Smart Performance Hacks for Faster Python Code
11 days ago
- #Python
- #Performance
- #Optimization
- Use sets for membership testing to achieve constant-time lookups (O(1)) instead of linear scans (O(n)).
- Avoid unnecessary copies of large objects; modify in place to save memory and time.
- Use `__slots__` in classes to reduce memory overhead and speed up attribute access.
- Prefer `math` module functions over operators for faster and more precise numerical computations.
- Pre-allocate memory for lists or arrays when the final size is known to avoid resizing overhead.
- Avoid exception handling in performance-critical loops; use conditional checks instead.
- Define local functions for repeated logic to benefit from faster name resolution.
- Use `itertools` for efficient combinatorial operations with lazy evaluation.
- Leverage `bisect` for fast insertions and searches in sorted lists (O(log n)).
- Cache function results outside loops to avoid redundant computations.