python - How can I combine datetime.date and datetime.time columns in pandas dataframe? -


given df

          date      time    data      3   2017-08-10  15:15:00        0   2017-08-11  15:15:00    b     1   2017-08-12  15:15:00    c     2   2017-08-13  15:15:00    d     1   2017-08-14  15:15:00    e

and

print (type(df['date'].iat[0])) <class 'datetime.date'>  print (type(df['time'].iat[0])) <class 'datetime.time'>

how can combine df.date , df.time datetime column datetime object ??:

        date        time    data  datetime    3   2017-08-10  15:15:00        2017-08-10 15:15:00 0   2017-08-11  15:15:00    b     2017-08-11 15:15:00 1   2017-08-12  15:15:00    c     2017-08-12 15:15:00 2   2017-08-13  15:15:00    d     2017-08-13 15:15:00 1   2017-08-14  15:15:00    e     2017-08-14 15:15:00

what tried:

df['datetime'] = df.apply(lambda r : pd.datetime.combine(r['date'],r['time']),1)

this works intended, however, prefer vectorized operation , following msg:

c:\users\user\anaconda3\lib\site-packages\ipykernel\__main__.py:1:  settingwithcopywarning:  value trying set on copy of slice dataframe. try using .loc[row_indexer,col_indexer] = value instead  see caveats in documentation: http://pandas.pydata.org/pandas- docs/stable/indexing.html#indexing-view-versus-copy if __name__ == '__main__':

the issue here both date , time in datetime format. try 

df['datetime'] = pd.to_datetime(df['date'].dt.strftime('%y-%m-%d') + df['time'].astype(str), format = '%y-%m-%d%h:%m:%s')

though don't know if more efficient using datetime.combine


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