d1 matplotlib & seaborn libraries Part 2

Seaborn is a data visualization library built on top of matplotlib and closely integrated with pandas data structures in Python. Visualization is the central part of Seaborn which helps in exploration and understanding of data.
One has to be familiar with Numpy and Matplotlib and Pandas to learn about Seaborn.
Seaborn offers the following functionalities:
Dataset oriented API to determine the relationship between variables.
Automatic estimation and plotting of linear regression plots.
It supports high-level abstractions for multi-plot grids.
Visualizing univariate and bivariate distribution.
These are only some of the functionalities offered by Seaborn, there are many more of them, and we can explore all of them here.
To initialize the Seaborn library, the command used is:
import seaborn as sns
Using Seaborn we can plot wide varieties of plots like:
Distribution Plots
Pie Chart & Bar Chart
Scatter Plots
Pair Plots
Heat maps
For this entirety of the article, we are using the dataset of Google Playstore downloaded from Kaggle.
1. Distribution Plots
We can compare the distribution plot in Seaborn to histograms in Matplotlib. They both offer pretty similar functionalities. Instead of frequency plots in the histogram, here we’ll plot an approximate probability density across the y-axis.
We will be using sns.distplot() in the code to plot distribution graphs.
Before going further, first, let’s access our dataset,

Seaborn is a library for making statistical graphics in Python. It is built on top of matplotlib and closely integrated with pandas data structures.

Here is some of the functionality that seaborn offers:

A dataset-oriented API for examining relationships between multiple variables
Specialized support for using categorical variables to show observations or aggregate statistics
Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
Automatic estimation and plotting of linear regression models for different kinds dependent variables
Convenient views onto the overall structure of complex datasets
High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
Concise control over matplotlib figure styling with several built-in themes
Tools for choosing color palettes that faithfully reveal patterns in your data
Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.

The Seaborn library is built on top of Matplotlib and offers many advanced data visualization capabilities.

Though, the Seaborn library can be used to draw a variety of charts such as matrix plots, grid plots, regression plots etc., in this article we will see how the Seaborn library can be used to draw distributional and categorial plots. In the second part of the series, we will see how to draw regression plots, matrix plots, and grid plots.

Through this video, we will discuss the following points in detail:

How to use Seaborn
Visualizing different statistical charts
Various plotting functions in Seaborn
Different parameters for seaborn visualization.
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Data Visualization is an accessible way to represent the patterns, outliers, anomalies, etc. that are available in data by plotting graphs and charts. Data Visualization is a powerful tool because as soon as the human eyes see a chart or plot they try to find out a pattern in it because we get attracted to colours and patterns. Python provides different visualization libraries but Seaborn is the most commonly used library for statistical data visualization.

It can be used to build almost each and every statistical chart. It is built on matplotlib which is also a visualization library. Seaborn provides highly attractive and informative charts/plots. It is easy to use and is blazingly fast. Seaborn is a dataset oriented plotting function that can be used on both data frames and arrays. It enhances the visualization power of matplotlib which is only used for basic plotting like a bar graph, line chart, pie chart, etc.Through this article, we will discuss the following points in detail:

How to use Seaborn
Visualizing different statistical charts
Various plotting functions in Seaborn
Different parameters for seaborn visualization.