📄 Abstract
Frequency analysis is one of the most important operations in computer science, statistics, data mining, and machine learning. Identifying the element that occurs the maximum number of times in a dataset helps in discovering patterns, trends, and useful insights. This research paper presents the implementation of a user-defined Python function named count_frequency(), which determines the most frequently occurring element in a vector. The results obtained from the custom implementation are verified using Python's standard library collections.Counter class. The study compares both methodologies in terms of implementation complexity, execution speed, memory utilization, readability, maintainability, and computational efficiency. Experimental analysis is performed on datasets of different sizes to evaluate performance. The results demonstrate that both methods generate identical outputs and exhibit linear time complexity. However, the Counter class offers improved readability, reduced coding effort, and better runtime performance due to internal optimization. The study concludes that while the custom implementation is useful for educational purposes and understanding algorithm design, the Counter class is more suitable for practical applications involving large datasets. The findings of this research contribute to a better understanding of frequency analysis techniques and their applications in modern computing systems.
🏷️ Keywords
📚 How to Cite:
M Gobi Krishna, M K Koushik Raja , IMPLEMENTATION OF count_frequency() FUNCTION WHICH RETURNS THE ELEMENT PRESENT MAXIMUM NUMBER OF TIMES IN A VECTOR. FURTHER CHECKING WITH PYTHON STANDARD LIBRARY COLLECTIONS BY IMPORTING Counter FUNCTION AND ANALYSING THE EFFICIENCY OF THESE TWO METHODOLOGIES – A CASE STUDY , Volume 12 , Issue 6, June 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 663 - 665 ,