7 个程序员应该知道的Python开发库
在我多年的 Python 编程经历以及在 Github 上的探索漫游过程中，我发掘到一些很不错的 Python 开发包，这些包大大简化了开发过程，而本文就是为了向大家推荐这些开发包。
请注意我特别排除了像 SQLAlchemy 和 Flask 这样的库，因为其实在太优秀了，无需多提。
下面开始：
1. PyQuery (with lxml)
安装方法 pip install pyquery
Python 解析 HTML 时最经常被推荐的是 Beautiful Soup ，而且它的确也表现很好。提供良好的 Python 风格的 API，而且很容易在网上找到相关的资料文档，但是当你需要在短时间内解析大量文档时便会碰到性能的问题，简单，但是真的非常慢。
下图是 08 年的一份性能比较图：
这个图里我们发现 lxml 的性能是如此之好，不过文档就很少，而且使用上相当的笨拙！那么是选择一个使用简单但是速度奇慢的库呢，还是选择一个速度飞快但是用起来巨复杂的库呢？
谁说二者一定要选其一呢，我们要的是用起来方便，速度也一样飞快的 XML/HTML 解析库！
而 PyQuery 就可以同时满足你的易用性和解析速度方面的苛刻要求。
看看下面这几行代码：
from pyquery import PyQuery page = PyQuery(some_html) last_red_anchor = page('#container > a.red:last')很简单吧，很像是 jQuery，但它却是 Python。
不过也有一些不足，在使用迭代时需要对文本进行重新封装：
for paragraph in page('#container > p'): paragraph = PyQuery(paragraph) text = paragraph.text()
2. dateutil
安装方法：pip install dateutil
处理日期很痛苦，多亏有了 dateutil
from dateutil.parser import parse >>> parse('Mon, 11 Jul 2011 10:01:56 +0200 (CEST)') datetime.datetime(2011, 7, 11, 10, 1, 56, tzinfo=tzlocal()) # fuzzy ignores unknown tokens >>> s = """Today is 25 of September of 2003, exactly ... at 10:49:41 with timezone -03:00.""" >>> parse(s, fuzzy=True) datetime.datetime(2003, 9, 25, 10, 49, 41, tzinfo=tzoffset(None, -10800))
3. fuzzywuzzy
安装方法：pip install fuzzywuzzy
fuzzywuzzy 可以让你对两个字符串进行模糊比较，当你需要处理一些人类产生的数据时，这非常有用。下面代码使用Levenshtein 距离比较方法来匹配用户输入数组和可能的选择。
from Levenshtein import distance countries = ['Canada', 'Antarctica', 'Togo', ...] def choose_least_distant(element, choices): 'Return the one element of choices that is most similar to element' return min(choices, key=lambda s: distance(element, s)) user_input = 'canaderp' choose_least_distant(user_input, countries) >>> 'Canada'这已经不错了，但还可以做的更好：
from fuzzywuzzy import process process.extractOne("canaderp", countries) >>> ("Canada", 97)
4. watchdog
安装方法：pip install watchdog
watchdog 是一个用来监控文件系统事件的 Python API和shell实用工具。
5. sh
安装方法：pip install sh
sh 可让你调用任意程序，就好象是一个函数一般：
from sh import git, ls, wc # checkout master branch git(checkout="master") # print(the contents of this directory print(ls("-l")) # get the longest line of this file longest_line = wc(__file__, "-L")
6. pattern
安装方法：pip install pattern
Pattern 是 Python 的一个 Web 数据挖掘模块。可用于数据挖掘、自然语言处理、机器学习和网络分析。
7. path.py
安装方法：pip install path.py
当我开始学习 Python 时，os.path 是我最不喜欢的 stdlib 的一部分。尽管在一个目录下创建一组文件很简单。
import os some_dir = '/some_dir' files = [] for f in os.listdir(some_dir): files.append(os.path.joinpath(some_dir, f))
但 listdir 在 os 而不是 os.path 中。
而有了 path.py ，处理文件路径变得简单：
from path import path some_dir = path('/some_dir') files = some_dir.files()其他的用法： >>> path('/').owner 'root' >>> path('a/b/c').splitall() [path(''), 'a', 'b', 'c'] # overriding __div__ >>> path('a') / 'b' / 'c' path('a/b/c') >>> path('ab/c').relpathto('ab/d/f') path('../d/f')是不是要好很多？ 英文原文：
In my years of programming in Python and roaming around GitHub's Explore section, I've come across a few libraries that stood out to me as being particularly enjoyable to use. This blog post is an effort to further spread that knowledge.
I specifically excluded awesome libs like requests, SQLAlchemy, Flask, fabric etc. because I think they're already pretty "main-stream". If you know what you're trying to do, it's almost guaranteed that you'll stumble over the aforementioned. This is a list of libraries that in my opinion should be better known, but aren't.
1. pyquery (with lxml)
pip install pyquery
For parsing HTML in Python, Beautiful Soup is oft recommended and it does a great job. It sports a good pythonic API and it's easy to find introductory guides on the web. All is good in parsing-land .. until you want to parse more than a dozen documents at a time and immediately run head-first into performance problems. It's - simply put - very, very slow.
Just how slow? Check out this chart from the excellent Python HTML Parser comparison Ian Bicking compiled in 2008:
What immediately stands out is how fast lxml is. Compared to Beautiful Soup, the lxml docs are pretty sparse and that's what originally kept me from adopting this mustang of a parsing library. lxml is pretty clunky to use. Yeah you can learn and use Xpath or cssselect to select specific elements out of the tree and it becomes kind of tolerable. But once you've selected the elements that you actually want to get, you have to navigate the labyrinth of attributes lxml exposes, some containing the bits you want to get at, but the vast majority just returning None. This becomes easier after a couple dozen uses but it remains unintuitive.
So either slow and easy to use or fast and hard to use, right?
Wrong!
Enter PyQuery
Oh PyQuery you beautiful seductress:
from pyquery import PyQuery page = PyQuery(some_html) last_red_anchor = page('#container > a.red:last') Easy as pie. It's ever-beloved jQuery but in Python!
There are some gotchas, like for example that PyQuery, like jQuery, exposes its internals upon iteration, forcing you to re-wrap:
for paragraph in page('#container > p'): paragraph = PyQuery(paragraph) text = paragraph.text() That's a wart the PyQuery creators ported over from jQuery (where they'd fix it if it didn't break compatability). Understandable but still unfortunate for such a great library.
2. dateutil
pip install dateutil
Handling dates is a pain. Thank god dateutil exists. I won't even go near parsing dates without trying dateutil.parser first:
from dateutil.parser import parse >>> parse('Mon, 11 Jul 2011 10:01:56 +0200 (CEST)') datetime.datetime(2011, 7, 11, 10, 1, 56, tzinfo=tzlocal()) # fuzzy ignores unknown tokens >>> s = """Today is 25 of September of 2003, exactly ... at 10:49:41 with timezone -03:00.""" >>> parse(s, fuzzy=True) datetime.datetime(2003, 9, 25, 10, 49, 41, tzinfo=tzoffset(None, -10800)) Another thing that dateutil does for you, that would be a total pain to do manually, is recurrence:
>>> list(rrule(DAILY, count=3, byweekday=(TU,TH), ... dtstart=datetime(2007,1,1))) [datetime.datetime(2007, 1, 2, 0, 0), datetime.datetime(2007, 1, 4, 0, 0), datetime.datetime(2007, 1, 9, 0, 0)] 3. fuzzywuzzy
pip install fuzzywuzzy
fuzzywuzzy allows you to do fuzzy comparison on wuzzes strings. This has a whole host of use cases and is especially nice when you have to deal with human-generated data.
Consider the following code that uses the Levenshtein distance comparing some user input to an array of possible choices.
from Levenshtein import distance countries = ['Canada', 'Antarctica', 'Togo', ...] def choose_least_distant(element, choices): 'Return the one element of choices that is most similar to element' return min(choices, key=lambda s: distance(element, s)) user_input = 'canaderp' choose_least_distant(user_input, countries) >>> 'Canada' This is all nice and dandy but we can do better. The ocean of 3rd party libs in Python is so vast, that in most cases we can just import something and be on our way:
from fuzzywuzzy import process process.extractOne("canaderp", countries) >>> ("Canada", 97) More has been written about fuzzywuzzy here.
4. watchdog
pip install watchdog
watchdog is a Python API and shell utilities to monitor file system events. This means you can watch some directory and define a "push-based" system. Watchdog supports all kinds of problems. A solid piece of engineering that does it much better than the 5 or so libraries I tried before finding out about it.
5. sh
pip install sh
sh allows you to call any program as if it were a function:
from sh import git, ls, wc # checkout master branch git(checkout="master") # print(the contents of this directory print(ls("-l")) # get the longest line of this file longest_line = wc(__file__, "-L") 6. pattern
pip install pattern
This behemoth of a library advertises itself quite modestly:
Pattern is a web mining module for the Python programming language.
... that does Data Mining, Natural Language Processing, Machine Learning and Network Analysis all in one. I myself yet have to play with it but a friend's verdict was very positive.
7. path.py
pip install path.py
When I first learned Python os.path was my least favorite part of the stdlib.
Even something as simple as creating a list of files in a directory turned out to be grating:
import os some_dir = '/some_dir' files = [] for f in os.listdir(some_dir): files.append(os.path.joinpath(some_dir, f)) That listdir is in os and not os.path is unfortunate and unexpected and one would really hope for more from such a prominent module. And then all this manual fiddling for what really should be as simple as possible.
But with the power of path, handling file paths becomes fun again:
from path import path some_dir = path('/some_dir') files = some_dir.files() Done!
Other goodies include:
>>> path('/').owner 'root' >>> path('a/b/c').splitall() [path(''), 'a', 'b', 'c'] # overriding __div__ >>> path('a') / 'b' / 'c' path('a/b/c') >>> path('ab/c').relpathto('ab/d/f') path('../d/f') Best part of it all? path subclasses Python's str so you can use it completely guilt-free without constantly being forced to cast it to str and worrying about libraries that check isinstance(s, basestring) (or even worse isinstance(s, str)).