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Check if NPY_NAT is NA for int64 in rank() (#32859) #35533

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Check if NPY_NAT is NA for int64 in rank() (#32859) #35533

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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v1.2.0.rst
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Expand Up @@ -157,7 +157,7 @@ ExtensionArray

Other
^^^^^
-
- Bug in :meth:`Series.rank` incorrectly treating int64 min value as NaN (:issue:`32859`)
-

.. ---------------------------------------------------------------------------
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2 changes: 1 addition & 1 deletion pandas/_libs/algos.pyx
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Expand Up @@ -840,7 +840,7 @@ def rank_1d(
elif rank_t is float64_t:
mask = np.isnan(values)
elif rank_t is int64_t:
mask = values == NPY_NAT
mask = missing.isnaobj(values)
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this is going to be all-False

We probably need to specifically handle datetimelike case in which the existing check is correct


# create copy in case of NPY_NAT
# values are mutated inplace
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13 changes: 13 additions & 0 deletions pandas/tests/test_algos.py
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Expand Up @@ -1769,6 +1769,19 @@ def test_basic(self):
s = Series([1, 100], dtype=dtype)
tm.assert_numpy_array_equal(algos.rank(s), exp)

@pytest.mark.parametrize("dtype", ["int32", "int64"])
def test_negative_min_rank(self, dtype):
# GH#32859
# Check that nan is respected on float64
s = pd.Series(np.array([np.inf, np.nan, -np.inf]))
expected = pd.Series(np.array([2.0, np.nan, 1.0]))
tm.assert_series_equal(s.rank(na_option="keep"), expected)

# Rank works if coverted to most negative value
s = pd.Series(np.array([np.inf, np.nan, -np.inf]).astype(dtype))
expected = pd.Series(np.array([2.0, 2.0, 2.0]))
tm.assert_series_equal(s.rank(na_option="keep"), expected)

def test_uint64_overflow(self):
exp = np.array([1, 2], dtype=np.float64)

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