Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations

Abstract

Python is the general purpose, high-level, scriptable, interpreted, object oriented and easy to learn language was developed by Guido van Rossum. Python is a popular programming language because of its simplicity, ease of use, open-source licensing, and accessibility. It provides various libraries and frameworks for learning and real world programming. Python’s versatility and active community make it an ideal language for Machine Learning, Data Science, Artificial Intelligence, Networking, Game Development, Web Development and various other domains. In this paper, we will provide an introduction to the main Python programming software tools used for Web development, Data science, Machine learning techniques, Educational Development, etc. In this paper will first introduce Python as a language, and give introduction about Web Development, Data science, Machine learning, Educational Development & Financial Analysis and then describe packages that are popular in that sector.

Introduction

I. INTRODUCTION

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language.  Python is developed by Guido van Rossum in the late 1980s and implemented it in 1990s. Simple syntax makes Python an excellent language to learn to program for new learners.  It uses English keywords frequently where as other languages use punctuation, and it has fewer syntactical constructions than other languages.  Python supports automatic memory management and wide range of comprehensive library that makes Python suitable for every application.

II. WHY PYTHON IS POPULAR TO NEW LEARNER’S?

1. Easy-to-learn & read : Python is very easy to learn because it has few keywords, simple structure and a clearly defined syntax. This allows the programmers to pick up the language quickly. Python's source code is fairly easy-to-maintain.
2. Interpreted Language : Python is an interpreted language because code is executed line by line at a time like other language C, C++, Java, etc.
3. Free & Open Source : Python is freely available at official website since it is an open source.
4. Object Oriented Programming : Python supports object oriented language and its concepts of class, object, inheritance, encapsulation, etc.
5. A broad standard library : Python's broad standard library is very convenient and cross-platform compatible on UNIX, Windows, and Macintosh.
6. Portable : Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
7. GUI Programming : GUI applications supports by Python that can be created  to many system calls, libraries and windows systems, such as Windows MFC, Macintosh, and the X-Window system of Unix.
8. Databases : Python provides interfaces to all major commercial databases including RDBMS & NoSQL Databases.

Fig.1  Features of Python

III. APPLICATIONS OF PYTHON

Fig. 2      Common Applications of Python Programming Language [8]

The above mentioned are common applications which are built through Python Programming Language. In this paper, we will discuss some of the areas where Python Programming Language provides ease of building an application, web development and many more.

1. Web Development: Python comes up with a wide range of web frameworks like Django, Flask, Pyramid, and a lot more that provide ease to developers.
2. Machine Learning & Data Science : In the current scenario, Machine Learning & Data Science is such a demandable topics. For the development of ML & AI, Python offers inbuilt libraries and tools such as Scipy, Panda, TensorFlow, Keras, etc.
3. Financial Applications : Python provides inbuilt libraries and tools like Odoo & Tryton that provides a variety of commercial applications with exclusive security features that delivers high performance business applications.
4. Educational Development : Python, a versatile and user-friendly programming language, has gained prominence in classrooms worldwide. The reason is simple: it offers students a gateway to the world of coding and computer science. This, in turn, aligns with the surging demand for coding skills in the modern workforce. As we navigate an ever-evolving technological landscape, the ability to understand and work with code has emerged as a fundamental skill, transcending traditional computer science domains.

IV. PYTHON FOR WEB DEVELOPMENT

Python comes with a wide array of web frameworks that exist to simplify a web developer’s life. Popular examples of these web development frameworks include Flask, Django, Pyramid, and Bottle. [1]

Using Python for web development also offers several other benefits, such as security, easy scalability, and convenience in the development process. More so, Python comes with out-of-the-box support for various web protocols such as HTML, XML, frequently used e-mail protocols, FTP. Python also has one of the largest collections of libraries that not only enhance the functionality of web applications but also make it easier to do so. [1]

V. PYTHON FOR DATA SCIENCE

Data Science is an interdisciplinary field that uses scientific method, processes, algorithm and system to extract to knowledge and insights form structured and unstructured data. In simple words we can say it combines domain of programming skills, expertise, and knowledge of math and statistics to extract meaningful insights from the data [2]. 

These are the most essential Data Science libraries you have to know:

A. Numpy

It will help us to handle multi-dimensional arrays very efficiently. Maybe it is difficult to do that directly, but since the concept is a crucial part of data science [3], many other libraries (well, almost all of them) are built on Numpy.

B. Matplotlib

Data visualization is very important. Data visualization helps us to better understand the data, discover things that wouldn’t discover in raw format and communicate findings more efficiently to others. The best and most well-known Python data visualization library is Matplotlib [3]. It is not easy to use, but usually it provides many functions like barchart, scatterplot, piechart, histogram, etc. which are useful for projecting many dimensions of data.

C. Scipy

Using Scipy, we can deal a huge number of concepts of Mathematics that are very vital. However, Python provides the full-fledged scipy library that resolves this issue for us. Using Scipy, we will be learning how to make use of this library along with a few functions and their examples [3].

VI. PYTHON FOR MACHINE LEARNING

Machine learning is a type of learning in which the machine learns on its own without being explicitly programmed. It is a type of application of AI that gives systems the ability to learn and improve on their own from their experience. Here we can create a program which is made by integrating the input and output of the same program.[4]

The Numpy, Matplotlib and Scipy libraries are general-purpose libraries for anything involving advanced data manipulation as discussed above. To implement Machine Learning, we can also use other modules like Tensorflow, Keras, also.    

A. Tensorflow : [3]

Google developed Tensorflow, the open source machine learning library for Python. Google applications also utilize machine learning, when we used Google Voice Search and Google Photos, then we are used Tensorflow. Tensorflow is optimized for speed and particularly well documented and supported.

B. Keras :[3]

Keras is the high level library for functioning with datasets. Keras is the easiest machine learning library that is coded exclusively in Python where as Tensorflow as a back-end. It is the most beginner-friendly library for machine learning, and includes functions for creating training datasets and more. Keras' neural networks API was developed for fast experimentation and is a good choice for any deep learning project that requires fast prototyping.

VII. PYTHON IN EDUCATION

As Python gains popularity, more educational institutions are incorporating it into their curriculum to develop students' data processing and analysis skills. However, Zhang Jingshu (2019) pointed out that despite the widespread promotion of digital education in accounting teaching, disconnect still exists between the programming knowledge students learn in school and its practical application in business finance. This gap is mainly evident in students' lack of practical experience in applying programming skills to specific financial issues.

VIII. PYTHON IN FINANCIAL ANALYSIS

Python's applications in corporate finance are continually expanding, encompassing data mining, automated reporting, risk management, and more. According to Zhang Wen-Hui and Zhang Yanjin (2022), Python effectively handles large data sets, significantly enhancing accounting efficiency and the visualization of financial data. Similarly, Li Miao (2021) confirmed the advantages of Python in big data analysis within finance, particularly noting its higher efficiency and accuracy compared to traditional financial software when dealing with complex datasets.

Conclusion

In spite of availability of so many languages, Python is evolving as the popular language among developers. This paper thoroughly explores the potential and value of Python in enhancing the efficiency of various different sectors like web development, data science, machine learning, educational development & corporate financial analysis.

References

[1] Dr. Uday Patkar et all, “PYTHON FOR WEB DEVELOPMENT”, International Journal of Computer Science and Mobile Computing(IJCSMC), ISSN 2320–088X, Vol. 11, Issue. 4, April 2022, pg.36-48. [2] Rajkumar Chouhan, Kapil Singh, Ritu Vashistha, “Review Paper on Python for Data Science & Web Development”, World Journal of Research and Review (WJRR), ISSN: 2455-3956, Volume-14, Issue-5, May 2022, pg. 15-19. [3] P.N.Siva jyothi, Rohita Yamaganti, “A Review on Python for Data Science, Machine Learning and IoT”, International Journal of Scientific & Engineering Research, ISSN 2229-5518, Volume 10, Issue 12, December-2019, pg. 851-858. [4] Dr. Priyanka Sisodia, Dr. Bhaskar Seth, “An Implementation on Python for Data Science and Machine Learning”, International Journal of Creative Research Thoughts (IJCRT), ISSN : 2320-2882, Volume 10, Issue 3 March 2022, pg. a904-a907. [5] Zhang Jingshu. Application of Data Analysis in the Reform of College Accounting Teaching [J]. Learning of Finance and Accounting, 2019, 228(19): 200-201+204 [6] Zhang Wenhui, Zhang Yanjin. Fundamentals of Financial Big Data [M]. Lixin Accounting Publishing House, July 2022 [7] Li Miao. Application of Python in Financial Big Data [J]. Today\'s Wealth (China Intellectual Property), 2021, (11): 115-117. [8] https://www.javatpoint.com/python-applications

Copyright

Copyright © 2024 Piyush D. Pitroda, Bhavika C. Donga, Dr. Hasmukh B. Domadiya, Dr. Dipti H. Domadiya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.