#Convert String to datetime
>>> x=datetime.strptime('Jun 1 2005', '%b %d %Y').date()
>>> print(x,type(x))
2005-06-01 00:00:00 <class 'datetime.datetime'>


#Convert datetime to String (Reverse above process)
>>> y=x.strftime('%b %d %Y')
>>> print(y,type(y))
Jun 01 2005 <class 'str'>

如果您不想明确指定字符串相对于日期时间格式的格式，可以使用此黑客绕过该步骤：

from dateutil.parser import parse

# Function that'll guess the format and convert it into the python datetime format
def update_event(start_datetime=None, end_datetime=None, description=None):
    if start_datetime is not None:
        new_start_time = parse(start_datetime)

        return new_start_time

# Sample input dates in different formats
d = ['06/07/2021 06:40:23.277000', '06/07/2021 06:40', '06/07/2021']

new = [update_event(i) for i in d]

for date in new:
    print(date)
    # Sample output dates in Python datetime object
    #   2014-04-23 00:00:00
    #   2013-04-24 00:00:00
    #   2014-04-25 00:00:00

如果要将其转换为其他日期时间格式，只需使用您喜欢的格式修改最后一行，例如date.strftime（“%Y/%m/%d%H:%m:%S.%f”）：

from dateutil.parser import parse

def update_event(start_datetime=None, end_datetime=None, description=None):
    if start_datetime is not None:
        new_start_time = parse(start_datetime)

        return new_start_time

# Sample input dates in different formats
d = ['06/07/2021 06:40:23.277000', '06/07/2021 06:40', '06/07/2021']

# Passing the dates one by one through the function
new = [update_event(i) for i in d]

for date in new:
    print(date.strftime('%Y/%m/%d %H:%M:%S.%f'))
    # Sample output dates in required Python datetime object
    #   2021/06/07 06:40:23.277000
    #   2021/06/07 06:40:00.000000
    #   2021/06/07 00:00:00.000000

尝试运行上面的代码段以获得更好的清晰度。

Use:

emp = pd.read_csv("C:\\py\\programs\\pandas_2\\pandas\\employees.csv")
emp.info()

它显示“开始日期时间”列和“上次登录时间”都是数据帧中的“对象=字符串”：

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
First Name           933 non-null object
Gender               855 non-null object

    Start Date           1000 non-null object

    Last Login Time      1000 non-null object

Salary               1000 non-null int64
Bonus %              1000 non-null float64
Senior Management    933 non-null object
Team                 957 non-null object
dtypes: float64(1), int64(1), object(6)
memory usage: 62.6+ KB

通过使用read_csv中的parse_dates选项，可以将字符串datetime转换为panda datetime格式。

emp = pd.read_csv("C:\\py\\programs\\pandas_2\\pandas\\employees.csv", parse_dates=["Start Date", "Last Login Time"])
emp.info()

输出：

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
First Name           933 non-null object
Gender               855 non-null object

     Start Date           1000 non-null datetime64[ns]
     Last Login Time      1000 non-null datetime64[ns]

Salary               1000 non-null int64
Bonus %              1000 non-null float64
Senior Management    933 non-null object
Team                 957 non-null object
dtypes: datetime64[ns](2), float64(1), int64(1), object(4)
memory usage: 62.6+ KB

arrow为日期和时间提供了许多有用的函数。这段代码为这个问题提供了答案，并表明箭头还能够轻松格式化日期并显示其他地区的信息。

>>> import arrow
>>> dateStrings = [ 'Jun 1  2005 1:33PM', 'Aug 28 1999 12:00AM' ]
>>> for dateString in dateStrings:
...     dateString
...     arrow.get(dateString.replace('  ',' '), 'MMM D YYYY H:mmA').datetime
...     arrow.get(dateString.replace('  ',' '), 'MMM D YYYY H:mmA').format('ddd, Do MMM YYYY HH:mm')
...     arrow.get(dateString.replace('  ',' '), 'MMM D YYYY H:mmA').humanize(locale='de')
...
'Jun 1  2005 1:33PM'
datetime.datetime(2005, 6, 1, 13, 33, tzinfo=tzutc())
'Wed, 1st Jun 2005 13:33'
'vor 11 Jahren'
'Aug 28 1999 12:00AM'
datetime.datetime(1999, 8, 28, 0, 0, tzinfo=tzutc())
'Sat, 28th Aug 1999 00:00'
'vor 17 Jahren'

看见http://arrow.readthedocs.io/en/latest/了解更多信息。

使用熊猫时间戳似乎是最快的：

import pandas as pd

N = 1000

l = ['Jun 1 2005  1:33PM'] * N

list(pd.to_datetime(l, format=format))

%timeit _ = list(pd.to_datetime(l, format=format))
1.58 ms ± 21.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

其他解决方案

from datetime import datetime
%timeit _ = list(map(lambda x: datetime.strptime(x, format), l))
9.41 ms ± 95.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

from dateutil.parser import parse
%timeit _ = list(map(lambda x: parse(x), l))
73.8 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

如果字符串是ISO 8601字符串，请使用csio8601：

import ciso8601

l = ['2014-01-09'] * N

%timeit _ = list(map(lambda x: ciso8601.parse_datetime(x), l))
186 µs ± 4.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Python>=3.7

要将YYYY-MM-DD字符串转换为datetime对象，可以使用datetime.fromisoformat。

from datetime import datetime

date_string = "2012-12-12 10:10:10"
print (datetime.fromisoformat(date_string))
2012-12-12 10:10:10

文档中的注意事项：

这不支持解析任意的ISO 8601字符串-它只是作为datetime.isoformat（）的反操作。第三方包dateutil中提供了一个功能更全面的ISO 8602解析器dateutil.parser.isorse。