8th day of python challenges 111-117
This commit is contained in:
abd.shallal
@@ -0,0 +1,3 @@
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from .array import DecimalArray, DecimalDtype, make_data, to_decimal
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__all__ = ["DecimalArray", "DecimalDtype", "to_decimal", "make_data"]
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@@ -0,0 +1,189 @@
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import decimal
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import numbers
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import random
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import sys
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import numpy as np
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from pandas.core.dtypes.base import ExtensionDtype
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import pandas as pd
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from pandas.api.extensions import register_extension_dtype
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from pandas.core.arrays import ExtensionArray, ExtensionScalarOpsMixin
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@register_extension_dtype
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class DecimalDtype(ExtensionDtype):
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type = decimal.Decimal
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name = "decimal"
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na_value = decimal.Decimal("NaN")
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_metadata = ("context",)
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def __init__(self, context=None):
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self.context = context or decimal.getcontext()
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def __repr__(self):
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return "DecimalDtype(context={})".format(self.context)
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@classmethod
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def construct_array_type(cls):
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"""Return the array type associated with this dtype
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Returns
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-------
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type
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"""
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return DecimalArray
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@classmethod
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def construct_from_string(cls, string):
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if string == cls.name:
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return cls()
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else:
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raise TypeError("Cannot construct a '{}' from '{}'".format(cls, string))
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@property
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def _is_numeric(self):
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return True
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class DecimalArray(ExtensionArray, ExtensionScalarOpsMixin):
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__array_priority__ = 1000
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def __init__(self, values, dtype=None, copy=False, context=None):
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for val in values:
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if not isinstance(val, decimal.Decimal):
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raise TypeError("All values must be of type " + str(decimal.Decimal))
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values = np.asarray(values, dtype=object)
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self._data = values
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# Some aliases for common attribute names to ensure pandas supports
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# these
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self._items = self.data = self._data
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# those aliases are currently not working due to assumptions
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# in internal code (GH-20735)
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# self._values = self.values = self.data
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self._dtype = DecimalDtype(context)
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@property
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def dtype(self):
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return self._dtype
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@classmethod
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def _from_sequence(cls, scalars, dtype=None, copy=False):
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return cls(scalars)
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@classmethod
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def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
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return cls._from_sequence([decimal.Decimal(x) for x in strings], dtype, copy)
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@classmethod
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def _from_factorized(cls, values, original):
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return cls(values)
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_HANDLED_TYPES = (decimal.Decimal, numbers.Number, np.ndarray)
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def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
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#
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if not all(
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isinstance(t, self._HANDLED_TYPES + (DecimalArray,)) for t in inputs
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):
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return NotImplemented
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inputs = tuple(x._data if isinstance(x, DecimalArray) else x for x in inputs)
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result = getattr(ufunc, method)(*inputs, **kwargs)
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def reconstruct(x):
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if isinstance(x, (decimal.Decimal, numbers.Number)):
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return x
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else:
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return DecimalArray._from_sequence(x)
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if isinstance(result, tuple):
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return tuple(reconstruct(x) for x in result)
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else:
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return reconstruct(result)
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def __getitem__(self, item):
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if isinstance(item, numbers.Integral):
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return self._data[item]
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else:
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return type(self)(self._data[item])
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def take(self, indexer, allow_fill=False, fill_value=None):
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from pandas.api.extensions import take
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data = self._data
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if allow_fill and fill_value is None:
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fill_value = self.dtype.na_value
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result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill)
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return self._from_sequence(result)
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def copy(self):
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return type(self)(self._data.copy())
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def astype(self, dtype, copy=True):
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if isinstance(dtype, type(self.dtype)):
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return type(self)(self._data, context=dtype.context)
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return np.asarray(self, dtype=dtype)
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def __setitem__(self, key, value):
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if pd.api.types.is_list_like(value):
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if pd.api.types.is_scalar(key):
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raise ValueError("setting an array element with a sequence.")
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value = [decimal.Decimal(v) for v in value]
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else:
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value = decimal.Decimal(value)
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self._data[key] = value
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def __len__(self):
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return len(self._data)
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@property
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def nbytes(self):
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n = len(self)
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if n:
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return n * sys.getsizeof(self[0])
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return 0
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def isna(self):
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return np.array([x.is_nan() for x in self._data], dtype=bool)
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@property
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def _na_value(self):
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return decimal.Decimal("NaN")
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def _formatter(self, boxed=False):
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if boxed:
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return "Decimal: {0}".format
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return repr
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@classmethod
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def _concat_same_type(cls, to_concat):
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return cls(np.concatenate([x._data for x in to_concat]))
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def _reduce(self, name, skipna=True, **kwargs):
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if skipna:
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raise NotImplementedError("decimal does not support skipna=True")
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try:
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op = getattr(self.data, name)
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except AttributeError:
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raise NotImplementedError(
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"decimal does not support the {} operation".format(name)
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)
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return op(axis=0)
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def to_decimal(values, context=None):
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return DecimalArray([decimal.Decimal(x) for x in values], context=context)
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def make_data():
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return [decimal.Decimal(random.random()) for _ in range(100)]
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DecimalArray._add_arithmetic_ops()
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DecimalArray._add_comparison_ops()
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@@ -0,0 +1,439 @@
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import decimal
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import math
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import operator
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import numpy as np
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import pytest
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import pandas as pd
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from pandas.tests.extension import base
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import pandas.util.testing as tm
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from .array import DecimalArray, DecimalDtype, make_data, to_decimal
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@pytest.fixture
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def dtype():
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return DecimalDtype()
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@pytest.fixture
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def data():
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return DecimalArray(make_data())
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@pytest.fixture
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def data_for_twos():
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return DecimalArray([decimal.Decimal(2) for _ in range(100)])
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@pytest.fixture
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def data_missing():
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return DecimalArray([decimal.Decimal("NaN"), decimal.Decimal(1)])
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@pytest.fixture
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def data_for_sorting():
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return DecimalArray(
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[decimal.Decimal("1"), decimal.Decimal("2"), decimal.Decimal("0")]
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)
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@pytest.fixture
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def data_missing_for_sorting():
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return DecimalArray(
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[decimal.Decimal("1"), decimal.Decimal("NaN"), decimal.Decimal("0")]
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)
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@pytest.fixture
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def na_cmp():
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return lambda x, y: x.is_nan() and y.is_nan()
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@pytest.fixture
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def na_value():
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return decimal.Decimal("NaN")
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@pytest.fixture
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def data_for_grouping():
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b = decimal.Decimal("1.0")
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a = decimal.Decimal("0.0")
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c = decimal.Decimal("2.0")
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na = decimal.Decimal("NaN")
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return DecimalArray([b, b, na, na, a, a, b, c])
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class BaseDecimal:
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def assert_series_equal(self, left, right, *args, **kwargs):
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def convert(x):
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# need to convert array([Decimal(NaN)], dtype='object') to np.NaN
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# because Series[object].isnan doesn't recognize decimal(NaN) as
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# NA.
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try:
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return math.isnan(x)
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except TypeError:
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return False
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if left.dtype == "object":
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left_na = left.apply(convert)
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else:
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left_na = left.isna()
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if right.dtype == "object":
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right_na = right.apply(convert)
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else:
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right_na = right.isna()
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tm.assert_series_equal(left_na, right_na)
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return tm.assert_series_equal(left[~left_na], right[~right_na], *args, **kwargs)
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def assert_frame_equal(self, left, right, *args, **kwargs):
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# TODO(EA): select_dtypes
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tm.assert_index_equal(
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left.columns,
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right.columns,
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exact=kwargs.get("check_column_type", "equiv"),
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check_names=kwargs.get("check_names", True),
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check_exact=kwargs.get("check_exact", False),
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check_categorical=kwargs.get("check_categorical", True),
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obj="{obj}.columns".format(obj=kwargs.get("obj", "DataFrame")),
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)
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decimals = (left.dtypes == "decimal").index
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for col in decimals:
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self.assert_series_equal(left[col], right[col], *args, **kwargs)
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left = left.drop(columns=decimals)
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right = right.drop(columns=decimals)
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tm.assert_frame_equal(left, right, *args, **kwargs)
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class TestDtype(BaseDecimal, base.BaseDtypeTests):
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def test_hashable(self, dtype):
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pass
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class TestInterface(BaseDecimal, base.BaseInterfaceTests):
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pass
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class TestConstructors(BaseDecimal, base.BaseConstructorsTests):
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@pytest.mark.skip(reason="not implemented constructor from dtype")
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def test_from_dtype(self, data):
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# construct from our dtype & string dtype
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pass
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class TestReshaping(BaseDecimal, base.BaseReshapingTests):
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pass
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class TestGetitem(BaseDecimal, base.BaseGetitemTests):
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def test_take_na_value_other_decimal(self):
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arr = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
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result = arr.take([0, -1], allow_fill=True, fill_value=decimal.Decimal("-1.0"))
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expected = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("-1.0")])
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self.assert_extension_array_equal(result, expected)
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class TestMissing(BaseDecimal, base.BaseMissingTests):
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pass
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class Reduce:
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def check_reduce(self, s, op_name, skipna):
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if skipna or op_name in ["median", "skew", "kurt"]:
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with pytest.raises(NotImplementedError):
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getattr(s, op_name)(skipna=skipna)
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else:
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result = getattr(s, op_name)(skipna=skipna)
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expected = getattr(np.asarray(s), op_name)()
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tm.assert_almost_equal(result, expected)
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class TestNumericReduce(Reduce, base.BaseNumericReduceTests):
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pass
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class TestBooleanReduce(Reduce, base.BaseBooleanReduceTests):
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pass
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class TestMethods(BaseDecimal, base.BaseMethodsTests):
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@pytest.mark.parametrize("dropna", [True, False])
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@pytest.mark.xfail(reason="value_counts not implemented yet.")
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def test_value_counts(self, all_data, dropna):
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all_data = all_data[:10]
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if dropna:
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other = np.array(all_data[~all_data.isna()])
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else:
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other = all_data
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result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
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expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
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tm.assert_series_equal(result, expected)
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class TestCasting(BaseDecimal, base.BaseCastingTests):
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pass
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class TestGroupby(BaseDecimal, base.BaseGroupbyTests):
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@pytest.mark.xfail(
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reason="needs to correctly define __eq__ to handle nans, xref #27081."
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)
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def test_groupby_apply_identity(self, data_for_grouping):
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super().test_groupby_apply_identity(data_for_grouping)
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class TestSetitem(BaseDecimal, base.BaseSetitemTests):
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pass
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class TestPrinting(BaseDecimal, base.BasePrintingTests):
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def test_series_repr(self, data):
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# Overriding this base test to explicitly test that
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# the custom _formatter is used
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ser = pd.Series(data)
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assert data.dtype.name in repr(ser)
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assert "Decimal: " in repr(ser)
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# TODO(extension)
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@pytest.mark.xfail(
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reason=(
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"raising AssertionError as this is not implemented, though easy enough to do"
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)
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)
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def test_series_constructor_coerce_data_to_extension_dtype_raises():
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xpr = (
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"Cannot cast data to extension dtype 'decimal'. Pass the "
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"extension array directly."
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)
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with pytest.raises(ValueError, match=xpr):
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pd.Series([0, 1, 2], dtype=DecimalDtype())
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def test_series_constructor_with_dtype():
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arr = DecimalArray([decimal.Decimal("10.0")])
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result = pd.Series(arr, dtype=DecimalDtype())
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expected = pd.Series(arr)
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tm.assert_series_equal(result, expected)
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result = pd.Series(arr, dtype="int64")
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expected = pd.Series([10])
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tm.assert_series_equal(result, expected)
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def test_dataframe_constructor_with_dtype():
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arr = DecimalArray([decimal.Decimal("10.0")])
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result = pd.DataFrame({"A": arr}, dtype=DecimalDtype())
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expected = pd.DataFrame({"A": arr})
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tm.assert_frame_equal(result, expected)
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arr = DecimalArray([decimal.Decimal("10.0")])
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result = pd.DataFrame({"A": arr}, dtype="int64")
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expected = pd.DataFrame({"A": [10]})
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize("frame", [True, False])
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def test_astype_dispatches(frame):
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# This is a dtype-specific test that ensures Series[decimal].astype
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# gets all the way through to ExtensionArray.astype
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# Designing a reliable smoke test that works for arbitrary data types
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# is difficult.
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data = pd.Series(DecimalArray([decimal.Decimal(2)]), name="a")
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ctx = decimal.Context()
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ctx.prec = 5
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if frame:
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data = data.to_frame()
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result = data.astype(DecimalDtype(ctx))
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if frame:
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result = result["a"]
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assert result.dtype.context.prec == ctx.prec
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class TestArithmeticOps(BaseDecimal, base.BaseArithmeticOpsTests):
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def check_opname(self, s, op_name, other, exc=None):
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super().check_opname(s, op_name, other, exc=None)
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def test_arith_series_with_array(self, data, all_arithmetic_operators):
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op_name = all_arithmetic_operators
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s = pd.Series(data)
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context = decimal.getcontext()
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divbyzerotrap = context.traps[decimal.DivisionByZero]
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invalidoptrap = context.traps[decimal.InvalidOperation]
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context.traps[decimal.DivisionByZero] = 0
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context.traps[decimal.InvalidOperation] = 0
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# Decimal supports ops with int, but not float
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other = pd.Series([int(d * 100) for d in data])
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self.check_opname(s, op_name, other)
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if "mod" not in op_name:
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self.check_opname(s, op_name, s * 2)
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self.check_opname(s, op_name, 0)
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self.check_opname(s, op_name, 5)
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context.traps[decimal.DivisionByZero] = divbyzerotrap
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context.traps[decimal.InvalidOperation] = invalidoptrap
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def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
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# We implement divmod
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super()._check_divmod_op(s, op, other, exc=None)
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def test_error(self):
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pass
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class TestComparisonOps(BaseDecimal, base.BaseComparisonOpsTests):
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def check_opname(self, s, op_name, other, exc=None):
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super().check_opname(s, op_name, other, exc=None)
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def _compare_other(self, s, data, op_name, other):
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self.check_opname(s, op_name, other)
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def test_compare_scalar(self, data, all_compare_operators):
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op_name = all_compare_operators
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s = pd.Series(data)
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self._compare_other(s, data, op_name, 0.5)
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|
||||
def test_compare_array(self, data, all_compare_operators):
|
||||
op_name = all_compare_operators
|
||||
s = pd.Series(data)
|
||||
|
||||
alter = np.random.choice([-1, 0, 1], len(data))
|
||||
# Randomly double, halve or keep same value
|
||||
other = pd.Series(data) * [decimal.Decimal(pow(2.0, i)) for i in alter]
|
||||
self._compare_other(s, data, op_name, other)
|
||||
|
||||
|
||||
class DecimalArrayWithoutFromSequence(DecimalArray):
|
||||
"""Helper class for testing error handling in _from_sequence."""
|
||||
|
||||
def _from_sequence(cls, scalars, dtype=None, copy=False):
|
||||
raise KeyError("For the test")
|
||||
|
||||
|
||||
class DecimalArrayWithoutCoercion(DecimalArrayWithoutFromSequence):
|
||||
@classmethod
|
||||
def _create_arithmetic_method(cls, op):
|
||||
return cls._create_method(op, coerce_to_dtype=False)
|
||||
|
||||
|
||||
DecimalArrayWithoutCoercion._add_arithmetic_ops()
|
||||
|
||||
|
||||
def test_combine_from_sequence_raises():
|
||||
# https://github.com/pandas-dev/pandas/issues/22850
|
||||
ser = pd.Series(
|
||||
DecimalArrayWithoutFromSequence(
|
||||
[decimal.Decimal("1.0"), decimal.Decimal("2.0")]
|
||||
)
|
||||
)
|
||||
result = ser.combine(ser, operator.add)
|
||||
|
||||
# note: object dtype
|
||||
expected = pd.Series(
|
||||
[decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
|
||||
)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"class_", [DecimalArrayWithoutFromSequence, DecimalArrayWithoutCoercion]
|
||||
)
|
||||
def test_scalar_ops_from_sequence_raises(class_):
|
||||
# op(EA, EA) should return an EA, or an ndarray if it's not possible
|
||||
# to return an EA with the return values.
|
||||
arr = class_([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
|
||||
result = arr + arr
|
||||
expected = np.array(
|
||||
[decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
|
||||
)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"reverse, expected_div, expected_mod",
|
||||
[(False, [0, 1, 1, 2], [1, 0, 1, 0]), (True, [2, 1, 0, 0], [0, 0, 2, 2])],
|
||||
)
|
||||
def test_divmod_array(reverse, expected_div, expected_mod):
|
||||
# https://github.com/pandas-dev/pandas/issues/22930
|
||||
arr = to_decimal([1, 2, 3, 4])
|
||||
if reverse:
|
||||
div, mod = divmod(2, arr)
|
||||
else:
|
||||
div, mod = divmod(arr, 2)
|
||||
expected_div = to_decimal(expected_div)
|
||||
expected_mod = to_decimal(expected_mod)
|
||||
|
||||
tm.assert_extension_array_equal(div, expected_div)
|
||||
tm.assert_extension_array_equal(mod, expected_mod)
|
||||
|
||||
|
||||
def test_ufunc_fallback(data):
|
||||
a = data[:5]
|
||||
s = pd.Series(a, index=range(3, 8))
|
||||
result = np.abs(s)
|
||||
expected = pd.Series(np.abs(a), index=range(3, 8))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
def test_formatting_values_deprecated():
|
||||
class DecimalArray2(DecimalArray):
|
||||
def _formatting_values(self):
|
||||
return np.array(self)
|
||||
|
||||
ser = pd.Series(DecimalArray2([decimal.Decimal("1.0")]))
|
||||
|
||||
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
|
||||
repr(ser)
|
||||
|
||||
|
||||
def test_array_ufunc():
|
||||
a = to_decimal([1, 2, 3])
|
||||
result = np.exp(a)
|
||||
expected = to_decimal(np.exp(a._data))
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
|
||||
|
||||
def test_array_ufunc_series():
|
||||
a = to_decimal([1, 2, 3])
|
||||
s = pd.Series(a)
|
||||
result = np.exp(s)
|
||||
expected = pd.Series(to_decimal(np.exp(a._data)))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
def test_array_ufunc_series_scalar_other():
|
||||
# check _HANDLED_TYPES
|
||||
a = to_decimal([1, 2, 3])
|
||||
s = pd.Series(a)
|
||||
result = np.add(s, decimal.Decimal(1))
|
||||
expected = pd.Series(np.add(a, decimal.Decimal(1)))
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
def test_array_ufunc_series_defer():
|
||||
a = to_decimal([1, 2, 3])
|
||||
s = pd.Series(a)
|
||||
|
||||
expected = pd.Series(to_decimal([2, 4, 6]))
|
||||
r1 = np.add(s, a)
|
||||
r2 = np.add(a, s)
|
||||
|
||||
tm.assert_series_equal(r1, expected)
|
||||
tm.assert_series_equal(r2, expected)
|
||||
Reference in New Issue
Block a user