106 lines
2.8 KiB
Python
106 lines
2.8 KiB
Python
|
import decimal
|
||
|
|
import numbers
|
||
|
|
import random
|
||
|
|
import sys
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas.core.arrays import ExtensionArray
|
||
|
|
from pandas.core.dtypes.base import ExtensionDtype
|
||
|
|
|
||
|
|
|
||
|
|
class DecimalDtype(ExtensionDtype):
|
||
|
|
type = decimal.Decimal
|
||
|
|
name = 'decimal'
|
||
|
|
na_value = decimal.Decimal('NaN')
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def construct_from_string(cls, string):
|
||
|
|
if string == cls.name:
|
||
|
|
return cls()
|
||
|
|
else:
|
||
|
|
raise TypeError("Cannot construct a '{}' from "
|
||
|
|
"'{}'".format(cls, string))
|
||
|
|
|
||
|
|
|
||
|
|
class DecimalArray(ExtensionArray):
|
||
|
|
dtype = DecimalDtype()
|
||
|
|
|
||
|
|
def __init__(self, values):
|
||
|
|
assert all(isinstance(v, decimal.Decimal) for v in values)
|
||
|
|
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
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def _from_sequence(cls, scalars):
|
||
|
|
return cls(scalars)
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def _from_factorized(cls, values, original):
|
||
|
|
return cls(values)
|
||
|
|
|
||
|
|
def __getitem__(self, item):
|
||
|
|
if isinstance(item, numbers.Integral):
|
||
|
|
return self._data[item]
|
||
|
|
else:
|
||
|
|
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)
|
||
|
|
|
||
|
|
def copy(self, deep=False):
|
||
|
|
if deep:
|
||
|
|
return type(self)(self._data.copy())
|
||
|
|
return type(self)(self)
|
||
|
|
|
||
|
|
def __setitem__(self, key, value):
|
||
|
|
if pd.api.types.is_list_like(value):
|
||
|
|
value = [decimal.Decimal(v) for v in value]
|
||
|
|
else:
|
||
|
|
value = decimal.Decimal(value)
|
||
|
|
self._data[key] = value
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return len(self._data)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return 'DecimalArray({!r})'.format(self._data)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def nbytes(self):
|
||
|
|
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')
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def _concat_same_type(cls, to_concat):
|
||
|
|
return cls(np.concatenate([x._data for x in to_concat]))
|
||
|
|
|
||
|
|
|
||
|
|
def make_data():
|
||
|
|
return [decimal.Decimal(random.random()) for _ in range(100)]
|