Copying from https://github.com/pandas-dev/pandas/issues/14448#issuecomment-255068330 (as the issue is closed now):
In [1]: pd.to_datetime('13000101', errors='ignore')
Out[1]: '13000101'
In [2]: pd.to_datetime('13000101', errors='ignore', format='%Y%m%d')
Out[2]: datetime.datetime(1300, 1, 1, 0, 0)
The above inconsistency (which is also not documented at all I think), is that something we want to fix?
Comment From: jreback
i would say we should return the same as [1] though is it then not possible to get a datetime for out of bounds dates?
Comment From: tacaswell
maybe bread crumbs back to https://github.com/pandas-dev/pandas/issues/6415
Comment From: jorisvandenbossche
It also depends on which format is specified:
In [38]: pd.to_datetime(['15010101', '20150101'], format="%Y%m%d", errors='ignore')
Out[38]: Index([1501-01-01 00:00:00, 2015-01-01 00:00:00], dtype='object') <-- datetime.datetime objects
In [40]: pd.to_datetime(['1501-01-01', '2015-01-01'], format="%Y-%m-%d", errors='ignore')
Out[40]: Index(['1501-01-01', '2015-01-01'], dtype='object')
Comment From: jorisvandenbossche
On the original question of which of the two behaviours we would want: whether it is for errors='ignore' or not, I think there is some value in having the option to get datetime.datetime values for out of bound dates
Comment From: nathalier
Yet another example of unpredictable behaviour:
pd.to_datetime(12912, format="%Y%m%d", errors='ignore')
Out[4]: 12912
pd.to_datetime(21213, format="%Y%m%d", errors='ignore')
Out[5]: datetime.datetime(2, 12, 13, 0, 0)
Comment From: MarcoGorelli
yeah it's the %Y%m%d fastpath and its myriad of bugs
this'll be addressed in https://github.com/pandas-dev/pandas/pull/50242
Comment From: jorisvandenbossche
Resolved by https://github.com/pandas-dev/pandas/pull/50242