Mastering Seaborn: A Visual Infographic

🌈 Seaborn Master Reference Table

Your quick guide to high-level statistical plotting. Click any function to visit the official Seaborn documentation.

Category	Core / Must-Know Functions & Methods	Other Useful Functions & Methods
Relational Plots	sns.scatterplot() sns.lineplot()	sns.relplot() sns.regplot() sns.lmplot()
Categorical Plots	sns.barplot() sns.countplot() sns.boxplot() sns.violinplot() sns.stripplot() sns.swarmplot()	sns.catplot() sns.pointplot() sns.boxenplot()
Distribution Plots	sns.histplot() sns.kdeplot() sns.displot()	sns.ecdfplot() sns.rugplot()
Matrix Plots / Heatmaps	sns.heatmap() sns.clustermap()	sns.pairplot() sns.jointplot()
Regression Plots	sns.regplot() sns.lmplot()	sns.residplot() sns.jointplot(kind='reg')
Themes & Styles	sns.set_style() sns.set_context() sns.set_palette() sns.set_theme()	sns.despine() sns.color_palette() sns.axes_style()
Faceting / Multi-Plot Grids	sns.FacetGrid() sns.PairGrid()	sns.JointGrid() .map() .map_dataframe()

📚 Key Documentation Links

Seaborn API Reference → The complete API reference for all Seaborn functions.

Seaborn Tutorials & Examples → In-depth guides and examples for creating beautiful plots.

Key Concepts (The Seaborn Way)

Seaborn's philosophy for high-level plotting.

Tidy DataData-AwareFigure-levelAxes-levelSemantic MappingMatplotlib Backend

Core Functions (The Starting Points)

The building blocks for any Seaborn visualization.

sns.set_theme()sns.load_dataset()sns.relplot()sns.displot()sns.catplot()sns.lmplot()

Recommended Learning Path

A structured approach to creating statistical graphics with Seaborn.

Load Data & Set Style

sns.load_dataset('tips'), sns.set_theme()

Choose a Plot

sns.scatterplot(data=..., x=..., y=...)

Add Semantics

hue='smoker', style='time', size='size'

Customize & Show

plt.title('My Plot'), plt.show()

Core Plot Categories

Seaborn groups its functions into logical categories for easy discovery.

Relational Plots

Understand relationships between variables. (`scatterplot`, `lineplot`)

Distribution Plots

Visualize the distribution of a dataset. (`histplot`, `kdeplot`, `ecdfplot`)

Categorical Plots

Explore relationships with categorical data. (`barplot`, `boxplot`, `violinplot`)

Matrix Plots

Visualize matrix data as color-encoded grids. (`heatmap`, `clustermap`)

Full A-Z Quick Reference

A searchable list of the most common Seaborn functions.

Function	Description

Complete Function Inventory

A visual guide to the most common functions in seaborn.

Plotting Functions

barplot()

Bar plots with confidence intervals.

sns.barplot(data=df, x="day", y="total_bill")

boxplot()

Box and whisker plots.

sns.boxplot(data=df, x="day", y="total_bill")

catplot()

Figure-level interface for categorical plots.

sns.catplot(data=df, x="day", y="total_bill", kind="box", col="time")

countplot()

Show counts of categories.

sns.countplot(data=df, x="day")

displot()

Figure-level interface for distribution plots.

sns.displot(data=df, x="total_bill", col="time", kind="kde")

distplot() (deprecated)

Histogram with KDE.

sns.distplot(df["total_bill"])

ecdfplot()

Empirical Cumulative Distribution Function.

sns.ecdfplot(data=df, x="total_bill")

histplot()

Histogram with optional KDE.

sns.histplot(data=df, x="total_bill", kde=True)

jointplot()

Scatter plot with marginal distributions.

sns.jointplot(data=df, x="total_bill", y="tip")

kdeplot()

Kernel Density Estimate plot.

sns.kdeplot(data=df, x="total_bill")

lmplot()

Figure-level regression plots.

sns.lmplot(data=df, x="total_bill", y="tip", col="time")

pairplot()

Scatter plot matrix.

sns.pairplot(df)

pointplot()

Point estimates with confidence intervals.

sns.pointplot(data=df, x="day", y="tip")

regplot()

Linear regression plot.

sns.regplot(data=df, x="total_bill", y="tip")

relplot()

Figure-level relational plots.

sns.relplot(data=df, x="total_bill", y="tip", hue="smoker", col="time")

residplot()

Residual plot for regression.

sns.residplot(data=df, x="total_bill", y="tip")

rugplot()

Marginal tick plots.

sns.rugplot(data=df, x="total_bill")

scatterplot()

Scatter plot with grouping.

sns.scatterplot(data=df, x="total_bill", y="tip", hue="smoker")

stripplot()

Scatter plot for categorical data.

sns.stripplot(data=df, x="day", y="total_bill")

swarmplot()

Non-overlapping categorical scatter plot.

sns.swarmplot(data=df, x="day", y="total_bill")

violinplot()

Violin plots.

sns.violinplot(data=df, x="day", y="total_bill")

Figure-Level Functions

FacetGrid()

Multi-plot grid for conditional relationships.

g = sns.FacetGrid(df, col="time", row="sex")
g.map(sns.scatterplot, "total_bill", "tip")

PairGrid()

Grid for pairwise relationships.

g = sns.PairGrid(df)
g.map(sns.scatterplot)

JointGrid()

Grid for joint and marginal distributions.

g = sns.JointGrid(data=df, x="total_bill", y="tip")
g.plot(sns.scatterplot, sns.histplot)

Styling Functions

Control the look and feel of your plots with these aesthetic functions.

set()

Set aesthetic parameters in one step.

sns.set(style="whitegrid", palette="muted")

set_style()

Set the aesthetic style of the plots.

sns.set_style("darkgrid")

set_context()

Set plotting context parameters.

sns.set_context("talk")

set_palette()

Set the color palette for all plots.

sns.set_palette("viridis")

axes_style()

Return parameters of a style, or temporarily set style.

with sns.axes_style("whitegrid"):
    sns.boxplot(...)

plotting_context()

Return parameters of a context, or temporarily set context.

with sns.plotting_context("paper"):
    sns.lineplot(...)

color_palette()

Return a list of colors defining a palette.

sns.color_palette("pastel")

diverging_palette()

Make a diverging palette between two HUSL colors.

sns.diverging_palette(220, 20, as_cmap=True)

light_palette() / dark_palette()

Make a sequential palette from a single color.

sns.light_palette("green", as_cmap=True)

cubehelix_palette()

Make a sequential palette from the cubehelix system.

sns.cubehelix_palette(as_cmap=True)

husl_palette() / hls_palette()

Make a palette with evenly spaced hues in HUSL or HLS.

sns.husl_palette(n_colors=8)

blend_palette()

Make a palette by blending a list of colors.

sns.blend_palette(["blue", "pink"], n_colors=10)

xkcd_palette()

Make a palette with colors from the xkcd color survey.

sns.xkcd_palette(["windows blue", "amber"])

Critical Functions (The 20% You'll Use 80% of the Time)

The minimal set needed for the vast majority of data visualization tasks.

scatterplot()

Fundamental visualization for relationships.

sns.scatterplot(data=df, x="bill", y="tip")

lineplot()

Essential for time series and trend visualization.

sns.lineplot(data=df, x="date", y="price")

barplot()

Most common categorical comparison.

sns.barplot(data=df, x="day", y="total_bill")

histplot()

Primary distribution visualization.

sns.histplot(data=df, x="age")

boxplot()

Standard statistical representation.

sns.boxplot(data=df, x="class", y="score")

heatmap()

Critical for correlation and matrix data.

sns.heatmap(df.corr(), annot=True)

pairplot()

Key EDA tool for multivariate data.

sns.pairplot(df, hue="species")

lmplot() / regplot()

Core regression visualization.

sns.regplot(data=df, x="bill", y="tip")

FacetGrid()

Enables complex multi-plot layouts.

g = sns.FacetGrid(df, col="time")
g.map(sns.histplot, "tip")

set_style() / set_palette()

Critical for visual polish and aesthetics.

sns.set_style("whitegrid")
sns.set_palette("flare")

Important Functions (The Supporting Cast)

Enhance critical plots and provide essential supporting functionality.

violinplot()

Enhanced distribution view combining a boxplot with a KDE.

sns.violinplot(data=df, x="day", y="tip")

stripplot() / swarmplot()

Categorical scatter variants for individual data points.

sns.swarmplot(data=df, x="day", y="total_bill")

kdeplot()

Smooth density visualization using Kernel Density Estimation.

sns.kdeplot(data=df, x="total_bill", hue="time")

jointplot()

Visualize bivariate distributions with marginal plots.

sns.jointplot(data=df, x="bill", y="tip", kind="hex")

countplot()

Show the counts of observations in each category.

sns.countplot(data=df, x="day", hue="sex")

relplot() / catplot()

Figure-level interfaces for relational and categorical plots.

sns.catplot(data=df, x="day", y="tip", kind="swarm")

displot()

Modern figure-level interface for distribution plots.

sns.displot(data=df, x="bill", kind="ecdf")

color_palette()

Define or return a color palette for customization.

my_palette = sns.color_palette("pastel")
sns.barplot(..., palette=my_palette)

load_dataset()

Convenient access to example datasets.

tips_df = sns.load_dataset("tips")

despine()

Aesthetic refinement by removing top and right spines.

sns.lineplot(...)
sns.despine(left=True)

Utility Functions (The Tool-belt)

Convenience functions for data, aesthetics, and plot management.

load_dataset()

Load an example dataset from the online repository.

flights_df = sns.load_dataset("flights")

despine()

Remove the top and right axis spines from a plot.

sns.lineplot(...)
sns.despine()

mpl.pyplot.show()

Display all open figures (from Matplotlib).

import matplotlib.pyplot as plt
sns.histplot(...)
plt.show()

move_legend()

Re-position a plot's legend.

g = sns.lineplot(...)
sns.move_legend(g, "lower left")

clustermap()

Plot a hierarchically-clustered heatmap.

sns.clustermap(df, metric="correlation")

heatmap()

Create an annotated heatmap of matrix data.

sns.heatmap(df.corr(), annot=True, fmt=".2f")

lineplot() (aggregation)

Line plot with confidence interval aggregation.

sns.lineplot(data=df, x="time", y="value")

palplot()

Plot the values in a color palette as a horizontal array.

sns.palplot(sns.color_palette("husl", 8))

Advanced & Internal Utilities

These are less common, used for specific cases or internally by other Seaborn functions.

regression.regplot() utils.ci_to_errsize() utils.get_dataset_names() axisgrid.Grid() _DistributionPlotter _DendrogramPlotter

🌈 Seaborn Plotting Examples

Creating advanced statistical graphics from a Pandas DataFrame with simple commands.

Step 1: The Sample Dataset

Monthly Sales Data

Month	Product A	Product B	Product C
Jan	150	200	180
Feb	180	210	190
Mar	170	220	200
Apr	160	230	210
May	190	240	220

Create DataFrame

We start with the same Pandas DataFrame.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = {
    "Month": ["Jan", "Feb", "Mar", "Apr", "May"],
    "Product A": [150, 180, 170, 160, 190],
    "Product B": [200, 210, 220, 230, 240],
    "Product C": [180, 190, 200, 210, 220]
}
df = pd.DataFrame(data)

Step 2: Convert to Long-Form Data (Tidy Data)

For many categorical plots, Seaborn works best with "long-form" or "tidy" data, where each row is a single observation. We use `df.melt()` for this transformation.

Using `df.melt()`

df_melted = df.melt(
    id_vars="Month", 
    var_name="Product", 
    value_name="Sales"
)

Resulting `df_melted`

"Wide" format (3 columns of values) is transformed into "Long" format (1 column for categories, 1 for values).

Wide DF → Long DF

1. Relational Plots

Line Plot

sns.lineplot(data=df_melted, x="Month", y="Sales", hue="Product", marker="o")
plt.title("Monthly Sales Line Plot")
plt.show()

Line Plot Visualization

2. Categorical Plots

Bar Plot

sns.barplot(data=df_melted, x="Month", y="Sales", hue="Product")
plt.title("Monthly Sales Bar Plot")
plt.show()

Bar Plot Visualization

Box Plot

sns.boxplot(data=df_melted, x="Product", y="Sales")
plt.title("Sales Distribution per Product")
plt.show()

Box Plot Visualization

3. Distribution Plots

Histogram + KDE

sns.histplot(df["Product A"], bins=5, kde=True)
plt.title("Product A Sales Distribution")
plt.show()

Histogram Visualization

4. Matrix / Heatmap Plots

Correlation Heatmap

corr = df.drop("Month", axis=1).corr()
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.title("Product Correlation")
plt.show()

Heatmap Visualization

1.0

0.4

0.2

0.4

1.0

0.9

0.2

0.9

1.0

5. Regression & Statistical Plots

Regression Plot

sns.regplot(x=df.index, y=df["Product A"])
plt.title("Product A Trend")
plt.show()

Regression Plot Visualization

6. Themes & Styles

Styling a Plot

sns.set_style("whitegrid")
sns.set_context("talk")
sns.set_palette("Set2")

sns.lineplot(data=df, x="Month", y="Product A")
plt.title("Styled Plot Example")
plt.show()

Month	Product A	Product B	Product C
Jan	150	200	180
Feb	180	210	190
Mar	170	220	200
Apr	160	230	210
May	190	240	220

Month	Product A	Product B	Product C
Jan	150	200	180
Feb	180	210	190
Mar	170	220	200
Apr	160	230	210
May	190	240	220

Month	Product A	Product B	Product C
Jan	150	200	180
Feb	180	210	190
Mar	170	220	200
Apr	160	230	210
May	190	240	220