本文主要向大家介绍了Python语言标准库 - re，通过具体的内容向大家展示，希望对大家学习Python语言有所帮助。

编写代码时, 经常要匹配特定字符串, 或某个模式的字符串, 一般会借助字符串函数, 或正则表达式完成.

对于正则表达式, 有些字符具有特殊含义, 需使用反斜杠字符'\'转义, 使其表示本身含义. 如想匹配字符'\', 却要写成'\\\\', 很是困扰. Python中Raw string解决了该问题, 只需给'\'加上前缀'r'即可, 如r'\n', 表示'\'和'n'两个普通字符, 而不是原来的换行. 前缀'r'类似于sed命令的-r(use extended regular expressions)参数.  

正则表达式可包括两部分, 一是正常字符, 表本身含义; 二是特殊字符, 表一类正常字符, 或字符数量...

re模块提供了诸多方法进行正则匹配.

match    Match a regular expression pattern to the beginning of a string.

search   Search a string for the presence of a pattern.

sub      Substitute occurrences of a pattern found in a string.

subn     Same as sub, but also return the number of substitutions made.

split    Split a string by the occurrences of a pattern.

findall  Find all occurrences of a pattern in a string.

finditer Return an iterator yielding a match object for each match.

purge    Clear the regular expression cache.

escape   Backslash all non-alphanumerics in a string.

还有compile函数, 其较特殊, 将匹配模式编译为一个正则表达式对象(RegexObject, _sre.SRE_Pattern), 并返回, 该对象仍然可以使用上述这些函数. 这也从侧面说明了, 对于re模块, 有非编译和编译两种使用方式, 如下所示.

1.

result = re.match(pattern, string)

2.

prog = re.compile(pattern)

result = prog.match(string)

它们达到的效果是相同的, 只是后者暂存了正则表达式对象, 对于某块代码中频繁使用该正则表达式的情形, 后者性能一般会高于前者.

对于match()和search()匹配成功, 会返回一个匹配对象(Match Object, _sre.SRE_Match), 其也有若干方法, 下面几个较常用.

group 

    group([group1, ...]) -> str or tuple.

    Return subgroup(s) of the match by indices or names.

    For 0 returns the entire match.

groups(...)

    groups([default=None]) -> tuple.

    Return a tuple containing all the subgroups of the match, from 1.

    The default argument is used for groups

    that did not participate in the match

end(...)

    end([group=0]) -> int.

    Return index of the end of the substring matched by group.

start(...)

    start([group=0]) -> int.

    Return index of the start of the substring matched by group.

至此对re模块框架性梳理就这样了, 给出些例子, 对上面的内容总结下.

1.

In [23]: text = "He was carefully disguised but captured quickly by police."

In [24]: re.findall(r"\w+ly", text)

Out[24]: ['carefully', 'quickly']

2.

In [25]: m = re.match(r"(\w+) (\w+)", "Isaac Newton, physicist")

In [26]: m.group(0)

Out[26]: 'Isaac Newton'

In [27]: m.group(1)

Out[27]: 'Isaac'

In [28]: m.group(2)

Out[28]: 'Newton'

In [29]: m.group(1, 2)

Out[29]: ('Isaac', 'Newton')

3.

In [31]: account = "abcxyz_"

In [32]: replace_regex = re.compile(r'_$')

In [33]: replace_regex.sub(account[0], account)

Out[33]: 'abcxyza'

正则表达式使用中的细节还有很多, 这里无法尽数, 实践过程中慢慢体会和总结吧.

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