每个人都用日期回答得很好， 让我用熊猫来回答这个问题

dt = pd.to_datetime('2008/08/18', format='%Y/%m/%d')
dt1 = pd.to_datetime('2008/09/26', format='%Y/%m/%d')

(dt1-dt).days

这就会给出答案。 如果其中一个输入是dataframe列。简单地使用dt。日子换成了日子

(dt1-dt).dt.days

对于计算日期和时间，有几个选项，但我将写简单的方式:

from datetime import timedelta, datetime, date
import dateutil.relativedelta

# current time
date_and_time = datetime.now()
date_only = date.today()
time_only = datetime.now().time()

# calculate date and time
result = date_and_time - timedelta(hours=26, minutes=25, seconds=10)

# calculate dates: years (-/+)
result = date_only - dateutil.relativedelta.relativedelta(years=10)

# months
result = date_only - dateutil.relativedelta.relativedelta(months=10)

# week
results = date_only - dateutil.relativedelta.relativedelta(weeks=1)

# days
result = date_only - dateutil.relativedelta.relativedelta(days=10)

# calculate time 
result = date_and_time - timedelta(hours=26, minutes=25, seconds=10)
result.time()

希望能有所帮助

圣诞节前几天:

>>> import datetime
>>> today = datetime.date.today()
>>> someday = datetime.date(2008, 12, 25)
>>> diff = someday - today
>>> diff.days
86

这里有更多的算术。

使用datetime函数:

from datetime import datetime
date_format = "%m/%d/%Y"
a = datetime.strptime('8/18/2008', date_format)
b = datetime.strptime('9/26/2008', date_format)
delta = b - a
print delta.days # that's it

如果你想自己编写计算代码，那么这里有一个函数，它将返回给定年、月和日的序数:

def ordinal(year, month, day):
    return ((year-1)*365 + (year-1)//4 - (year-1)//100 + (year-1)//400
         + [ 0,31,59,90,120,151,181,212,243,273,304,334][month - 1]
         + day
         + int(((year%4==0 and year%100!=0) or year%400==0) and month > 2))

此功能与日期兼容。datetime模块中的Toordinal方法。

两个日期的差日数如下所示:

print(ordinal(2021, 5, 10) - ordinal(2001, 9, 11))

您需要datetime模块。

>>> from datetime import datetime 
>>> datetime(2008,08,18) - datetime(2008,09,26) 
datetime.timedelta(4) 

另一个例子:

>>> import datetime 
>>> today = datetime.date.today() 
>>> print(today)
2008-09-01 
>>> last_year = datetime.date(2007, 9, 1) 
>>> print(today - last_year)
366 days, 0:00:00 

正如这里所指出的