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I think this can be fixed by just padding tokens in the inference step. The dates do parse, just always fall back to the slow path, even with infer_datetime_format=True
In [22]: from pandas.tseries.tools import _guess_datetime_format
In [23]: _guess_datetime_format('02/02/2014')
Out[23]: '%m/%d/%Y'
In [24]: _guess_datetime_format('2/2/2014')
The text was updated successfully, but these errors were encountered:
* commit 'v0.17.0rc1-92-gc6bcc99': (29 commits)
CI: tests latest versions of openpyxl
COMPAT: openpyxl >= 2.2 support, pandas-dev#10125
Tests demonstrating how to use sqlalchemy.text() objects in read_sql()
TST: Capture warnings in _check_plot_works
COMPAT/BUG: color handling in scatter
COMPAT: Support for matplotlib 1.5
ERR/API: Raise NotImplementedError when Panel operator function is not implemented, pandas-dev#7692
DOC: minor doc formatting fixes
PERF: nested dict DataFrame construction
DEPR: deprecate SparsePanel
BLD: dateutil->python-dateutil in conda recipe
BUG/API: GH11086 where freq is not inferred if both freq is None
ENH: add merge indicator to DataFrame.merge
PERF: improves performance in groupby.size
BUG: DatetimeTZBlock.fillna raises TypeError
PERF: infer_datetime_format without padding pandas-dev#11142
PERF: improves performance in SeriesGroupBy.transform
TST: Verify fix for buffer overflow in read_csv with engine='c' (GH pandas-dev#9735)
DEPR: Series.is_timeseries
BUG: nested construction with timedelta pandas-dev#11129
...
I think this can be fixed by just padding tokens in the inference step. The dates do parse, just always fall back to the slow path, even with
infer_datetime_format=True
The text was updated successfully, but these errors were encountered: