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BUG: Avoid ambiguous condition in GroupBy.first / last #32124

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Merged
merged 10 commits into from
Feb 23, 2020
Merged

BUG: Avoid ambiguous condition in GroupBy.first / last #32124

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627410d
Add test
dsaxton Feb 20, 2020
bb4ad11
Check for NA
dsaxton Feb 20, 2020
3213718
Update whatsnew
dsaxton Feb 20, 2020
58bbb57
Use nulls_fixture
dsaxton Feb 20, 2020
4f2d126
Use checknull
dsaxton Feb 20, 2020
c4a6518
Merge branch 'master' into grpby-nth
dsaxton Feb 20, 2020
e09bbe6
Merge remote-tracking branch 'upstream/master' into grpby-nth
dsaxton Feb 20, 2020
c7c7a95
Merge remote-tracking branch 'upstream/master' into grpby-nth
dsaxton Feb 21, 2020
4329021
Merge remote-tracking branch 'upstream/master' into grpby-nth
dsaxton Feb 23, 2020
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Move and add test
dsaxton Feb 23, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.0.2.rst
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Expand Up @@ -45,6 +45,7 @@ Bug fixes

- Fix bug in :meth:`DataFrame.convert_dtypes` for columns that were already using the ``"string"`` dtype (:issue:`31731`).
- Fixed bug in setting values using a slice indexer with string dtype (:issue:`31772`)
- Fixed bug where :meth:`GroupBy.first` and :meth:`GroupBy.last` would raise a ``TypeError`` when groups contained ``pd.NA`` in a column of object dtype (:issue:`32123`)

.. ---------------------------------------------------------------------------

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6 changes: 4 additions & 2 deletions pandas/_libs/groupby.pyx
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Expand Up @@ -22,6 +22,8 @@ from pandas._libs.algos cimport (swap, TiebreakEnumType, TIEBREAK_AVERAGE,
from pandas._libs.algos import (take_2d_axis1_float64_float64,
groupsort_indexer, tiebreakers)

from pandas._libs.missing import NA

cdef int64_t NPY_NAT = get_nat()
_int64_max = np.iinfo(np.int64).max

Expand Down Expand Up @@ -887,7 +889,7 @@ def group_last(rank_t[:, :] out,
for j in range(K):
val = values[i, j]

if val == val:
if (val is not NA) and (val == val):
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I think can use the checknull function from missing instead

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Maybe just not checknull(val) for the whole condition? I think there was an issue with checknull not catching decimal.Decimal("nan") but I could try to add that here

# NB: use _treat_as_na here once
# conditional-nogil is available.
nobs[lab, j] += 1
Expand Down Expand Up @@ -976,7 +978,7 @@ def group_nth(rank_t[:, :] out,
for j in range(K):
val = values[i, j]

if val == val:
if (val is not NA) and (val == val):
# NB: use _treat_as_na here once
# conditional-nogil is available.
nobs[lab, j] += 1
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17 changes: 17 additions & 0 deletions pandas/tests/groupby/test_nth.py
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Expand Up @@ -530,3 +530,20 @@ def test_nth_nan_in_grouper(dropna):
)

tm.assert_frame_equal(result, expected)


@pytest.mark.parametrize("method", ["first", "last"])
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can you also test .nth(0) and .nth(-1) which are the same results (except the nth -1 will have the null as the result

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Added a test for nth with nulls; I gave it its own test rather than putting it with first / last to avoid too much awkward if / else branching inside the test

def test_first_last_with_na_object(method):
# https://github.com/pandas-dev/pandas/issues/32123
groups = pd.DataFrame({"a": [1, 1, 2, 2], "b": [1, 2, 3, pd.NA]}).groupby("a")
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Should also use nulls_fixture here after #31799

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Can you merge master? This should be available now

result = getattr(groups, method)()

if method == "first":
values = {"b": [1, 3]}
else:
values = {"b": [2, 3]}
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This raises a question (that perhaps should be handled in another issue).
GroupBy.last() returns [2, 3] however GroupBy.tail(1) returns [2, pd.NA].
Is this intended behaviour? it is consistent with pandas 0.25, but undocumented.

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It seems that last gives the last non-null value but that doesn't seem to be well-documented like you say (probably does warrant a separate issue)


idx = pd.Index([1, 2], name="a")
expected = pd.DataFrame(values, index=idx)

tm.assert_frame_equal(result, expected)