Ali Hassan Sial et al., International Journal of Advanced Trends in Computer Science and Engineering, 10(1), January – February 2021, 277 - 281 277 ABSTRACT With the tremendous growth in the areas of computing, statistics, and mathematics has led to the rise of the emerging field of expertise, named ‘Data Science’. This paper focuses on the comparative study and evaluation of the data science libraries used in Python Programming Languages, named ‘Matplotlib’ and ‘Seaborn’. The sole purpose of this paper is to identify areas and evaluate the strengths and weaknesses of these libraries with the implementation of code and identify the classification of the univariate and multivariate plotting of data concerned with patterns of data visualization and computational modelling of data in the form of processed information using techniques of big data and data mining. Key words : Data Visualization, Computational Modelling, Univariate, Multivariate, Big Data 1. INTRODUCTION Data Visualization is the graphical illustration for a pictorial representation of data with the integrated use of illustrated design. The sole perspective is to provide in a visualized form that is easier to understand and presented. In a general perspective, data visualization techniques can be classified into two categories 1) univariate and 2) multivariate data visualizations. The first category, univariate data visualization constitutes of plotting a specific variable to identify and understand relatively more about the distribution and scattering of plots whereas, multivariate plots classifies the relationship of several datasets and variables [2, 3]. The popular data visualization techniques that comprise of scatter plots, bar charts, pie charts, and line charts, are extensively used in the areas of data science, mathematical modelling, and computational research. In a greater extent of the rapid transformation of data or information, although new techniques are used to visualize quantitative and qualitative information for data researchers to incorporate data analytics and mathematical computations for better efficiency and performance metrics. The tools of data mining are broadly categorized into three types 1) programming languages 2) Statistical tools 3) visualization tools. The merits of SPSS and Stata both fall under the category of statistical analysis software packages that are solely used for the management or organization of the datasets. The researchers have identified that SPSS in various data visualizations areas of complicated and complex data analysis alongside Stata can be utilized for high-level areas in the research and development industry. Furthermore, R is a high-level, resource-oriented data analysis package and high-level programming language that is used for numerical and statistical analysis, data visualization and reporting. Significantly, Python is a powerful, high-level, general-purpose, and resource intensive programming language. Thus, the key difference among R and Python is that R is a statistical, numerical and data analysis-driven programming language whereas Python is a general-purpose programming language. A. Matplotlib Matplotlib is one of the most popularly used data visualization libraries of python. This library was built by a John Hunter who is along with several contributors, and it had put in a greater amount of time into prompting this software used by every scientist and philosopher across the globe [4]. Matplotlib is a graphics library for data visualization package in Python which encompasses as an integral aspect in the python data science stack and it is easily supported with NumPy, Pandas and other relevant libraries. B. Seaborn Seaborn is a graphic visualization library that is built on the primary configurations of Matplotlib. It provides accessibility to the users with some of the most commonly provides data visualizations processes with certain data visualizations necessities such as mapping colour to a variable or using faceting requirements across the globe. It provides seaborn is more integrated for working with Pandas DataFrames [4]. C. Pandas Pandas is an open-source library used in Python that provides enhanced performance metrics, easy to use data structures and data analysis packages, tools and libraries for Python Programming Language [4][5][6]. The use of pandas with Comparative Analysis of Data Visualization Libraries Matplotlib and Seaborn in Python Ali Hassan Sial 1 , Syed Yahya Shah Rashdi 2 , Dr. Abdul Hafeez Khan 3 1 Department of Computer Science, SMI University, Karachi, Pakistan, sial_alihassan@yahoo.com 2 Department of Computer Science, SMI University, Karachi, Pakistan, yahyarashdi6@gmail.com 3 Department of Software Engineering, SMI University, Karachi, Pakistan, ahkhan@smiu.edu.pk ISSN 2278-3091 Volume 10, No.1, January - February 2021 International Journal of Advanced Trends in Computer Science and Engineering Available Online at http://www.warse.org/IJATCSE/static/pdf/file/ijatcse391012021.pdf https://doi.org/10.30534/ijatcse/2021/391012021