4 字符串操作
4.1 字符串对象方法
大部分的字符处理，内置方法已经够用了。结合“split”和“strip”、‘+’、“join”等方法。
In [1]: a = 'a,b, guide'
In [2]: a.split(',')
Out[2]: ['a', 'b', ' guide']
In [4]: [x.strip() for x in a.split(',')]
Out[4]: ['a', 'b', 'guide']
In [5]: first,second,third = [x.strip() for x in a.split(',')]
In [6]: first
Out[6]: 'a'
In [7]: second
Out[7]: 'b'
In [8]: third
Out[8]: 'guide'
In [9]: first + third
Out[9]: 'aguide'
In [10]: first.join(third)
Out[10]: 'gauaiadae'
In [12]: ''.join([third,first])
Out[12]: 'guidea'
In [13]: 'a' in a
Out[13]: True
In [14]: a.find(':')
Out[14]: -1
In [15]: a.index(':')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-15-e54927e30300> in <module>()
----> 1 a.index(':')
ValueError: substring not found
In [16]: a.count(',')
Out[16]: 2
字符串内置方法：
4.2 正则表达式
复习一下正则表达式
In [17]: import re
In [18]: text = 'foo bar baz qux'
In [19]: re.split('s+', text) # s+ 表示一个或多个空格
Out[19]: ['foo', 'bar', 'baz', 'qux']
In [20]: regex = re.compile('s+') # 创建regex对象，多次复用，减少大量CPU时间
In [21]: regex.split(text)
Out[21]: ['foo', 'bar', 'baz', 'qux']
In [22]: regex.findall(text) # 所有text中匹配到的对象
Out[22]: [' ', ' ', ' ']
# match ：从头开始匹配
# search：第一个
# findall：找到所有
In [23]: text = '''
...: Dave dave@google.com
...: Steve steve@gmail.com
...: Rob rob@gmail.com
...: Ryan ryan@yahoo.com
...: '''
In [24]: pattern = r'[A-Z0-9._%+-]+@[A-Z0-9.-]+.[A-Z]{2,4}'
# flags=re.IGNORECASE 表示忽略大小写
In [25]: regex = re.compile(pattern, flags=re.IGNORECASE)
In [26]: regex.findall(text)
Out[26]: ['dave@google.com', 'steve@gmail.com', 'rob@gmail.com', 'ryan@yahoo.com']
In [27]: regex.search(text)
Out[27]: <_sre.SRE_Match at 0x104227b28>
In [29]: regex.search(text).group(0)
Out[29]: 'dave@google.com'
# sub 替换
In [30]: regex.sub('HAHAHA', text)
Out[30]: '
Dave HAHAHA
Steve HAHAHA
Rob HAHAHA
Ryan HAHAHA
'
In [31]: print(regex.sub("HAHAHAAHA", text))
Dave HAHAHAAHA
Steve HAHAHAAHA
Rob HAHAHAAHA
Ryan HAHAHAAHA
# 分组
In [33]: pattern = r'([A-Z0-9._%+-]+)@([A-Z0-9.-]+).([A-Z]{2,4})'
In [34]: regex = re.compile(pattern, flags=re.IGNORECASE)
In [35]: m=regex.match('wesm@bright.net')
In [36]: m.groups()
Out[36]: ('wesm', 'bright', 'net')
In [37]: m.group(0)
Out[37]: 'wesm@bright.net'
In [38]: m.group(1)
Out[38]: 'wesm'
In [39]: m.group(2)
Out[39]: 'bright'
In [40]: m.group(3)
Out[40]: 'net'
# 返回一个大的列表
In [41]: regex.findall(text)
Out[41]:
[('dave', 'google', 'com'),
('steve', 'gmail', 'com'),
('rob', 'gmail', 'com'),
('ryan', 'yahoo', 'com')]
# 指定替换
In [48]: regex.sub(r'Username:1,Domain:2,Suffix:3',text)
Out[48]: '
Dave Username:dave,Domain:google,Suffix:com
Steve Username:steve,Domain:gmail,Suffix:com
Rob Username:rob,Domain:gmail,Suffix:com
Ryan Username:ryan,Domain:yahoo,Suffix:com
'
4.3 pandas矢量化的字符串函数
In [61]: data = {
...: 'Dave':'dave@google.com',
...: 'Steve':'steve@gmail.com',
...: 'Rob':'rob@gmail.com',
...: 'Ryan':'ryan@yahoo.com',
...: 'Wes':np.nan}
In [62]: data = Series(data)
In [63]: data
Out[63]:
Dave dave@google.com
Rob rob@gmail.com
Ryan ryan@yahoo.com
Steve steve@gmail.com
Wes NaN
dtype: object
In [64]: data.isnull()
Out[64]:
Dave False
Rob False
Ryan False
Steve False
Wes True
dtype: bool
# 查看是否包含，返回一个bool Series
In [65]: data.str.contains('gmail')
Out[65]:
Dave False
Rob True
Ryan False
Steve True
Wes NaN
dtype: object
In [66]: pattern
Out[66]: '([A-Z0-9._%+-]+)@([A-Z0-9.-]+)\.([A-Z]{2,4})'
# 返回一个匹配好的Series
In [67]: data.str.findall(pattern, flags=re.IGNORECASE)
Out[67]:
Dave [(dave, google, com)]
Rob [(rob, gmail, com)]
Ryan [(ryan, yahoo, com)]
Steve [(steve, gmail, com)]
Wes NaN
dtype: object
In [68]: matches = data.str.match(pattern, flags=re.IGNORECASE)
/Users/yangfeilong/anaconda/bin/ipython:1: FutureWarning: In future versions of pandas, match will change to always return a bool indexer.
#!/bin/bash /Users/yangfeilong/anaconda/bin/python.app
In [69]: matches
Out[69]:
Dave (dave, google, com)
Rob (rob, gmail, com)
Ryan (ryan, yahoo, com)
Steve (steve, gmail, com)
Wes NaN
dtype: object
In [70]: matches.str.get(1)
Out[70]:
Dave google
Rob gmail
Ryan yahoo
Steve gmail
Wes NaN
dtype: object
In [71]: matches.str.get(2)
Out[71]:
Dave com
Rob com
Ryan com
Steve com
Wes NaN
dtype: object
In [72]: matches.str[0]
Out[72]:
Dave dave
Rob rob
Ryan ryan
Steve steve
Wes NaN
dtype: object
In [73]: data.str[:5]
Out[73]:
Dave dave@
Rob rob@g
Ryan ryan@
Steve steve
Wes NaN
dtype: object
完。