将DateTimeindex转换为仅包含年，时和日，而不包含时间信息 [英] convert DateTimeindex to contain only year, hour and day not time information

问题描述

我有一个带有索引的数据框，该索引还包含Hour:second:...，但是我只希望它包含日期，如年，月，日.

I have a dataframe with an index that also contains the Hour:second:... but instead I just want it to contain the date as year, month, day.

索引是否仍可能是DateTimeIndex但仅包含年，月，日?

Is it possible for the index to still be a DateTimeIndex but contain only year, month, day?

当前索引如下:

my_index = DatetimeIndex(['2017-08-25', '2017-08-24', '2017-08-23', '2017-08-22',
           '2017-08-21', '2017-08-20', '2017-08-19', '2017-08-18',
           '2017-08-17', '2017-08-16',
           ...
           '2015-07-19', '2015-07-18', '2015-07-17', '2015-07-16',
           '2015-07-15', '2015-07-14', '2015-07-13', '2015-07-12',
           '2015-07-11', '2015-07-10'],
          dtype='datetime64[ns]', length=778, freq=None)

我可以做到:

only_date_index = [el.date() for el in my_index]

但是如果我想在熊猫中使用重采样功能，则会收到错误消息: TypeError:仅对DatetimeIndex，TimedeltaIndex或PeriodIndex有效，但具有"Index"的实例.

But then if I want to use the resample function in pandas I get the error: TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'.

推荐答案

您需要 DatetimeIndex.floor :

myIndex = df.index.floor('D')

示例:

rng = pd.date_range('2017-04-03 15:00:45', periods=10, freq='24T')
df = pd.DataFrame({'a': range(10)}, index=rng)  
print (df)
                     a
2017-04-03 15:00:45  0
2017-04-03 15:24:45  1
2017-04-03 15:48:45  2
2017-04-03 16:12:45  3
2017-04-03 16:36:45  4
2017-04-03 17:00:45  5
2017-04-03 17:24:45  6
2017-04-03 17:48:45  7
2017-04-03 18:12:45  8
2017-04-03 18:36:45  9

myIndex = df.index.floor('D')
print (myIndex)
DatetimeIndex(['2017-04-03', '2017-04-03', '2017-04-03', '2017-04-03',
               '2017-04-03', '2017-04-03', '2017-04-03', '2017-04-03',
               '2017-04-03', '2017-04-03'],
              dtype='datetime64[ns]', freq=None)

感谢其他解决方案 :

Thanks for another solution Deckerz - using DatetimeIndex.normalize:

myIndex = df.index.normalize()
print (myIndex)
DatetimeIndex(['2017-04-03', '2017-04-03', '2017-04-03', '2017-04-03',
               '2017-04-03', '2017-04-03', '2017-04-03', '2017-04-03',
               '2017-04-03', '2017-04-03'],
              dtype='datetime64[ns]', freq=None)

时间:

ix = pd.date_range('1970-01-01', '2200-01-15', freq='1H')

print (len(ix))
2016481

In [68]: %timeit (ix.normalize())
10 loops, best of 3: 178 ms per loop

In [69]: %timeit (ix.floor('d'))
10 loops, best of 3: 38.4 ms per loop

#solution from Dror (https://stackoverflow.com/questions/45954497/in-pandas-group-by-date-from-datetimeindex)
In [70]: %timeit pd.to_datetime(ix.date)
1 loop, best of 3: 5.09 s per loop

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