I have this column in a df:
> df["time"]
0 2007-02-01 22:00:00+00:00
1 2007-02-01 22:00:00+00:00
2 2007-02-01 22:00:00+00:00
3 2007-02-01 22:00:00+00:00
4 2007-02-01 22:00:00+00:00
I want to create three new columns with day, month, and year but I can't figure out a way to extract each one of these of the time column.
In order to not modify your existing time column, create a separate datetime series using pd.to_datetime and then use the dt accessor:
# obtain datetime series:
datetimes = pd.to_datetime(df['time'])
# assign your new columns
df['day'] = datetimes.dt.day
df['month'] = datetimes.dt.month
df['year'] = datetimes.dt.year
>>> df
time day month year
0 2007-02-01 22:00:00+00:00 1 2 2007
1 2007-02-01 22:00:00+00:00 1 2 2007
2 2007-02-01 22:00:00+00:00 1 2 2007
3 2007-02-01 22:00:00+00:00 1 2 2007
4 2007-02-01 22:00:00+00:00 1 2 2007
An alternative would be to use str.split('-') on the datetime.dt.date series:
datetimes = pd.to_datetime(df['time'])
df[['year','month','day']] = datetimes.dt.date.astype(str).str.split('-',expand=True)
>>> df
time year month day
0 2007-02-01 22:00:00+00:00 2007 02 01
1 2007-02-01 22:00:00+00:00 2007 02 01
2 2007-02-01 22:00:00+00:00 2007 02 01
3 2007-02-01 22:00:00+00:00 2007 02 01
4 2007-02-01 22:00:00+00:00 2007 02 01
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