BUG: combination of out of bound date and nan with errors='ignore' gives nonsense data

[jorisvandenbossche]

In [46]: pd.__version__        
Out[46]: '0.25.0.dev0+593.g307265e28'

In [47]: pd.to_datetime(['15010101', '20150101', np.nan], format="%Y%m%d", errors='ignore')                                                                      
Out[47]: DatetimeIndex(['2085-07-20 23:34:33.709551616', '2015-01-01 00:00:00', 'NaT'], dtype='datetime64[ns]', freq=None)

[makbigc]

pandas/pandas/core/tools/datetimes.py

Line 772 in ba48fc4

result[mask] = masked_result.astype('M8[ns]')

The astype conversion turns all elements no matter the element is out of bound.

[jorisvandenbossche]

I can't say for sure (without looking more in detail) if that is indeed the reason (I would think that the array_to_datetime in the calc function a just above should still raise for out of bounds datetimes). But welcome to check further! Eg does it work correctly if you leave out the astype?

[another-green]

What is the expected behavior of to_datetime in this case? If there is time out of bound, should it just ignore all the elements in the array and return an Index? For example:

Index(['15010101', '20150101', nan], dtype='object')

Or we want to convert the date like element, such as

Index([1501-01-01 00:00:00, 2015-01-01 00:00:00, 'NaT'], dtype='object')

[jorisvandenbossche]

That's not fully clear. See also #14487

[MarcoGorelli]

this'll be addressed by #50242