Follow

Keep Up to Date with the Most Important News

By pressing the Subscribe button, you confirm that you have read and are agreeing to our Privacy Policy and Terms of Use
Contact

How to convert csv date and time with milliseconds to datetime with milliseconds

I have a difficult time converting separated date and time columns from a csv file into a merged dataframe datetime column with milliseconds.

original data:

    Date    Time
0   2014/9/2    08:30:00.0
1   2014/9/2    08:37:39.21
2   2014/9/2    08:39:41.2
3   2014/9/2    08:41:23.9
4   2014/9/2    09:13:01.1
5   2014/9/2    09:43:02.49
6   2014/9/2    10:49:16.115
7   2014/9/2    10:58:46.39
8   2014/9/2    11:46:18.5
9   2014/9/2    12:03:43.0
10  2014/9/2    12:56:22.0
11  2014/9/2    13:13:01.0
12  2014/9/2    14:42:22.39
13  2014/9/2    14:50:00.74
14  2014/9/3    08:30:00.0
15  2014/9/3    08:30:11.57
16  2014/9/3    08:39:02.18
17  2014/9/3    08:44:31.74
18  2014/9/3    08:45:16.105
19  2014/9/3    08:47:52.57

concatenating date + time column

MEDevel.com: Open-source for Healthcare and Education

Collecting and validating open-source software for healthcare, education, enterprise, development, medical imaging, medical records, and digital pathology.

Visit Medevel

df['datetime'] = df.Date + str(' ') + df.Time 

0      2014/9/2 08:30:00.0
1     2014/9/2 08:37:39.21
2      2014/9/2 08:39:41.2
3      2014/9/2 08:41:23.9
4      2014/9/2 09:13:01.1
5     2014/9/2 09:43:02.49
6    2014/9/2 10:49:16.115
7     2014/9/2 10:58:46.39
8      2014/9/2 11:46:18.5
9      2014/9/2 12:03:43.0

Trying to parse the string to datetime object:

df['datetime'] = df['datetime'].apply(lambda x: datetime.strptime(x, '%Y/%m/%d %H:%M:%S.f%'))

fails:

ValueError: stray % in format '%Y/%m/%d %H:%M:%S.f%'

What is wrong with that and how to solve it?

>Solution :

The format code for microseconds is %f and not f% as per the documentation.

Try this :

df['datetime'] = df['datetime'].apply(lambda x: datetime.strptime(x, '%Y/%m/%d %H:%M:%S.%f'))

Or, in one shot :

(
    pd.read_csv("test.csv")
        .astype(str).agg(" ".join, axis=1)
        .to_frame("datetime")
        .apply(lambda _: pd.to_datetime(_, format= '%Y/%m/%d %H:%M:%S.%f'))
)

# Output :

                  datetime
0  2014-09-02 08:30:00.000
1  2014-09-02 08:37:39.210
2  2014-09-02 08:39:41.200
3  2014-09-02 08:41:23.900
4  2014-09-02 09:13:01.100
..                     ...
15 2014-09-03 08:30:11.570
16 2014-09-03 08:39:02.180
17 2014-09-03 08:44:31.740
18 2014-09-03 08:45:16.105
19 2014-09-03 08:47:52.570

[20 rows x 1 columns]

#dtypes
datetime    datetime64[ns]
dtype: object
Add a comment

Leave a Reply

Keep Up to Date with the Most Important News

By pressing the Subscribe button, you confirm that you have read and are agreeing to our Privacy Policy and Terms of Use

Discover more from Dev solutions

Subscribe now to keep reading and get access to the full archive.

Continue reading