@ coding和@user1767754:下面两行可以工作。我看到没有人贴出被问到的例子问题的完整解决方案。希望这个解释足够了。

import datetime

x = datetime.datetime.strptime("Mon Feb 15 2010", "%a %b %d %Y").strftime("%d/%m/%Y")
print(x)

输出:

15/02/2010

Datetime模块可以帮助你:

datetime.datetime.strptime(date_string, format1).strftime(format2)

对于具体的例子

>>> import datetime
>>> datetime.datetime.strptime('Mon Feb 15 2010', '%a %b %d %Y').strftime('%d/%m/%Y')
'15/02/2010'
>>>

使用datetime库 http://docs.python.org/library/datetime.html查看9.1.7。 特别是strptime() strftime()行为¶ 例子 http://pleac.sourceforge.net/pleac_python/datesandtimes.html

>>> from_date="Mon Feb 15 2010"
>>> import time                
>>> conv=time.strptime(from_date,"%a %b %d %Y")
>>> time.strftime("%d/%m/%Y",conv)
'15/02/2010'

如果你不想定义输入日期格式，那么安装dateparser (pip Install dateparser)和，

from dateparser import parse
parse("Mon Feb 15 2010").strftime("%d/%m/%Y")

你也可以使用熊猫来实现这一点:

import pandas as pd

pd.to_datetime('Mon Feb 15 2010', format='%a %b %d %Y').strftime('%d/%m/%Y')

输出:

'15/02/2010'

你可以对不同的数据类型应用pandas方法:

import pandas as pd
import numpy as np

def reformat_date(date_string, old_format, new_format):
    return pd.to_datetime(date_string, format=old_format, errors='ignore').strftime(new_format)

date_string = 'Mon Feb 15 2010'
date_list = ['Mon Feb 15 2010', 'Wed Feb 17 2010']
date_array = np.array(date_list)
date_series = pd.Series(date_list)

old_format = '%a %b %d %Y'
new_format = '%d/%m/%Y'

print(reformat_date(date_string, old_format, new_format))
print(reformat_date(date_list, old_format, new_format).values)
print(reformat_date(date_array, old_format, new_format).values)
print(date_series.apply(lambda x: reformat_date(x, old_format, new_format)).values)

输出:

15/02/2010
['15/02/2010' '17/02/2010']
['15/02/2010' '17/02/2010']
['15/02/2010' '17/02/2010']