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Deprecated fastpath parameter in Categorial constructor #17889

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Deprecated fastpath parameter in Categorial constructor #17889

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d649f9e
Deprecated fastpath parameter in Categorial constructor
kaichogami Oct 16, 2017
92303ec
added validate_dtype and changed code using fastpaath paremeter
kaichogami Nov 13, 2017
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125 changes: 53 additions & 72 deletions pandas/core/categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
is_timedelta64_dtype,
is_categorical,
is_categorical_dtype,
is_integer_dtype,
is_list_like, is_sequence,
is_scalar,
is_dict_like)
Expand Down Expand Up @@ -244,49 +245,16 @@ class Categorical(PandasObject):
def __init__(self, values, categories=None, ordered=None, dtype=None,
fastpath=False):

# Ways of specifying the dtype (prioritized ordered)
# 1. dtype is a CategoricalDtype
# a.) with known categories, use dtype.categories
# b.) else with Categorical values, use values.dtype
# c.) else, infer from values
# d.) specifying dtype=CategoricalDtype and categories is an error
# 2. dtype is a string 'category'
# a.) use categories, ordered
# b.) use values.dtype
# c.) infer from values
# 3. dtype is None
# a.) use categories, ordered
# b.) use values.dtype
# c.) infer from values

if dtype is not None:
# The dtype argument takes precedence over values.dtype (if any)
if isinstance(dtype, compat.string_types):
if dtype == 'category':
dtype = CategoricalDtype(categories, ordered)
else:
msg = "Unknown `dtype` {dtype}"
raise ValueError(msg.format(dtype=dtype))
elif categories is not None or ordered is not None:
raise ValueError("Cannot specify both `dtype` and `categories`"
" or `ordered`.")

categories = dtype.categories
ordered = dtype.ordered

elif is_categorical(values):
# If no "dtype" was passed, use the one from "values", but honor
# the "ordered" and "categories" arguments
dtype = values.dtype._from_categorical_dtype(values.dtype,
categories, ordered)
else:
# If dtype=None and values is not categorical, create a new dtype
dtype = CategoricalDtype(categories, ordered)
categories, ordered, dtype = validate_dtype(values, categories,
ordered, dtype)

# At this point, dtype is always a CategoricalDtype
# if dtype.categories is None, we are inferring

if fastpath:
warn("`fastpath` parameter is deprecated. Use `dtype` parameter "
"to construct or pass `CategorialDtype` instance instead.",
DeprecationWarning, stacklevel=2)
self._codes = coerce_indexer_dtype(values, categories)
self._dtype = dtype
return
Expand Down Expand Up @@ -344,6 +312,13 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
dtype.categories)

else:
# there were two ways if categories are present
# - the old one, where each value is a int pointer to the levels
# array -> not anymore possible, but code outside of pandas could
# call us like that, so make some checks
# - the new one, where each value is also in the categories array
# (or np.nan)

codes = _get_codes_for_values(values, dtype.categories)

if null_mask.any():
Expand Down Expand Up @@ -413,7 +388,7 @@ def copy(self):
return self._constructor(values=self._codes.copy(),
categories=self.categories,
ordered=self.ordered,
fastpath=True)
dtype=self.dtype)

def astype(self, dtype, copy=True):
"""
Expand Down Expand Up @@ -545,7 +520,7 @@ def _from_inferred_categories(cls, inferred_categories, inferred_codes,
dtype = CategoricalDtype(cats, ordered=False)
codes = inferred_codes

return cls(codes, dtype=dtype, fastpath=True)
return cls(codes, dtype=dtype)
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I am quite confused on what should be done for the fastpath parameters used in class method's to create a new Categorial. Should I replace them with dtype or let the __init__ handle CategorialDtype construction?


@classmethod
def from_array(cls, data, **kwargs):
Expand Down Expand Up @@ -640,13 +615,15 @@ def _get_labels(self):

labels = property(fget=_get_labels, fset=_set_codes)

def _set_categories(self, categories, fastpath=False):
def _set_categories(self, categories=None, dtype=None):
""" Sets new categories inplace

Parameters
----------
fastpath : boolean (default: False)
Don't perform validation of the categories for uniqueness or nulls
categories : Index-like (unique)
Unique new categories
dtype : CategorialDtype or string (Default: None)
An instance of ``CategorialDtype``.

Examples
--------
Expand All @@ -661,13 +638,35 @@ def _set_categories(self, categories, fastpath=False):
Categories (2, object): [a, c]
"""

if fastpath:
new_dtype = CategoricalDtype._from_fastpath(categories,
self.ordered)
if dtype is None and categories is None:
raise ValueError("categories and dtype both cannot be None")

if dtype is not None:
if isinstance(dtype, compat.string_types):
if dtype == 'category':
new_dtype = CategorialDtype(categories, self.ordered)

else:
msg = "Unknown `dtype` {dtype}"
raise ValueError(msg.format(dtype=dtype))

elif categories is not None:
raise ValueError("Cannot specify both categories and dtype")


else:
if not(isinstance(dtype, CategorialDtype)):
raise ValueError("dtype must be an instance of "
"CategorialDtype")
else:
new_dtype = dtype

else:
new_dtype = CategoricalDtype(categories, ordered=self.ordered)
if (not fastpath and self.dtype.categories is not None and
len(new_dtype.categories) != len(self.dtype.categories)):
new_dtype = CategorialDtype(categories, self.ordered)


if (self.dtype.categories is not None and
len(new_dtype.categories) != len(self.dtype.categories)):
raise ValueError("new categories need to have the same number of "
"items than the old categories!")

Expand Down Expand Up @@ -848,6 +847,11 @@ def set_categories(self, new_categories, ordered=None, rename=False,
def rename_categories(self, new_categories, inplace=False):
""" Renames categories.

The new categories can be either a list-like dict-like object.
If it is list-like, all items must be unique and the number of items
in the new categories must be the same as the number of items in the
old categories.

Raises
------
ValueError
Expand Down Expand Up @@ -888,33 +892,10 @@ def rename_categories(self, new_categories, inplace=False):
remove_categories
remove_unused_categories
set_categories

Examples
--------
>>> c = Categorical(['a', 'a', 'b'])
>>> c.rename_categories([0, 1])
[0, 0, 1]
Categories (2, int64): [0, 1]

For dict-like ``new_categories``, extra keys are ignored and
categories not in the dictionary are passed through

>>> c.rename_categories({'a': 'A', 'c': 'C'})
[A, A, b]
Categories (2, object): [A, b]
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
cat = self if inplace else self.copy()

if isinstance(new_categories, ABCSeries):
msg = ("Treating Series 'new_categories' as a list-like and using "
"the values. In a future version, 'rename_categories' will "
"treat Series like a dictionary.\n"
"For dict-like, use 'new_categories.to_dict()'\n"
"For list-like, use 'new_categories.values'.")
warn(msg, FutureWarning, stacklevel=2)
new_categories = list(new_categories)

if is_dict_like(new_categories):
cat.categories = [new_categories.get(item, item)
for item in cat.categories]
Expand Down