An Introduction to Seaborn Library in Python

Python evolved from a coding language, web development language, and automating tasks to a crucial player in data visualization by adding Matplotlib and Seaborn libraries.

Initially, Python lacked strong data visualization features but after the introduction of NumPy, and SciPy libraries, it gained attention among scientists, engineers, and researchers and has become a popular choice for their computational work. The release of Matplotlib in 2003 offered Python basic plotting and visualization capabilities. The introduction of Seaborn in Python changed the game by enhancing Python’s data visualization capabilities with high-level interface and specialized statistical visualizations enabling the end user to get valuable insights from raw data.

Introduction to Seaborn Library:

Seaborn, introduced in 2012 by Michael Waskom, a PhD student at UC Berkeley, aimed to simplify statistical data visualization in Python. Its release on GitHub in 2013 marked the beginning of its widespread adoption and continuous improvement. The active user community has played a crucial role in refining Seaborn's capabilities. Interestingly, Waskom drew inspiration from "Sam Seaborn" of The West Wing for the library's name and the import convention "sns."

Seaborn architecture:

Seaborn is built upon matplotlib and panda data structures, imagine like one stacked upon another in an architectural framework.

Pandas form the base layer, providing data structures like Data Frames to store and manipulate the tabular data, Matplotlib is the next layer, which acts like a low-level plotting library that does actual visualizations. Seaborn is built on top of Matplotlib, utilizing its capabilities while providing a higher-level interface customized for statistical data visualization.

Advantages over Matplotlib:

The advantages of Seaborn over Matplotlib are

When to use Seaborn and Matplotlib:

Both Seaborn and Matplotlib are powerful visualization libraries in Python. While Matplotlib provides a wide range of customizable options for creating basic plots, Seaborn offers more advanced statistical visualizations with less code.

If you need to create simple plots or customize your graphs extensively, Matplotlib is a great choice. On the other hand, if you want to create more complex visualizations with minimal code and display advanced statistical information, Seaborn is the way to go.

In the end, the choice between Seaborn and Matplotlib depends on your specific needs and preferences. It’s always a good idea to experiment with both libraries and see which one works best for you.

Installation of seaborn:

One can install Seaborn using the below command,

pip install seaborn

This will result in the basic invocation of pip will install seaborn.

If one wants to include optional dependencies that give access to a few advanced features

pip install seaborn[stats]

The library is also included as part of the Anaconda distribution, and it can be installed with conda.

conda install seaborn

Mandatory dependencies for installing seaborn are numpy, pandas, matplotlib. For that we have to use below commands.

• import numpy as np

• import pandas as pd

• import matplotlib as mpl

• import matplotlib.pyplot as plt

• import seaborn as sns

Charts in Seaborn:

Seaborn offers a wide range of plot types to visualize different types of data and relationships, one can categorize them based on chart purpose and the type of data they want to visualize.

1. Relational plots:

As the name suggests, these charts explore the statistical relationship between two or more variables in a dataset, help in understanding the relationship between each other. In these plots, the relationship between two numerical variables or between one numerical variable and categorical variable could be explored.

o   Scatter Plot: Visualizes the relationship between two continuous variables.

The syntax is sns.scatterplot()

o   Line Plot: It plots one continuous variable against another, typically over time.

The syntax is sns.lineplot()

o   Joint Plot: It displays the joint distribution of two variables along with their distributions.

The syntax is sns.jointplot()

2. Distribution Plots:

As the name says, these plots describe univariate and bivariate distributions.

o   Histogram: Plots distribution of a single variable.

Syntax is sns.histplot()

o   KDE Plot: Plots the univariate or bivariate distribution of a dataset using kernel density estimation.

Syntax is sns.kdeplot()

o   Rug Plot: Plots data points in an array as sticks on an axis. It shows marginal distribution.

Syntax is sns.rugplot()

3. Categorical Plots:

These plots visualize the relationship between a numerical variable and one or more categorical variables.

o   Bar Plot: Shows the relationship between a categorical variable and a continuous variable.

Syntax is sns.barplot()

o   Box Plot: Summarizes the distribution of a continuous variable across levels of one or more categorical variables.

Syntax is sns.boxplot()

o   Violin Plot: It is similar to a box plot, but provides a rich description of the distribution by showing the kernel density estimation.

Syntax is sns.violinplot()

o   Swarm Plot: Shows each observation in a dataset along with a categorical variable.

Syntax is sns.swarmplot()

o   Point Plot: Shows point estimates and confidence intervals using categorical data.

Syntax is sns.pointplot()

4. Matrix Plots:

It can be used to visualize high-dimensional data and relationships between multiple variables.

o   Heatmap: Plots rectangular data as a color-encoded matrix.

Syntax is sns.heatmap()

o   Clustermap: Plots a matrix dataset as a hierarchically clustered heatmap.

Syntax is sns.clustermap()

5. Regression Plots:

This plot visualizes the linear regression model and fit.

o   lmplot: Plots data and regression model fits across a Facet Grid.

Syntax is sns.lmplot()

o   regplot: plots data and a linear regression model fit.

Syntax is sns.regplot()

o   Residplot: plots the residuals of a linear regression.

Syntax is sns.residplot()

6. Pairwise Plots:

This plots pairwise relationship in a dataset as a matrix of scatter plots.

o   Pairplot: Plots pairwise relationships in a dataset.

Syntax is sns.pairplot()

o   Pairgrid: It shows a subplot grid for plotting pairwise relationships in a dataset.

Syntax is sns.PairGrid()

7. Time Series Plots:

This plot supports time series visualization through relational plots like line plot when working with time-indexed data.

o   Time Series Plot: Plots time series data.

Syntax is sns.lineplot()

These are some of the commonly used plot types in Seaborn, but the library offers even more specialized plots and customization options for advanced data visualization needs. Each plot type has its own set of parameters for customization, allowing you to create a wide variety of visualizations tailored to your specific data and analysis goals.

Tips and best practices for effective data visualization:

These could be summarized as:

Conclusion:

Seaborn is a powerful data visualization library in Python offering a wide range of plot types, using best practices, one can create effective and useful visualizations representing your data story ultimately serving its purpose.

References:

https://seaborn.pydata.org