Often companies deal with massive data that are hard to understand or analyze. Also, it is always better for humans to understand the relationship between multiple data through visualization because the more we see things in a graphical format, the clearer it becomes for our mind to perceive. That is where we use data visualization as a primary approach to interacting with the data in data science and analytics. Matplotlib is a data science library in which you will learn to create graphs and plots to interact visually with data.
What Is Data Visualization?
Data visualization is the technique of representing data or values in a visual context. These visual representations of data can be charts, maps, graphs, and other forms of plots. Through such visualization, it becomes easier for the human brain to extract meaning from datasets and derive meaningful insights without any loads. The primary purpose of rendering data visualization is to identify trends, patterns, data-driven behaviors, and outliers in large and complex data sets. Data visualization has become an essential part or stage of data science. Data collected through programming languages and various modules are meaningfully organized, processed, and modeled, then taken for visualization to draw conclusions from the data. Matplotlib and Seaborn are two well-known data visualization libraries. This tutorial will use Python to render graphs and plots using the popular Matplotlib library.
What Is Matplotlib?
Matplotlib is the most popular data visualization library that converts any statistical or mathematical data or analysis into different plots. It mainly displays the graph in 2D format. It provides an object-oriented API for embedding charts and maps into applications through various general-purpose GUI toolkits such as Qt, wxPython, Tkinter, GTK, etc. This open-source plotting library also provides diverse drawing elements such as histograms, line graphs, bar charts, scatter diagrams, and other types of charts/graphs just by typing a few lines of code. John D. Hunter is the person who wrote this visualization library in 2003 and distributed it under a BSD-style license. It has an active developer community, and shortly before the death of John Hunter in August 2012, Mr. Michael Droietboom got nominated as the lead developer of Matplotlieb. Many other advanced visualization modules and APIs largely depend on Matplotlib.