Python有哪些黑魔法？
回复内容：
ctypes ，当年有个人靠这个省了好几个月的加班------------------------劳动节补充-----------------------------
回答 @于酥酥
1. ipython + ctypes: 调试/测试Linux API的交互式运行环境
ipython是最好的REPL！（我喜欢Python，至少有30%的好感来自ipython）
REPL的好处不言自明，在开发和调试时能大大的提高效率。尤其是需要对API进行快速验证时。
我最早是用gdb来做一些REPL的事情，但毕竟操作复杂，交互式功能有限。而用ctypes，就爽快多了，ctypes可以直接人so中提取出函数，在Python层面稍加包装，就能直接使用，不用编译/连接，保持运行状态，结果出来直接用Python分析……简直是画面太美
真实场景：
我们的运营环境有数十万host，host上去除了编译环境，某一天，我们对某个系统调用的返回产生怀疑。于是，按照通常的作法，在开发机上写一个示例程序，编译，拷贝到运营机，运行，反复执行这一个过程。
那么有了ctypes，直接在python/ipython的REPL里调试就好了。还不容易留下可疑的可执行程序。
2. ctypes作为胶水
ctypes增强了python作为胶水语言的能力，从进程调用/统一协议级别的脱水直接深入到二进制级别的脱水。这样看来，C++对C的兼容就显得没那么重要了
真实场景：
某个执行框架，插件以so的形式提供，so提供固定的函数入口。重构时打算去除语言耦合，改用进程调用的方式调用插件（类似于cgi server，这样可以减少对插件编写的限制，插件本身也更容易测试，防止so崩溃造成框架整体崩溃）。但是很多插件的作者已离职，于是只需要框架额外增加从so里调用函数出来即可，做到平滑升级。
3. ctypes与系统编程
ctypes作为一种轻量并且内置的c语言“代理”，使得python极大地增强了系统编程的能力。
从此，系统编程的代码也可以变得更加优雅。
真实场景：
sdn/vpc方案需要对内核协议栈做较多的调整，从管理的层面上，网络配置由中央控制并下发。因此，host上存在一个daemon，一方面要接受zookeeper的配置变更通知，另一方面要把配置解析后通过netlink与内核通信。
这个daemon大概几乎没有人会用python去做。但是我看到iotop里用到ctypes对netlink接口的封装，惊为天人，并且python更加适合对配置解析与处理。我斗胆用python实现了这个daemon，调试起来如丝般顺滑，然后就减少了好几个月的加班。
回答我成电师兄 @韦易笑 大神
没有有生产环境用过cffi，以前在自己电脑上简单用过，感觉不如ctypes简单粗暴。当然我没去用的主要原因还是不想在部署的时候附带太多东西。 在Quora上看到的, 不算黑科技吧, 但感觉挺有意思的. turtle是内置库
======================================================================
补充一个最近才看到的:
要对字典里面的键嵌套赋值, 对键不存在时候的解决方案:
<span class="kn">import</span> <span class="nn">collections</span> <span class="n">tree</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="n">collections</span><span class="o">.</span><span class="n">defaultdict</span><span class="p">(</span><span class="n">tree</span><span class="p">)</span> <span class="n">some_dict</span> <span class="o">=</span> <span class="n">tree</span><span class="p">()</span> <span class="n">some_dict</span><span class="p">[</span><span class="s">"colors"</span><span class="p">][</span><span class="s">"favourite"</span><span class="p">]</span> <span class="o">=</span> <span class="s">"yellow"</span>
<span class="k">with</span> <span class="n">timeout</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
<span class="n">balabala</span><span class="p">()</span>
difflib，它是个official的module哦，用来比较串的相似度。
(difflib)[difflib – Compare sequences],
另外常用的functools和collections也都是Python吸引人的地方。
很多第三方的库窃以为不能算了。
另外doctest在进行单元测试的时候也是棒呆
25.2. doctest 说到python黑魔法，必然要提到python的第三方协程库gevent的底层实现——greenlet。
greenlet直接在内存层面，通过保存和替换Python进程的运行栈来实现不同协程的切换。
这个切换对于python解释器是透明的，如果python解释器对环境有感知的话，则每当协程切换的时候，它的感觉可能类似一个人前一秒还在在路上走路，下一秒突然自己又出现在了地铁上。
对于普通python用户而言，直接操作python的运行时栈，这就是在刀尖上跳舞有木有，这要求对内存的操作100%精确，任何错误都可能导致python进程崩溃！
那作者又是如何又是如何来保证正确性呢？除了要熟悉python、操作系统、编译器等等的底层机制，明确设计方案，还需要对不同的系统以及硬件环境做对应的适配工作。我们在使用python的时候，在不同的系统或者硬件下感觉都一样，那是因为python本身为我们屏蔽了底层细节，在做这种python底层hack的事情的时候，显然就没那么轻松了。
举个例子，由于CPU有很多种，例如i386、x86_64、arm等等，每种CPU的设计不尽相同，于是作者为每种CPU写了对应的汇编操作指令来完成栈的保存和替换，这些操作都是与操作系统和硬件高度绑定的。
虽然greenlet的实现这么bt，但就是有人做到了，加上gevent的封装，用起来比python自带协程好用太多。
我想任何对python比较熟悉的童鞋，在初次接触gevent的时候，都会好奇它是如何做到的，在进一步了解其底层greenlet实现机理之后，无不惊叹其鬼斧神工。
这种事情就是那种，别人不说，你可能永远不会想到的事情。 pip一下啥都有呀，比如微信接口 itchat
pip install itchat
- Flask 中的插件是怎么做的? 为何能用
from flask.ext.sqlalchemy import SQLAlchemy
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