Data Visualization using Python and Matplotlib

Abstract

In today's data-focused world, the ability to analyze and communicate complex data is vital. Data visualization is a key link between data and information you can use. This case study investigates the practical application of Python and its effective Matplotlib library for producing insightful data visualizations. Using a real-world public dataset of global COVID-19 statistics,this paper carefully demonstrates the complete process of analyzing data. The process includes data cleaning and preprocessing with the Pandas library, subsequent arrangements of static, animated, and interactive visualizations completed with Matplotlib. The case study process highlights the creation of basic plots such as bar plots, line plots, scatter plots, and histograms with the objective of revealing visual trends, patterns, or anomalies. This study demonstrates how the capabilities of Matplotlib's landscape framework allow us to convert dense data into clear visual understandings. The study concludes that Python and Matplotlib work together to create a potent, accessible, and indispensable grid for researchers, analysts, and decision-makers, while also serving as a key resource in modern data science.