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BUG: astype_nansafe with copy=False #38835

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Merged
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Dec 31, 2020
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BUG: astype_nansafe with copy=False #38835

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a7cf00d
BUG: astype_nansafe with copy=False
jbrockmendel Dec 30, 2020
1bccacc
whatsnew
jbrockmendel Dec 30, 2020
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v1.3.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -296,7 +296,7 @@ Sparse
^^^^^^

- Bug in :meth:`DataFrame.sparse.to_coo` raising ``KeyError`` with columns that are a numeric :class:`Index` without a 0 (:issue:`18414`)
-
- Bug in :meth:`SparseArray.astype` with ``copy=False`` producing incorrect results when going from integer dtype to floating dtype (:issue:`34456`)
-

ExtensionArray
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4 changes: 2 additions & 2 deletions pandas/core/dtypes/cast.py
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Expand Up @@ -1133,11 +1133,11 @@ def astype_nansafe(
)
raise ValueError(msg)

if copy or is_object_dtype(arr) or is_object_dtype(dtype):
if copy or is_object_dtype(arr.dtype) or is_object_dtype(dtype):
# Explicit copy, or required since NumPy can't view from / to object.
return arr.astype(dtype, copy=True)

return arr.view(dtype)
return arr.astype(dtype, copy=copy)


def soft_convert_objects(
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8 changes: 8 additions & 0 deletions pandas/tests/arrays/sparse/test_array.py
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Expand Up @@ -563,6 +563,14 @@ def test_astype_nan_raises(self):
with pytest.raises(ValueError, match="Cannot convert non-finite"):
arr.astype(int)

def test_astype_copy_false(self):
# GH#34456 bug caused by using .view instead of .astype in astype_nansafe
arr = SparseArray([1, 2, 3])

result = arr.astype(float, copy=False)
expected = SparseArray([1.0, 2.0, 3.0], fill_value=0.0)
tm.assert_sp_array_equal(result, expected)

def test_set_fill_value(self):
arr = SparseArray([1.0, np.nan, 2.0], fill_value=np.nan)
arr.fill_value = 2
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11 changes: 11 additions & 0 deletions pandas/tests/dtypes/test_common.py
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Expand Up @@ -724,6 +724,17 @@ def test_astype_nansafe(val, typ):
astype_nansafe(arr, dtype=typ)


def test_astype_nansafe_copy_false(any_int_dtype):
# GH#34457 use astype, not view
arr = np.array([1, 2, 3], dtype=any_int_dtype)

dtype = np.dtype("float64")
result = astype_nansafe(arr, dtype, copy=False)

expected = np.array([1.0, 2.0, 3.0], dtype=dtype)
tm.assert_numpy_array_equal(result, expected)


@pytest.mark.parametrize("from_type", [np.datetime64, np.timedelta64])
@pytest.mark.parametrize(
"to_type",
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