我正在尝试使用pandas date_range功能生成一组日期.然后我想迭代这个范围并从每个日期减去几个月(确切的月数在循环中确定)以获得新的日期.
当我这样做时,我得到一些非常奇怪的结果.
MVP：
#get date range
dates = pd.date_range(start = '1/1/2013', end='1/1/2018', freq=str(test_size)+'MS', closed='left', normalize=True)
#take first date as example
date = dates[0]
date
Timestamp('2013-01-01 00:00:00', freq='3MS')
到现在为止还挺好.
现在让我们说我想从这个日期回来一个月.我定义numpy timedelta(它支持定义的月份,而pandas的timedelta不支持)：
#get timedelta of 1 month
deltaGap = np.timedelta64(1,'M')
#subtract one month from date
date - deltaGap
Timestamp('2012-12-01 13:30:54', freq='3MS')
为什么这样？为什么我得到13:30:54的时间组件而不是午夜.
此外,如果我减去超过1个月,它的变化就会变得很大,以至于我失去了一整天：
#let's say I want to subtract both 2 years and then 1 month
deltaTrain = np.timedelta64(2,'Y')
#subtract 2 years and then subtract 1 month
date - deltaTrain - deltaGap
Timestamp('2010-12-02 01:52:30', freq='3MS')
解决方法:
我和timedelta有类似的问题,我最终使用的解决方案是使用来自dateutil的relativedelta,它专门为这种应用程序而构建(考虑到所有日历的怪异,如闰年,工作日等). ).例如给出：
from dateutil.relativedelta import relativedelta
date = dates[0]
>>> date
Timestamp('2013-01-01 00:00:00', freq='10MS')
deltaGap = relativedelta(months=1)
>>> date-deltaGap
Timestamp('2012-12-01 00:00:00', freq='10MS')
deltaGap = relativedelta(years=2, months=1)
>>> date-deltaGap
Timestamp('2010-12-01 00:00:00', freq='10MS')
有关relativedelta的更多信息,请查看documentation
numpy.timedelta64的问题
我认为np.timedelta的问题在docs的这两部分中有所体现：
There are two timedelta units (‘Y’, years and ‘M’, months) which are treated specially, because how much time they represent changes depending on when they are used. While a timedelta day unit is equivalent to 24 hours, there is no way to convert a month unit into days, because different months have different numbers of days.
和
The length of the span is the range of a 64-bit integer times the length of the date or unit. For example, the time span for ‘W’ (week) is exactly 7 times longer than the time span for ‘D’ (day), and the time span for ‘D’ (day) is exactly 24 times longer than the time span for ‘h’ (hour).
因此,timedeltas适用于数小时,数周,数月,数天,因为这些是不可变的时间跨度.然而,几个月和几年的长度是可变的(想想闰年),所以考虑到这一点,numpy采取某种“平均”(我猜).一个numpy“年”似乎是一年,5小时49分12秒,而一个numpy“月”似乎是30天,10小时,29分钟和6秒.
# Adding one numpy month adds 30 days + 10:29:06:
deltaGap = np.timedelta64(1,'M')
date+deltaGap
# Timestamp('2013-01-31 10:29:06', freq='10MS')
# Adding one numpy year adds 1 year + 05:49:12:
deltaGap = np.timedelta64(1,'Y')
date+deltaGap
# Timestamp('2014-01-01 05:49:12', freq='10MS')
这不是那么容易使用,这就是为什么我会去相对的deltadelta,这对我来说更直观.
