[Image](/uploads/documents/d/c/f/f/dcff3d9f70403fc7fe9fcc283f05b024/p2_2.jpg) ## 大家以前都是怎样调用C++代码呢？ 1. Cython 2. boost.python/pybind11 3. ctypes ![Image](/uploads/documents/d/c/f/f/dcff3d9f70403fc7fe9fcc283f05b024/p3_2 |cCThostFtdcBrokerDepositField|vnctp| |cCThostFtdcBrokerField|vnctp| 如果把调用C++代码当作吃饭，那么： |工具|科技等级| |---|---| |手撸Cython|手撕猎物；钻木取火| |Boost.python/pybind11|用猎枪打猎；在现代化的厨房里做菜| |ctypes|在某个便利店里买吃的。（只卖番茄炒蛋）| |c2py|外卖app| ![

☐ Interoperability with data structures in Python – shared memory space ## How? Python/C API Cython ○ Numba / PyPy Boost::Python ○ Pybind11 ○ cppyy ☐ Mix and match! ## Hello, world (not really) argument, increments it and returns) C++ • Python • C++ with pybind11 C++ with boost::python • Cython int increment(int); // defined in a separate translation unit to prevent inlining int main(int argc PyInit_hello_world_python(void) { return PyModule_Create(&hello_world_python); } ## CYTHON #cython: Language_Level=3 # distutils: Language = c++ cdef extern from "cy.h": int cpp_increment(int

STATA Format ..... 408 18.10 Data Reader ..... 409 19 Enhancing Performance ..... 411 19.1 Cython (Writing C extensions for pandas) ..... 411 20 Sparse data structures ..... 417 20.1 SparseArray notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else, generalization usually sacrifices performance. So if you focus and control over how the intervals and result labeling are defined. A suite of high performance Cython/C-based resampling functions (including Open-High-Low-Close) have also been implemented. • Revamp

jpg) 为什么编译器从字节码开始着手？ 栈机(stack machine)语义的优化问题? Julia后端和Cython后端的差别？ 栈机到基于寄存器(register based)的语义，控制流分析，SSA 和 $ \Phi $ 节点和栈机语义消除。 Cython JIT 基础架构。 ## 为什么编译器从字节码开始着手？ 因为运行时一旦开始你是拿不到源代码的。 如果你拿到了， Pop。 Julia后端就是这样做的。 导致For循环编译后并不快。 而基本上编译器0优化的Cython端， 你模拟栈后， 还远不如Python解释器快， 所以必须写分析去掉栈机语义。 那用Julia后端就可以不写了？ 来谈后端的问题。 ## JIT 后端问题 ||Julia|Cython| |---|---|---| |常见性能提升|100x|1-10x| |小函数JIT|比Py |每次打log等待30s|瞬秒(ms级)| 同时，由于Julia端目前固有的问题，不支持动态链接编译的Python(例如Anaconda Python), hmmmmm 我投Cython亿票, debug福音。跨语言含JAOT的debug是屑。 ## Cython问题 没有goto啊。我怎么jump啊，Python大量jump指令要怎么办啊？ ## 没问题，程序分析来帮你 ## ● ● ● while True:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 21 Enhancing Performance 535 21.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus In [131]: s.where(s>1) Out[131]: 0 NaN 1 2 2 3 3 4 dtype: float64 • Passing a Series directly to a cython function expecting an ndarray type will no long work directly, you must pass Series.values, See Enhancing

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 21 Enhancing Performance 595 21.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus dropna=’any’) Out[34]: A B 0 1 4 1 5 6 • groupby will now not return the grouped column for non-cython functions (GH5610, GH5614, GH6732), as its already the index In [35]: df = DataFrame([[1, np.nan]

About Apache 2.0 • Falcon License • Installation • Install from PyPI • Installing Cython on OS X • WSGI Server • Source Code • Quickstart • The Big Picture • Learning compile itself with Cython for an extra speed boost. The following will make sure Cython is installed first, and that you always have the latest and greatest. $ pip install --upgrade cython falcon Note that that if you are running on PyPy, you won’t need Cython, so you can just type: $ pip install --upgrade falcon ## Note When using Cython, you should always recompile Falcon after upgrading Python. To do

compile itself with Cython for an extra speed boost. The following will make sure Cython is installed first, and that you always have the latest and greatest. $ pip install --upgrade cython falcon Note that that if you are running on PyPy, you won’t need Cython, so you can just type: $ pip install --upgrade falcon Note: When using Cython, you should always recompile Falcon after upgrading Python. To do this run: $ pip install --force-reinstall --upgrade cython $ pip install --force-reinstall --upgrade falcon ## I nstalling Cython on OS X In order to get Cython working on OS X Mavericks with Xcode 5.1, you

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 25 Enhancing Performance 745 25.1 Cython (Writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus nth(0, dropna=’any’) Out[34]: B A 1 4 5 6 • groupby will now not return the grouped column for non-cython functions (GH5610, GH5614, GH6732), as its already the index In [35]: df = DataFrame([[1, np.nan]

performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 943 2.23.1 Cython (writing C extensions for pandas) . . . . . . . . . . . . . . . . . . . . . . . . . . . 944 2.23 higher. • bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.3.1 or higher. Note: You are other notes • pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus