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