Comparative Analysis of Arrays and Linked Lists in Python and C#: Impact on Memory Efficiency
DOI:
https://doi.org/10.55873/rad.v4i2.422Keywords:
arrays, C#, memory efficiency, data structures, linked lists, programming, PythonAbstract
This article analyzes the impact of arrays and linked lists on memory efficiency in Python and C#. Both structures were implemented and three operations were evaluated on collections of 10,000 elements: middle access, insertion, and deletion. Execution times were measured using timeit in Python and Stopwatch in C#, complemented with an estimation of memory consumption. Statistical analysis through a two-factor ANOVA was applied to contrast the effect of the programming language and the data structure. The results show that arrays are systematically more efficient than linked lists in both platforms. In central access, arrays exhibited nearly constant execution times (0.0010 ms in Python and 0.0012 ms in C#), clearly outperforming linked lists due to their contiguous memory organization. They also showed superior performance in intermediate insertions and deletions. The ANOVA results indicated that the programming language does not have a statistically significant effect on execution times (p > 0.05), whereas the data structure is the main determinant of performance; therefore, arrays represent the most efficient option in scenarios dominated by random access and intermediate operations, regardless of the programming language.
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Copyright (c) 2025 Aldo Alarcón-Sucasaca , Nestor Antonio Gallegos-Ramos
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