** Type - Description**
from datetime import datetime
from dateutil.parser import parse
import pandas as pd
war_start = '2011-01-03'
datetime.strptime(war_start, '%Y-%m-%d')
datetime.datetime(2011, 1, 3, 0, 0)
attack_dates = ['7/2/2011', '8/6/2012', '11/13/2013', '5/26/2011', '5/2/2001']
[datetime.strptime(x, '%m/%d/%Y') for x in attack_dates]
[datetime.datetime(2011, 7, 2, 0, 0), datetime.datetime(2012, 8, 6, 0, 0), datetime.datetime(2013, 11, 13, 0, 0), datetime.datetime(2011, 5, 26, 0, 0), datetime.datetime(2001, 5, 2, 0, 0)]
parse(war_start)
datetime.datetime(2011, 1, 3, 0, 0)
[parse(x) for x in attack_dates]
[datetime.datetime(2011, 7, 2, 0, 0), datetime.datetime(2012, 8, 6, 0, 0), datetime.datetime(2013, 11, 13, 0, 0), datetime.datetime(2011, 5, 26, 0, 0), datetime.datetime(2001, 5, 2, 0, 0)]
parse(war_start, dayfirst=True)
datetime.datetime(2011, 1, 3, 0, 0)
data = {'date': ['2014-05-01 18:47:05.069722', '2014-05-01 18:47:05.119994', '2014-05-02 18:47:05.178768', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.230071', '2014-05-02 18:47:05.280592', '2014-05-03 18:47:05.332662', '2014-05-03 18:47:05.385109', '2014-05-04 18:47:05.436523', '2014-05-04 18:47:05.486877'],
'value': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
df = pd.DataFrame(data, columns = ['date', 'value'])
print(df)
date value 0 2014-05-01 18:47:05.069722 1 1 2014-05-01 18:47:05.119994 1 2 2014-05-02 18:47:05.178768 1 3 2014-05-02 18:47:05.230071 1 4 2014-05-02 18:47:05.230071 1 5 2014-05-02 18:47:05.280592 1 6 2014-05-03 18:47:05.332662 1 7 2014-05-03 18:47:05.385109 1 8 2014-05-04 18:47:05.436523 1 9 2014-05-04 18:47:05.486877 1 [10 rows x 2 columns]
pd.to_datetime(df['date'])
0 2014-05-01 18:47:05.069722 1 2014-05-01 18:47:05.119994 2 2014-05-02 18:47:05.178768 3 2014-05-02 18:47:05.230071 4 2014-05-02 18:47:05.230071 5 2014-05-02 18:47:05.280592 6 2014-05-03 18:47:05.332662 7 2014-05-03 18:47:05.385109 8 2014-05-04 18:47:05.436523 9 2014-05-04 18:47:05.486877 Name: date, dtype: datetime64[ns]