BUG GH23224 Allow integer_array to be initialized with all None #23237
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Hello @JustinZhengBC! Thanks for submitting the PR.
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pandas/core/arrays/integer.py
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| @@ -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']: | |||
| 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
pandas/tests/arrays/test_integer.py
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| [ | ||
| (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
pandas/core/arrays/integer.py
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| raise TypeError("{} cannot be converted to an IntegerDtype".format( | ||
| values.dtype)) | ||
| 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
pandas/core/arrays/integer.py
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| @@ -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)
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jreback
added
Bug
Missing-data
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lgtm. i think you need to merge master and push again. ping on green. |
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@jreback green now. I also moved my test cases to another more appropriate test that someone added since I last pulled. |
pandas/core/arrays/integer.py
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| if inferred_type not in ['floating', 'integer', | ||
| 'mixed-integer', 'mixed-integer-float']: | ||
| if inferred_type is 'mixed' and isna(values).any(): | ||
| values = np.array([np.nan] * len(values)) # GH 23224 |
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use
values = np.empty(len(values))
values.fill(np.nan)
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thanks @JustinZhengBC |
| @@ -173,8 +173,11 @@ def coerce_to_array(values, dtype, mask=None, copy=False): | |||
| values = np.array(values, copy=copy) | |||
| if is_object_dtype(values): | |||
| inferred_type = lib.infer_dtype(values) | |||
| if inferred_type not in ['floating', 'integer', | |||
| 'mixed-integer', 'mixed-integer-float']: | |||
| if inferred_type is 'mixed' and isna(values).any(): | |||
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Shoudn't this any be all ?
Because now this allows something like this:
In [14]: pd.core.arrays.integer_array([np.nan, {'a':1}])
Out[14]: IntegerArray([nan, nan], dtype='Int64')
A cornercase, but basically any python object that is not a datetime or string (something that numpy will convert to an object array).
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good catch, yep need to have all here
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@JustinZhengBC feel free to open a follow-up PR |
git diff upstream/master -u -- "*.py" | flake8 --diffAllows for initialization of
integer_arraywith[None], lists ofNone, and lists made of onlyNoneand other na types