将Pandas Series转换为DataFrame中的DateTime

Question

我有一个Pandas DataFrame,如下所示

        ReviewID       ID      Type               TimeReviewed
205     76032930  51936827  ReportID 2015-01-15 00:05:27.513000
232     76032930  51936854  ReportID 2015-01-15 00:06:46.703000
233     76032930  51936855  ReportID 2015-01-15 00:06:56.707000
413     76032930  51937035  ReportID 2015-01-15 00:14:24.957000
565     76032930  51937188  ReportID 2015-01-15 00:23:07.220000

>>> type(df)
<class 'pandas.core.frame.DataFrame'>

TimeReviewed是一个系列类型

>>> type(df.TimeReviewed)
<class 'pandas.core.series.Series'>

我在下面试过,但它仍然没有改变系列类型

import pandas as pd
review = pd.to_datetime(pd.Series(df.TimeReviewed))
>>> type(review)
<class 'pandas.core.series.Series'>

如何将df.TimeReviewed更改为DateTime类型并分别提取年,月,日,小时,分钟？我是python的新手,谢谢你的帮助.

Answer 1

根据定义,您不能:DataFrame列Series.也就是说,如果您使dtype(所有元素的类型)与datetime类似,那么您可以通过.dt访问者(docs)访问所需的数量:

>>> df["TimeReviewed"] = pd.to_datetime(df["TimeReviewed"])
>>> df["TimeReviewed"]
205  76032930   2015-01-24 00:05:27.513000
232  76032930   2015-01-24 00:06:46.703000
233  76032930   2015-01-24 00:06:56.707000
413  76032930   2015-01-24 00:14:24.957000
565  76032930   2015-01-24 00:23:07.220000
Name: TimeReviewed, dtype: datetime64[ns]
>>> df["TimeReviewed"].dt
<pandas.tseries.common.DatetimeProperties object at 0xb10da60c>
>>> df["TimeReviewed"].dt.year
205  76032930    2015
232  76032930    2015
233  76032930    2015
413  76032930    2015
565  76032930    2015
dtype: int64
>>> df["TimeReviewed"].dt.month
205  76032930    1
232  76032930    1
233  76032930    1
413  76032930    1
565  76032930    1
dtype: int64
>>> df["TimeReviewed"].dt.minute
205  76032930     5
232  76032930     6
233  76032930     6
413  76032930    14
565  76032930    23
dtype: int64

如果您使用的是旧版本pandas,则可以手动访问各种元素(再次将其转换为datetime-dtyped系列).它会慢一些,但有时这不是问题:

>>> df["TimeReviewed"].apply(lambda x: x.year)
205  76032930    2015
232  76032930    2015
233  76032930    2015
413  76032930    2015
565  76032930    2015
Name: TimeReviewed, dtype: int64