Cython

这本书最打动人的地方，是它把 Python 与 C 之间那道看似不可逾越的鸿沟，变成了一条可以稳步通行的桥。它不满足于告诉你「Cython 能提速」，而是手把手教你如何在享受性能红利的同时，把内存管理这类最容易出错的细节稳稳接住——用得好是利器，用错则隐患重重，这一点贯穿全书。书中以 Pandas、SymPy 等真实项目为锚点，让读者看到它并非理论空谈，而是被无数底层工程反复验证过的选择。它适合那些已能熟练写 Python、却常在性能瓶颈前束手无策的开发者：当你需要把关键路径压到最快，又不想放弃 Python 的简洁时，这本书会是你工具箱里那把最趁手的扳手。

Kurt Smith has been using Python in scientific computing ever since his college days, looking for any opportunity to incorporate it into his computational physics classes. He has contributed to the Cython project as part of the 2009 Google Summer of Code, implementing the initial version of typed memoryviews and native cython arrays. He uses Cython extensively in his consulting work at Enthought, training hundreds of scientists, engineers, and researchers in Python, NumPy, Cython, and parallel and high-performance computing.