CHARACTERISTICS OF SORTING ALGORITHMS OPERATION IN PYTHON
Keywords:
Python, Cython, sorting algorithms, large datasets, localization, Ukrainian alphabet, sorting performance, data visualizationAbstract
This article is dedicated to the analysis of various sorting algorithms using the Python programming language and sorting algorithms in Cython. The study compares classic sorting methods, such as bubble sort, insertion sort, and quicksort, with the aim of determining their effectiveness for large datasets. Special attention is given to localization issues when sorting strings in non-English languages, particularly, the application of a specialized dictionary for the correct processing of the Ukrainian alphabet. Techniques for measuring performance and visualizing results in the form of graphs are presented, allowing a deeper assessment of the scaling of each algorithm depending on the dataset size.
References
1. Van Rossum, G., & Drake, F. L. (2019). Python: A guide for developers. Dialog.
2. Bekhterev, A. M. (2018). Cython: Optimizing Python programs. Lviv Technological.
3. Orlov, S. A. (2020). Python for complex tasks: Data science and machine learning. Fakt.
4. Sedgwick, R., & Wayne, C. (2017). Algorithms: Sorting, searching, basic data structures. Pleiades.
5. Cormack, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2019). Algorithms: Design and analysis (3rd ed.). Williams Publishing House.
6. McKinney, W. (2018). Python for data analysis (2nd ed.). Osnovy.
7. Cython: The complete guide to optimizing Python code. (2022). https://cython.readthedocs.io
8. Grossman, S. A., & McKinney, W. (2016). Effective Python: 59 specific ways to write better code. Diafilm.
9. Lutz, M. (2021). Programming in Python: Vol. 1. Introduction to programming. Diafilm.
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