Python之Regular Expressions（正则表达式）

     在编写处理字符串的程序或网页时，经常会有查找符合某些复杂规则的字符串的需要。正则表达式就是用于描述这些规则的工具。换句话说，正则表达式就是记录文本规则的代码。

     很可能你使用过Windows/Dos下用于文件查找的通配符(wildcard)，也就是*和?。如果你想查找某个目录下的所有的Word文档的话，你会搜索*.doc。在这里，*会被解释成任意的字符串。和通配符类似，正则表达式也是用来进行文本匹配的工具，只不过比起通配符，它能更精确地描述你的需求——当然，代价就是更复杂——比如你可以编写一个正则表达式，用来查找所有以0开头，后面跟着2-3个数字，然后是一个连字号“-”，最后是7或8位数字的字符串(像010-12345678或0376-7654321)。学习正则表达式的一个好网站戳这里！

     好，言归正传，我们来谈谈在Python中的正则表达式！我们在日常对字符串处理（诸如搜索，替换和解析操作）的时候，往往会被大小写的问题所困扰。ref：戳这里

The search methods look for a single, hard-coded substring, and they are always case-sensitive. To do case-insensitive searches of a string s, you must call s.lower() or s.upper() and make sure your search strings are the appropriate case to match. The replace and split methods have the same limitations.

If what you're trying to do can be accomplished with string functions, you should use them. They're fast and simple and easy to read, and there's a lot to be said for fast, simple, readable code. But if you find yourself using a lot of different string functions with if statements to handle special cases, or if you're combining them with split and join and list comprehensions in weird unreadable ways, you may need to move up to regular expressions.

Although the regular expression syntax is tight and unlike normal code, the result can end up being more readable than a hand-rolled solution that uses a long chain of string functions. There are even ways of embedding comments within regular expressions to make them practically self-documenting.

举例说明：

Case Study: Street Addresses

This series of examples was inspired by a real-life problem I had in my day job several years ago, when I needed to scrub and standardize street addresses exported from a legacy system before importing them into a newer system. (See, I don't just make this stuff up; it's actually useful.) This example shows how I approached the problem.

Example 7.1. Matching at the End of a String

>>> s = '100 NORTH MAIN ROAD'
>>> s.replace('ROAD', 'RD.')          

'100 NORTH MAIN RD.'
>>> s = '100 NORTH BROAD ROAD'
>>> s.replace('ROAD', 'RD.')               

'100 NORTH BRD. RD.'
>>> s[:-4] + s[-4:].replace('ROAD', 'RD.') 

'100 NORTH BROAD RD.'
>>> import re                              

>>> re.sub('ROAD$', 'RD.', s)              

'100 NORTH BROAD RD.'

My goal is to standardize a street address so that 'ROAD' is always abbreviated as 'RD.'. At first glance, I thought this was simple enough that I could just use the string method replace. After all, all the data was already uppercase, so case mismatches would not be a problem. And the search string, 'ROAD', was a constant. And in this deceptively simple example, s.replace does indeed work.

Life, unfortunately, is full of counterexamples, and I quickly discovered this one. The problem here is that 'ROAD' appears twice in the address, once as part of the street name 'BROAD' and once as its own word. The replace method sees these two occurrences and blindly replaces both of them; meanwhile, I see my addresses getting destroyed.

To solve the problem of addresses with more than one 'ROAD' substring, you could resort to something like this: only search and replace 'ROAD' in the last four characters of the address (s[-4:]), and leave the string alone (s[:-4]). But you can see that this is already getting unwieldy. For example, the pattern is dependent on the length of the string you're replacing (if you were replacing 'STREET' with 'ST.', you would need to use s[:-6] and s[-6:].replace(...)). Would you like to come back in six months and debug this? I know I wouldn't.

It's time to move up to regular expressions. In Python, all functionality related to regular expressions is contained in the re module.

Take a look at the first parameter: 'ROAD$'. This is a simple regular expression that matches 'ROAD' only when it occurs at the end of a string. The $ means “end of the string”. (There is a corresponding character, the caret ^, which means “beginning of the string”.)

Using the re.sub function, you search the string s for the regular expression 'ROAD$' and replace it with 'RD.'. This matches the ROAD at the end of the strings, but does not match the ROAD that's part of the word BROAD, because that's in the middle of s.

Continuing with my story of scrubbing addresses, I soon discovered that the previous example, matching 'ROAD' at the end of the address, was not good enough, because not all addresses included a street designation at all; some just ended with the street name. Most of the time, I got away with it, but if the street name was'BROAD', then the regular expression would match 'ROAD' at the end of the string as part of the word 'BROAD', which is not what I wanted.

Example 7.2. Matching Whole Words

>>> s = '100 BROAD'
>>> re.sub('ROAD$', 'RD.', s)
'100 BRD.'
>>> re.sub('\\bROAD$', 'RD.', s)  

'100 BROAD'
>>> re.sub(r'\bROAD$', 'RD.', s)  

'100 BROAD'
>>> s = '100 BROAD ROAD APT. 3'
>>> re.sub(r'\bROAD$', 'RD.', s)  

'100 BROAD ROAD APT. 3'
>>> re.sub(r'\bROAD\b', 'RD.', s) 

'100 BROAD RD. APT 3'

What I really wanted was to match 'ROAD' when it was at the end of the string and it was its own whole word, not a part of some larger word. To express this in a regular expression, you use \b, which means “a word boundary must occur right here”. In Python, this is complicated by the fact that the '\' character in a string must itself be escaped. This is sometimes referred to as the backslash plague, and it is one reason why regular expressions are easier in Perl than in Python. On the down side, Perl mixes regular expressions with other syntax, so if you have a bug, it may be hard to tell whether it's a bug in syntax or a bug in your regular expression.

To work around the backslash plague, you can use what is called a raw string, by prefixing the string with the letter r. This tells Python that nothing in this string should be escaped; '\t' is a tab character, but r'\t' is really the backslash character \ followed by the letter t. I recommend always using raw strings when dealing with regular expressions; otherwise, things get too confusing too quickly (and regular expressions get confusing quickly enough all by themselves).

 *sigh* Unfortunately, I soon found more cases that contradicted my logic. In this case, the street address contained the word 'ROAD' as a whole word by itself, but it wasn't at the end, because the address had an apartment number after the street designation. Because 'ROAD' isn't at the very end of the string, it doesn't match, so the entire call to re.sub ends up replacing nothing at all, and you get the original string back, which is not what you want.

To solve this problem, I removed the $ character and added another \b. Now the regular expression reads “match 'ROAD' when it's a whole word by itself anywhere in the string,” whether at the end, the beginning, or somewhere in the middle.