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from __future__ import annotations
import decimal
import numbers
import sys
from typing import TYPE_CHECKING
import numpy as np
from pandas.core.dtypes.base import ExtensionDtype
from pandas.core.dtypes.common import (
is_dtype_equal,
is_float,
is_integer,
pandas_dtype,
)
import pandas as pd
from pandas.api.extensions import (
no_default,
register_extension_dtype,
)
from pandas.api.types import (
is_list_like,
is_scalar,
)
from pandas.core import arraylike
from pandas.core.algorithms import value_counts_internal as value_counts
from pandas.core.arraylike import OpsMixin
from pandas.core.arrays import (
ExtensionArray,
ExtensionScalarOpsMixin,
)
from pandas.core.indexers import check_array_indexer
if TYPE_CHECKING:
from pandas._typing import type_t
@register_extension_dtype
class DecimalDtype(ExtensionDtype):
type = decimal.Decimal
name = "decimal"
na_value = decimal.Decimal("NaN")
_metadata = ("context",)
def __init__(self, context=None) -> None:
self.context = context or decimal.getcontext()
def __repr__(self) -> str:
return f"DecimalDtype(context={self.context})"
@classmethod
def construct_array_type(cls) -> type_t[DecimalArray]:
"""
Return the array type associated with this dtype.
Returns
-------
type
"""
return DecimalArray
@property
def _is_numeric(self) -> bool:
return True
class DecimalArray(OpsMixin, ExtensionScalarOpsMixin, ExtensionArray):
__array_priority__ = 1000
def __init__(self, values, dtype=None, copy=False, context=None) -> None:
for i, val in enumerate(values):
if is_float(val) or is_integer(val):
if np.isnan(val):
values[i] = DecimalDtype.na_value
else:
# error: Argument 1 has incompatible type "float | int |
# integer[Any]"; expected "Decimal | float | str | tuple[int,
# Sequence[int], int]"
values[i] = DecimalDtype.type(val) # type: ignore[arg-type]
elif not isinstance(val, decimal.Decimal):
raise TypeError("All values must be of type " + str(decimal.Decimal))
values = np.asarray(values, dtype=object)
self._data = values
# Some aliases for common attribute names to ensure pandas supports
# these
self._items = self.data = self._data
# those aliases are currently not working due to assumptions
# in internal code (GH-20735)
# self._values = self.values = self.data
self._dtype = DecimalDtype(context)
@property
def dtype(self):
return self._dtype
@classmethod
def _from_sequence(cls, scalars, *, dtype=None, copy=False):
return cls(scalars)
@classmethod
def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
return cls._from_sequence(
[decimal.Decimal(x) for x in strings], dtype=dtype, copy=copy
)
@classmethod
def _from_factorized(cls, values, original):
return cls(values)
_HANDLED_TYPES = (decimal.Decimal, numbers.Number, np.ndarray)
def to_numpy(
self,
dtype=None,
copy: bool = False,
na_value: object = no_default,
decimals=None,
) -> np.ndarray:
result = np.asarray(self, dtype=dtype)
if decimals is not None:
result = np.asarray([round(x, decimals) for x in result])
return result
def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
#
if not all(
isinstance(t, self._HANDLED_TYPES + (DecimalArray,)) for t in inputs
):
return NotImplemented
result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
# e.g. test_array_ufunc_series_scalar_other
return result
if "out" in kwargs:
return arraylike.dispatch_ufunc_with_out(
self, ufunc, method, *inputs, **kwargs
)
inputs = tuple(x._data if isinstance(x, DecimalArray) else x for x in inputs)
result = getattr(ufunc, method)(*inputs, **kwargs)
if method == "reduce":
result = arraylike.dispatch_reduction_ufunc(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
def reconstruct(x):
if isinstance(x, (decimal.Decimal, numbers.Number)):
return x
else:
return type(self)._from_sequence(x, dtype=self.dtype)
if ufunc.nout > 1:
return tuple(reconstruct(x) for x in result)
else:
return reconstruct(result)
def __getitem__(self, item):
if isinstance(item, numbers.Integral):
return self._data[item]
else:
# array, slice.
item = pd.api.indexers.check_array_indexer(self, item)
return type(self)(self._data[item])
def take(self, indexer, allow_fill=False, fill_value=None):
from pandas.api.extensions import take
data = self._data
if allow_fill and fill_value is None:
fill_value = self.dtype.na_value
result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill)
return self._from_sequence(result, dtype=self.dtype)
def copy(self):
return type(self)(self._data.copy(), dtype=self.dtype)
def astype(self, dtype, copy=True):
if is_dtype_equal(dtype, self._dtype):
if not copy:
return self
dtype = pandas_dtype(dtype)
if isinstance(dtype, type(self.dtype)):
return type(self)(self._data, copy=copy, context=dtype.context)
return super().astype(dtype, copy=copy)
def __setitem__(self, key, value) -> None:
if is_list_like(value):
if is_scalar(key):
raise ValueError("setting an array element with a sequence.")
value = [decimal.Decimal(v) for v in value]
else:
value = decimal.Decimal(value)
key = check_array_indexer(self, key)
self._data[key] = value
def __len__(self) -> int:
return len(self._data)
def __contains__(self, item) -> bool | np.bool_:
if not isinstance(item, decimal.Decimal):
return False
elif item.is_nan():
return self.isna().any()
else:
return super().__contains__(item)
@property
def nbytes(self) -> int:
n = len(self)
if n:
return n * sys.getsizeof(self[0])
return 0
def isna(self):
return np.array([x.is_nan() for x in self._data], dtype=bool)
@property
def _na_value(self):
return decimal.Decimal("NaN")
def _formatter(self, boxed=False):
if boxed:
return "Decimal: {}".format
return repr
@classmethod
def _concat_same_type(cls, to_concat):
return cls(np.concatenate([x._data for x in to_concat]))
def _reduce(
self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
):
if skipna and self.isna().any():
# If we don't have any NAs, we can ignore skipna
other = self[~self.isna()]
result = other._reduce(name, **kwargs)
elif name == "sum" and len(self) == 0:
# GH#29630 avoid returning int 0 or np.bool_(False) on old numpy
result = decimal.Decimal(0)
else:
try:
op = getattr(self.data, name)
except AttributeError as err:
raise NotImplementedError(
f"decimal does not support the {name} operation"
) from err
result = op(axis=0)
if keepdims:
return type(self)([result])
else:
return result
def _cmp_method(self, other, op):
# For use with OpsMixin
def convert_values(param):
if isinstance(param, ExtensionArray) or is_list_like(param):
ovalues = param
else:
# Assume it's an object
ovalues = [param] * len(self)
return ovalues
lvalues = self
rvalues = convert_values(other)
# If the operator is not defined for the underlying objects,
# a TypeError should be raised
res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
return np.asarray(res, dtype=bool)
def value_counts(self, dropna: bool = True):
return value_counts(self.to_numpy(), dropna=dropna)
# We override fillna here to simulate a 3rd party EA that has done so. This
# lets us test the deprecation telling authors to implement _pad_or_backfill
# Simulate a 3rd-party EA that has not yet updated to include a "copy"
# keyword in its fillna method.
# error: Signature of "fillna" incompatible with supertype "ExtensionArray"
def fillna( # type: ignore[override]
self,
value=None,
method=None,
limit: int | None = None,
):
return super().fillna(value=value, method=method, limit=limit, copy=True)
def to_decimal(values, context=None):
return DecimalArray([decimal.Decimal(x) for x in values], context=context)
def make_data():
return [decimal.Decimal(val) for val in np.random.default_rng(2).random(100)]
DecimalArray._add_arithmetic_ops()
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