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dtypes.py
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dtypes.py
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""" define extension dtypes """
import re
import warnings
import numpy as np
import pytz
from pandas._libs.interval import Interval
from pandas._libs.tslibs import NaT, Period, Timestamp, timezones
from pandas.core.dtypes.generic import ABCCategoricalIndex, ABCIndexClass
from pandas import compat
from .base import ExtensionDtype, _DtypeOpsMixin
from .inference import is_list_like
def register_extension_dtype(cls):
"""Class decorator to register an ExtensionType with pandas.
.. versionadded:: 0.24.0
This enables operations like ``.astype(name)`` for the name
of the ExtensionDtype.
Examples
--------
>>> from pandas.api.extensions import register_extension_dtype
>>> from pandas.api.extensions import ExtensionDtype
>>> @register_extension_dtype
... class MyExtensionDtype(ExtensionDtype):
... pass
"""
registry.register(cls)
return cls
class Registry(object):
"""
Registry for dtype inference
The registry allows one to map a string repr of a extension
dtype to an extension dtype. The string alias can be used in several
places, including
* Series and Index constructors
* :meth:`pandas.array`
* :meth:`pandas.Series.astype`
Multiple extension types can be registered.
These are tried in order.
"""
def __init__(self):
self.dtypes = []
def register(self, dtype):
"""
Parameters
----------
dtype : ExtensionDtype
"""
if not issubclass(dtype, (PandasExtensionDtype, ExtensionDtype)):
raise ValueError("can only register pandas extension dtypes")
self.dtypes.append(dtype)
def find(self, dtype):
"""
Parameters
----------
dtype : PandasExtensionDtype or string
Returns
-------
return the first matching dtype, otherwise return None
"""
if not isinstance(dtype, compat.string_types):
dtype_type = dtype
if not isinstance(dtype, type):
dtype_type = type(dtype)
if issubclass(dtype_type, ExtensionDtype):
return dtype
return None
for dtype_type in self.dtypes:
try:
return dtype_type.construct_from_string(dtype)
except TypeError:
pass
return None
registry = Registry()
class PandasExtensionDtype(_DtypeOpsMixin):
"""
A np.dtype duck-typed class, suitable for holding a custom dtype.
THIS IS NOT A REAL NUMPY DTYPE
"""
type = None
subdtype = None
kind = None
str = None
num = 100
shape = tuple()
itemsize = 8
base = None
isbuiltin = 0
isnative = 0
_cache = {}
def __unicode__(self):
return self.name
def __str__(self):
"""
Return a string representation for a particular Object
Invoked by str(df) in both py2/py3.
Yields Bytestring in Py2, Unicode String in py3.
"""
if compat.PY3:
return self.__unicode__()
return self.__bytes__()
def __bytes__(self):
"""
Return a string representation for a particular object.
Invoked by bytes(obj) in py3 only.
Yields a bytestring in both py2/py3.
"""
from pandas.core.config import get_option
encoding = get_option("display.encoding")
return self.__unicode__().encode(encoding, 'replace')
def __repr__(self):
"""
Return a string representation for a particular object.
Yields Bytestring in Py2, Unicode String in py3.
"""
return str(self)
def __hash__(self):
raise NotImplementedError("sub-classes should implement an __hash__ "
"method")
def __getstate__(self):
# pickle support; we don't want to pickle the cache
return {k: getattr(self, k, None) for k in self._metadata}
@classmethod
def reset_cache(cls):
""" clear the cache """
cls._cache = {}
class CategoricalDtypeType(type):
"""
the type of CategoricalDtype, this metaclass determines subclass ability
"""
pass
@register_extension_dtype
class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
"""
Type for categorical data with the categories and orderedness
.. versionchanged:: 0.21.0
Parameters
----------
categories : sequence, optional
Must be unique, and must not contain any nulls.
ordered : bool, default False
Attributes
----------
categories
ordered
Methods
-------
None
See Also
--------
pandas.Categorical
Notes
-----
This class is useful for specifying the type of a ``Categorical``
independent of the values. See :ref:`categorical.categoricaldtype`
for more.
Examples
--------
>>> t = pd.CategoricalDtype(categories=['b', 'a'], ordered=True)
>>> pd.Series(['a', 'b', 'a', 'c'], dtype=t)
0 a
1 b
2 a
3 NaN
dtype: category
Categories (2, object): [b < a]
"""
# TODO: Document public vs. private API
name = 'category'
type = CategoricalDtypeType
kind = 'O'
str = '|O08'
base = np.dtype('O')
_metadata = ('categories', 'ordered')
_cache = {}
def __init__(self, categories=None, ordered=None):
self._finalize(categories, ordered, fastpath=False)
@classmethod
def _from_fastpath(cls, categories=None, ordered=None):
self = cls.__new__(cls)
self._finalize(categories, ordered, fastpath=True)
return self
@classmethod
def _from_categorical_dtype(cls, dtype, categories=None, ordered=None):
if categories is ordered is None:
return dtype
if categories is None:
categories = dtype.categories
if ordered is None:
ordered = dtype.ordered
return cls(categories, ordered)
@classmethod
def _from_values_or_dtype(cls, values=None, categories=None, ordered=None,
dtype=None):
"""
Construct dtype from the input parameters used in :class:`Categorical`.
This constructor method specifically does not do the factorization
step, if that is needed to find the categories. This constructor may
therefore return ``CategoricalDtype(categories=None, ordered=None)``,
which may not be useful. Additional steps may therefore have to be
taken to create the final dtype.
The return dtype is specified from the inputs in this prioritized
order:
1. if dtype is a CategoricalDtype, return dtype
2. if dtype is the string 'category', create a CategoricalDtype from
the supplied categories and ordered parameters, and return that.
3. if values is a categorical, use value.dtype, but override it with
categories and ordered if either/both of those are not None.
4. if dtype is None and values is not a categorical, construct the
dtype from categories and ordered, even if either of those is None.
Parameters
----------
values : list-like, optional
The list-like must be 1-dimensional.
categories : list-like, optional
Categories for the CategoricalDtype.
ordered : bool, optional
Designating if the categories are ordered.
dtype : CategoricalDtype or the string "category", optional
If ``CategoricalDtype``, cannot be used together with
`categories` or `ordered`.
Returns
-------
CategoricalDtype
Examples
--------
>>> CategoricalDtype._from_values_or_dtype()
CategoricalDtype(categories=None, ordered=None)
>>> CategoricalDtype._from_values_or_dtype(categories=['a', 'b'],
... ordered=True)
CategoricalDtype(categories=['a', 'b'], ordered=True)
>>> dtype1 = CategoricalDtype(['a', 'b'], ordered=True)
>>> dtype2 = CategoricalDtype(['x', 'y'], ordered=False)
>>> c = Categorical([0, 1], dtype=dtype1, fastpath=True)
>>> CategoricalDtype._from_values_or_dtype(c, ['x', 'y'], ordered=True,
... dtype=dtype2)
ValueError: Cannot specify `categories` or `ordered` together with
`dtype`.
The supplied dtype takes precedence over values' dtype:
>>> CategoricalDtype._from_values_or_dtype(c, dtype=dtype2)
CategoricalDtype(['x', 'y'], ordered=False)
"""
from pandas.core.dtypes.common import is_categorical
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!r}"
raise ValueError(msg.format(dtype=dtype))
elif categories is not None or ordered is not None:
raise ValueError("Cannot specify `categories` or `ordered` "
"together with `dtype`.")
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.
# Note: This could potentially have categories=None and
# ordered=None.
dtype = CategoricalDtype(categories, ordered)
return dtype
def _finalize(self, categories, ordered, fastpath=False):
if ordered is not None:
self.validate_ordered(ordered)
if categories is not None:
categories = self.validate_categories(categories,
fastpath=fastpath)
self._categories = categories
self._ordered = ordered
def __setstate__(self, state):
self._categories = state.pop('categories', None)
self._ordered = state.pop('ordered', False)
def __hash__(self):
# _hash_categories returns a uint64, so use the negative
# space for when we have unknown categories to avoid a conflict
if self.categories is None:
if self.ordered:
return -1
else:
return -2
# We *do* want to include the real self.ordered here
return int(self._hash_categories(self.categories, self.ordered))
def __eq__(self, other):
"""
Rules for CDT equality:
1) Any CDT is equal to the string 'category'
2) Any CDT is equal to itself
3) Any CDT is equal to a CDT with categories=None regardless of ordered
4) A CDT with ordered=True is only equal to another CDT with
ordered=True and identical categories in the same order
5) A CDT with ordered={False, None} is only equal to another CDT with
ordered={False, None} and identical categories, but same order is
not required. There is no distinction between False/None.
6) Any other comparison returns False
"""
if isinstance(other, compat.string_types):
return other == self.name
elif other is self:
return True
elif not (hasattr(other, 'ordered') and hasattr(other, 'categories')):
return False
elif self.categories is None or other.categories is None:
# We're forced into a suboptimal corner thanks to math and
# backwards compatibility. We require that `CDT(...) == 'category'`
# for all CDTs **including** `CDT(None, ...)`. Therefore, *all*
# CDT(., .) = CDT(None, False) and *all*
# CDT(., .) = CDT(None, True).
return True
elif self.ordered or other.ordered:
# At least one has ordered=True; equal if both have ordered=True
# and the same values for categories in the same order.
return ((self.ordered == other.ordered) and
self.categories.equals(other.categories))
else:
# Neither has ordered=True; equal if both have the same categories,
# but same order is not necessary. There is no distinction between
# ordered=False and ordered=None: CDT(., False) and CDT(., None)
# will be equal if they have the same categories.
return hash(self) == hash(other)
def __repr__(self):
tpl = u'CategoricalDtype(categories={}ordered={})'
if self.categories is None:
data = u"None, "
else:
data = self.categories._format_data(name=self.__class__.__name__)
return tpl.format(data, self.ordered)
@staticmethod
def _hash_categories(categories, ordered=True):
from pandas.core.util.hashing import (
hash_array, _combine_hash_arrays, hash_tuples
)
from pandas.core.dtypes.common import is_datetime64tz_dtype, _NS_DTYPE
if len(categories) and isinstance(categories[0], tuple):
# assumes if any individual category is a tuple, then all our. ATM
# I don't really want to support just some of the categories being
# tuples.
categories = list(categories) # breaks if a np.array of categories
cat_array = hash_tuples(categories)
else:
if categories.dtype == 'O':
types = [type(x) for x in categories]
if not len(set(types)) == 1:
# TODO: hash_array doesn't handle mixed types. It casts
# everything to a str first, which means we treat
# {'1', '2'} the same as {'1', 2}
# find a better solution
hashed = hash((tuple(categories), ordered))
return hashed
if is_datetime64tz_dtype(categories.dtype):
# Avoid future warning.
categories = categories.astype(_NS_DTYPE)
cat_array = hash_array(np.asarray(categories), categorize=False)
if ordered:
cat_array = np.vstack([
cat_array, np.arange(len(cat_array), dtype=cat_array.dtype)
])
else:
cat_array = [cat_array]
hashed = _combine_hash_arrays(iter(cat_array),
num_items=len(cat_array))
return np.bitwise_xor.reduce(hashed)
@classmethod
def construct_array_type(cls):
"""
Return the array type associated with this dtype
Returns
-------
type
"""
from pandas import Categorical
return Categorical
@classmethod
def construct_from_string(cls, string):
"""
attempt to construct this type from a string, raise a TypeError if
it's not possible """
try:
if string == 'category':
return cls()
else:
raise TypeError("cannot construct a CategoricalDtype")
except AttributeError:
pass
@staticmethod
def validate_ordered(ordered):
"""
Validates that we have a valid ordered parameter. If
it is not a boolean, a TypeError will be raised.
Parameters
----------
ordered : object
The parameter to be verified.
Raises
------
TypeError
If 'ordered' is not a boolean.
"""
from pandas.core.dtypes.common import is_bool
if not is_bool(ordered):
raise TypeError("'ordered' must either be 'True' or 'False'")
@staticmethod
def validate_categories(categories, fastpath=False):
"""
Validates that we have good categories
Parameters
----------
categories : array-like
fastpath : bool
Whether to skip nan and uniqueness checks
Returns
-------
categories : Index
"""
from pandas import Index
if not fastpath and not is_list_like(categories):
msg = "Parameter 'categories' must be list-like, was {!r}"
raise TypeError(msg.format(categories))
elif not isinstance(categories, ABCIndexClass):
categories = Index(categories, tupleize_cols=False)
if not fastpath:
if categories.hasnans:
raise ValueError('Categorial categories cannot be null')
if not categories.is_unique:
raise ValueError('Categorical categories must be unique')
if isinstance(categories, ABCCategoricalIndex):
categories = categories.categories
return categories
def update_dtype(self, dtype):
"""
Returns a CategoricalDtype with categories and ordered taken from dtype
if specified, otherwise falling back to self if unspecified
Parameters
----------
dtype : CategoricalDtype
Returns
-------
new_dtype : CategoricalDtype
"""
if isinstance(dtype, compat.string_types) and dtype == 'category':
# dtype='category' should not change anything
return self
elif not self.is_dtype(dtype):
msg = ('a CategoricalDtype must be passed to perform an update, '
'got {dtype!r}').format(dtype=dtype)
raise ValueError(msg)
elif dtype.categories is not None and dtype.ordered is self.ordered:
return dtype
# dtype is CDT: keep current categories/ordered if None
new_categories = dtype.categories
if new_categories is None:
new_categories = self.categories
new_ordered = dtype.ordered
if new_ordered is None:
new_ordered = self.ordered
return CategoricalDtype(new_categories, new_ordered)
@property
def categories(self):
"""
An ``Index`` containing the unique categories allowed.
"""
return self._categories
@property
def ordered(self):
"""
Whether the categories have an ordered relationship.
"""
return self._ordered
@property
def _is_boolean(self):
from pandas.core.dtypes.common import is_bool_dtype
return is_bool_dtype(self.categories)
@register_extension_dtype
class DatetimeTZDtype(PandasExtensionDtype, ExtensionDtype):
"""
A np.dtype duck-typed class, suitable for holding a custom datetime with tz
dtype.
THIS IS NOT A REAL NUMPY DTYPE, but essentially a sub-class of
np.datetime64[ns]
"""
type = Timestamp
kind = 'M'
str = '|M8[ns]'
num = 101
base = np.dtype('M8[ns]')
na_value = NaT
_metadata = ('unit', 'tz')
_match = re.compile(r"(datetime64|M8)\[(?P<unit>.+), (?P<tz>.+)\]")
_cache = {}
def __init__(self, unit="ns", tz=None):
"""
An ExtensionDtype for timezone-aware datetime data.
Parameters
----------
unit : str, default "ns"
The precision of the datetime data. Currently limited
to ``"ns"``.
tz : str, int, or datetime.tzinfo
The timezone.
Raises
------
pytz.UnknownTimeZoneError
When the requested timezone cannot be found.
Examples
--------
>>> pd.core.dtypes.dtypes.DatetimeTZDtype(tz='UTC')
datetime64[ns, UTC]
>>> pd.core.dtypes.dtypes.DatetimeTZDtype(tz='dateutil/US/Central')
datetime64[ns, tzfile('/usr/share/zoneinfo/US/Central')]
"""
if isinstance(unit, DatetimeTZDtype):
unit, tz = unit.unit, unit.tz
if unit != 'ns':
if isinstance(unit, compat.string_types) and tz is None:
# maybe a string like datetime64[ns, tz], which we support for
# now.
result = type(self).construct_from_string(unit)
unit = result.unit
tz = result.tz
msg = (
"Passing a dtype alias like 'datetime64[ns, {tz}]' "
"to DatetimeTZDtype is deprecated. Use "
"'DatetimeTZDtype.construct_from_string()' instead."
)
warnings.warn(msg.format(tz=tz), FutureWarning, stacklevel=2)
else:
raise ValueError("DatetimeTZDtype only supports ns units")
if tz:
tz = timezones.maybe_get_tz(tz)
elif tz is not None:
raise pytz.UnknownTimeZoneError(tz)
elif tz is None:
raise TypeError("A 'tz' is required.")
self._unit = unit
self._tz = tz
@property
def unit(self):
"""The precision of the datetime data."""
return self._unit
@property
def tz(self):
"""The timezone."""
return self._tz
@classmethod
def construct_array_type(cls):
"""
Return the array type associated with this dtype
Returns
-------
type
"""
from pandas.core.arrays import DatetimeArray
return DatetimeArray
@classmethod
def construct_from_string(cls, string):
"""
Construct a DatetimeTZDtype from a string.
Parameters
----------
string : str
The string alias for this DatetimeTZDtype.
Should be formatted like ``datetime64[ns, <tz>]``,
where ``<tz>`` is the timezone name.
Examples
--------
>>> DatetimeTZDtype.construct_from_string('datetime64[ns, UTC]')
datetime64[ns, UTC]
"""
if isinstance(string, compat.string_types):
msg = "Could not construct DatetimeTZDtype from '{}'"
try:
match = cls._match.match(string)
if match:
d = match.groupdict()
return cls(unit=d['unit'], tz=d['tz'])
except Exception:
# TODO(py3): Change this pass to `raise TypeError(msg) from e`
pass
raise TypeError(msg.format(string))
raise TypeError("Could not construct DatetimeTZDtype")
def __unicode__(self):
return "datetime64[{unit}, {tz}]".format(unit=self.unit, tz=self.tz)
@property
def name(self):
"""A string representation of the dtype."""
return str(self)
def __hash__(self):
# make myself hashable
# TODO: update this.
return hash(str(self))
def __eq__(self, other):
if isinstance(other, compat.string_types):
return other == self.name
return (isinstance(other, DatetimeTZDtype) and
self.unit == other.unit and
str(self.tz) == str(other.tz))
def __setstate__(self, state):
# for pickle compat.
self._tz = state['tz']
self._unit = state['unit']
@register_extension_dtype
class PeriodDtype(ExtensionDtype, PandasExtensionDtype):
"""
A Period duck-typed class, suitable for holding a period with freq dtype.
THIS IS NOT A REAL NUMPY DTYPE, but essentially a sub-class of np.int64.
"""
type = Period
kind = 'O'
str = '|O08'
base = np.dtype('O')
num = 102
_metadata = ('freq',)
_match = re.compile(r"(P|p)eriod\[(?P<freq>.+)\]")
_cache = {}
def __new__(cls, freq=None):
"""
Parameters
----------
freq : frequency
"""
if isinstance(freq, PeriodDtype):
return freq
elif freq is None:
# empty constructor for pickle compat
return object.__new__(cls)
from pandas.tseries.offsets import DateOffset
if not isinstance(freq, DateOffset):
freq = cls._parse_dtype_strict(freq)
try:
return cls._cache[freq.freqstr]
except KeyError:
u = object.__new__(cls)
u.freq = freq
cls._cache[freq.freqstr] = u
return u
@classmethod
def _parse_dtype_strict(cls, freq):
if isinstance(freq, compat.string_types):
if freq.startswith('period[') or freq.startswith('Period['):
m = cls._match.search(freq)
if m is not None:
freq = m.group('freq')
from pandas.tseries.frequencies import to_offset
freq = to_offset(freq)
if freq is not None:
return freq
raise ValueError("could not construct PeriodDtype")
@classmethod
def construct_from_string(cls, string):
"""
Strict construction from a string, raise a TypeError if not
possible
"""
from pandas.tseries.offsets import DateOffset
if (isinstance(string, compat.string_types) and
(string.startswith('period[') or
string.startswith('Period[')) or
isinstance(string, DateOffset)):
# do not parse string like U as period[U]
# avoid tuple to be regarded as freq
try:
return cls(freq=string)
except ValueError:
pass
raise TypeError("could not construct PeriodDtype")
def __unicode__(self):
return compat.text_type(self.name)
@property
def name(self):
return str("period[{freq}]".format(freq=self.freq.freqstr))
@property
def na_value(self):
return NaT
def __hash__(self):
# make myself hashable
return hash(str(self))
def __eq__(self, other):
if isinstance(other, compat.string_types):
return other == self.name or other == self.name.title()
return isinstance(other, PeriodDtype) and self.freq == other.freq
@classmethod
def is_dtype(cls, dtype):
"""
Return a boolean if we if the passed type is an actual dtype that we
can match (via string or type)
"""
if isinstance(dtype, compat.string_types):
# PeriodDtype can be instantiated from freq string like "U",
# but doesn't regard freq str like "U" as dtype.
if dtype.startswith('period[') or dtype.startswith('Period['):
try:
if cls._parse_dtype_strict(dtype) is not None:
return True
else:
return False
except ValueError:
return False
else:
return False
return super(PeriodDtype, cls).is_dtype(dtype)
@classmethod
def construct_array_type(cls):
from pandas.core.arrays import PeriodArray
return PeriodArray
@register_extension_dtype
class IntervalDtype(PandasExtensionDtype, ExtensionDtype):
"""
A Interval duck-typed class, suitable for holding an interval
THIS IS NOT A REAL NUMPY DTYPE
"""
name = 'interval'
kind = None
str = '|O08'
base = np.dtype('O')
num = 103
_metadata = ('subtype',)
_match = re.compile(r"(I|i)nterval\[(?P<subtype>.+)\]")
_cache = {}
def __new__(cls, subtype=None):
"""
Parameters
----------
subtype : the dtype of the Interval
"""
from pandas.core.dtypes.common import (
is_categorical_dtype, is_string_dtype, pandas_dtype)
if isinstance(subtype, IntervalDtype):
return subtype
elif subtype is None:
# we are called as an empty constructor
# generally for pickle compat
u = object.__new__(cls)
u.subtype = None
return u
elif (isinstance(subtype, compat.string_types) and
subtype.lower() == 'interval'):
subtype = None
else:
if isinstance(subtype, compat.string_types):
m = cls._match.search(subtype)
if m is not None:
subtype = m.group('subtype')
try:
subtype = pandas_dtype(subtype)
except TypeError:
raise TypeError("could not construct IntervalDtype")
if is_categorical_dtype(subtype) or is_string_dtype(subtype):
# GH 19016
msg = ('category, object, and string subtypes are not supported '
'for IntervalDtype')
raise TypeError(msg)
try:
return cls._cache[str(subtype)]
except KeyError:
u = object.__new__(cls)
u.subtype = subtype
cls._cache[str(subtype)] = u
return u
@classmethod
def construct_array_type(cls):
"""
Return the array type associated with this dtype
Returns
-------
type
"""
from pandas.core.arrays import IntervalArray
return IntervalArray
@classmethod
def construct_from_string(cls, string):
"""
attempt to construct this type from a string, raise a TypeError
if its not possible
"""
if (isinstance(string, compat.string_types) and
(string.startswith('interval') or
string.startswith('Interval'))):
return cls(string)
msg = "a string needs to be passed, got type {typ}"
raise TypeError(msg.format(typ=type(string)))
@property
def type(self):
return Interval
def __unicode__(self):
if self.subtype is None:
return "interval"
return "interval[{subtype}]".format(subtype=self.subtype)
def __hash__(self):
# make myself hashable
return hash(str(self))
def __eq__(self, other):
if isinstance(other, compat.string_types):
return other.lower() in (self.name.lower(), str(self).lower())
elif not isinstance(other, IntervalDtype):
return False
elif self.subtype is None or other.subtype is None:
# None should match any subtype
return True
else:
from pandas.core.dtypes.common import is_dtype_equal
return is_dtype_equal(self.subtype, other.subtype)
@classmethod
def is_dtype(cls, dtype):
"""
Return a boolean if we if the passed type is an actual dtype that we
can match (via string or type)
"""
if isinstance(dtype, compat.string_types):
if dtype.lower().startswith('interval'):
try:
if cls.construct_from_string(dtype) is not None:
return True
else:
return False
except ValueError:
return False
else:
return False
return super(IntervalDtype, cls).is_dtype(dtype)