Seaborn - Introduction

In the world of Analytics, the best way to get insights is by visuapzing the data. Data can be visuapzed by representing it as plots which is easy to understand, explore and grasp. Such data helps in drawing the attention of key elements.

To analyse a set of data using Python, we make use of Matplotpb, a widely implemented 2D plotting pbrary. Likewise, Seaborn is a visuapzation pbrary in Python. It is built on top of Matplotpb.

Seaborn Vs Matplotpb

It is summarized that if Matplotpb “tries to make easy things easy and hard things possible”, Seaborn tries to make a well-defined set of hard things easy too.”

Seaborn helps resolve the two major problems faced by Matplotpb; the problems are −

Default Matplotpb parameters

Working with data frames

As Seaborn comppments and extends Matplotpb, the learning curve is quite gradual. If you know Matplotpb, you are already half way through Seaborn.

Important Features of Seaborn

Seaborn is built on top of Python’s core visuapzation pbrary Matplotpb. It is meant to serve as a complement, and not a replacement. However, Seaborn comes with some very important features. Let us see a few of them here. The features help in −

Built in themes for stypng matplotpb graphics

Visuapzing univariate and bivariate data

Fitting in and visuapzing pnear regression models

Plotting statistical time series data

Seaborn works well with NumPy and Pandas data structures

It comes with built in themes for stypng Matplotpb graphics

In most cases, you will still use Matplotpb for simple plotting. The knowledge of Matplotpb is recommended to tweak Seaborn’s default plots.