Infer which columns are datetime

Question

I have a huge dataframe with many columns, many of which are of type datetime.datetime. The problem is that many also have mixed types, including for instance datetime.datetime values and None values (and potentially other invalid values):

0         2017-07-06 00:00:00
1         2018-02-27 21:30:05
2         2017-04-12 00:00:00
3         2017-05-21 22:05:00
4         2018-01-22 00:00:00
                 ...         
352867    2019-10-04 00:00:00
352868                   None
352869            some_string
Name: colx, Length: 352872, dtype: object

Hence resulting in an object type column. This can be solved with df.colx.fillna(pd.NaT). The problem is that the dataframe is too big to search for individual columns.

Another approach is to use pd.to_datetime(col, errors='coerce'), however this will cast to datetime many columns that contain numerical values.

I could also do df.fillna(float('nan'), inplace=True), though the columns containing dates are still of object type, and would still have the same problem.

What approach could I follow to cast to datetime those columns whose values really do contain datetime values, but could also contain None, and potentially some invalid values (mentioning since otherwise a pd.to_datetime in a try/except clause would do)? Something like a flexible version of pd.to_datetime(col)

Answer 1

The main problem I see is when parsing numerical values.

I'd propose converting them to strings first

---

Setup

dat = {
    'index': [0, 1, 2, 3, 4, 352867, 352868, 352869],
    'columns': ['Mixed', 'Numeric Values', 'Strings'],
    'data': [
        ['2017-07-06 00:00:00', 1, 'HI'],
        ['2018-02-27 21:30:05', 1, 'HI'],
        ['2017-04-12 00:00:00', 1, 'HI'],
        ['2017-05-21 22:05:00', 1, 'HI'],
        ['2018-01-22 00:00:00', 1, 'HI'],
        ['2019-10-04 00:00:00', 1, 'HI'],
        ['None', 1, 'HI'],
        ['some_string', 1, 'HI']
    ]
}

df = pd.DataFrame(**dat)

df

                      Mixed  Numeric Values Strings
0       2017-07-06 00:00:00               1      HI
1       2018-02-27 21:30:05               1      HI
2       2017-04-12 00:00:00               1      HI
3       2017-05-21 22:05:00               1      HI
4       2018-01-22 00:00:00               1      HI
352867  2019-10-04 00:00:00               1      HI
352868                 None               1      HI
352869          some_string               1      HI

---

Solution

df.astype(str).apply(pd.to_datetime, errors='coerce')

                     Mixed Numeric Values Strings
0      2017-07-06 00:00:00            NaT     NaT
1      2018-02-27 21:30:05            NaT     NaT
2      2017-04-12 00:00:00            NaT     NaT
3      2017-05-21 22:05:00            NaT     NaT
4      2018-01-22 00:00:00            NaT     NaT
352867 2019-10-04 00:00:00            NaT     NaT
352868                 NaT            NaT     NaT
352869                 NaT            NaT     NaT