移动 pandas 日期时间索引

发布于 2025-01-17 05:39:43 字数 591 浏览 0 评论 0原文

我猜这是一个非常愚蠢的问题，但无论如何，这里是：

我有一个带有日期时间索引的数据框，如下所示：

DatetimeIndex(['1995-01-02', '1995-01-03', '1995-01-04', '1995-01-05',
               '1995-01-06', '1995-01-09', '1995-01-10', '1995-01-11',
                '1995-01-12', '1995-01-13'])

毫不奇怪，time字段是“00:00: 00":

bets.index.map(pd.datetime.time)

给出：

 Index([00:00:00, 00:00:00, 00:00:00, 00:00:00, 00:00:00, 00:00:00, 00:00:00,
       00:00:00, 00:00:00, 00:00:00])

我想将所有索引元素的时间字段设置为其他内容（例如中午）。最简单的方法是什么？

原文

This is a really stupid question, I am guessing, but anyway, here goes:

I have a dataframe with a datetime index which looks like this:

DatetimeIndex(['1995-01-02', '1995-01-03', '1995-01-04', '1995-01-05',
               '1995-01-06', '1995-01-09', '1995-01-10', '1995-01-11',
                '1995-01-12', '1995-01-13'])

Not too surprisingly, the time field is "00:00:00":

bets.index.map(pd.datetime.time)

gives:

 Index([00:00:00, 00:00:00, 00:00:00, 00:00:00, 00:00:00, 00:00:00, 00:00:00,
       00:00:00, 00:00:00, 00:00:00])

I would like to set the time field to something else (e.g., noon), for all the index elements. What is the simplest way to do this?

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花心好男孩 2025-01-24 05:39:43

IIUC，您可以使用 to_timedelta 并传入所需的小时数：

bets.index += pd.to_timedelta('12h')

输出：

DatetimeIndex(['1995-01-02 12:00:00', '1995-01-03 12:00:00',
               '1995-01-04 12:00:00', '1995-01-05 12:00:00',
               '1995-01-06 12:00:00', '1995-01-09 12:00:00',
               '1995-01-10 12:00:00', '1995-01-11 12:00:00',
               '1995-01-12 12:00:00', '1995-01-13 12:00:00'],
              dtype='datetime64[ns]', freq=None)

IIUC, you could use to_timedelta and pass in the required number of hours:

bets.index += pd.to_timedelta('12h')

Output:

DatetimeIndex(['1995-01-02 12:00:00', '1995-01-03 12:00:00',
               '1995-01-04 12:00:00', '1995-01-05 12:00:00',
               '1995-01-06 12:00:00', '1995-01-09 12:00:00',
               '1995-01-10 12:00:00', '1995-01-11 12:00:00',
               '1995-01-12 12:00:00', '1995-01-13 12:00:00'],
              dtype='datetime64[ns]', freq=None)

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