A naukri.com initiative
Dev
4w
214
Image Credit: Dev
7 Powerful Python Performance Optimization Techniques for Faster Code
- Python developers often think of performance optimization to create high-performance applications. Here, we present seven efficient techniques that help improve Python code's performance by faster execution speed and efficient memory management.
- Generators and iterators are particularly useful while working with large datasets because they can process data without loading everything into memory at once.
- List comprehensions and generator expressions are faster alternatives to traditional loops while creating new lists or iterating over sequences. Generator expressions are preferred for larger sequences to save memory.
- The collections module in Python provides several high-performance container datatypes, such as deque, that can significantly improve code efficiency.
- Sets and dictionaries are extremely fast for lookups and membership testing, and they use hash tables internally.
- Numba provides significant speed improvements through just-in-time compilation for numerical computations.
- Cython allows Python code to compile to C, resulting in significant performance improvements.
- Tools like cProfile for timing and memory_profiler for memory usage analysis can identify where the bottleneck is while optimizing it.
- Memoization is used to cache the results of expensive function calls, helpful for recursive functions, just like itertools module provides a collection of fast, memory-efficient tools for creating iterators.
- Using built-in functions and libraries, minimizing object creation, optimizing loops, reducing function call overhead, using appropriate data structures, using 'in' for membership testing, and avoiding global variables are some best practices for writing performant Python code.
Read Full Article
12 Likes
For uninterrupted reading, download the app