Convert Pandas Series to DateTime in a DataFrame

Question

I have a Pandas DataFrame as below

        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 is a series type

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

I've tried below, but it still doesn't change the Series type

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

How can I change the df.TimeReviewed to DateTime type and pull out year, month, day, hour, min, sec separately? I'm kinda new to python, thanks for your help.

Answer 1

You can't: DataFrame columns are Series, by definition. That said, if you make the dtype (the type of all the elements) datetime-like, then you can access the quantities you want via the .dt accessor (docs):

>>> 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 

---

If you're stuck using an older version of pandas, you can always access the various elements manually (again, after converting it to a datetime-dtyped Series). It'll be slower, but sometimes that isn't an issue:

>>> 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