Python-String parsing for extraction of date and time

Question

The datetime is given in the format YY-MM-DD HH:MM:SS in a dataframe.I want new Series of year,month and hour for which I am trying the below code. But the problem is that Month and Hour are getting the same value,Year is fine.

Can anyone help me with this ? I am using Ipthon notebook and Pandas and numpy.

Here is the code :

        def extract_hour(X):
            cnv=datetime.strptime(X, '%Y-%m-%d %H:%M:%S')
            return cnv.hour

        def extract_month(X):
            cnv=datetime.strptime(X, '%Y-%m-%d %H:%M:%S')
            return cnv.month

        def extract_year(X):
            cnv=datetime.strptime(X, '%Y-%m-%d %H:%M:%S')
            return cnv.year

        #month column 
        train['Month']=train['datetime'].apply((lambda x: extract_month(x)))
        test['Month']=test['datetime'].apply((lambda x: extract_month(x)))

        #year column 
        train['Year']=train['datetime'].apply((lambda x: extract_year(x)))
        test['Year']=test['datetime'].apply((lambda x: extract_year(x)))

        #Hour column 
        train['Hour']=train['datetime'].apply((lambda x: extract_hour(x)))
        test['Hour']=test['datetime'].apply((lambda x: extract_hour(x)))

Answer 1

you can use .dt accessors instead: train['datetime'].dt.month, train['datetime'].dt.year, train['datetime'].dt.hour (see the full list below)

Demo:

In [81]: train = pd.DataFrame(pd.date_range('2016-01-01', freq='1999H', periods=10), columns=['datetime'])

In [82]: train
Out[82]:
             datetime
0 2016-01-01 00:00:00
1 2016-03-24 07:00:00
2 2016-06-15 14:00:00
3 2016-09-06 21:00:00
4 2016-11-29 04:00:00
5 2017-02-20 11:00:00
6 2017-05-14 18:00:00
7 2017-08-06 01:00:00
8 2017-10-28 08:00:00
9 2018-01-19 15:00:00

In [83]: train.datetime.dt.year
Out[83]:
0    2016
1    2016
2    2016
3    2016
4    2016
5    2017
6    2017
7    2017
8    2017
9    2018
Name: datetime, dtype: int64

In [84]: train.datetime.dt.month
Out[84]:
0     1
1     3
2     6
3     9
4    11
5     2
6     5
7     8
8    10
9     1
Name: datetime, dtype: int64

In [85]: train.datetime.dt.hour
Out[85]:
0     0
1     7
2    14
3    21
4     4
5    11
6    18
7     1
8     8
9    15
Name: datetime, dtype: int64

In [86]: train.datetime.dt.day
Out[86]:
0     1
1    24
2    15
3     6
4    29
5    20
6    14
7     6
8    28
9    19
Name: datetime, dtype: int64

List of all .dt accessors:

In [77]: train.datetime.dt.
train.datetime.dt.ceil             train.datetime.dt.hour             train.datetime.dt.month            train.datetime.dt.to_pydatetime
train.datetime.dt.date             train.datetime.dt.is_month_end     train.datetime.dt.nanosecond       train.datetime.dt.tz
train.datetime.dt.day              train.datetime.dt.is_month_start   train.datetime.dt.normalize        train.datetime.dt.tz_convert
train.datetime.dt.dayofweek        train.datetime.dt.is_quarter_end   train.datetime.dt.quarter          train.datetime.dt.tz_localize
train.datetime.dt.dayofyear        train.datetime.dt.is_quarter_start train.datetime.dt.round            train.datetime.dt.week
train.datetime.dt.days_in_month    train.datetime.dt.is_year_end      train.datetime.dt.second           train.datetime.dt.weekday
train.datetime.dt.daysinmonth      train.datetime.dt.is_year_start    train.datetime.dt.strftime         train.datetime.dt.weekday_name
train.datetime.dt.floor            train.datetime.dt.microsecond      train.datetime.dt.time             train.datetime.dt.weekofyear
train.datetime.dt.freq             train.datetime.dt.minute           train.datetime.dt.to_period        train.datetime.dt.year