cat = pd.Categorical([3, 1], categories=[3, 2, 1], ordered=True) res = pd.Categorical(cat, dtype="category") >>> res.dtype CategoricalDtype(categories=[3, 2, 1], ordered=False) >>> cat.dtype CategoricalDtype(categories=[3, 2, 1], ordered=True)
By contrast, if we do cat.astype("category") we do retain orderedness.
Found when refactoring sanitize_array. We have two code paths effectively:
if isinstance(data, ExtensionArray):
if dtype is not None:
return data.astype(dtype, copy=copy)
elif copy:
return data.copy()
return data
if isinstance(dtype, ExtensionDtype):
cls = dtype.construct_array_type()
return cls._from_sequence(data, dtype=dtype, copy=copy)
Switching the order of these checks causes a few Categorical tests to break bc of the Categorical constructor behavior.
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