Python松散正则表达式用法分析
本文实例讲述了Python松散正则表达式用法。分享给大家供大家参考，具体如下：
Python 允许用户利用所谓的 松散正则表达式来完成这个任务。一个松散正则表达式和一个紧凑正则表达式主要区别表现在两个方面：
1. 忽略空白符。空格符，制表符，回车符不匹配它们自身，他们根本不参与匹配。(如果你想在松散正则表达式中匹配一个空格符，你必须在它前面添加一个反斜线符号对他进行转义)
2. 忽略注释。在松散正则表达式中的注释和在普通Python代码中的一样：开始于一个#符号，结束于行尾。这种情况下，采用在一个多行字符串中注释，而不是在源代码中注释，他们以相同的方式工作。
下面是一个松散正则表达式的例子，直观地看，正则表达式模式被分成好几行来写了，我们可以为每行配上我们的注释。这样在过段时间后回头过来看我们可以很快地知道这个正则表达式的作用，增强代码的可读性。
>>> import re
>>> pattern = """
^ # beginning of string
M{0,4} # thousands - 0 to 4 M's
(CM|CD|D?C{0,3}) # hundreds - 900 (CM), 400 (CD), 0-300 (0 to 3 C's),
# or 500-800 (D, followed by 0 to 3 C's)
(XC|XL|L?X{0,3}) # tens - 90 (XC), 40 (XL), 0-30 (0 to 3 X's),
# or 50-80 (L, followed by 0 to 3 X's)
(IX|IV|V?I{0,3}) # ones - 9 (IX), 4 (IV), 0-3 (0 to 3 I's),
# or 5-8 (V, followed by 0 to 3 I's)
$ # end of string
"""
>>> re.search(pattern, 'M', re.VERBOSE)
<_sre.SRE_Match object at 0x01401570>
>>> re.search(pattern, 'MCMLXXXIX', re.VERBOSE)
<_sre.SRE_Match object at 0x014015C0>
>>> re.search(pattern, 'M')
>>>
使用松散正则表达式时必须传递另外一个参数re.VERBOSE，该参数是定义在re 模块中的一个常量，标志着待匹配的正则表达式是一个松散正则表达式。Python 不能自动检测一个正则表达式是为松散类型还是紧凑类型，所以必须显式的标明一个正则表达式为松散类型。所以
re.search(pattern, 'M', re.VERBOSE)#松散正则表达式
跟：
re.search(pattern, 'M'))#默认为“紧凑”正则表达式
得到的结果就不一样了。
下面是很常见的一些正则表达式：
^ 匹配字符串的开始。
$ 匹配字符串的结尾。
\b 匹配一个单词的边界。
\d 匹配任意数字。
\D 匹配任意非数字字符。
x? 匹配一个可选的x字符（换句话说，它匹配1次或者0次x 字符）。
x* 匹配0次或者多次x字符。
x+匹配1次或者多次x字符。
x{n,m} 匹配x字符，至少n次，至多m次。
(a|b|c)要么匹配a，要么匹配b，要么匹配c。
(x) 一般情况下表示一个记忆组(remembered group). 我们可以利用re.search函数返回对象的groups()函数获取它的值。
更多关于Python相关内容可查看本站专题：《Python正则表达式用法总结》、《Python数据结构与算法教程》、《Python Socket编程技巧总结》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》、《Python入门与进阶经典教程》及《Python文件与目录操作技巧汇总》
希望本文所述对大家Python程序设计有所帮助。
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