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BUG: Fix Series.groupby raising OutOfBoundsDatetime with DatetimeIndex and month name #54306

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Aug 1, 2023
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BUG: Fix Series.groupby raising OutOfBoundsDatetime with DatetimeIndex and month name #54306

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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
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Expand Up @@ -628,6 +628,7 @@ Groupby/resample/rolling
- Bug in :meth:`GroupBy.var` failing to raise ``TypeError`` when called with datetime64, timedelta64 or :class:`PeriodDtype` values (:issue:`52128`, :issue:`53045`)
- Bug in :meth:`DataFrameGroupby.resample` with ``kind="period"`` raising ``AttributeError`` (:issue:`24103`)
- Bug in :meth:`Resampler.ohlc` with empty object returning a :class:`Series` instead of empty :class:`DataFrame` (:issue:`42902`)
- Bug in :meth:`Series.groupby` raising an error when grouped :class:`Series` has a :class:`DatetimeIndex` index and a :class:`Series` with a name that is a month is given to the ``by`` argument (:issue:`48509`)
- Bug in :meth:`SeriesGroupBy.count` and :meth:`DataFrameGroupBy.count` where the dtype would be ``np.int64`` for data with :class:`ArrowDtype` or masked dtypes (e.g. ``Int64``) (:issue:`53831`)
- Bug in :meth:`SeriesGroupBy.nth` and :meth:`DataFrameGroupBy.nth` after performing column selection when using ``dropna="any"`` or ``dropna="all"`` would not subset columns (:issue:`53518`)
- Bug in :meth:`SeriesGroupBy.nth` and :meth:`DataFrameGroupBy.nth` raised after performing column selection when using ``dropna="any"`` or ``dropna="all"`` resulted in rows being dropped (:issue:`53518`)
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7 changes: 5 additions & 2 deletions pandas/core/groupby/grouper.py
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Expand Up @@ -15,6 +15,7 @@
from pandas._config import using_copy_on_write

from pandas._libs import lib
from pandas._libs.tslibs import OutOfBoundsDatetime
from pandas.errors import InvalidIndexError
from pandas.util._decorators import cache_readonly
from pandas.util._exceptions import find_stack_level
Expand Down Expand Up @@ -969,7 +970,7 @@ def is_in_obj(gpr) -> bool:
# series is part of the object
try:
obj_gpr_column = obj[gpr.name]
except (KeyError, IndexError, InvalidIndexError):
except (KeyError, IndexError, InvalidIndexError, OutOfBoundsDatetime):
return False
if isinstance(gpr, Series) and isinstance(obj_gpr_column, Series):
return gpr._mgr.references_same_values( # type: ignore[union-attr]
Expand All @@ -978,11 +979,13 @@ def is_in_obj(gpr) -> bool:
return False
try:
return gpr is obj[gpr.name]
except (KeyError, IndexError, InvalidIndexError):
Comment on lines 980 to -981
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It seems like any error we hit here will want to be ignored; what are thoughts on just doing try: ... except Exception: return False?

cc @jbrockmendel @mroeschke

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How would you get an arbitrary exception here? A couple years back we made a concerted effort to be more specific in what we caught

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I think there are other issues caused by not being strict enough here e.g. #51818

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@rhshadrach rhshadrach Jul 31, 2023 •
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I don't think you can; what I'm thinking is that any exception that can possibly be hit here we want to ignore. I'm not certain that's true though.

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I think the listed exceptions here are ones that make sense if gpr.name is not found in obj.columns. Any other exceptions mean Something Has Gone Wrong and we would want a bug report to be filed

except (KeyError, IndexError, InvalidIndexError, OutOfBoundsDatetime):
# IndexError reached in e.g. test_skip_group_keys when we pass
# lambda here
# InvalidIndexError raised on key-types inappropriate for index,
# e.g. DatetimeIndex.get_loc(tuple())
# OutOfBoundsDatetime raised when obj is a Series with DatetimeIndex
# and gpr.name is month str
return False

for gpr, level in zip(keys, levels):
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9 changes: 9 additions & 0 deletions pandas/tests/groupby/test_groupby.py
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Expand Up @@ -3124,3 +3124,12 @@ def test_groupby_with_Time_Grouper():
df = test_data.groupby(Grouper(key="time2", freq="1T")).count().reset_index()

tm.assert_frame_equal(df, expected_output)


def test_groupby_series_with_datetimeindex_month_name():
# GH 48509
s = Series([0, 1, 0], index=date_range("2022-01-01", periods=3), name="jan")
result = s.groupby(s).count()
expected = Series([2, 1], name="jan")
expected.index.name = "jan"
tm.assert_series_equal(result, expected)