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BUG fix IntegerArray.astype int -> uint #22441

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Aug 22, 2018
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BUG fix IntegerArray.astype int -> uint #22441

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BUG fix IntegerArray.astype int -> uint
jorisvandenbossche Aug 21, 2018
26afccf
whatsnew
jreback Aug 22, 2018
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.24.0.txt
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Expand Up @@ -42,7 +42,7 @@ Pandas has gained the ability to hold integer dtypes with missing values. This l
Here is an example of the usage.

We can construct a ``Series`` with the specified dtype. The dtype string ``Int64`` is a pandas ``ExtensionDtype``. Specifying a list or array using the traditional missing value
marker of ``np.nan`` will infer to integer dtype. The display of the ``Series`` will also use the ``NaN`` to indicate missing values in string outputs. (:issue:`20700`, :issue:`20747`)
marker of ``np.nan`` will infer to integer dtype. The display of the ``Series`` will also use the ``NaN`` to indicate missing values in string outputs. (:issue:`20700`, :issue:`20747`, :issue:`22441`)

.. ipython:: python

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3 changes: 1 addition & 2 deletions pandas/core/arrays/integer.py
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Expand Up @@ -409,8 +409,7 @@ def astype(self, dtype, copy=True):

# if we are astyping to an existing IntegerDtype we can fastpath
if isinstance(dtype, _IntegerDtype):
result = self._data.astype(dtype.numpy_dtype,
casting='same_kind', copy=False)
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@jreback do you remember what the reason was to explicitly put casting='same_kind' ?

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iirc was to make sure we were
only coercing actual ints (and would raise on floats)

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But since we have the if isinstance(dtype, _IntegerDtype):, we are sure about that? (so it can never be a float dtype)

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it might have been an artificat.

result = self._data.astype(dtype.numpy_dtype, copy=False)
return type(self)(result, mask=self._mask, copy=False)

# coerce
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7 changes: 4 additions & 3 deletions pandas/tests/extension/integer/test_integer.py
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Expand Up @@ -566,16 +566,17 @@ def test_astype(self, all_data):
expected = pd.Series(np.asarray(mixed))
tm.assert_series_equal(result, expected)

@pytest.mark.parametrize('dtype', [Int8Dtype(), 'Int8'])
@pytest.mark.parametrize('dtype', [Int8Dtype(), 'Int8',
UInt32Dtype(), 'UInt32'])
def test_astype_specific_casting(self, dtype):
s = pd.Series([1, 2, 3], dtype='Int64')
result = s.astype(dtype)
expected = pd.Series([1, 2, 3], dtype='Int8')
expected = pd.Series([1, 2, 3], dtype=dtype)
self.assert_series_equal(result, expected)

s = pd.Series([1, 2, 3, None], dtype='Int64')
result = s.astype(dtype)
expected = pd.Series([1, 2, 3, None], dtype='Int8')
expected = pd.Series([1, 2, 3, None], dtype=dtype)
self.assert_series_equal(result, expected)

def test_construct_cast_invalid(self, dtype):
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