- Hack 2: Avoid unnecessary copies
Copying large objects like lists, dictionaries, or arrays can be costly in both time and memory. Each copy creates a new object in memory, which can lead to significant overhead, especially when working with large datasets or within tight loops.
- Hack 4: Use math functions instead of operators
For numerical computations, Python’s math module provides functions that are implemented in C, offering better performance and precision than equivalent operations written in pure Python.
- Hack 6: Avoid exception handling in hot loops
While Python’s exception handling is powerful and clean for managing unexpected behavior, it’s not designed for high-frequency use inside performance-critical loops. Raising and catching exceptions involves stack unwinding and context switching, which are relatively expensive operations
- Hack 9: Use bisect for sorted list operations
When working with sorted lists, using linear search or manual insertion logic can be inefficient – especially as the list grows. Python’s bisect module provides fast, efficient tools for maintaining sorted order using binary search.
- Hack 10: Avoid repeated function calls in loops
Calling the same function multiple times inside a loop – especially if the function is expensive or produces the same result each time – can lead to unnecessary overhead. Even relatively fast functions can accumulate significant cost when called repeatedly in large loops.
