Python Seaborn

                                                                      Python Seaborn

Definition of Seaborn

Seaborn is a Python package that allows you to build easy-to-manage visuals. Seaborn is a Python package that helps to visualize data and make it more understandable to the user. We may plot our data and create a graphical representation of it with the help of the library. Internally, this library makes use of matplotlib; in other words, it is entirely built on matplotlib. This also makes it easier to produce visually appealing and more informative data visualizations. The Panda's data structure is connected with this library. In this course, we'll look at how to use a seaborn library to create interactive visuals and learn more about how it works.

Why Do We Need Seaborn?

We always have a lot of data, or we may have some applications that deal with a lot of data, thus in order to represent it well, we may need a library that can efficiently represent data that is first saved in a table, array, list, or other data structure. So seaborn is a library that can display data stored in an array, list, or any other data structure in a graphical format, which aids users and developers in understanding data when dealing with enormous amounts of data in their applications. So, to visualize our data, we can use the python seaborn library, which is solely based on matplotlib. This can be used for model fitting and data visualization, among other things. This library is data-oriented. However, it must first be installed before it can be used.

The Objective of Python Seaborn Library

The goal of the Seaborn library is to make the central component of comprehending and analyzing data more visually appealing. It is based on the matplotlib library's core and has dataset-oriented APIs.

Seaborn is also tightly interwoven with Panda's data structures, allowing us to quickly switch between the multiple visual representations for a specific variable in order to better comprehend the information.

Categories of Plots in Python's Seaborn Library

Plots are commonly used to visualize the relationships between two or more variables. These variables can be either categorical, such as a group, division, or class, or completely numerical. Using the seaborn library, we can generate plots in a variety of different categories.

The plots that we develop in the seaborn library are categorized into the following categories:

• Distribution plots: This sort of plot is used to examine both univariate and bivariate distributions.

• Relational plots: This type of plot is used to comprehend the relation between the 2given variables.

• Regression plots: Regression plots in the seaborn library are primarily envisioned to add an additional visual guide that will assist to emphasize dataset patterns during the analysis of exploratory data.

• Categorical plots: The categorical plots are used to deals with categories of variables and how we can envisage them.

• Multi-plot grids: The multi-plot grids are also a type of plot that is a useful approach is to draw multiple instances for the same plot with dissimilar subsets of a single dataset.

• Matrix plots: The matrix plots are a sort of arrays of the scatterplots.

Installation of a Seaborn Library For Python

We'll learn how to install the seaborn Python library in this tutorial. 'pip installs seaborn' will import the seaborn library into our Python program and allow us to use it in Python.

Required dependencies or prerequisites for the seaborn library are as follows:

We must have the below,

• Python installed with the latest version (3.6+)

• Numpy must be installed with version 1.13.3 or higher

• SciPy must be installed with 1.0.1 or higher versions

• Must have panda library with 0.22.0 or higher versions

• Statsmodel library must be installed with version 0.8.0 or higher

• And should have Matplotlib installed with 2.1.2 or higher versions

Plotting Chart Using Seaborn Library

1. Line Plot: One of the most basic plots in the seaborn library is the seaborn line plot. The seaborn line plot is mostly used to show data in a time-series format, that is, in a continuous manner with respect to time.

2. Dist Plot: As a consequence, we draw histograms with the specified variables and data using the seaborn dist plots. Using a dist plot, we may plot histograms with several more modifications, such as rugplot and kdeplot.

3. Lmplot: The Lmplot is another of the seaborn library's fundamental plots. On the provided two-dimensional (2-D) space, the Lmplot displays a line that reflects a linear regression model with the data points. We can use the x and y variables as the vertical and horizontal labels, respectively, in this 2-D space.

Advantages of Python Seaborn

We have some advantages of using seaborn, which is as follows:

• We can easily represent our data on a plot using the seaborn library.

• This library is used to visualize our data; we don't have to worry about the internals; all we have to do is provide our data set or data into the relplot() function, and it will compute and place the value appropriately.

• Using the 'kind' property within this, we can swap to any other data representation.

• It generates a dynamic and informative plot to represent our data; it also makes it simple for the user to comprehend and view the application's records.

• It generates plots in Python using static aggregation.

• Because it is built on matplotlib, we also have additional libraries loaded when we install seaborn, one of which is matplotlib, which has various features and functions for creating more interactive plots in python.

Usage of seaborn are as given below:

• Data visualization

• Use to demonstration data as a line plot

• Use to generate high level informative and attractive plots to show the data

About This Course

If you want to learn Python Seaborn online, this is an ideal place to kick start with. The presentation is 1.0 hours long and is presented in video format along with one quiz.

Introduction to Python Seaborn and Line Plot, Bar Plot in Seaborn, Scatterplot using Seaborn, Histograms and Joint plot, Working with Boxplots, and Seaborn Implementation in EDA are all covered in detail in the Python Seaborn course curriculum. You will receive a certificate from Great Learning upon completion, which you can use on your LinkedIn page, printed resumes and CVs, and other documents.

Enroll in this free beginner Python Seaborn certification course right away and get started learning.