C Sorting Algorithms With Python Visualizations

This project demonstrates the implementation of eight classic sorting algorithms in C, coupled with Python-based visualizations to analyze and compare their performance.

C Implementation

The following sorting algorithms were implemented in C, utilizing efficient memory management and function-based modularity:

• Bubble Sort
• Selection Sort
• Insertion Sort
• Heap Sort
• Merge Sort
• Quick Sort
• Count Sort
• Radix Sort

Each algorithm is benchmarked for runtime performance on datasets of varying sizes and ranges, leveraging a custom benchmarking function written in C. The implementation includes utility functions to generate randomized input arrays, validate sorting correctness, and handle edge cases. Memory-efficient techniques such as in-place operations and heap-based structures were used to optimize performance.

Python Visualization

A Python script leverages the shared library (sort_funcs.so) generated from the C implementation. Using ctypes, the benchmarking function is integrated into Python, allowing runtime measurements for each algorithm across varying input sizes. Libraries such as Matplotlib, Pandas, and Seaborn are used to create detailed visualizations.

• Runtime Comparisons: Line plots compare the runtime of all eight algorithms under identical conditions.
• Algorithm-Specific Performance: Dedicated graphs for individual algorithms reveal scaling behavior and highlight strengths and weaknesses.
• Special Focus on Non-Comparison Sorts: Visualizations for Count Sort and Radix Sort include a breakdown of performance across datasets with varying digit lengths.

Technical Highlights

• Benchmarking Framework: A clock-based benchmarking function in C measures and verifies the performance of each algorithm.
• Python-C Interfacing: ctypes enables seamless integration of the C backend with Python’s visualization ecosystem.
• Scalability Testing: The project tests algorithms on datasets ranging from a few elements to millions, providing insights into their theoretical time complexities in practice.