cpython

I am trying to understand how Python works (because I use it all the time!). To my understanding, when you run something like python script.py, the script is converted to bytecode and then the interpreter/VM/CPython–really just a C Program–reads in the python bytecode and executes the program accordingly. How is this bytecode read in? Is it similar to how a text file is read in C? I am unsure how the Python code is converted to machine code. Is it the case that the Python interpreter (the python command in the CLI) is really just a precompiled C program that is already converted to machine

In doing some bioinformatics work, I've been pondering the ramifications of storing object instances in a Numpy array rather than a Python list, but in all the testing I've done the performance was worse in every instance. I am using CPython. Does anyone know the reason why? Specifically: What are the performance impacts of using a fixed-length array numpy.ndarray(dtype=object) vs. a regular Python list? Initial tests I performed showed that accessing the Numpy array elements was slower than iteration through the Python list, especially when using object methods. Why is it faster to

文章首发于我的技术博客：你可以在上面看到更多的 Python教程 和 python爬虫 Python语言最初是用C语言实现的一种脚本语言，后来被称为CPython，是因为后来又有其它语言实现的Python，比如Python实现的Python——PyPy，Java语言实现的Python——Jython，.Net实现的Python——IronPython。 CPython具有优良的开放性和可扩展性，并提供了方便灵活的应用程序接口(API)，从而使得C/C++程序员能够在各个级别上对Python解释器的功能进行扩展。 Python的C语言接口很适合封装C语言实现的各种函数，如果要封装C++的类，使用boost_python或者SWIG更方便和合适。 1 模块封装 假设我们有一个C函数： /* 文件名： mylib.c */ int addone(int a) { return a+1; } 如果想在Python解释器中调用该函数，则应该首先将其实现为Python中的一个模块，这需要编写相应的封装接口，如下所示： /* wrap_mylib.c */ #include <Python.h> #include "mylib.h" PyObject* wrap_addone(PyObject* self, PyObject* args) { int n, result; if (! PyArg

Python 2.7.2 (default, Jun 12 2011, 14:24:46) [MSC v.1500 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> None > 0 False >>> None == 0 False >>> None < 0 True Is comparing None using arithmetic operators well defined for built-in types (integers in this case)? Is the difference between the first two and the third comparison part of language specification (Python's specification - you must be kidding :)) or is it CPython's implementation detail? The only meaningful comparison you can really use with None is if obj is None: (or if obj is not

If I open up interactive mode and type: __builtins__ = 0 # breaks everything have I completely broken the session? If so, what is going on behind the scenes to assign __builtins__ to the builtin module that can't be handled by the interpreter? If not, how can I recover from this? Just a few of my own attempts to fix it: Any attempt to import anything results in an error "ImportError __import__ not found" all functions I might use to do anything other than evaluate numerical expressions are broken There is another variable __package__ still accessible, but I don't know if/how it can be used.

Can I extend syntax in python for dict comprehensions for other dicts, like the OrderedDict in collections module or my own types which inherit from dict ? Just rebinding the dict name obviously doesn't work, the {key: value} comprehension syntax still gives you a plain old dict for comprehensions and literals. >>> from collections import OrderedDict >>> olddict, dict = dict, OrderedDict >>> {i: i*i for i in range(3)}.__class__ <type 'dict'> So, if it's possible how would I go about doing that? It's OK if it only works in CPython. For syntax I guess I would try it with a O{k: v} prefix like we