提取所有以'st'，'nd'，'rd'，'th'结尾的日期，同时使用RegEx将日期与月份交换

如何解决提取所有以'st'，'nd'，'rd'，'th'结尾的日期，同时使用RegEx将日期与月份交换

我在pandas数据框列的文本中输入了这些日期。

import pandas as pd
sr = pd.Series(['text Mar 20,2009 text','text march 20,'text 20 Mar. 2009 text','text Sep 2010 text','text Mar 20th,2009 text ','text Mar 21st,2009 text'])

当我使用正则表达式时，会得到这个。

a=sr.str.extractall(r'((?P<day>(?:\d{2} )?(?P<month>(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z,.]*)) (?:\d{2}[-/th|st|nd|rd\s]*[,.]* )?(?P<year>\d{4}))')


       all              day     month  year
match               
0   0   Mar 20,2009    Mar      Mar    2009
1   0   march 20,2009  march    march  2009
2   0   20 Mar. 2009    20 Mar.  Mar.   2009
3   0   Sep 2010        Sep      Sep    2010
4   0   Mar 20th,2009  Mar      Mar    2009
5   0   Mar 21st,2009  Mar      Mar    2009

如何将日期（20、20、21st ...）放入“天”列？

解决方法

使用熊猫的一种解决方案（为什么要重新发明轮子？）

    import pandas as pd
    df = sr.to_frame(name='all')
    df['all'] = pd.to_datetime(df['all'])
    df['day'] = df['all'].dt.day
    df['month'] = df['all'].dt.strftime('%b')
    df['year'] = df['all'].dt.year

输出：

         all  day month  year
0 2009-03-20   20   Mar  2009
1 2009-03-20   20   Mar  2009
2 2009-03-20   20   Mar  2009
3 2010-09-01    1   Sep  2010
4 2009-03-20   20   Mar  2009
5 2009-03-21   21   Mar  2009

,

也许另一种解决方案可能是使用PyPi regex module和分支重置组(?|来匹配日期和月份。

没有命名组的模式：

\b((?|(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z,.]*(?: (\d{2}(?:th|st|nd|rd)?)?[,.])?|(\d{2}) (?:(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z,.]*)?) (\d{4}))

Regex demo

import pandas as pd
import regex

pattern = r"\b(?P<all>(?|(?P<month>Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z,.]*(?: (?P<day>\d{2}(?:th|st|nd|rd)?)?[,.])?|(?P<day>\d{2}) (?:(?P<month>Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z,.]*)?) (?P<year>\d{4}))"

items = [
    'text Mar 20,2009 text','text March 20,'text 20 Mar. 2009 text','text Sep 2010 text','text Mar 20th,2009 text ','text Mar 21st,2009 text'
]
res = map(lambda x: regex.findall(pattern,x)[0],items)
df = pd.DataFrame(res)
df.columns = ['all','month','day','year']
print(df)

输出

              all month   day  year
0    Mar 20,2009   Mar    20  2009
1  March 20,2009   Mar    20  2009
2    20 Mar. 2009   Mar    20  2009
3        Sep 2010   Sep        2010
4  Mar 20th,2009   Mar  20th  2009
5  Mar 21st,2009   Mar  21st  2009