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BUG: Fix qcut for nullable integers #31440

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Merged
merged 21 commits into from
Feb 2, 2020
Merged

BUG: Fix qcut for nullable integers #31440

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a0d2d95
Add test
Jan 30, 2020
fdfb2e9
Cast BooleanArray to bool
Jan 30, 2020
a35c7a6
Update 1.1 whatsnew
Jan 30, 2020
e2d1a46
Update test
Jan 30, 2020
bee26b1
Use place holder for NA values
Jan 30, 2020
516dbe3
Issue number
Jan 30, 2020
3f09385
Cast to numpy
Jan 31, 2020
dde3b61
Merge branch 'master' into qcut-null-int
Jan 31, 2020
5579381
Move note
Jan 31, 2020
93ef631
Fixup
Jan 31, 2020
f0990b1
Don't check for BooleanArray
Jan 31, 2020
99e6080
Make test data deterministic
Jan 31, 2020
5694ab6
Update test scripts
Feb 1, 2020
e26a13b
Merge branch 'master' into qcut-null-int
Feb 1, 2020
1bf4cee
Move test_cut for nullable int
Feb 1, 2020
4eb9313
Just import pandas
Feb 1, 2020
6767ea4
Cast to float instead of object in test
Feb 2, 2020
fc0a0ba
Move conversion into _coerce_to_type
Feb 2, 2020
465bb0e
Merge branch 'master' into qcut-null-int
Feb 2, 2020
b67abad
Move comments
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Feb 2, 2020
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.0.1.rst
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Expand Up @@ -118,6 +118,7 @@ ExtensionArray
^^^^^^^^^^^^^^

- Bug in dtype being lost in ``__invert__`` (``~`` operator) for extension-array backed ``Series`` and ``DataFrame`` (:issue:`23087`)
- Bug where :meth:`qcut` would raise when passed a nullable integer. (:issue:`31389`)
-


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3 changes: 3 additions & 0 deletions pandas/core/reshape/tile.py
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Expand Up @@ -340,6 +340,9 @@ def qcut(
x = _preprocess_for_cut(x)
x, dtype = _coerce_to_type(x)

if is_extension_array_dtype(x.dtype) and is_integer_dtype(x.dtype):
x = x.to_numpy(dtype=np.float64, na_value=np.nan)

if is_integer(q):
quantiles = np.linspace(0, 1, q + 1)
else:
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11 changes: 11 additions & 0 deletions pandas/tests/arrays/test_integer.py
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Expand Up @@ -1074,6 +1074,17 @@ def test_cut(bins, right, include_lowest):
tm.assert_categorical_equal(result, expected)


@pytest.mark.parametrize("q", [2, 5, 10])
def test_qcut_nullable_integer(q, any_nullable_int_dtype):
arr = pd.array(np.arange(100), dtype=any_nullable_int_dtype)
arr[::2] = pd.NA

result = pd.qcut(arr, q)
expected = pd.qcut(arr.astype(float), q)

tm.assert_categorical_equal(result, expected)


# TODO(jreback) - these need testing / are broken

# shift
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