

`sns.lineplot()`

Explanation

Joint Plot

`sns.jointplot()`

Output

KDE Joint Plot

Explanation

Bar Plot

`sns.barplot()`

Explanation

Count Plot

`sns.countplot()`

Output

Box Plot

`sns.boxplot()`

Output

Violin Plot

`sns.violinplot()`

Explanation

Split Violin Plot

Explanation

Inner Quartiles

Explanation

Swarm + Violin Combined

Explanation

KDE Plot

`sns.kdeplot()`

Explanation

Heatmap

`sns.heatmap()`

Explanation

Output

Rug Plot