def __new__(cls,
values,
missing_value,
categories=None,
sort=True):
# Numpy's fixed-width string types aren't very efficient. Working with
# object arrays is faster than bytes or unicode arrays in almost all
# cases.
if not is_object(values):
values = values.astype(object)
if categories is None:
codes, categories, reverse_categories = factorize_strings(
values.ravel(),
missing_value=missing_value,
sort=sort,
)
else:
codes, categories, reverse_categories = (
factorize_strings_known_categories(
values.ravel(),
categories=categories,
missing_value=missing_value,
sort=sort,
)
)
categories.setflags(write=False)
return cls._from_codes_and_metadata(
codes=codes.reshape(values.shape),
categories=categories,
reverse_categories=reverse_categories,
missing_value=missing_value,
)
def __new__(cls, values, missing_value, categories=None, sort=True):
# Numpy's fixed-width string types aren't very efficient. Working with
# object arrays is faster than bytes or unicode arrays in almost all
# cases.
if not is_object(values):
values = values.astype(object)
if categories is None:
codes, categories, reverse_categories = factorize_strings(
values.ravel(),
missing_value=missing_value,
sort=sort,
)
else:
codes, categories, reverse_categories = (
factorize_strings_known_categories(
values.ravel(),
categories=categories,
missing_value=missing_value,
sort=sort,
))
categories.setflags(write=False)
return cls._from_codes_and_metadata(
codes=codes.reshape(values.shape),
categories=categories,
reverse_categories=reverse_categories,
missing_value=missing_value,
)
def __new__(cls, values, missing_value, categories=None, sort=True):
# Numpy's fixed-width string types aren't very efficient. Working with
# object arrays is faster than bytes or unicode arrays in almost all
# cases.
if not is_object(values):
values = values.astype(object)
if values.flags.f_contiguous:
ravel_order = 'F'
else:
ravel_order = 'C'
if categories is None:
codes, categories, reverse_categories = factorize_strings(
values.ravel(ravel_order),
missing_value=missing_value,
sort=sort,
)
else:
codes, categories, reverse_categories = (
factorize_strings_known_categories(
values.ravel(ravel_order),
categories=categories,
missing_value=missing_value,
sort=sort,
))
# 确保 code of missing_value = -1
# code in [-1, len(categories) - 1]
# codes, reverse_categories = _to_pandas_code(
# reverse_categories, codes, missing_value)
# categories = np.delete(categories, categories == None, None)
categories.setflags(write=False)
return cls.from_codes_and_metadata(
codes=codes.reshape(values.shape, order=ravel_order),
categories=categories,
reverse_categories=reverse_categories,
missing_value=missing_value,
)
