How to convert time object to datetime format in pandas (python)?

Question

I am new to pandas and practicing some basic functionalities. I have a CSV file which contains some data of every minute of some date. After reading CSV, df.head() gives the following result :

        Time            C1  C2  C3  C4  C5  C6
0  2016-05-25 03:15:00  0   0   0   0   0   0
1  2016-05-25 03:16:00  0   0   0   0   0   0
2  2016-05-25 03:17:00  0   0   2   0   0   0  
3  2016-05-25 03:18:00  0   0   0   5   0   2
4  2016-05-25 03:19:00  0   0   0   0   0   5

I have used parse_dates option of pd.read_csv . Hence, Time is in datetime64[ns] format. Since, the date is the same I don't want to have that on my column. So, I use

df['Time']=df['Time'].dt.time

It does what I want but it changes the format to object , which I didn't want. Upon suggestions of some other answers, I did the following :

df['Time']=pd.to_datetime(df['Time'], format="%H:%M:%S")
df['Time'].head()

0      1900-01-01 03:15:00
1      1900-01-01 03:16:00
2      1900-01-01 03:17:00
3      1900-01-01 03:18:00
4      1900-01-01 03:19:00
Name: Time, dtype: datetime64[ns]

This converted the column into datetime64[ns] but added an additional date. Is it possible to convert just time into datetime64[ns] ?

Answer 1

No, it is not possible. For datetimes always need dates.

But if need working with times, better is use timedelta s by strftime for strings HH:MM:SS with to_timedelta :

df['Time'] = pd.to_timedelta(df['Time'].dt.strftime('%H:%M:%S'))
print (df)
      Time  C1  C2  C3  C4  C5  C6
0 03:15:00   0   0   0   0   0   0
1 03:16:00   0   0   0   0   0   0
2 03:17:00   0   0   2   0   0   0
3 03:18:00   0   0   0   5   0   2
4 03:19:00   0   0   0   0   0   5

print (df.dtypes)
Time    timedelta64[ns]
C1                int64
C2                int64
C3                int64
C4                int64
C5                int64
C6                int64
dtype: object