This project was developed as part of the Math 608 - Data Science course. It explores the transition from TimSort to PowerSort in Python 3.11, explaining the underlying reasons, working mechanisms, and performance differences between the two sorting algorithms.
To compare TimSort and PowerSort with a focus on:
- Algorithm structure and internal steps
- Edge-case performance
- Time and space efficiency
- Real-world relevance and application in Python
- A hybrid sorting algorithm (Merge Sort + Insertion Sort)
- Default in Python from version 2.3 to 3.10
- Optimized for partially sorted data
- Stable sort
- Introduced in Python 3.11
- Improved worst-case performance over TimSort
- More consistent in edge-case behavior
- Uses "power values" to determine merge order
- Identifying Runs: Sub-sequences of ordered elements
- Merge Rules: How runs are combined in each algorithm
- Performance Analysis: On average and worst-case datasets
- Python 3.11 Transition: Why the standard sorting method changed
- Step-by-step simulation of sorting
[5, 7, 8, 4, 2, 1, 3, 10, 12, 11, 9, 15] - Diagrams of merge stacks in TimSort
- Power calculations and merge strategy in PowerSort
- Summary tables comparing speed, space, and reliability
Math608-DataScienceProject/
│
├── Data Science Project.pptx # Full slide deck
├── README.md # Project documentation
📈 Why Python Switched to PowerSort
More predictable behavior with irregular datasets
Lower memory usage
Tighter performance bounds
Alignment with CPython’s performance goals📜 License This project is for educational and research purposes. Please contact for reuse or collaboration.
Zakir Elaskar
📧 zelaskar@csuchico.edu | elaskarzakir@gmail.com
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