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Implementation of Data Management using Object-Oriented Programming (OOP) in Python

by Ahmad Farhan AlShammari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 49
Year of Publication: 2025
Authors: Ahmad Farhan AlShammari
10.5120/ijca2025925844

Ahmad Farhan AlShammari . Implementation of Data Management using Object-Oriented Programming (OOP) in Python. International Journal of Computer Applications. 187, 49 ( Oct 2025), 54-61. DOI=10.5120/ijca2025925844

@article{ 10.5120/ijca2025925844,
author = { Ahmad Farhan AlShammari },
title = { Implementation of Data Management using Object-Oriented Programming (OOP) in Python },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2025 },
volume = { 187 },
number = { 49 },
month = { Oct },
year = { 2025 },
issn = { 0975-8887 },
pages = { 54-61 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number49/implementation-of-data-management-using-object-oriented-programming-oop-in-python/ },
doi = { 10.5120/ijca2025925844 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-10-23T00:18:28+05:30
%A Ahmad Farhan AlShammari
%T Implementation of Data Management using Object-Oriented Programming (OOP) in Python
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 49
%P 54-61
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The goal of this research is to implement data management using object-oriented programming (OOP) in Python. Data management is the process of handling data efficiently by performing the basic operations on data. It helps to keep data organized, accessible, accurate, and secure. It also provides a solid foundation for data analysis and decision making. The basic operations of data management are explained: defining table, creating table, displaying table, displaying shape, displaying field names, displaying data types, displaying row, displaying column, adding row, adding column, updating row, updating column, deleting row, deleting column, getting values, counting values, computing statistics (count, min, max, mean, and std), searching by value, sorting by column, grouping by column, and clearing table. The developed program was tested on an experimental dataset. The program has successfully performed the basic operations of data management using object-oriented programming and provided the required results.

References
  1. Sammut, C., & Webb, G. I. (2011). "Encyclopedia of Machine Learning". Springer.
  2. Jung, A. (2022). "Machine Learning: The Basics". Springer.
  3. Kubat, M. (2021). "An Introduction to Machine Learning". Springer.
  4. Li, H. (2023). "Machine Learning Methods". Springer.
  5. Zollanvari, A. (2023). " Machine Learning with Python". Springer.
  6. Chopra, D., & Khurana, R. (2023). "Introduction to Machine Learning with Python". Bentham Science Publishers.
  7. Müller, A. C., & Guido, S. (2016). "Introduction to Machine Learning with Python: A Guide for Data Scientists". O'Reilly Media.
  8. Raschka, S. (2015). "Python Machine Learning". Packt Publishing.
  9. Forsyth, D. (2019). "Applied Machine Learning". Springer.
  10. Sarkar, D., Bali, R., & Sharma, T. (2018). "Practical Machine Learning with Python". Apress.
  11. Bonaccorso, G. (2018). "Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning". Packt Publishing.
  12. Teoh, T., & Rong, Z. (2022). "Artificial Intelligence with Python". Springer.
  13. Igual, L., & Seguí, S. (2017). "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications". Springer.
  14. VanderPlas, J. (2017). "Python Data Science Handbook: Essential Tools for Working with Data". O'Reilly Media.
  15. Muddana, A., & Vinayakam, S. (2024). "Python for Data Science". Springer.
  16. Unpingco, J. (2022). "Python for Probability, Statistics, and Machine Learning". Springer.
  17. Unpingco, J. (2021). "Python Programming for Data Analysis". Springer.
  18. Blazewicz, J., Kubiak, W., Morzy, T., & Rusinkiewicz, M. (2003). "Handbook on Data Management in Information Systems". Springer.
  19. Purba, S. (2019). "Handbook of Data Management". CRC Press.
  20. Gray, J. (1996). "Data Management: Past, Present, and Future". IEEE Computer 29(10), 38-46.
  21. Gordon, K. (2022). "Principles of Data Management: Facilitating Information Sharing". BCS.
  22. Bressoud, T., & White, D. (2020). "Introduction to Data Systems: Building from Python". Springer.
  23. Cao, J. (2023). "E-Commerce Big Data Mining and Analytics". Springer.
  24. Zelle, J. (2017). "Python Programming: An Introduction to Computer Science". Franklin, Beedle & Associates.
  25. Xanthidis, D., Manolas, C., Xanthidou, O. K., & Wang, H. I. (2022). "Handbook of Computer Programming with Python". CRC Press.
  26. Chun, W. (2001). "Core Python Programming". Prentice Hall Professional.
  27. Padmanabhan, T. (2016). "Programming with Python". Springer.
  28. Beazley, D., & Jones, B. K. (2013). "Python Cookbook: Recipes for Mastering Python 3". O'Reilly Media.
  29. Lott, S. (2014). "Mastering Object-Oriented Python". Packt Publishing.
  30. Phillips, D. (2015). "Python 3 Object-Oriented Programming: Harness the Power of Python 3 Objects". Packt Publishing.
  31. Lott, S., & Phillips, D. (2021). "Python Object-Oriented Programming: Build Robust and Maintainable Object-Oriented Python Applications and Libraries". Packt Publishing.
  32. Goldwasser, M. H., & Letscher, D. (2008). "Object-Oriented Programming in Python". Pearson Prentice Hall.
  33. Downey, A. (2012). "Think Python". O'Reilly Media
  34. Lutz, M. (2013). "Learning Python: Powerful Object-Oriented Programming". O'Reilly Media.
  35. Rangisetti, A. (2024). "Hands-On Object-Oriented Programming: Mastering OOP Features for Real-World Software Systems Development". Apress.
  36. Hillar, G. (2015). "Learning Object-Oriented Programming". Packt Publishing.
  37. Codd, E. (1970). "A Relational Model of Data for Large Shared Data Banks". Communications of the ACM. 13 (6), 377–87.
  38. Chamberlin, D., Boyce, R. (1974). "SEQUEL: A Structured English Query Language". Proceedings of the 1974 ACM SIGFIDET Workshop on Data Description, Access and Control, 249–64.
  39. Python: http://www.python.org
  40. Numpy: http://www.numpy.org
  41. Pandas: http://pandas.pydata.org
  42. Matplotlib: http://www. matplotlib.org
  43. Seaborn: http://seaborn.pydata.org
  44. NLTK: http://www.nltk.org
  45. SciPy: http://scipy.org
  46. SK Learn: http://scikit-learn.org
Index Terms

Computer Science
Information Sciences

Keywords

Computer Science Artificial Intelligence Machine Learning Data Science Data Management Object-Oriented Programming OOP Python Programming