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BUG GH23224 Allow integer_array to be initialized with all None #23237

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Merged
merged 6 commits into from
Oct 23, 2018
Merged

BUG GH23224 Allow integer_array to be initialized with all None #23237

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4fd3202
BUG-23224 Allow integer_array to be initialized with all None
JustinZhengBC Oct 19, 2018
0590df8
BUG-23224 Add tests and other requested changes
JustinZhengBC Oct 19, 2018
9a2a053
BUG-23224 Merge master and fix conflict
JustinZhengBC Oct 19, 2018
349ca0a
Fix existing tests
JustinZhengBC Oct 19, 2018
a679c15
Merge branch 'master' into PR_TOOL_MERGE_PR_23237
jreback Oct 23, 2018
14399b1
use idiomatic
jreback Oct 23, 2018
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8 changes: 6 additions & 2 deletions pandas/core/arrays/integer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -174,8 +174,12 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
inferred_type = infer_dtype(values)
if inferred_type not in ['floating', 'integer',
'mixed-integer', 'mixed-integer-float']:
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i don’t recall if we are checking for exactly ndim == 1 right after we convert to ndarray

can u add this as well ( and some tests)

raise TypeError("{} cannot be converted to an IntegerDtype".format(
values.dtype))
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put this before the

if inferred_type not in

and make that an elif

if inferred_type is 'mixed' and all(isna(x) for x in values):
values = np.array([np.nan] * len(values)) # GH 23224
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you can use

isna(values).any() as we always return an. array

else:
raise TypeError(
"{} cannot be converted to an IntegerDtype".format(
values.dtype))

elif not (is_integer_dtype(values) or is_float_dtype(values)):
raise TypeError("{} cannot be converted to an IntegerDtype".format(
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9 changes: 9 additions & 0 deletions pandas/tests/arrays/test_integer.py
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Expand Up @@ -560,6 +560,15 @@ def test_to_integer_array_float():
assert result.dtype == Int64Dtype()


@pytest.mark.parametrize(
'result, expected',
[
(integer_array([None]), integer_array([np.nan])),
(integer_array([None, np.nan]), integer_array([np.nan, np.nan]))])
def test_to_integer_array_none(result, expected):
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can u also try an array of all np.nan

tm.assert_extension_array_equal(result, expected)


@pytest.mark.parametrize(
'values, to_dtype, result_dtype',
[
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