Python Performance Optimization Examples

This repository contains a collection of 30 practical examples demonstrating various Python performance optimization techniques and best practices.

Project Structure

project/
├── code/         # Python implementation files
├── doc/          # Detailed documentation for each day
└── requirements.txt

Topics Covered

1. List Comprehensions.
2. List Comprehensions vs Loops.
3. Generator Expressions.
4. Breaking Early from Loops.
5. Using any() and all().
6. Built-in Functions.
7. Replacing Recursion.
8. String Join Operations.
9. Using enumerate().
10. Dictionary Comprehensions.
11. ZIP Function Usage.
12. NumPy Vectorization.
13. Memoization Techniques.
14. Code Profiling.
15. Math and Itertools.
16. Set Lookups.
17. Context Managers.
18. Efficient Data Structures.
19. Batch API Requests.
20. Local Caching.
21. Pandas usecols.
22. Managing Memory with del.
23. Data Compression.
24. Optimizing Logging.
25. Efficient Serialization.
26. Database Connection Reuse.
27. Resource Management.
28. Lazy Loading.
29. Stream Processing.
30. Multiprocessing.

Getting Started

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt

3. Navigate to the code directory to run examples:

   cd code
   python day1.py  # Replace with any day's script

Documentation

Each optimization technique has its own detailed documentation file in the doc/ directory. The documentation includes:

• Explanation of the technique.
• Use cases.
• Performance comparisons.
• Best practices.
• Code examples.

Project Organization

• code/: Contains the implementation files for each day's optimization technique.
• doc/: Contains detailed markdown documentation for each technique.
• requirements.txt: Lists all Python dependencies required to run the examples.

Dependencies

The project uses several Python libraries for demonstration purposes:

• NumPy: For numerical computations and array operations.
• Pandas: For data manipulation and analysis.
• SQLite3: For database operations.
• Other standard Python libraries.

Best Practices Demonstrated

• Resource management.
• Memory optimization.
• CPU optimization.
• I/O optimization.
• Database optimization.
• Algorithm optimization.
• Code organization.
• Error handling.

Contributing

Feel free to contribute by:

1. Forking the repository.
2. Creating a feature branch.
3. Committing your changes.
4. Opening a pull request.

License

This project is open source and available under the MIT License.