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duality-group / pandas python
Repository URL to install this package:
|
Version:
1.4.3 ▾
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from __future__ import annotations
from csv import QUOTE_NONNUMERIC
from functools import partial
import operator
from shutil import get_terminal_size
from typing import (
TYPE_CHECKING,
Hashable,
Sequence,
TypeVar,
Union,
cast,
overload,
)
from warnings import (
catch_warnings,
simplefilter,
warn,
)
import numpy as np
from pandas._config import get_option
from pandas._libs import (
NaT,
algos as libalgos,
hashtable as htable,
lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.lib import no_default
from pandas._typing import (
ArrayLike,
AstypeArg,
Dtype,
NpDtype,
Ordered,
Shape,
npt,
type_t,
)
from pandas.compat.numpy import function as nv
from pandas.util._decorators import (
cache_readonly,
deprecate_kwarg,
)
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import validate_bool_kwarg
from pandas.core.dtypes.cast import (
coerce_indexer_dtype,
maybe_cast_to_extension_array,
)
from pandas.core.dtypes.common import (
ensure_int64,
ensure_platform_int,
is_categorical_dtype,
is_datetime64_dtype,
is_dict_like,
is_dtype_equal,
is_extension_array_dtype,
is_hashable,
is_integer_dtype,
is_list_like,
is_object_dtype,
is_scalar,
is_timedelta64_dtype,
needs_i8_conversion,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
ExtensionDtype,
)
from pandas.core.dtypes.generic import (
ABCIndex,
ABCSeries,
)
from pandas.core.dtypes.missing import (
is_valid_na_for_dtype,
isna,
notna,
)
from pandas.core import (
arraylike,
ops,
)
from pandas.core.accessor import (
PandasDelegate,
delegate_names,
)
import pandas.core.algorithms as algorithms
from pandas.core.algorithms import (
factorize,
take_nd,
unique1d,
)
from pandas.core.arrays._mixins import (
NDArrayBackedExtensionArray,
ravel_compat,
)
from pandas.core.base import (
ExtensionArray,
NoNewAttributesMixin,
PandasObject,
)
import pandas.core.common as com
from pandas.core.construction import (
extract_array,
sanitize_array,
)
from pandas.core.ops.common import unpack_zerodim_and_defer
from pandas.core.sorting import nargsort
from pandas.core.strings.object_array import ObjectStringArrayMixin
from pandas.io.formats import console
if TYPE_CHECKING:
from pandas import Index
CategoricalT = TypeVar("CategoricalT", bound="Categorical")
def _cat_compare_op(op):
opname = f"__{op.__name__}__"
fill_value = True if op is operator.ne else False
@unpack_zerodim_and_defer(opname)
def func(self, other):
hashable = is_hashable(other)
if is_list_like(other) and len(other) != len(self) and not hashable:
# in hashable case we may have a tuple that is itself a category
raise ValueError("Lengths must match.")
if not self.ordered:
if opname in ["__lt__", "__gt__", "__le__", "__ge__"]:
raise TypeError(
"Unordered Categoricals can only compare equality or not"
)
if isinstance(other, Categorical):
# Two Categoricals can only be compared if the categories are
# the same (maybe up to ordering, depending on ordered)
msg = "Categoricals can only be compared if 'categories' are the same."
if not self._categories_match_up_to_permutation(other):
raise TypeError(msg)
if not self.ordered and not self.categories.equals(other.categories):
# both unordered and different order
other_codes = recode_for_categories(
other.codes, other.categories, self.categories, copy=False
)
else:
other_codes = other._codes
ret = op(self._codes, other_codes)
mask = (self._codes == -1) | (other_codes == -1)
if mask.any():
ret[mask] = fill_value
return ret
if hashable:
if other in self.categories:
i = self._unbox_scalar(other)
ret = op(self._codes, i)
if opname not in {"__eq__", "__ge__", "__gt__"}:
# GH#29820 performance trick; get_loc will always give i>=0,
# so in the cases (__ne__, __le__, __lt__) the setting
# here is a no-op, so can be skipped.
mask = self._codes == -1
ret[mask] = fill_value
return ret
else:
return ops.invalid_comparison(self, other, op)
else:
# allow categorical vs object dtype array comparisons for equality
# these are only positional comparisons
if opname not in ["__eq__", "__ne__"]:
raise TypeError(
f"Cannot compare a Categorical for op {opname} with "
f"type {type(other)}.\nIf you want to compare values, "
"use 'np.asarray(cat) <op> other'."
)
if isinstance(other, ExtensionArray) and needs_i8_conversion(other.dtype):
# We would return NotImplemented here, but that messes up
# ExtensionIndex's wrapped methods
return op(other, self)
return getattr(np.array(self), opname)(np.array(other))
func.__name__ = opname
return func
def contains(cat, key, container):
"""
Helper for membership check for ``key`` in ``cat``.
This is a helper method for :method:`__contains__`
and :class:`CategoricalIndex.__contains__`.
Returns True if ``key`` is in ``cat.categories`` and the
location of ``key`` in ``categories`` is in ``container``.
Parameters
----------
cat : :class:`Categorical`or :class:`categoricalIndex`
key : a hashable object
The key to check membership for.
container : Container (e.g. list-like or mapping)
The container to check for membership in.
Returns
-------
is_in : bool
True if ``key`` is in ``self.categories`` and location of
``key`` in ``categories`` is in ``container``, else False.
Notes
-----
This method does not check for NaN values. Do that separately
before calling this method.
"""
hash(key)
# get location of key in categories.
# If a KeyError, the key isn't in categories, so logically
# can't be in container either.
try:
loc = cat.categories.get_loc(key)
except (KeyError, TypeError):
return False
# loc is the location of key in categories, but also the *value*
# for key in container. So, `key` may be in categories,
# but still not in `container`. Example ('b' in categories,
# but not in values):
# 'b' in Categorical(['a'], categories=['a', 'b']) # False
if is_scalar(loc):
return loc in container
else:
# if categories is an IntervalIndex, loc is an array.
return any(loc_ in container for loc_ in loc)
class Categorical(NDArrayBackedExtensionArray, PandasObject, ObjectStringArrayMixin):
"""
Represent a categorical variable in classic R / S-plus fashion.
`Categoricals` can only take on only a limited, and usually fixed, number
of possible values (`categories`). In contrast to statistical categorical
variables, a `Categorical` might have an order, but numerical operations
(additions, divisions, ...) are not possible.
All values of the `Categorical` are either in `categories` or `np.nan`.
Assigning values outside of `categories` will raise a `ValueError`. Order
is defined by the order of the `categories`, not lexical order of the
values.
Parameters
----------
values : list-like
The values of the categorical. If categories are given, values not in
categories will be replaced with NaN.
categories : Index-like (unique), optional
The unique categories for this categorical. If not given, the
categories are assumed to be the unique values of `values` (sorted, if
possible, otherwise in the order in which they appear).
ordered : bool, default False
Whether or not this categorical is treated as a ordered categorical.
If True, the resulting categorical will be ordered.
An ordered categorical respects, when sorted, the order of its
`categories` attribute (which in turn is the `categories` argument, if
provided).
dtype : CategoricalDtype
An instance of ``CategoricalDtype`` to use for this categorical.
Attributes
----------
categories : Index
The categories of this categorical
codes : ndarray
The codes (integer positions, which point to the categories) of this
categorical, read only.
ordered : bool
Whether or not this Categorical is ordered.
dtype : CategoricalDtype
The instance of ``CategoricalDtype`` storing the ``categories``
and ``ordered``.
Methods
-------
from_codes
__array__
Raises
------
ValueError
If the categories do not validate.
TypeError
If an explicit ``ordered=True`` is given but no `categories` and the
`values` are not sortable.
See Also
--------
CategoricalDtype : Type for categorical data.
CategoricalIndex : An Index with an underlying ``Categorical``.
Notes
-----
See the `user guide
<https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>`__
for more.
Examples
--------
>>> pd.Categorical([1, 2, 3, 1, 2, 3])
[1, 2, 3, 1, 2, 3]
Categories (3, int64): [1, 2, 3]
>>> pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'])
['a', 'b', 'c', 'a', 'b', 'c']
Categories (3, object): ['a', 'b', 'c']
Missing values are not included as a category.
>>> c = pd.Categorical([1, 2, 3, 1, 2, 3, np.nan])
>>> c
[1, 2, 3, 1, 2, 3, NaN]
Categories (3, int64): [1, 2, 3]
However, their presence is indicated in the `codes` attribute
by code `-1`.
>>> c.codes
array([ 0, 1, 2, 0, 1, 2, -1], dtype=int8)
Ordered `Categoricals` can be sorted according to the custom order
of the categories and can have a min and max value.
>>> c = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'], ordered=True,
... categories=['c', 'b', 'a'])
>>> c
['a', 'b', 'c', 'a', 'b', 'c']
Categories (3, object): ['c' < 'b' < 'a']
>>> c.min()
'c'
"""
# For comparisons, so that numpy uses our implementation if the compare
# ops, which raise
__array_priority__ = 1000
# tolist is not actually deprecated, just suppressed in the __dir__
_hidden_attrs = PandasObject._hidden_attrs | frozenset(["tolist"])
_typ = "categorical"
_dtype: CategoricalDtype
def __init__(
self,
values,
categories=None,
ordered=None,
dtype: Dtype | None = None,
fastpath: bool = False,
copy: bool = True,
):
dtype = CategoricalDtype._from_values_or_dtype(
values, categories, ordered, dtype
)
# At this point, dtype is always a CategoricalDtype, but
# we may have dtype.categories be None, and we need to
# infer categories in a factorization step further below
if fastpath:
codes = coerce_indexer_dtype(values, dtype.categories)
dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
super().__init__(codes, dtype)
return
if not is_list_like(values):
# GH#38433
warn(
"Allowing scalars in the Categorical constructor is deprecated "
"and will raise in a future version. Use `[value]` instead",
FutureWarning,
stacklevel=find_stack_level(),
)
values = [values]
# null_mask indicates missing values we want to exclude from inference.
# This means: only missing values in list-likes (not arrays/ndframes).
null_mask = np.array(False)
# sanitize input
if is_categorical_dtype(values):
if dtype.categories is None:
dtype = CategoricalDtype(values.categories, dtype.ordered)
elif not isinstance(values, (ABCIndex, ABCSeries, ExtensionArray)):
values = com.convert_to_list_like(values)
if isinstance(values, list) and len(values) == 0:
# By convention, empty lists result in object dtype:
values = np.array([], dtype=object)
elif isinstance(values, np.ndarray):
if values.ndim > 1:
# preempt sanitize_array from raising ValueError
raise NotImplementedError(
"> 1 ndim Categorical are not supported at this time"
)
values = sanitize_array(values, None)
else:
# i.e. must be a list
arr = sanitize_array(values, None)
null_mask = isna(arr)
if null_mask.any():
# We remove null values here, then below will re-insert
# them, grep "full_codes"
arr_list = [values[idx] for idx in np.where(~null_mask)[0]]
# GH#44900 Do not cast to float if we have only missing values
if arr_list or arr.dtype == "object":
sanitize_dtype = None
else:
sanitize_dtype = arr.dtype
arr = sanitize_array(arr_list, None, dtype=sanitize_dtype)
values = arr
if dtype.categories is None:
try:
codes, categories = factorize(values, sort=True)
except TypeError as err:
codes, categories = factorize(values, sort=False)
if dtype.ordered:
# raise, as we don't have a sortable data structure and so
# the user should give us one by specifying categories
raise TypeError(
"'values' is not ordered, please "
"explicitly specify the categories order "
"by passing in a categories argument."
) from err
# we're inferring from values
dtype = CategoricalDtype(categories, dtype.ordered)
elif is_categorical_dtype(values.dtype):
# error: Item "ExtensionArray" of "Union[Any, ExtensionArray]" has no
# attribute "_codes"
old_codes = extract_array(values)._codes # type: ignore[union-attr]
codes = recode_for_categories(
old_codes, values.dtype.categories, dtype.categories, copy=copy
)
else:
codes = _get_codes_for_values(values, dtype.categories)
if null_mask.any():
# Reinsert -1 placeholders for previously removed missing values
full_codes = -np.ones(null_mask.shape, dtype=codes.dtype)
full_codes[~null_mask] = codes
codes = full_codes
dtype = CategoricalDtype(ordered=False).update_dtype(dtype)
arr = coerce_indexer_dtype(codes, dtype.categories)
# error: Argument 1 to "__init__" of "NDArrayBacked" has incompatible
# type "Union[ExtensionArray, ndarray]"; expected "ndarray"
super().__init__(arr, dtype) # type: ignore[arg-type]
@property
def dtype(self) -> CategoricalDtype:
"""
The :class:`~pandas.api.types.CategoricalDtype` for this instance.
"""
return self._dtype
@property
def _constructor(self) -> type[Categorical]:
return Categorical
@classmethod
def _from_sequence(cls, scalars, *, dtype: Dtype | None = None, copy=False):
return Categorical(scalars, dtype=dtype, copy=copy)
@overload
def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
...
@overload
def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
...
@overload
def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
...
def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
"""
Coerce this type to another dtype
Parameters
----------
dtype : numpy dtype or pandas type
copy : bool, default True
By default, astype always returns a newly allocated object.
If copy is set to False and dtype is categorical, the original
object is returned.
"""
dtype = pandas_dtype(dtype)
if self.dtype is dtype:
result = self.copy() if copy else self
elif is_categorical_dtype(dtype):
dtype = cast("Union[str, CategoricalDtype]", dtype)
# GH 10696/18593/18630
dtype = self.dtype.update_dtype(dtype)
self = self.copy() if copy else self
result = self._set_dtype(dtype)
elif isinstance(dtype, ExtensionDtype):
return super().astype(dtype, copy=copy)
elif is_integer_dtype(dtype) and self.isna().any():
raise ValueError("Cannot convert float NaN to integer")
elif len(self.codes) == 0 or len(self.categories) == 0:
result = np.array(
self,
dtype=dtype,
copy=copy,
)
else:
# GH8628 (PERF): astype category codes instead of astyping array
new_cats = self.categories._values
try:
new_cats = new_cats.astype(dtype=dtype, copy=copy)
fill_value = self.categories._na_value
if not is_valid_na_for_dtype(fill_value, dtype):
fill_value = lib.item_from_zerodim(
np.array(self.categories._na_value).astype(dtype)
)
except (
TypeError, # downstream error msg for CategoricalIndex is misleading
ValueError,
):
msg = f"Cannot cast {self.categories.dtype} dtype to {dtype}"
raise ValueError(msg)
result = take_nd(
new_cats, ensure_platform_int(self._codes), fill_value=fill_value
)
return result
@cache_readonly
def itemsize(self) -> int:
"""
return the size of a single category
"""
return self.categories.itemsize
def to_list(self):
"""
Alias for tolist.
"""
return self.tolist()
@classmethod
def _from_inferred_categories(
cls, inferred_categories, inferred_codes, dtype, true_values=None
):
"""
Construct a Categorical from inferred values.
For inferred categories (`dtype` is None) the categories are sorted.
For explicit `dtype`, the `inferred_categories` are cast to the
appropriate type.
Parameters
----------
inferred_categories : Index
inferred_codes : Index
dtype : CategoricalDtype or 'category'
true_values : list, optional
If none are provided, the default ones are
"True", "TRUE", and "true."
Returns
-------
Categorical
"""
from pandas import (
Index,
to_datetime,
to_numeric,
to_timedelta,
)
cats = Index(inferred_categories)
known_categories = (
isinstance(dtype, CategoricalDtype) and dtype.categories is not None
)
if known_categories:
# Convert to a specialized type with `dtype` if specified.
if dtype.categories.is_numeric():
cats = to_numeric(inferred_categories, errors="coerce")
elif is_datetime64_dtype(dtype.categories):
cats = to_datetime(inferred_categories, errors="coerce")
elif is_timedelta64_dtype(dtype.categories):
cats = to_timedelta(inferred_categories, errors="coerce")
elif dtype.categories.is_boolean():
if true_values is None:
true_values = ["True", "TRUE", "true"]
# error: Incompatible types in assignment (expression has type
# "ndarray", variable has type "Index")
cats = cats.isin(true_values) # type: ignore[assignment]
if known_categories:
# Recode from observation order to dtype.categories order.
categories = dtype.categories
codes = recode_for_categories(inferred_codes, cats, categories)
elif not cats.is_monotonic_increasing:
# Sort categories and recode for unknown categories.
unsorted = cats.copy()
categories = cats.sort_values()
codes = recode_for_categories(inferred_codes, unsorted, categories)
dtype = CategoricalDtype(categories, ordered=False)
else:
dtype = CategoricalDtype(cats, ordered=False)
codes = inferred_codes
return cls(codes, dtype=dtype, fastpath=True)
@classmethod
def from_codes(
cls, codes, categories=None, ordered=None, dtype: Dtype | None = None
):
"""
Make a Categorical type from codes and categories or dtype.
This constructor is useful if you already have codes and
categories/dtype and so do not need the (computation intensive)
factorization step, which is usually done on the constructor.
If your data does not follow this convention, please use the normal
constructor.
Parameters
----------
codes : array-like of int
An integer array, where each integer points to a category in
categories or dtype.categories, or else is -1 for NaN.
categories : index-like, optional
The categories for the categorical. Items need to be unique.
If the categories are not given here, then they must be provided
in `dtype`.
ordered : bool, optional
Whether or not this categorical is treated as an ordered
categorical. If not given here or in `dtype`, the resulting
categorical will be unordered.
dtype : CategoricalDtype or "category", optional
If :class:`CategoricalDtype`, cannot be used together with
`categories` or `ordered`.
Returns
-------
Categorical
Examples
--------
>>> dtype = pd.CategoricalDtype(['a', 'b'], ordered=True)
>>> pd.Categorical.from_codes(codes=[0, 1, 0, 1], dtype=dtype)
['a', 'b', 'a', 'b']
Categories (2, object): ['a' < 'b']
"""
dtype = CategoricalDtype._from_values_or_dtype(
categories=categories, ordered=ordered, dtype=dtype
)
if dtype.categories is None:
msg = (
"The categories must be provided in 'categories' or "
"'dtype'. Both were None."
)
raise ValueError(msg)
if is_extension_array_dtype(codes) and is_integer_dtype(codes):
# Avoid the implicit conversion of Int to object
if isna(codes).any():
raise ValueError("codes cannot contain NA values")
codes = codes.to_numpy(dtype=np.int64)
else:
codes = np.asarray(codes)
if len(codes) and not is_integer_dtype(codes):
raise ValueError("codes need to be array-like integers")
if len(codes) and (codes.max() >= len(dtype.categories) or codes.min() < -1):
raise ValueError("codes need to be between -1 and len(categories)-1")
return cls(codes, dtype=dtype, fastpath=True)
# ------------------------------------------------------------------
# Categories/Codes/Ordered
@property
def categories(self):
"""
The categories of this categorical.
Setting assigns new values to each category (effectively a rename of
each individual category).
The assigned value has to be a list-like object. 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.
Assigning to `categories` is a inplace operation!
Raises
------
ValueError
If the new categories do not validate as categories or if the
number of new categories is unequal the number of old categories
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
"""
return self.dtype.categories
@categories.setter
def categories(self, categories):
new_dtype = CategoricalDtype(categories, ordered=self.ordered)
if self.dtype.categories is not None and len(self.dtype.categories) != len(
new_dtype.categories
):
raise ValueError(
"new categories need to have the same number of "
"items as the old categories!"
)
super().__init__(self._ndarray, new_dtype)
@property
def ordered(self) -> Ordered:
"""
Whether the categories have an ordered relationship.
"""
return self.dtype.ordered
@property
def codes(self) -> np.ndarray:
"""
The category codes of this categorical.
Codes are an array of integers which are the positions of the actual
values in the categories array.
There is no setter, use the other categorical methods and the normal item
setter to change values in the categorical.
Returns
-------
ndarray[int]
A non-writable view of the `codes` array.
"""
v = self._codes.view()
v.flags.writeable = False
return v
def _set_categories(self, categories, fastpath=False):
"""
Sets new categories inplace
Parameters
----------
fastpath : bool, default False
Don't perform validation of the categories for uniqueness or nulls
Examples
--------
>>> c = pd.Categorical(['a', 'b'])
>>> c
['a', 'b']
Categories (2, object): ['a', 'b']
>>> c._set_categories(pd.Index(['a', 'c']))
>>> c
['a', 'c']
Categories (2, object): ['a', 'c']
"""
if fastpath:
new_dtype = CategoricalDtype._from_fastpath(categories, self.ordered)
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)
):
raise ValueError(
"new categories need to have the same number of "
"items than the old categories!"
)
super().__init__(self._ndarray, new_dtype)
def _set_dtype(self, dtype: CategoricalDtype) -> Categorical:
"""
Internal method for directly updating the CategoricalDtype
Parameters
----------
dtype : CategoricalDtype
Notes
-----
We don't do any validation here. It's assumed that the dtype is
a (valid) instance of `CategoricalDtype`.
"""
codes = recode_for_categories(self.codes, self.categories, dtype.categories)
return type(self)(codes, dtype=dtype, fastpath=True)
def set_ordered(self, value, inplace=False):
"""
Set the ordered attribute to the boolean value.
Parameters
----------
value : bool
Set whether this categorical is ordered (True) or not (False).
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to the value.
"""
inplace = validate_bool_kwarg(inplace, "inplace")
new_dtype = CategoricalDtype(self.categories, ordered=value)
cat = self if inplace else self.copy()
NDArrayBacked.__init__(cat, cat._ndarray, new_dtype)
if not inplace:
return cat
def as_ordered(self, inplace=False):
"""
Set the Categorical to be ordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to True.
Returns
-------
Categorical or None
Ordered Categorical or None if ``inplace=True``.
"""
inplace = validate_bool_kwarg(inplace, "inplace")
return self.set_ordered(True, inplace=inplace)
def as_unordered(self, inplace=False):
"""
Set the Categorical to be unordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to False.
Returns
-------
Categorical or None
Unordered Categorical or None if ``inplace=True``.
"""
inplace = validate_bool_kwarg(inplace, "inplace")
return self.set_ordered(False, inplace=inplace)
def set_categories(
self, new_categories, ordered=None, rename=False, inplace=no_default
):
"""
Set the categories to the specified new_categories.
`new_categories` can include new categories (which will result in
unused categories) or remove old categories (which results in values
set to NaN). If `rename==True`, the categories will simple be renamed
(less or more items than in old categories will result in values set to
NaN or in unused categories respectively).
This method can be used to perform more than one action of adding,
removing, and reordering simultaneously and is therefore faster than
performing the individual steps via the more specialised methods.
On the other hand this methods does not do checks (e.g., whether the
old categories are included in the new categories on a reorder), which
can result in surprising changes, for example when using special string
dtypes, which does not considers a S1 string equal to a single char
python string.
Parameters
----------
new_categories : Index-like
The categories in new order.
ordered : bool, default False
Whether or not the categorical is treated as a ordered categorical.
If not given, do not change the ordered information.
rename : bool, default False
Whether or not the new_categories should be considered as a rename
of the old categories or as reordered categories.
inplace : bool, default False
Whether or not to reorder the categories in-place or return a copy
of this categorical with reordered categories.
.. deprecated:: 1.3.0
Returns
-------
Categorical with reordered categories or None if inplace.
Raises
------
ValueError
If new_categories does not validate as categories
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
"""
if inplace is not no_default:
warn(
"The `inplace` parameter in pandas.Categorical."
"set_categories is deprecated and will be removed in "
"a future version. Removing unused categories will always "
"return a new Categorical object.",
FutureWarning,
stacklevel=2,
)
else:
inplace = False
inplace = validate_bool_kwarg(inplace, "inplace")
if ordered is None:
ordered = self.dtype.ordered
new_dtype = CategoricalDtype(new_categories, ordered=ordered)
cat = self if inplace else self.copy()
if rename:
if cat.dtype.categories is not None and len(new_dtype.categories) < len(
cat.dtype.categories
):
# remove all _codes which are larger and set to -1/NaN
cat._codes[cat._codes >= len(new_dtype.categories)] = -1
codes = cat._codes
else:
codes = recode_for_categories(
cat.codes, cat.categories, new_dtype.categories
)
NDArrayBacked.__init__(cat, codes, new_dtype)
if not inplace:
return cat
def rename_categories(self, new_categories, inplace=no_default):
"""
Rename categories.
Parameters
----------
new_categories : list-like, dict-like or callable
New categories which will replace old categories.
* list-like: all items must be unique and the number of items in
the new categories must match the existing number of categories.
* dict-like: specifies a mapping from
old categories to new. Categories not contained in the mapping
are passed through and extra categories in the mapping are
ignored.
* callable : a callable that is called on all items in the old
categories and whose return values comprise the new categories.
inplace : bool, default False
Whether or not to rename the categories inplace or return a copy of
this categorical with renamed categories.
.. deprecated:: 1.3.0
Returns
-------
cat : Categorical or None
Categorical with removed categories or None if ``inplace=True``.
Raises
------
ValueError
If new categories are list-like and do not have the same number of
items than the current categories or do not validate as categories
See Also
--------
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> c = pd.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']
You may also provide a callable to create the new categories
>>> c.rename_categories(lambda x: x.upper())
['A', 'A', 'B']
Categories (2, object): ['A', 'B']
"""
if inplace is not no_default:
warn(
"The `inplace` parameter in pandas.Categorical."
"rename_categories is deprecated and will be removed in "
"a future version. Removing unused categories will always "
"return a new Categorical object.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
inplace = False
inplace = validate_bool_kwarg(inplace, "inplace")
cat = self if inplace else self.copy()
if is_dict_like(new_categories):
cat.categories = [new_categories.get(item, item) for item in cat.categories]
elif callable(new_categories):
cat.categories = [new_categories(item) for item in cat.categories]
else:
cat.categories = new_categories
if not inplace:
return cat
def reorder_categories(self, new_categories, ordered=None, inplace=no_default):
"""
Reorder categories as specified in new_categories.
`new_categories` need to include all old categories and no new category
items.
Parameters
----------
new_categories : Index-like
The categories in new order.
ordered : bool, optional
Whether or not the categorical is treated as a ordered categorical.
If not given, do not change the ordered information.
inplace : bool, default False
Whether or not to reorder the categories inplace or return a copy of
this categorical with reordered categories.
.. deprecated:: 1.3.0
Returns
-------
cat : Categorical or None
Categorical with removed categories or None if ``inplace=True``.
Raises
------
ValueError
If the new categories do not contain all old category items or any
new ones
See Also
--------
rename_categories : Rename categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
"""
if inplace is not no_default:
warn(
"The `inplace` parameter in pandas.Categorical."
"reorder_categories is deprecated and will be removed in "
"a future version. Reordering categories will always "
"return a new Categorical object.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
inplace = False
inplace = validate_bool_kwarg(inplace, "inplace")
if set(self.dtype.categories) != set(new_categories):
raise ValueError(
"items in new_categories are not the same as in old categories"
)
with catch_warnings():
simplefilter("ignore")
return self.set_categories(new_categories, ordered=ordered, inplace=inplace)
def add_categories(self, new_categories, inplace=no_default):
"""
Add new categories.
`new_categories` will be included at the last/highest place in the
categories and will be unused directly after this call.
Parameters
----------
new_categories : category or list-like of category
The new categories to be included.
inplace : bool, default False
Whether or not to add the categories inplace or return a copy of
this categorical with added categories.
.. deprecated:: 1.3.0
Returns
-------
cat : Categorical or None
Categorical with new categories added or None if ``inplace=True``.
Raises
------
ValueError
If the new categories include old categories or do not validate as
categories
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
remove_categories : Remove the specified categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> c = pd.Categorical(['c', 'b', 'c'])
>>> c
['c', 'b', 'c']
Categories (2, object): ['b', 'c']
>>> c.add_categories(['d', 'a'])
['c', 'b', 'c']
Categories (4, object): ['b', 'c', 'd', 'a']
"""
if inplace is not no_default:
warn(
"The `inplace` parameter in pandas.Categorical."
"add_categories is deprecated and will be removed in "
"a future version. Removing unused categories will always "
"return a new Categorical object.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
inplace = False
inplace = validate_bool_kwarg(inplace, "inplace")
if not is_list_like(new_categories):
new_categories = [new_categories]
already_included = set(new_categories) & set(self.dtype.categories)
if len(already_included) != 0:
raise ValueError(
f"new categories must not include old categories: {already_included}"
)
new_categories = list(self.dtype.categories) + list(new_categories)
new_dtype = CategoricalDtype(new_categories, self.ordered)
cat = self if inplace else self.copy()
codes = coerce_indexer_dtype(cat._ndarray, new_dtype.categories)
NDArrayBacked.__init__(cat, codes, new_dtype)
if not inplace:
return cat
def remove_categories(self, removals, inplace=no_default):
"""
Remove the specified categories.
`removals` must be included in the old categories. Values which were in
the removed categories will be set to NaN
Parameters
----------
removals : category or list of categories
The categories which should be removed.
inplace : bool, default False
Whether or not to remove the categories inplace or return a copy of
this categorical with removed categories.
.. deprecated:: 1.3.0
Returns
-------
cat : Categorical or None
Categorical with removed categories or None if ``inplace=True``.
Raises
------
ValueError
If the removals are not contained in the categories
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_unused_categories : Remove categories which are not used.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> c = pd.Categorical(['a', 'c', 'b', 'c', 'd'])
>>> c
['a', 'c', 'b', 'c', 'd']
Categories (4, object): ['a', 'b', 'c', 'd']
>>> c.remove_categories(['d', 'a'])
[NaN, 'c', 'b', 'c', NaN]
Categories (2, object): ['b', 'c']
"""
if inplace is not no_default:
warn(
"The `inplace` parameter in pandas.Categorical."
"remove_categories is deprecated and will be removed in "
"a future version. Removing unused categories will always "
"return a new Categorical object.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
inplace = False
inplace = validate_bool_kwarg(inplace, "inplace")
if not is_list_like(removals):
removals = [removals]
removal_set = set(removals)
not_included = removal_set - set(self.dtype.categories)
new_categories = [c for c in self.dtype.categories if c not in removal_set]
# GH 10156
if any(isna(removals)):
not_included = {x for x in not_included if notna(x)}
new_categories = [x for x in new_categories if notna(x)]
if len(not_included) != 0:
raise ValueError(f"removals must all be in old categories: {not_included}")
with catch_warnings():
simplefilter("ignore")
return self.set_categories(
new_categories, ordered=self.ordered, rename=False, inplace=inplace
)
def remove_unused_categories(self, inplace=no_default):
"""
Remove categories which are not used.
Parameters
----------
inplace : bool, default False
Whether or not to drop unused categories inplace or return a copy of
this categorical with unused categories dropped.
.. deprecated:: 1.2.0
Returns
-------
cat : Categorical or None
Categorical with unused categories dropped or None if ``inplace=True``.
See Also
--------
rename_categories : Rename categories.
reorder_categories : Reorder categories.
add_categories : Add new categories.
remove_categories : Remove the specified categories.
set_categories : Set the categories to the specified ones.
Examples
--------
>>> c = pd.Categorical(['a', 'c', 'b', 'c', 'd'])
>>> c
['a', 'c', 'b', 'c', 'd']
Categories (4, object): ['a', 'b', 'c', 'd']
>>> c[2] = 'a'
>>> c[4] = 'c'
>>> c
['a', 'c', 'a', 'c', 'c']
Categories (4, object): ['a', 'b', 'c', 'd']
>>> c.remove_unused_categories()
['a', 'c', 'a', 'c', 'c']
Categories (2, object): ['a', 'c']
"""
if inplace is not no_default:
warn(
"The `inplace` parameter in pandas.Categorical."
"remove_unused_categories is deprecated and "
"will be removed in a future version.",
FutureWarning,
stacklevel=find_stack_level(),
)
else:
inplace = False
inplace = validate_bool_kwarg(inplace, "inplace")
cat = self if inplace else self.copy()
idx, inv = np.unique(cat._codes, return_inverse=True)
if idx.size != 0 and idx[0] == -1: # na sentinel
idx, inv = idx[1:], inv - 1
new_categories = cat.dtype.categories.take(idx)
new_dtype = CategoricalDtype._from_fastpath(
new_categories, ordered=self.ordered
)
new_codes = coerce_indexer_dtype(inv, new_dtype.categories)
NDArrayBacked.__init__(cat, new_codes, new_dtype)
if not inplace:
return cat
# ------------------------------------------------------------------
def map(self, mapper):
"""
Map categories using an input mapping or function.
Maps the categories to new categories. If the mapping correspondence is
one-to-one the result is a :class:`~pandas.Categorical` which has the
same order property as the original, otherwise a :class:`~pandas.Index`
is returned. NaN values are unaffected.
If a `dict` or :class:`~pandas.Series` is used any unmapped category is
mapped to `NaN`. Note that if this happens an :class:`~pandas.Index`
will be returned.
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
Returns
-------
pandas.Categorical or pandas.Index
Mapped categorical.
See Also
--------
CategoricalIndex.map : Apply a mapping correspondence on a
:class:`~pandas.CategoricalIndex`.
Index.map : Apply a mapping correspondence on an
:class:`~pandas.Index`.
Series.map : Apply a mapping correspondence on a
:class:`~pandas.Series`.
Series.apply : Apply more complex functions on a
:class:`~pandas.Series`.
Examples
--------
>>> cat = pd.Categorical(['a', 'b', 'c'])
>>> cat
['a', 'b', 'c']
Categories (3, object): ['a', 'b', 'c']
>>> cat.map(lambda x: x.upper())
['A', 'B', 'C']
Categories (3, object): ['A', 'B', 'C']
>>> cat.map({'a': 'first', 'b': 'second', 'c': 'third'})
['first', 'second', 'third']
Categories (3, object): ['first', 'second', 'third']
If the mapping is one-to-one the ordering of the categories is
preserved:
>>> cat = pd.Categorical(['a', 'b', 'c'], ordered=True)
>>> cat
['a', 'b', 'c']
Categories (3, object): ['a' < 'b' < 'c']
>>> cat.map({'a': 3, 'b': 2, 'c': 1})
[3, 2, 1]
Categories (3, int64): [3 < 2 < 1]
If the mapping is not one-to-one an :class:`~pandas.Index` is returned:
>>> cat.map({'a': 'first', 'b': 'second', 'c': 'first'})
Index(['first', 'second', 'first'], dtype='object')
If a `dict` is used, all unmapped categories are mapped to `NaN` and
the result is an :class:`~pandas.Index`:
>>> cat.map({'a': 'first', 'b': 'second'})
Index(['first', 'second', nan], dtype='object')
"""
new_categories = self.categories.map(mapper)
try:
return self.from_codes(
self._codes.copy(), categories=new_categories, ordered=self.ordered
)
except ValueError:
# NA values are represented in self._codes with -1
# np.take causes NA values to take final element in new_categories
if np.any(self._codes == -1):
new_categories = new_categories.insert(len(new_categories), np.nan)
return np.take(new_categories, self._codes)
__eq__ = _cat_compare_op(operator.eq)
__ne__ = _cat_compare_op(operator.ne)
__lt__ = _cat_compare_op(operator.lt)
__gt__ = _cat_compare_op(operator.gt)
__le__ = _cat_compare_op(operator.le)
__ge__ = _cat_compare_op(operator.ge)
# -------------------------------------------------------------
# Validators; ideally these can be de-duplicated
def _validate_setitem_value(self, value):
if not is_hashable(value):
# wrap scalars and hashable-listlikes in list
return self._validate_listlike(value)
else:
return self._validate_scalar(value)
_validate_searchsorted_value = _validate_setitem_value
def _validate_scalar(self, fill_value):
"""
Convert a user-facing fill_value to a representation to use with our
underlying ndarray, raising TypeError if this is not possible.
Parameters
----------
fill_value : object
Returns
-------
fill_value : int
Raises
------
TypeError
"""
if is_valid_na_for_dtype(fill_value, self.categories.dtype):
fill_value = -1
elif fill_value in self.categories:
fill_value = self._unbox_scalar(fill_value)
else:
raise TypeError(
"Cannot setitem on a Categorical with a new "
f"category ({fill_value}), set the categories first"
)
return fill_value
# -------------------------------------------------------------
@ravel_compat
def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
"""
The numpy array interface.
Returns
-------
numpy.array
A numpy array of either the specified dtype or,
if dtype==None (default), the same dtype as
categorical.categories.dtype.
"""
ret = take_nd(self.categories._values, self._codes)
if dtype and not is_dtype_equal(dtype, self.categories.dtype):
return np.asarray(ret, dtype)
# When we're a Categorical[ExtensionArray], like Interval,
# we need to ensure __array__ gets all the way to an
# ndarray.
return np.asarray(ret)
def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
# for binary ops, use our custom dunder methods
result = ops.maybe_dispatch_ufunc_to_dunder_op(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
if method == "reduce":
# e.g. TestCategoricalAnalytics::test_min_max_ordered
result = arraylike.dispatch_reduction_ufunc(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
# for all other cases, raise for now (similarly as what happens in
# Series.__array_prepare__)
raise TypeError(
f"Object with dtype {self.dtype} cannot perform "
f"the numpy op {ufunc.__name__}"
)
def __setstate__(self, state):
"""Necessary for making this object picklable"""
if not isinstance(state, dict):
return super().__setstate__(state)
if "_dtype" not in state:
state["_dtype"] = CategoricalDtype(state["_categories"], state["_ordered"])
if "_codes" in state and "_ndarray" not in state:
# backward compat, changed what is property vs attribute
state["_ndarray"] = state.pop("_codes")
super().__setstate__(state)
@property
def nbytes(self) -> int:
return self._codes.nbytes + self.dtype.categories.values.nbytes
def memory_usage(self, deep: bool = False) -> int:
"""
Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False
See Also
--------
numpy.ndarray.nbytes
"""
return self._codes.nbytes + self.dtype.categories.memory_usage(deep=deep)
def isna(self) -> np.ndarray:
"""
Detect missing values
Missing values (-1 in .codes) are detected.
Returns
-------
np.ndarray[bool] of whether my values are null
See Also
--------
isna : Top-level isna.
isnull : Alias of isna.
Categorical.notna : Boolean inverse of Categorical.isna.
"""
return self._codes == -1
isnull = isna
def notna(self) -> np.ndarray:
"""
Inverse of isna
Both missing values (-1 in .codes) and NA as a category are detected as
null.
Returns
-------
np.ndarray[bool] of whether my values are not null
See Also
--------
notna : Top-level notna.
notnull : Alias of notna.
Categorical.isna : Boolean inverse of Categorical.notna.
"""
return ~self.isna()
notnull = notna
def value_counts(self, dropna: bool = True):
"""
Return a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : bool, default True
Don't include counts of NaN.
Returns
-------
counts : Series
See Also
--------
Series.value_counts
"""
from pandas import (
CategoricalIndex,
Series,
)
code, cat = self._codes, self.categories
ncat, mask = (len(cat), code >= 0)
ix, clean = np.arange(ncat), mask.all()
if dropna or clean:
obs = code if clean else code[mask]
count = np.bincount(obs, minlength=ncat or 0)
else:
count = np.bincount(np.where(mask, code, ncat))
ix = np.append(ix, -1)
ix = coerce_indexer_dtype(ix, self.dtype.categories)
ix = self._from_backing_data(ix)
return Series(count, index=CategoricalIndex(ix), dtype="int64")
# error: Argument 2 of "_empty" is incompatible with supertype
# "NDArrayBackedExtensionArray"; supertype defines the argument type as
# "ExtensionDtype"
@classmethod
def _empty( # type: ignore[override]
cls: type_t[Categorical], shape: Shape, dtype: CategoricalDtype
) -> Categorical:
"""
Analogous to np.empty(shape, dtype=dtype)
Parameters
----------
shape : tuple[int]
dtype : CategoricalDtype
"""
arr = cls._from_sequence([], dtype=dtype)
# We have to use np.zeros instead of np.empty otherwise the resulting
# ndarray may contain codes not supported by this dtype, in which
# case repr(result) could segfault.
backing = np.zeros(shape, dtype=arr._ndarray.dtype)
return arr._from_backing_data(backing)
def _internal_get_values(self):
"""
Return the values.
For internal compatibility with pandas formatting.
Returns
-------
np.ndarray or Index
A numpy array of the same dtype as categorical.categories.dtype or
Index if datetime / periods.
"""
# if we are a datetime and period index, return Index to keep metadata
if needs_i8_conversion(self.categories.dtype):
return self.categories.take(self._codes, fill_value=NaT)
elif is_integer_dtype(self.categories) and -1 in self._codes:
return self.categories.astype("object").take(self._codes, fill_value=np.nan)
return np.array(self)
def check_for_ordered(self, op):
"""assert that we are ordered"""
if not self.ordered:
raise TypeError(
f"Categorical is not ordered for operation {op}\n"
"you can use .as_ordered() to change the "
"Categorical to an ordered one\n"
)
def argsort(self, ascending=True, kind="quicksort", **kwargs):
"""
Return the indices that would sort the Categorical.
.. versionchanged:: 0.25.0
Changed to sort missing values at the end.
Parameters
----------
ascending : bool, default True
Whether the indices should result in an ascending
or descending sort.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
Sorting algorithm.
**kwargs:
passed through to :func:`numpy.argsort`.
Returns
-------
np.ndarray[np.intp]
See Also
--------
numpy.ndarray.argsort
Notes
-----
While an ordering is applied to the category values, arg-sorting
in this context refers more to organizing and grouping together
based on matching category values. Thus, this function can be
called on an unordered Categorical instance unlike the functions
'Categorical.min' and 'Categorical.max'.
Examples
--------
>>> pd.Categorical(['b', 'b', 'a', 'c']).argsort()
array([2, 0, 1, 3])
>>> cat = pd.Categorical(['b', 'b', 'a', 'c'],
... categories=['c', 'b', 'a'],
... ordered=True)
>>> cat.argsort()
array([3, 0, 1, 2])
Missing values are placed at the end
>>> cat = pd.Categorical([2, None, 1])
>>> cat.argsort()
array([2, 0, 1])
"""
return super().argsort(ascending=ascending, kind=kind, **kwargs)
def sort_values(
self, inplace: bool = False, ascending: bool = True, na_position: str = "last"
):
"""
Sort the Categorical by category value returning a new
Categorical by default.
While an ordering is applied to the category values, sorting in this
context refers more to organizing and grouping together based on
matching category values. Thus, this function can be called on an
unordered Categorical instance unlike the functions 'Categorical.min'
and 'Categorical.max'.
Parameters
----------
inplace : bool, default False
Do operation in place.
ascending : bool, default True
Order ascending. Passing False orders descending. The
ordering parameter provides the method by which the
category values are organized.
na_position : {'first', 'last'} (optional, default='last')
'first' puts NaNs at the beginning
'last' puts NaNs at the end
Returns
-------
Categorical or None
See Also
--------
Categorical.sort
Series.sort_values
Examples
--------
>>> c = pd.Categorical([1, 2, 2, 1, 5])
>>> c
[1, 2, 2, 1, 5]
Categories (3, int64): [1, 2, 5]
>>> c.sort_values()
[1, 1, 2, 2, 5]
Categories (3, int64): [1, 2, 5]
>>> c.sort_values(ascending=False)
[5, 2, 2, 1, 1]
Categories (3, int64): [1, 2, 5]
Inplace sorting can be done as well:
>>> c.sort_values(inplace=True)
>>> c
[1, 1, 2, 2, 5]
Categories (3, int64): [1, 2, 5]
>>>
>>> c = pd.Categorical([1, 2, 2, 1, 5])
'sort_values' behaviour with NaNs. Note that 'na_position'
is independent of the 'ascending' parameter:
>>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5])
>>> c
[NaN, 2, 2, NaN, 5]
Categories (2, int64): [2, 5]
>>> c.sort_values()
[2, 2, 5, NaN, NaN]
Categories (2, int64): [2, 5]
>>> c.sort_values(ascending=False)
[5, 2, 2, NaN, NaN]
Categories (2, int64): [2, 5]
>>> c.sort_values(na_position='first')
[NaN, NaN, 2, 2, 5]
Categories (2, int64): [2, 5]
>>> c.sort_values(ascending=False, na_position='first')
[NaN, NaN, 5, 2, 2]
Categories (2, int64): [2, 5]
"""
inplace = validate_bool_kwarg(inplace, "inplace")
if na_position not in ["last", "first"]:
raise ValueError(f"invalid na_position: {repr(na_position)}")
sorted_idx = nargsort(self, ascending=ascending, na_position=na_position)
if inplace:
self._codes[:] = self._codes[sorted_idx]
else:
codes = self._codes[sorted_idx]
return self._from_backing_data(codes)
def _rank(
self,
*,
axis: int = 0,
method: str = "average",
na_option: str = "keep",
ascending: bool = True,
pct: bool = False,
):
"""
See Series.rank.__doc__.
"""
if axis != 0:
raise NotImplementedError
vff = self._values_for_rank()
return algorithms.rank(
vff,
axis=axis,
method=method,
na_option=na_option,
ascending=ascending,
pct=pct,
)
def _values_for_rank(self):
"""
For correctly ranking ordered categorical data. See GH#15420
Ordered categorical data should be ranked on the basis of
codes with -1 translated to NaN.
Returns
-------
numpy.array
"""
from pandas import Series
if self.ordered:
values = self.codes
mask = values == -1
if mask.any():
values = values.astype("float64")
values[mask] = np.nan
elif self.categories.is_numeric():
values = np.array(self)
else:
# reorder the categories (so rank can use the float codes)
# instead of passing an object array to rank
values = np.array(
self.rename_categories(Series(self.categories).rank().values)
)
return values
def view(self, dtype=None):
if dtype is not None:
raise NotImplementedError(dtype)
return self._from_backing_data(self._ndarray)
def to_dense(self) -> np.ndarray:
"""
Return my 'dense' representation
For internal compatibility with numpy arrays.
Returns
-------
dense : array
"""
warn(
"Categorical.to_dense is deprecated and will be removed in "
"a future version. Use np.asarray(cat) instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return np.asarray(self)
# ------------------------------------------------------------------
# NDArrayBackedExtensionArray compat
@property
def _codes(self) -> np.ndarray:
return self._ndarray
@_codes.setter
def _codes(self, value: np.ndarray):
warn(
"Setting the codes on a Categorical is deprecated and will raise in "
"a future version. Create a new Categorical object instead",
FutureWarning,
stacklevel=find_stack_level(),
) # GH#40606
NDArrayBacked.__init__(self, value, self.dtype)
def _box_func(self, i: int):
if i == -1:
return np.NaN
return self.categories[i]
def _unbox_scalar(self, key) -> int:
# searchsorted is very performance sensitive. By converting codes
# to same dtype as self.codes, we get much faster performance.
code = self.categories.get_loc(key)
code = self._ndarray.dtype.type(code)
return code
# ------------------------------------------------------------------
def take_nd(self, indexer, allow_fill: bool = False, fill_value=None):
# GH#27745 deprecate alias that other EAs dont have
warn(
"Categorical.take_nd is deprecated, use Categorical.take instead",
FutureWarning,
stacklevel=find_stack_level(),
)
return self.take(indexer, allow_fill=allow_fill, fill_value=fill_value)
def __iter__(self):
"""
Returns an Iterator over the values of this Categorical.
"""
if self.ndim == 1:
return iter(self._internal_get_values().tolist())
else:
return (self[n] for n in range(len(self)))
def __contains__(self, key) -> bool:
"""
Returns True if `key` is in this Categorical.
"""
# if key is a NaN, check if any NaN is in self.
if is_valid_na_for_dtype(key, self.categories.dtype):
return bool(self.isna().any())
return contains(self, key, container=self._codes)
# ------------------------------------------------------------------
# Rendering Methods
def _formatter(self, boxed: bool = False):
# Defer to CategoricalFormatter's formatter.
return None
def _tidy_repr(self, max_vals: int = 10, footer: bool = True) -> str:
"""
a short repr displaying only max_vals and an optional (but default
footer)
"""
num = max_vals // 2
head = self[:num]._get_repr(length=False, footer=False)
tail = self[-(max_vals - num) :]._get_repr(length=False, footer=False)
result = f"{head[:-1]}, ..., {tail[1:]}"
if footer:
result = f"{result}\n{self._repr_footer()}"
return str(result)
def _repr_categories(self) -> list[str]:
"""
return the base repr for the categories
"""
max_categories = (
10
if get_option("display.max_categories") == 0
else get_option("display.max_categories")
)
from pandas.io.formats import format as fmt
format_array = partial(
fmt.format_array, formatter=None, quoting=QUOTE_NONNUMERIC
)
if len(self.categories) > max_categories:
num = max_categories // 2
head = format_array(self.categories[:num])
tail = format_array(self.categories[-num:])
category_strs = head + ["..."] + tail
else:
category_strs = format_array(self.categories)
# Strip all leading spaces, which format_array adds for columns...
category_strs = [x.strip() for x in category_strs]
return category_strs
def _repr_categories_info(self) -> str:
"""
Returns a string representation of the footer.
"""
category_strs = self._repr_categories()
dtype = str(self.categories.dtype)
levheader = f"Categories ({len(self.categories)}, {dtype}): "
width, height = get_terminal_size()
max_width = get_option("display.width") or width
if console.in_ipython_frontend():
# 0 = no breaks
max_width = 0
levstring = ""
start = True
cur_col_len = len(levheader) # header
sep_len, sep = (3, " < ") if self.ordered else (2, ", ")
linesep = sep.rstrip() + "\n" # remove whitespace
for val in category_strs:
if max_width != 0 and cur_col_len + sep_len + len(val) > max_width:
levstring += linesep + (" " * (len(levheader) + 1))
cur_col_len = len(levheader) + 1 # header + a whitespace
elif not start:
levstring += sep
cur_col_len += len(val)
levstring += val
start = False
# replace to simple save space by
return levheader + "[" + levstring.replace(" < ... < ", " ... ") + "]"
def _repr_footer(self) -> str:
info = self._repr_categories_info()
return f"Length: {len(self)}\n{info}"
def _get_repr(self, length: bool = True, na_rep="NaN", footer: bool = True) -> str:
from pandas.io.formats import format as fmt
formatter = fmt.CategoricalFormatter(
self, length=length, na_rep=na_rep, footer=footer
)
result = formatter.to_string()
return str(result)
def __repr__(self) -> str:
"""
String representation.
"""
_maxlen = 10
if len(self._codes) > _maxlen:
result = self._tidy_repr(_maxlen)
elif len(self._codes) > 0:
result = self._get_repr(length=len(self) > _maxlen)
else:
msg = self._get_repr(length=False, footer=True).replace("\n", ", ")
result = f"[], {msg}"
return result
# ------------------------------------------------------------------
def _validate_listlike(self, value):
# NB: here we assume scalar-like tuples have already been excluded
value = extract_array(value, extract_numpy=True)
# require identical categories set
if isinstance(value, Categorical):
if not is_dtype_equal(self.dtype, value.dtype):
raise TypeError(
"Cannot set a Categorical with another, "
"without identical categories"
)
# is_dtype_equal implies categories_match_up_to_permutation
value = self._encode_with_my_categories(value)
return value._codes
from pandas import Index
# tupleize_cols=False for e.g. test_fillna_iterable_category GH#41914
to_add = Index._with_infer(value, tupleize_cols=False).difference(
self.categories
)
# no assignments of values not in categories, but it's always ok to set
# something to np.nan
if len(to_add) and not isna(to_add).all():
raise TypeError(
"Cannot setitem on a Categorical with a new "
"category, set the categories first"
)
codes = self.categories.get_indexer(value)
return codes.astype(self._ndarray.dtype, copy=False)
def _reverse_indexer(self) -> dict[Hashable, npt.NDArray[np.intp]]:
"""
Compute the inverse of a categorical, returning
a dict of categories -> indexers.
*This is an internal function*
Returns
-------
Dict[Hashable, np.ndarray[np.intp]]
dict of categories -> indexers
Examples
--------
>>> c = pd.Categorical(list('aabca'))
>>> c
['a', 'a', 'b', 'c', 'a']
Categories (3, object): ['a', 'b', 'c']
>>> c.categories
Index(['a', 'b', 'c'], dtype='object')
>>> c.codes
array([0, 0, 1, 2, 0], dtype=int8)
>>> c._reverse_indexer()
{'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}
"""
categories = self.categories
r, counts = libalgos.groupsort_indexer(
ensure_platform_int(self.codes), categories.size
)
counts = ensure_int64(counts).cumsum()
_result = (r[start:end] for start, end in zip(counts, counts[1:]))
return dict(zip(categories, _result))
# ------------------------------------------------------------------
# Reductions
@deprecate_kwarg(old_arg_name="numeric_only", new_arg_name="skipna")
def min(self, *, skipna=True, **kwargs):
"""
The minimum value of the object.
Only ordered `Categoricals` have a minimum!
.. versionchanged:: 1.0.0
Returns an NA value on empty arrays
Raises
------
TypeError
If the `Categorical` is not `ordered`.
Returns
-------
min : the minimum of this `Categorical`
"""
nv.validate_minmax_axis(kwargs.get("axis", 0))
nv.validate_min((), kwargs)
self.check_for_ordered("min")
if not len(self._codes):
return self.dtype.na_value
good = self._codes != -1
if not good.all():
if skipna and good.any():
pointer = self._codes[good].min()
else:
return np.nan
else:
pointer = self._codes.min()
return self._wrap_reduction_result(None, pointer)
@deprecate_kwarg(old_arg_name="numeric_only", new_arg_name="skipna")
def max(self, *, skipna=True, **kwargs):
"""
The maximum value of the object.
Only ordered `Categoricals` have a maximum!
.. versionchanged:: 1.0.0
Returns an NA value on empty arrays
Raises
------
TypeError
If the `Categorical` is not `ordered`.
Returns
-------
max : the maximum of this `Categorical`
"""
nv.validate_minmax_axis(kwargs.get("axis", 0))
nv.validate_max((), kwargs)
self.check_for_ordered("max")
if not len(self._codes):
return self.dtype.na_value
good = self._codes != -1
if not good.all():
if skipna and good.any():
pointer = self._codes[good].max()
else:
return np.nan
else:
pointer = self._codes.max()
return self._wrap_reduction_result(None, pointer)
def mode(self, dropna: bool = True) -> Categorical:
"""
Returns the mode(s) of the Categorical.
Always returns `Categorical` even if only one value.
Parameters
----------
dropna : bool, default True
Don't consider counts of NaN/NaT.
Returns
-------
modes : `Categorical` (sorted)
"""
warn(
"Categorical.mode is deprecated and will be removed in a future version. "
"Use Series.mode instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self._mode(dropna=dropna)
def _mode(self, dropna: bool = True) -> Categorical:
codes = self._codes
if dropna:
good = self._codes != -1
codes = self._codes[good]
codes = htable.mode(codes, dropna)
codes.sort()
codes = coerce_indexer_dtype(codes, self.dtype.categories)
return self._from_backing_data(codes)
# ------------------------------------------------------------------
# ExtensionArray Interface
def unique(self):
"""
Return the ``Categorical`` which ``categories`` and ``codes`` are
unique.
.. versionchanged:: 1.3.0
Previously, unused categories were dropped from the new categories.
Returns
-------
Categorical
See Also
--------
pandas.unique
CategoricalIndex.unique
Series.unique : Return unique values of Series object.
Examples
--------
>>> pd.Categorical(list("baabc")).unique()
['b', 'a', 'c']
Categories (3, object): ['a', 'b', 'c']
>>> pd.Categorical(list("baab"), categories=list("abc"), ordered=True).unique()
['b', 'a']
Categories (3, object): ['a' < 'b' < 'c']
"""
unique_codes = unique1d(self.codes)
return self._from_backing_data(unique_codes)
def _values_for_factorize(self):
return self._ndarray, -1
@classmethod
def _from_factorized(cls, uniques, original):
return original._constructor(
original.categories.take(uniques), dtype=original.dtype
)
def equals(self, other: object) -> bool:
"""
Returns True if categorical arrays are equal.
Parameters
----------
other : `Categorical`
Returns
-------
bool
"""
if not isinstance(other, Categorical):
return False
elif self._categories_match_up_to_permutation(other):
other = self._encode_with_my_categories(other)
return np.array_equal(self._codes, other._codes)
return False
@classmethod
def _concat_same_type(
cls: type[CategoricalT], to_concat: Sequence[CategoricalT], axis: int = 0
) -> CategoricalT:
from pandas.core.dtypes.concat import union_categoricals
first = to_concat[0]
if axis >= first.ndim:
raise ValueError(
f"axis {axis} is out of bounds for array of dimension {first.ndim}"
)
if axis == 1:
# Flatten, concatenate then reshape
if not all(x.ndim == 2 for x in to_concat):
raise ValueError
# pass correctly-shaped to union_categoricals
tc_flat = []
for obj in to_concat:
tc_flat.extend([obj[:, i] for i in range(obj.shape[1])])
res_flat = cls._concat_same_type(tc_flat, axis=0)
result = res_flat.reshape(len(first), -1, order="F")
return result
result = union_categoricals(to_concat)
return result
# ------------------------------------------------------------------
def _encode_with_my_categories(self, other: Categorical) -> Categorical:
"""
Re-encode another categorical using this Categorical's categories.
Notes
-----
This assumes we have already checked
self._categories_match_up_to_permutation(other).
"""
# Indexing on codes is more efficient if categories are the same,
# so we can apply some optimizations based on the degree of
# dtype-matching.
codes = recode_for_categories(
other.codes, other.categories, self.categories, copy=False
)
return self._from_backing_data(codes)
def _categories_match_up_to_permutation(self, other: Categorical) -> bool:
"""
Returns True if categoricals are the same dtype
same categories, and same ordered
Parameters
----------
other : Categorical
Returns
-------
bool
"""
return hash(self.dtype) == hash(other.dtype)
def is_dtype_equal(self, other) -> bool:
warn(
"Categorical.is_dtype_equal is deprecated and will be removed "
"in a future version",
FutureWarning,
stacklevel=find_stack_level(),
)
try:
return self._categories_match_up_to_permutation(other)
except (AttributeError, TypeError):
return False
def describe(self):
"""
Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category.
"""
counts = self.value_counts(dropna=False)
freqs = counts / counts.sum()
from pandas import Index
from pandas.core.reshape.concat import concat
result = concat([counts, freqs], axis=1)
result.columns = Index(["counts", "freqs"])
result.index.name = "categories"
return result
def isin(self, values) -> npt.NDArray[np.bool_]:
"""
Check whether `values` are contained in Categorical.
Return a boolean NumPy Array showing whether each element in
the Categorical matches an element in the passed sequence of
`values` exactly.
Parameters
----------
values : set or list-like
The sequence of values to test. Passing in a single string will
raise a ``TypeError``. Instead, turn a single string into a
list of one element.
Returns
-------
np.ndarray[bool]
Raises
------
TypeError
* If `values` is not a set or list-like
See Also
--------
pandas.Series.isin : Equivalent method on Series.
Examples
--------
>>> s = pd.Categorical(['lama', 'cow', 'lama', 'beetle', 'lama',
... 'hippo'])
>>> s.isin(['cow', 'lama'])
array([ True, True, True, False, True, False])
Passing a single string as ``s.isin('lama')`` will raise an error. Use
a list of one element instead:
>>> s.isin(['lama'])
array([ True, False, True, False, True, False])
"""
if not is_list_like(values):
values_type = type(values).__name__
raise TypeError(
"only list-like objects are allowed to be passed "
f"to isin(), you passed a [{values_type}]"
)
values = sanitize_array(values, None, None)
null_mask = np.asarray(isna(values))
code_values = self.categories.get_indexer(values)
code_values = code_values[null_mask | (code_values >= 0)]
return algorithms.isin(self.codes, code_values)
def replace(self, to_replace, value, inplace: bool = False):
"""
Replaces all instances of one value with another
Parameters
----------
to_replace: object
The value to be replaced
value: object
The value to replace it with
inplace: bool
Whether the operation is done in-place
Returns
-------
None if inplace is True, otherwise the new Categorical after replacement
Examples
--------
>>> s = pd.Categorical([1, 2, 1, 3])
>>> s.replace(1, 3)
[3, 2, 3, 3]
Categories (2, int64): [2, 3]
"""
# GH#44929 deprecation
warn(
"Categorical.replace is deprecated and will be removed in a future "
"version. Use Series.replace directly instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return self._replace(to_replace=to_replace, value=value, inplace=inplace)
def _replace(self, *, to_replace, value, inplace: bool = False):
inplace = validate_bool_kwarg(inplace, "inplace")
cat = self if inplace else self.copy()
# build a dict of (to replace -> value) pairs
if is_list_like(to_replace):
# if to_replace is list-like and value is scalar
replace_dict = {replace_value: value for replace_value in to_replace}
else:
# if both to_replace and value are scalar
replace_dict = {to_replace: value}
# other cases, like if both to_replace and value are list-like or if
# to_replace is a dict, are handled separately in NDFrame
for replace_value, new_value in replace_dict.items():
if new_value == replace_value:
continue
if replace_value in cat.categories:
if isna(new_value):
with catch_warnings():
simplefilter("ignore")
cat.remove_categories(replace_value, inplace=True)
continue
categories = cat.categories.tolist()
index = categories.index(replace_value)
if new_value in cat.categories:
value_index = categories.index(new_value)
cat._codes[cat._codes == index] = value_index
with catch_warnings():
simplefilter("ignore")
cat.remove_categories(replace_value, inplace=True)
else:
categories[index] = new_value
with catch_warnings():
simplefilter("ignore")
cat.rename_categories(categories, inplace=True)
if not inplace:
return cat
# ------------------------------------------------------------------------
# String methods interface
def _str_map(
self, f, na_value=np.nan, dtype=np.dtype("object"), convert: bool = True
):
# Optimization to apply the callable `f` to the categories once
# and rebuild the result by `take`ing from the result with the codes.
# Returns the same type as the object-dtype implementation though.
from pandas.core.arrays import PandasArray
categories = self.categories
codes = self.codes
result = PandasArray(categories.to_numpy())._str_map(f, na_value, dtype)
return take_nd(result, codes, fill_value=na_value)
def _str_get_dummies(self, sep="|"):
# sep may not be in categories. Just bail on this.
from pandas.core.arrays import PandasArray
return PandasArray(self.astype(str))._str_get_dummies(sep)
# The Series.cat accessor
@delegate_names(
delegate=Categorical, accessors=["categories", "ordered"], typ="property"
)
@delegate_names(
delegate=Categorical,
accessors=[
"rename_categories",
"reorder_categories",
"add_categories",
"remove_categories",
"remove_unused_categories",
"set_categories",
"as_ordered",
"as_unordered",
],
typ="method",
)
class CategoricalAccessor(PandasDelegate, PandasObject, NoNewAttributesMixin):
"""
Accessor object for categorical properties of the Series values.
Be aware that assigning to `categories` is a inplace operation, while all
methods return new categorical data per default (but can be called with
`inplace=True`).
Parameters
----------
data : Series or CategoricalIndex
Examples
--------
>>> s = pd.Series(list("abbccc")).astype("category")
>>> s
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> s.cat.categories
Index(['a', 'b', 'c'], dtype='object')
>>> s.cat.rename_categories(list("cba"))
0 c
1 b
2 b
3 a
4 a
5 a
dtype: category
Categories (3, object): ['c', 'b', 'a']
>>> s.cat.reorder_categories(list("cba"))
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (3, object): ['c', 'b', 'a']
>>> s.cat.add_categories(["d", "e"])
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (5, object): ['a', 'b', 'c', 'd', 'e']
>>> s.cat.remove_categories(["a", "c"])
0 NaN
1 b
2 b
3 NaN
4 NaN
5 NaN
dtype: category
Categories (1, object): ['b']
>>> s1 = s.cat.add_categories(["d", "e"])
>>> s1.cat.remove_unused_categories()
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> s.cat.set_categories(list("abcde"))
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (5, object): ['a', 'b', 'c', 'd', 'e']
>>> s.cat.as_ordered()
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (3, object): ['a' < 'b' < 'c']
>>> s.cat.as_unordered()
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (3, object): ['a', 'b', 'c']
"""
def __init__(self, data):
self._validate(data)
self._parent = data.values
self._index = data.index
self._name = data.name
self._freeze()
@staticmethod
def _validate(data):
if not is_categorical_dtype(data.dtype):
raise AttributeError("Can only use .cat accessor with a 'category' dtype")
def _delegate_property_get(self, name):
return getattr(self._parent, name)
def _delegate_property_set(self, name, new_values):
return setattr(self._parent, name, new_values)
@property
def codes(self):
"""
Return Series of codes as well as the index.
"""
from pandas import Series
return Series(self._parent.codes, index=self._index)
def _delegate_method(self, name, *args, **kwargs):
from pandas import Series
method = getattr(self._parent, name)
res = method(*args, **kwargs)
if res is not None:
return Series(res, index=self._index, name=self._name)
# utility routines
def _get_codes_for_values(values, categories: Index) -> np.ndarray:
"""
utility routine to turn values into codes given the specified categories
If `values` is known to be a Categorical, use recode_for_categories instead.
"""
if values.ndim > 1:
flat = values.ravel()
codes = _get_codes_for_values(flat, categories)
return codes.reshape(values.shape)
if isinstance(categories.dtype, ExtensionDtype) and is_object_dtype(values):
# Support inferring the correct extension dtype from an array of
# scalar objects. e.g.
# Categorical(array[Period, Period], categories=PeriodIndex(...))
cls = categories.dtype.construct_array_type()
values = maybe_cast_to_extension_array(cls, values)
codes = categories.get_indexer_for(values)
return coerce_indexer_dtype(codes, categories)
def recode_for_categories(
codes: np.ndarray, old_categories, new_categories, copy: bool = True
) -> np.ndarray:
"""
Convert a set of codes for to a new set of categories
Parameters
----------
codes : np.ndarray
old_categories, new_categories : Index
copy: bool, default True
Whether to copy if the codes are unchanged.
Returns
-------
new_codes : np.ndarray[np.int64]
Examples
--------
>>> old_cat = pd.Index(['b', 'a', 'c'])
>>> new_cat = pd.Index(['a', 'b'])
>>> codes = np.array([0, 1, 1, 2])
>>> recode_for_categories(codes, old_cat, new_cat)
array([ 1, 0, 0, -1], dtype=int8)
"""
if len(old_categories) == 0:
# All null anyway, so just retain the nulls
if copy:
return codes.copy()
return codes
elif new_categories.equals(old_categories):
# Same categories, so no need to actually recode
if copy:
return codes.copy()
return codes
indexer = coerce_indexer_dtype(
new_categories.get_indexer(old_categories), new_categories
)
new_codes = take_nd(indexer, codes, fill_value=-1)
return new_codes
def factorize_from_iterable(values) -> tuple[np.ndarray, Index]:
"""
Factorize an input `values` into `categories` and `codes`. Preserves
categorical dtype in `categories`.
Parameters
----------
values : list-like
Returns
-------
codes : ndarray
categories : Index
If `values` has a categorical dtype, then `categories` is
a CategoricalIndex keeping the categories and order of `values`.
"""
from pandas import CategoricalIndex
if not is_list_like(values):
raise TypeError("Input must be list-like")
if is_categorical_dtype(values):
values = extract_array(values)
# The Categorical we want to build has the same categories
# as values but its codes are by def [0, ..., len(n_categories) - 1]
cat_codes = np.arange(len(values.categories), dtype=values.codes.dtype)
cat = Categorical.from_codes(cat_codes, dtype=values.dtype)
categories = CategoricalIndex(cat)
codes = values.codes
else:
# The value of ordered is irrelevant since we don't use cat as such,
# but only the resulting categories, the order of which is independent
# from ordered. Set ordered to False as default. See GH #15457
cat = Categorical(values, ordered=False)
categories = cat.categories
codes = cat.codes
return codes, categories
def factorize_from_iterables(iterables) -> tuple[list[np.ndarray], list[Index]]:
"""
A higher-level wrapper over `factorize_from_iterable`.
Parameters
----------
iterables : list-like of list-likes
Returns
-------
codes : list of ndarrays
categories : list of Indexes
Notes
-----
See `factorize_from_iterable` for more info.
"""
if len(iterables) == 0:
# For consistency, it should return two empty lists.
return [], []
codes, categories = zip(*(factorize_from_iterable(it) for it in iterables))
return list(codes), list(categories)