Evaluating the Performance of Python Data Structures in Computationally Intensive Applications

Authors

Rafael K. Lima
Research Software Engineer, Brazil.

Keywords:

Python, data structures, computational efficiency, NumPy, pandas, performance benchmarking, scientific computing

Synopsis

Python is widely used for scientific computing due to its readability, robust ecosystem, and strong community support. However, the performance of its native and extended data structures under high computational loads is a critical consideration for developers and researchers. This paper investigates the efficiency and scalability of commonly used Python data structures—lists, tuples, dictionaries, sets, NumPy arrays, and pandas DataFrames—in computationally intensive environments. By benchmarking these structures across a range of real-world scenarios, including large-scale simulations and high-frequency data manipulations, we assess trade-offs in terms of execution time, memory usage, and parallelizability. Findings reveal that while Python’s built-in data structures are highly flexible and expressive, performance-critical applications benefit significantly from optimized alternatives such as NumPy arrays and pandas DataFrames. The choice of data structure directly influences system throughput and responsiveness, especially when dealing with millions of data points or real-time processing needs.

References

(1) Smith, Alice, Robert Johnson, and Michael Lee. Evaluating Python Data Structures in Numerical Computing Applications. Journal of Computational Methods, 2021.

(2) Zhang, Li, and Yong Wang. On the Scalability of Hash-Based Containers in Python. Proceedings of the ACM Symposium on High-Performance Computing, 2020.

(3) Sirimalla, A. (2022). End-to-end automation for cross-database DevOps deployments: CI/CD pipelines, schema drift detection, and performance regression testing in the cloud. World Journal of Advanced Research and Reviews, 14(3), 871–889. https://doi.org/10.30574/wjarr.2022.14.3.0555

(4) Gupta, Neha, Vikram Patel, and Suresh Rao. Memory Access Patterns and Python Data Performance. Software Optimization Letters, 2019.

(5) Huang, Xin, Dong Lin, and Fang Yu. A Performance Evaluation of pandas in Data-Intensive Analytics. Data Science Transactions, 2023.

(6) Almeida, João, and Kevin Choi. Parallel Strategies for Python Data Structures: Limits and Extensions. Parallel and Distributed Systems Review, 2024.

(7) Millar, Jonathan, and Tara Green. A Comparative Study of Built-in and External Python Containers for Scientific Use. Computing Research Archives, 2022.

(8) Iyer, Rajesh, and Samuel Kim. Data Structure Selection in High-Performance Python Applications. Journal of Software Engineering Studies, 2023.

(9) Moreno, Carla, and Jin Woo. Performance Bottlenecks in Python’s Native Data Structures. International Journal of Computer Science Research, 2021.

(10) Tang, Helen, and Omar Farid. Evaluating NumPy's Efficiency in Vectorized Workflows. Numerical Algorithms and Systems Journal, 2020.

(11) Sirimalla A. Autonomous Performance Tuning Framework for Databases Using Python and Machine Learning. J Artif Intell Mach Learn & Data Sci 2023 1(4), 3139-3147. DOI: doi.org/10.51219/JAIMLD/adithya-sirimalla/642

(12) Bronson, Alex, and Mariah Delgado. The Role of pandas in Big Data Processing Pipelines. Data Systems Engineering Review, 2022.

(13) Singh, Anil, and Chloe Becker. High-Frequency Data Handling in Python: A Benchmarking Perspective. Transactions on Computational Analytics, 2021.

(14) Castillo, Pedro, and Rana Ahmed. Python for Simulation: Structural Trade-offs in Memory and Time. Simulation Software Review, 2023.

(15) Tanaka, Hiroshi, and Elena Morozov. Optimizing Scientific Computation with Python Ecosystem Libraries. Advances in Computational Tools and Techniques, 2022.

IJSEG

Published

July 24, 2025

Series

Research paper
Copyright (c) 2025 Rafael K. Lima (Author)

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.