Pandas Inconsistent Date-time Format

I started using pandas library about a fortnight back. Learning the new features. I would appreciate help on the following problem. I have a column with dates in mixed format. Thes

Solution 1:

The real problem is that there are ambiguous dates in your dataset (do you parse it as mm/dd/yyyy or dd/mm/yyyy if it could be either?? (I've been here, and we decided just to pick what the majority seemed to be; essentially the dataset was compromised... and we had to treat it as such).

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If it's a Series then hitting it with pd.to_datetime seems to work:

In [11]: s = pd.Series(['6/5/2016', '7/5/2016', '7/5/2016', '7/5/2016', '9/5/2016', '9/5/2016', '9/5/2016', '9/5/2016', '5/13/2016', '5/14/2016', '5/14/2016'])

In [12]: pd.to_datetime(s)
Out[12]:
0    2016-06-05
1    2016-07-05
2    2016-07-05
3    2016-07-05
4    2016-09-05
5    2016-09-05
6    2016-09-05
7    2016-09-05
8    2016-05-13
9    2016-05-14
10   2016-05-14
Name: 0, dtype: datetime64[ns]

Note: If you had a consistent format you can pass it in explicitly:

In [13]: pd.to_datetime(s, format="%m/%d/%Y")
Out[13]:
0    2016-06-05
1    2016-07-05
2    2016-07-05
3    2016-07-05
4    2016-09-05
5    2016-09-05
6    2016-09-05
7    2016-09-05
8    2016-05-13
9    2016-05-14
10   2016-05-14
Name: 0, dtype: datetime64[ns]