remove specific characters from time stamp

Question

I have a rather odd time format and the data frame df with the column TIMESTAMP:

2016-10-25T09:34:52.051713+01:00
2016-10-25T09:46:14.051620+01:00
2016-10-25T09:51:16.052435+01:00
....

but I need to use the data as time information. In the first place, I would like to get rid of the last 13 characters such that it looks like

2016-10-25T09:34:52
2016-10-25T09:46:14
2016-10-25T09:51:16
....

To do so, I tried

df['TIMESTAMP'] = df.apply(lambda x: x['TIMESTAMP'][:-13], axis = 1)

from Remove ends of string entries in pandas DataFrame column

but I receive the error:

TypeError: string indices must be integers

which I do not understand. I'm just fresh in python but I don't see much I've done wrong?

Answer 1

The strict answer to your question is to use the str accessor, which can take a slice, just like a normal string, and which will apply that slice to every value in the Series:

data = ['2016-10-25T09:34:52.051713+01:00',
        '2016-10-25T09:46:14.051620+01:00',
        '2016-10-25T09:51:16.052435+01:00']

s = pd.Series(data)

print(s.str[:-13])

Output:

0    2016-10-25T09:34:52
1    2016-10-25T09:46:14
2    2016-10-25T09:51:16

However, I think what you want is actually pd.to_datetime, which will (within reason) infer the format of your data and convert it into datetime objects:

print(pd.to_datetime(s))

Output:

0   2016-10-25 09:34:52.051713+01:00
1   2016-10-25 09:46:14.051620+01:00
2   2016-10-25 09:51:16.052435+01:00
dtype: datetime64[ns, pytz.FixedOffset(60)]

Answer 2

A simple way to do this is to use a list comprehension :

df = pd.DataFrame({'TIMESTAMP' : ["2016-10-25T09:34:52.051713+01:00", "2016-10-25T09:46:14.051620+01:00"]})

 TIMESTAMP
0  2016-10-25T09:34:52.051713+01:00
1  2016-10-25T09:46:14.051620+01:00

df['TIMESTAMP'] = [x[:-13] for x in df['TIMESTAMP']]

Output :

TIMESTAMP
0  2016-10-25T09:34:52
1  2016-10-25T09:46:14