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)
if name is None and hasattr(data, "name"):
name = data.name
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 = []
data = cls._create_categorical(data, dtype=dtype)
data = data.copy() if copy else data
return cls._simple_new(data, name=name)
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 __new__(cls, data=None, categories=None, ordered=None, dtype=None,
copy=False, name=None, fastpath=None):
if fastpath is not None:
warnings.warn("The 'fastpath' keyword is deprecated, and will be "
"removed in a future version.",
FutureWarning, stacklevel=2)
if fastpath:
return cls._simple_new(data, name=name, dtype=dtype)
dtype = CategoricalDtype._from_values_or_dtype(data, categories,
ordered, dtype)
if name is None and hasattr(data, 'name'):
name = data.name
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:
cls._scalar_data_error(data)
data = []
data = cls._create_categorical(data, dtype=dtype)
data = data.copy() if copy else data
return cls._simple_new(data, name=name)
def __new__(cls, data=None, categories=None, ordered=None, dtype=None,
copy=False, name=None, fastpath=None):
if fastpath is not None:
warnings.warn("The 'fastpath' keyword is deprecated, and will be "
"removed in a future version.",
FutureWarning, stacklevel=2)
if fastpath:
return cls._simple_new(data, name=name, dtype=dtype)
dtype = CategoricalDtype._from_values_or_dtype(data, categories,
ordered, dtype)
if name is None and hasattr(data, 'name'):
name = data.name
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:
cls._scalar_data_error(data)
data = []
data = cls._create_categorical(data, dtype=dtype)
data = data.copy() if copy else data
return cls._simple_new(data, name=name)
def test_from_values_or_dtype_raises(self, values, categories, ordered,
dtype):
msg = "Cannot specify `categories` or `ordered` together with `dtype`."
with pytest.raises(ValueError, match=msg):
CategoricalDtype._from_values_or_dtype(values, categories, ordered,
dtype)
def test_from_values_or_dtype(self, values, categories, ordered, dtype,
expected):
result = CategoricalDtype._from_values_or_dtype(
values, categories, ordered, dtype)
assert result == expected
def test_from_values_or_dtype_invalid_dtype(self):
msg = "Cannot not construct CategoricalDtype from <class 'object'>"
with pytest.raises(ValueError, match=msg):
CategoricalDtype._from_values_or_dtype(None, None, None, object)
def test_from_values_or_dtype_raises(self, values, categories,
ordered, dtype):
msg = "Cannot specify `categories` or `ordered` together with `dtype`."
with pytest.raises(ValueError, match=msg):
CategoricalDtype._from_values_or_dtype(values, categories,
ordered, dtype)
def test_from_values_or_dtype(
self, values, categories, ordered, dtype, expected):
result = CategoricalDtype._from_values_or_dtype(values, categories,
ordered, dtype)
assert result == expected