python - Converting different date time formats to MM/DD/YYYY format in pandas dataframe

Question

I have a date column in a pandas.DataFrame in various date time formats and stored as list object, like the following:

            date
1    [May 23rd, 2011]
2    [January 1st, 2010]
    ...
99   [Apr. 15, 2008]
100  [07-11-2013]
    ...
256  [9/01/1995]
257  [04/15/2000]
258  [11/22/68]
    ...
360  [12/1997]
361  [08/2002]
     ...
463  [2014]
464  [2016]

For the sake of convenience, I want to convert them all to MM/DD/YYYY format. It doesn't seem possible to use regex replace() function to do this, since one cannot execute this operation over list objects. Also, to use strptime() for each cell will be too time-consuming.

What will be the easier way to convert them all to the desired MM/DD/YYYY format? I found it very hard to do this on list objects within a dataframe.

Note: for cell values of the form [YYYY] (e.g., [2014] and [2016]), I will assume they are the first day of that year (i.e., January 1, 1968) and for cell values such as [08/2002] (or [8/2002]), I will assume they the first day of the month of that year (i.e., August 1, 2002).

Answer 1

Given your sample data, with the addition of a NaT, this works:

Code:

df.date.apply(lambda x: pd.to_datetime(x).strftime('%m/%d/%Y')[0])

Test Code:

import pandas as pd

df = pd.DataFrame([
    [['']],
    [['May 23rd, 2011']],
    [['January 1st, 2010']],
    [['Apr. 15, 2008']],
    [['07-11-2013']],
    [['9/01/1995']],
    [['04/15/2000']],
    [['11/22/68']],
    [['12/1997']],
    [['08/2002']],
    [['2014']],
    [['2016']],
], columns=['date'])

df['clean_date'] = df.date.apply(
    lambda x: pd.to_datetime(x).strftime('%m/%d/%Y')[0])

print(df)

Results:

                   date  clean_date
0                    []         NaT
1      [May 23rd, 2011]  05/23/2011
2   [January 1st, 2010]  01/01/2010
3       [Apr. 15, 2008]  04/15/2008
4          [07-11-2013]  07/11/2013
5           [9/01/1995]  09/01/1995
6          [04/15/2000]  04/15/2000
7            [11/22/68]  11/22/1968
8             [12/1997]  12/01/1997
9             [08/2002]  08/01/2002
10               [2014]  01/01/2014
11               [2016]  01/01/2016