Title: Converting Strings To Datetime
Slug: strings_to_datetime
Summary: Converting Strings To Datetime
Date: 2016-05-01 12:00
Category: Python
Tags: Basics
Authors: Chris Albon

Import modules



In [1]:

    
from datetime import datetime
from dateutil.parser import parse
import pandas as pd

Create a string variable with the war start time



In [2]:

    
war_start = '2011-01-03'

Convert the string to datetime format



In [3]:

    
datetime.strptime(war_start, '%Y-%m-%d')









    Out[3]:





datetime.datetime(2011, 1, 3, 0, 0)

Create a list of strings as dates



In [4]:

    
attack_dates = ['7/2/2011', '8/6/2012', '11/13/2013', '5/26/2011', '5/2/2001']

Convert attack_dates strings into datetime format



In [5]:

    
[datetime.strptime(x, '%m/%d/%Y') for x in attack_dates]









    Out[5]:





[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)]

Use parse() to attempt to auto-convert common string formats



In [6]:

    
parse(war_start)









    Out[6]:





datetime.datetime(2011, 1, 3, 0, 0)

Use parse() on every element of the attack_dates string



In [7]:

    
[parse(x) for x in attack_dates]









    Out[7]:





[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)]

Use parse, but designate that the day is first



In [8]:

    
parse(war_start, dayfirst=True)









    Out[8]:





datetime.datetime(2011, 3, 1, 0, 0)

Create a dataframe



In [9]:

    
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

Convert df['date'] from string to datetime



In [10]:

    
pd.to_datetime(df['date'])









    Out[10]:





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]