Python:降低精度 pandas 时间戳数据帧

2022-01-13 00:00:00 python pandas dataframe timestamp

问题描述

您好，我有以下数据框

df = 

       Record_ID       Time
        94704   2014-03-10 07:19:19.647342
        94705   2014-03-10 07:21:44.479363
        94706   2014-03-10 07:21:45.479581
        94707   2014-03-10 07:21:54.481588
        94708   2014-03-10 07:21:55.481804

有可能有以下吗?

df1 = 

       Record_ID       Time
        94704   2014-03-10 07:19:19
        94705   2014-03-10 07:21:44
        94706   2014-03-10 07:21:45
        94707   2014-03-10 07:21:54
        94708   2014-03-10 07:21:55

解决方案

你可以转换底层 datetime64[ns] 值使用 astype 转换为 datetime64[s] 值:

You could convert the underlying datetime64[ns] values to datetime64[s] values using astype:

In [11]: df['Time'] = df['Time'].astype('datetime64[s]')

In [12]: df
Out[12]: 
   Record_ID                Time
0      94704 2014-03-10 07:19:19
1      94705 2014-03-10 07:21:44
2      94706 2014-03-10 07:21:45
3      94707 2014-03-10 07:21:54
4      94708 2014-03-10 07:21:55

请注意，由于 Pandas 系列和 DataFrames 将所有日期时间值存储为 datetime64[ns] 这些 datetime64[s] 值会自动转换回 datetime64[ns]，因此最终结果仍存储为 datetime64[ns] 值，但对 astype 的调用会导致秒的小数部分被删除.

Note that since Pandas Series and DataFrames store all datetime values as datetime64[ns] these datetime64[s] values are automatically converted back to datetime64[ns], so the end result is still stored as datetime64[ns] values, but the call to astype causes the fractional part of the seconds to be removed.

如果您希望有一个 datetime64[s] 值的 NumPy 数组，您可以使用 df['Time'].values.astype('datetime64[s]').

If you wish to have a NumPy array of datetime64[s] values, you could use df['Time'].values.astype('datetime64[s]').

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