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Let _get_dtype accept Categoricals and CategoricalIndex #16887

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merged 4 commits into from
Jul 13, 2017
Merged

Let _get_dtype accept Categoricals and CategoricalIndex #16887

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7cf00a2
Make _get_dtype accept pd.Categorical and pd.CategoricalIndex instances
Jul 11, 2017
fb0fe34
small flake8 cleanup
Jul 11, 2017
3b4ef99
Added a note for change in dtypes.common._get_dtype in whatsnew
Jul 12, 2017
54da145
Removed entry in whatsnew
topper-123 Jul 13, 2017
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1 change: 0 additions & 1 deletion doc/source/whatsnew/v0.21.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,6 @@ Conversion
^^^^^^^^^^



Indexing
^^^^^^^^

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4 changes: 3 additions & 1 deletion pandas/core/dtypes/common.py
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Expand Up @@ -11,7 +11,7 @@
ExtensionDtype)
from .generic import (ABCCategorical, ABCPeriodIndex,
ABCDatetimeIndex, ABCSeries,
ABCSparseArray, ABCSparseSeries)
ABCSparseArray, ABCSparseSeries, ABCCategoricalIndex)
from .inference import is_string_like
from .inference import * # noqa

Expand Down Expand Up @@ -1713,6 +1713,8 @@ def _get_dtype(arr_or_dtype):
return PeriodDtype.construct_from_string(arr_or_dtype)
elif is_interval_dtype(arr_or_dtype):
return IntervalDtype.construct_from_string(arr_or_dtype)
elif isinstance(arr_or_dtype, (ABCCategorical, ABCCategoricalIndex)):
return arr_or_dtype.dtype

if hasattr(arr_or_dtype, 'dtype'):
arr_or_dtype = arr_or_dtype.dtype
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6 changes: 3 additions & 3 deletions pandas/tests/dtypes/test_common.py
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Expand Up @@ -532,16 +532,16 @@ def test_is_complex_dtype():
(float, np.dtype(float)),
('float64', np.dtype('float64')),
(np.dtype('float64'), np.dtype('float64')),
pytest.mark.xfail((str, np.dtype('<U')), ),
(str, np.dtype(str)),
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what fixed this one?

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@topper-123 topper-123 Jul 12, 2017 •
edited
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On both my system (Windows) and at try.jupyter.org (sys.platform is 'linux'), _get_dtype(str) returns np.dtype('<U'). In the jenkins/appvoyeur, it returns np.dtype('S').

To me it looks like a difference of how numpy is handled on different system and not a pandas issue. I therefore relax that test, to make it only check that _get_dtype(str) == np.dtype(str), which passes both locally and in the CI system.

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hmm, that looks like a py2/py3 thing. ok then. (you can maybe add a comment for that particular case)

(pd.Series([1, 2], dtype=np.dtype('int16')), np.dtype('int16')),
(pd.Series(['a', 'b']), np.dtype(object)),
(pd.Index([1, 2]), np.dtype('int64')),
(pd.Index(['a', 'b']), np.dtype(object)),
('category', 'category'),
(pd.Categorical(['a', 'b']).dtype, CategoricalDtype()),
pytest.mark.xfail((pd.Categorical(['a', 'b']), CategoricalDtype()),),
(pd.Categorical(['a', 'b']), CategoricalDtype()),
(pd.CategoricalIndex(['a', 'b']).dtype, CategoricalDtype()),
pytest.mark.xfail((pd.CategoricalIndex(['a', 'b']), CategoricalDtype()),),
(pd.CategoricalIndex(['a', 'b']), CategoricalDtype()),
(pd.DatetimeIndex([1, 2]), np.dtype('<M8[ns]')),
(pd.DatetimeIndex([1, 2]).dtype, np.dtype('<M8[ns]')),
('<M8[ns]', np.dtype('<M8[ns]')),
Expand Down