What are the performance benchmarks for Python extensions?

What are the performance benchmarks for Python extensions?

Performance benchmarks for Python extensions can vary widely depending on the type of extension and its implementation. Generally, C extensions tend to offer significant speed improvements over pure Python code, often achieving performance gains of 10x or more.

Can you give examples of specific extensions and their benchmarks?

Sure! For instance, NumPy, which is a C extension, can perform array operations much faster than native Python lists. Benchmarks show that operations on large arrays can be up to 100 times faster.

How do I measure the performance of my own Python extension?

You can use the timeit module to measure execution time for small code snippets. For more comprehensive profiling, consider using tools like cProfile or line_profiler.

Are there any best practices for optimizing Python extensions?

Yes, some best practices include minimizing Python calls from C, using efficient data structures, and leveraging parallel processing where applicable.