def __new__(
cls, data=None, categories=None, ordered=None, dtype=None, copy=False, name=None
):
dtype = CategoricalDtype._from_values_or_dtype(data, categories, ordered, dtype)
name = maybe_extract_name(name, data, cls)
if not is_categorical_dtype(data):
# don't allow scalars
# if data is None, then categories must be provided
if is_scalar(data):
if data is not None or categories is None:
raise cls._scalar_data_error(data)
data = []
assert isinstance(dtype, CategoricalDtype), dtype
data = extract_array(data, extract_numpy=True)
if not isinstance(data, Categorical):
data = Categorical(data, dtype=dtype)
elif isinstance(dtype, CategoricalDtype) and dtype != data.dtype:
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
data = data.copy() if copy else data
return cls._simple_new(data, name=name)
def _create_categorical(self,
data,
categories=None,
ordered=None,
dtype=None):
"""
*this is an internal non-public method*
create the correct categorical from data and the properties
Parameters
----------
data : data for new Categorical
categories : optional categories, defaults to existing
ordered : optional ordered attribute, defaults to existing
dtype : CategoricalDtype, defaults to existing
Returns
-------
Categorical
"""
if (isinstance(data, (ABCSeries, type(self)))
and is_categorical_dtype(data)):
data = data.values
if not isinstance(data, ABCCategorical):
if ordered is None and dtype is None:
ordered = False
data = Categorical(data,
categories=categories,
ordered=ordered,
dtype=dtype)
else:
if categories is not None:
data = data.set_categories(categories, ordered=ordered)
elif ordered is not None and ordered != data.ordered:
data = data.set_ordered(ordered)
if isinstance(dtype, CategoricalDtype):
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
return data
def _create_categorical(cls, data, categories=None, ordered=None,
dtype=None):
"""
*this is an internal non-public method*
create the correct categorical from data and the properties
Parameters
----------
data : data for new Categorical
categories : optional categories, defaults to existing
ordered : optional ordered attribute, defaults to existing
dtype : CategoricalDtype, defaults to existing
Returns
-------
Categorical
"""
if (isinstance(data, (cls, ABCSeries)) and
is_categorical_dtype(data)):
data = data.values
if not isinstance(data, ABCCategorical):
if ordered is None and dtype is None:
ordered = False
data = Categorical(data, categories=categories, ordered=ordered,
dtype=dtype)
else:
if categories is not None:
data = data.set_categories(categories, ordered=ordered)
elif ordered is not None and ordered != data.ordered:
data = data.set_ordered(ordered)
if isinstance(dtype, CategoricalDtype) and dtype != data.dtype:
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
return data
