High Performance Python

High Performance Python is the simplified Chinese edition of the bestselling O'Reilly book of the same name, introduced and published by Posts & Telecom Press. The book has 12 chapters. Its subject is not Python syntax itself but a core question: when a program is not fast enough, how can you find the real bottleneck and make it faster with the right methods? The book begins with background knowledge on the computer's internal structure to help readers understand what costs Python's interpreted execution, object model, and memory allocation actually incur at a low level, so they understand "why it is slow." It then discusses the time and space overhead of basic data structures such as lists and tuples, dictionaries and sets; the lazy evaluation approach of iterators and generators; and NumPy-based matrix and vector computation—achieving order-of-magnitude speedups by pushing loops down to the C layer. At the tool level, the book explains how to use profilers to locate hotspots in CPU and memory consumption, how to benchmark and visualize, and how to avoid optimizing by feel; it also explains paths such as Cython and PyPy that compile Python code to machine code or use just-in-time compilation. In the concurrency and parallelism section, the book distinguishes the scenarios suited to multithreading, multiprocessing, and asynchronous I/O, explains the impact of the GIL on CPU-bound tasks, and shows how to turn multiprocessing code into a task system that can run on local or remote clusters and work queues. The final chapter uses several real cases to show the complete process of locating and solving performance problems in practical applications such as social media analysis and machine learning. The book is aimed at beginner-to-intermediate developers who already know Python and want to write efficient code.