Updated script that can be controled by Nodejs web app
This commit is contained in:
mac OS
@ -0,0 +1,8 @@
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from pandas.tests.extension.decimal.array import (
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DecimalArray,
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DecimalDtype,
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make_data,
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to_decimal,
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)
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__all__ = ["DecimalArray", "DecimalDtype", "to_decimal", "make_data"]
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@ -0,0 +1,311 @@
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from __future__ import annotations
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import decimal
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import numbers
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import sys
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from typing import TYPE_CHECKING
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import numpy as np
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from pandas.core.dtypes.base import ExtensionDtype
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from pandas.core.dtypes.common import (
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is_dtype_equal,
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is_float,
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is_integer,
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pandas_dtype,
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)
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import pandas as pd
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from pandas.api.extensions import (
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no_default,
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register_extension_dtype,
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)
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from pandas.api.types import (
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is_list_like,
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is_scalar,
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)
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from pandas.core import arraylike
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from pandas.core.algorithms import value_counts_internal as value_counts
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from pandas.core.arraylike import OpsMixin
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from pandas.core.arrays import (
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ExtensionArray,
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ExtensionScalarOpsMixin,
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)
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from pandas.core.indexers import check_array_indexer
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if TYPE_CHECKING:
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from pandas._typing import type_t
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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) -> None:
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self.context = context or decimal.getcontext()
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def __repr__(self) -> str:
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return f"DecimalDtype(context={self.context})"
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@classmethod
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def construct_array_type(cls) -> type_t[DecimalArray]:
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"""
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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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@property
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def _is_numeric(self) -> bool:
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return True
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class DecimalArray(OpsMixin, ExtensionScalarOpsMixin, ExtensionArray):
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__array_priority__ = 1000
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def __init__(self, values, dtype=None, copy=False, context=None) -> None:
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for i, val in enumerate(values):
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if is_float(val) or is_integer(val):
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if np.isnan(val):
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values[i] = DecimalDtype.na_value
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else:
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# error: Argument 1 has incompatible type "float | int |
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# integer[Any]"; expected "Decimal | float | str | tuple[int,
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# Sequence[int], int]"
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values[i] = DecimalDtype.type(val) # type: ignore[arg-type]
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elif 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(
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[decimal.Decimal(x) for x in strings], dtype=dtype, copy=copy
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)
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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 to_numpy(
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self,
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dtype=None,
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copy: bool = False,
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na_value: object = no_default,
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decimals=None,
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) -> np.ndarray:
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result = np.asarray(self, dtype=dtype)
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if decimals is not None:
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result = np.asarray([round(x, decimals) for x in result])
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return result
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def __array_ufunc__(self, ufunc: np.ufunc, method: str, *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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result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
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self, ufunc, method, *inputs, **kwargs
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)
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if result is not NotImplemented:
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# e.g. test_array_ufunc_series_scalar_other
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return result
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if "out" in kwargs:
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return arraylike.dispatch_ufunc_with_out(
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self, ufunc, method, *inputs, **kwargs
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)
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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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if method == "reduce":
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result = arraylike.dispatch_reduction_ufunc(
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self, ufunc, method, *inputs, **kwargs
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)
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if result is not NotImplemented:
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return result
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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 type(self)._from_sequence(x, dtype=self.dtype)
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if ufunc.nout > 1:
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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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# array, slice.
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item = pd.api.indexers.check_array_indexer(self, item)
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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, dtype=self.dtype)
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def copy(self):
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return type(self)(self._data.copy(), dtype=self.dtype)
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def astype(self, dtype, copy=True):
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if is_dtype_equal(dtype, self._dtype):
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if not copy:
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return self
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dtype = pandas_dtype(dtype)
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if isinstance(dtype, type(self.dtype)):
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return type(self)(self._data, copy=copy, context=dtype.context)
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return super().astype(dtype, copy=copy)
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def __setitem__(self, key, value) -> None:
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if is_list_like(value):
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if 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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key = check_array_indexer(self, key)
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self._data[key] = value
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def __len__(self) -> int:
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return len(self._data)
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def __contains__(self, item) -> bool | np.bool_:
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if not isinstance(item, decimal.Decimal):
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return False
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elif item.is_nan():
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return self.isna().any()
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else:
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return super().__contains__(item)
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@property
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def nbytes(self) -> int:
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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: {}".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(
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self, name: str, *, skipna: bool = True, keepdims: bool = False, **kwargs
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):
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if skipna and self.isna().any():
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# If we don't have any NAs, we can ignore skipna
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other = self[~self.isna()]
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result = other._reduce(name, **kwargs)
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elif name == "sum" and len(self) == 0:
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# GH#29630 avoid returning int 0 or np.bool_(False) on old numpy
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result = decimal.Decimal(0)
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else:
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try:
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op = getattr(self.data, name)
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except AttributeError as err:
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raise NotImplementedError(
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f"decimal does not support the {name} operation"
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) from err
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result = op(axis=0)
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if keepdims:
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return type(self)([result])
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else:
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return result
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def _cmp_method(self, other, op):
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# For use with OpsMixin
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def convert_values(param):
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if isinstance(param, ExtensionArray) or is_list_like(param):
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ovalues = param
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else:
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# Assume it's an object
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ovalues = [param] * len(self)
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return ovalues
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lvalues = self
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rvalues = convert_values(other)
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# If the operator is not defined for the underlying objects,
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# a TypeError should be raised
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res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
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return np.asarray(res, dtype=bool)
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def value_counts(self, dropna: bool = True):
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return value_counts(self.to_numpy(), dropna=dropna)
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# We override fillna here to simulate a 3rd party EA that has done so. This
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# lets us test the deprecation telling authors to implement _pad_or_backfill
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# Simulate a 3rd-party EA that has not yet updated to include a "copy"
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# keyword in its fillna method.
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# error: Signature of "fillna" incompatible with supertype "ExtensionArray"
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def fillna( # type: ignore[override]
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self,
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value=None,
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method=None,
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limit: int | None = None,
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):
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return super().fillna(value=value, method=method, limit=limit, copy=True)
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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(val) for val in np.random.default_rng(2).random(100)]
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DecimalArray._add_arithmetic_ops()
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@ -0,0 +1,567 @@
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from __future__ import annotations
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import decimal
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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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import pandas._testing as tm
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from pandas.tests.extension import base
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from pandas.tests.extension.decimal.array import (
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DecimalArray,
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DecimalDtype,
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make_data,
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to_decimal,
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)
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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 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 TestDecimalArray(base.ExtensionTests):
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def _get_expected_exception(
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self, op_name: str, obj, other
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) -> type[Exception] | None:
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return None
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def _supports_reduction(self, ser: pd.Series, op_name: str) -> bool:
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return True
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def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool):
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if op_name == "count":
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return super().check_reduce(ser, op_name, skipna)
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else:
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result = getattr(ser, op_name)(skipna=skipna)
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expected = getattr(np.asarray(ser), op_name)()
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tm.assert_almost_equal(result, expected)
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def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna, request):
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if all_numeric_reductions in ["kurt", "skew", "sem", "median"]:
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mark = pytest.mark.xfail(raises=NotImplementedError)
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request.applymarker(mark)
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super().test_reduce_series_numeric(data, all_numeric_reductions, skipna)
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def test_reduce_frame(self, data, all_numeric_reductions, skipna, request):
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op_name = all_numeric_reductions
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if op_name in ["skew", "median"]:
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mark = pytest.mark.xfail(raises=NotImplementedError)
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request.applymarker(mark)
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return super().test_reduce_frame(data, all_numeric_reductions, skipna)
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def test_compare_scalar(self, data, comparison_op):
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ser = pd.Series(data)
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self._compare_other(ser, data, comparison_op, 0.5)
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def test_compare_array(self, data, comparison_op):
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ser = pd.Series(data)
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alter = np.random.default_rng(2).choice([-1, 0, 1], len(data))
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# Randomly double, halve or keep same value
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other = pd.Series(data) * [decimal.Decimal(pow(2.0, i)) for i in alter]
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self._compare_other(ser, data, comparison_op, other)
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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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ser = 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(ser, op_name, other)
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if "mod" not in op_name:
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self.check_opname(ser, op_name, ser * 2)
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self.check_opname(ser, op_name, 0)
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self.check_opname(ser, 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 test_fillna_frame(self, data_missing):
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msg = "ExtensionArray.fillna added a 'copy' keyword"
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with tm.assert_produces_warning(
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DeprecationWarning, match=msg, check_stacklevel=False
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):
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super().test_fillna_frame(data_missing)
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def test_fillna_limit_pad(self, data_missing):
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msg = "ExtensionArray.fillna 'method' keyword is deprecated"
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with tm.assert_produces_warning(
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DeprecationWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
|
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):
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super().test_fillna_limit_pad(data_missing)
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msg = "The 'method' keyword in DecimalArray.fillna is deprecated"
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with tm.assert_produces_warning(
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FutureWarning,
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match=msg,
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check_stacklevel=False,
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raise_on_extra_warnings=False,
|
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):
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super().test_fillna_limit_pad(data_missing)
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@pytest.mark.parametrize(
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"limit_area, input_ilocs, expected_ilocs",
|
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[
|
||||
("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
|
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("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
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("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
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("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
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("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
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("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
|
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("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
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("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
|
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],
|
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)
|
||||
def test_ffill_limit_area(
|
||||
self, data_missing, limit_area, input_ilocs, expected_ilocs
|
||||
):
|
||||
# GH#56616
|
||||
msg = "ExtensionArray.fillna 'method' keyword is deprecated"
|
||||
with tm.assert_produces_warning(
|
||||
DeprecationWarning,
|
||||
match=msg,
|
||||
check_stacklevel=False,
|
||||
raise_on_extra_warnings=False,
|
||||
):
|
||||
msg = "DecimalArray does not implement limit_area"
|
||||
with pytest.raises(NotImplementedError, match=msg):
|
||||
super().test_ffill_limit_area(
|
||||
data_missing, limit_area, input_ilocs, expected_ilocs
|
||||
)
|
||||
|
||||
def test_fillna_limit_backfill(self, data_missing):
|
||||
msg = "Series.fillna with 'method' is deprecated"
|
||||
with tm.assert_produces_warning(
|
||||
FutureWarning,
|
||||
match=msg,
|
||||
check_stacklevel=False,
|
||||
raise_on_extra_warnings=False,
|
||||
):
|
||||
super().test_fillna_limit_backfill(data_missing)
|
||||
|
||||
msg = "ExtensionArray.fillna 'method' keyword is deprecated"
|
||||
with tm.assert_produces_warning(
|
||||
DeprecationWarning,
|
||||
match=msg,
|
||||
check_stacklevel=False,
|
||||
raise_on_extra_warnings=False,
|
||||
):
|
||||
super().test_fillna_limit_backfill(data_missing)
|
||||
|
||||
msg = "The 'method' keyword in DecimalArray.fillna is deprecated"
|
||||
with tm.assert_produces_warning(
|
||||
FutureWarning,
|
||||
match=msg,
|
||||
check_stacklevel=False,
|
||||
raise_on_extra_warnings=False,
|
||||
):
|
||||
super().test_fillna_limit_backfill(data_missing)
|
||||
|
||||
def test_fillna_no_op_returns_copy(self, data):
|
||||
msg = "|".join(
|
||||
[
|
||||
"ExtensionArray.fillna 'method' keyword is deprecated",
|
||||
"The 'method' keyword in DecimalArray.fillna is deprecated",
|
||||
]
|
||||
)
|
||||
with tm.assert_produces_warning(
|
||||
(FutureWarning, DeprecationWarning), match=msg, check_stacklevel=False
|
||||
):
|
||||
super().test_fillna_no_op_returns_copy(data)
|
||||
|
||||
def test_fillna_series(self, data_missing):
|
||||
msg = "ExtensionArray.fillna added a 'copy' keyword"
|
||||
with tm.assert_produces_warning(
|
||||
DeprecationWarning, match=msg, check_stacklevel=False
|
||||
):
|
||||
super().test_fillna_series(data_missing)
|
||||
|
||||
def test_fillna_series_method(self, data_missing, fillna_method):
|
||||
msg = "|".join(
|
||||
[
|
||||
"ExtensionArray.fillna 'method' keyword is deprecated",
|
||||
"The 'method' keyword in DecimalArray.fillna is deprecated",
|
||||
]
|
||||
)
|
||||
with tm.assert_produces_warning(
|
||||
(FutureWarning, DeprecationWarning), match=msg, check_stacklevel=False
|
||||
):
|
||||
super().test_fillna_series_method(data_missing, fillna_method)
|
||||
|
||||
def test_fillna_copy_frame(self, data_missing, using_copy_on_write):
|
||||
warn = DeprecationWarning if not using_copy_on_write else None
|
||||
msg = "ExtensionArray.fillna added a 'copy' keyword"
|
||||
with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
|
||||
super().test_fillna_copy_frame(data_missing)
|
||||
|
||||
def test_fillna_copy_series(self, data_missing, using_copy_on_write):
|
||||
warn = DeprecationWarning if not using_copy_on_write else None
|
||||
msg = "ExtensionArray.fillna added a 'copy' keyword"
|
||||
with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
|
||||
super().test_fillna_copy_series(data_missing)
|
||||
|
||||
@pytest.mark.parametrize("dropna", [True, False])
|
||||
def test_value_counts(self, all_data, dropna, request):
|
||||
all_data = all_data[:10]
|
||||
if dropna:
|
||||
other = np.array(all_data[~all_data.isna()])
|
||||
else:
|
||||
other = all_data
|
||||
|
||||
vcs = pd.Series(all_data).value_counts(dropna=dropna)
|
||||
vcs_ex = pd.Series(other).value_counts(dropna=dropna)
|
||||
|
||||
with decimal.localcontext() as ctx:
|
||||
# avoid raising when comparing Decimal("NAN") < Decimal(2)
|
||||
ctx.traps[decimal.InvalidOperation] = False
|
||||
|
||||
result = vcs.sort_index()
|
||||
expected = vcs_ex.sort_index()
|
||||
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
def test_series_repr(self, data):
|
||||
# Overriding this base test to explicitly test that
|
||||
# the custom _formatter is used
|
||||
ser = pd.Series(data)
|
||||
assert data.dtype.name in repr(ser)
|
||||
assert "Decimal: " in repr(ser)
|
||||
|
||||
@pytest.mark.xfail(reason="Inconsistent array-vs-scalar behavior")
|
||||
@pytest.mark.parametrize("ufunc", [np.positive, np.negative, np.abs])
|
||||
def test_unary_ufunc_dunder_equivalence(self, data, ufunc):
|
||||
super().test_unary_ufunc_dunder_equivalence(data, ufunc)
|
||||
|
||||
|
||||
def test_take_na_value_other_decimal():
|
||||
arr = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
|
||||
result = arr.take([0, -1], allow_fill=True, fill_value=decimal.Decimal("-1.0"))
|
||||
expected = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("-1.0")])
|
||||
tm.assert_extension_array_equal(result, expected)
|
||||
|
||||
|
||||
def test_series_constructor_coerce_data_to_extension_dtype():
|
||||
dtype = DecimalDtype()
|
||||
ser = pd.Series([0, 1, 2], dtype=dtype)
|
||||
|
||||
arr = DecimalArray(
|
||||
[decimal.Decimal(0), decimal.Decimal(1), decimal.Decimal(2)],
|
||||
dtype=dtype,
|
||||
)
|
||||
exp = pd.Series(arr)
|
||||
tm.assert_series_equal(ser, exp)
|
||||
|
||||
|
||||
def test_series_constructor_with_dtype():
|
||||
arr = DecimalArray([decimal.Decimal("10.0")])
|
||||
result = pd.Series(arr, dtype=DecimalDtype())
|
||||
expected = pd.Series(arr)
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
result = pd.Series(arr, dtype="int64")
|
||||
expected = pd.Series([10])
|
||||
tm.assert_series_equal(result, expected)
|
||||
|
||||
|
||||
def test_dataframe_constructor_with_dtype():
|
||||
arr = DecimalArray([decimal.Decimal("10.0")])
|
||||
|
||||
result = pd.DataFrame({"A": arr}, dtype=DecimalDtype())
|
||||
expected = pd.DataFrame({"A": arr})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
arr = DecimalArray([decimal.Decimal("10.0")])
|
||||
result = pd.DataFrame({"A": arr}, dtype="int64")
|
||||
expected = pd.DataFrame({"A": [10]})
|
||||
tm.assert_frame_equal(result, expected)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("frame", [True, False])
|
||||
def test_astype_dispatches(frame):
|
||||
# This is a dtype-specific test that ensures Series[decimal].astype
|
||||
# gets all the way through to ExtensionArray.astype
|
||||
# Designing a reliable smoke test that works for arbitrary data types
|
||||
# is difficult.
|
||||
data = pd.Series(DecimalArray([decimal.Decimal(2)]), name="a")
|
||||
ctx = decimal.Context()
|
||||
ctx.prec = 5
|
||||
|
||||
if frame:
|
||||
data = data.to_frame()
|
||||
|
||||
result = data.astype(DecimalDtype(ctx))
|
||||
|
||||
if frame:
|
||||
result = result["a"]
|
||||
|
||||
assert result.dtype.context.prec == ctx.prec
|
||||
|
||||
|
||||
class DecimalArrayWithoutFromSequence(DecimalArray):
|
||||
"""Helper class for testing error handling in _from_sequence."""
|
||||
|
||||
@classmethod
|
||||
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(monkeypatch):
|
||||
# https://github.com/pandas-dev/pandas/issues/22850
|
||||
cls = DecimalArrayWithoutFromSequence
|
||||
|
||||
@classmethod
|
||||
def construct_array_type(cls):
|
||||
return DecimalArrayWithoutFromSequence
|
||||
|
||||
monkeypatch.setattr(DecimalDtype, "construct_array_type", construct_array_type)
|
||||
|
||||
arr = cls([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
|
||||
ser = pd.Series(arr)
|
||||
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_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)
|
||||
|
||||
|
||||
def test_groupby_agg():
|
||||
# Ensure that the result of agg is inferred to be decimal dtype
|
||||
# https://github.com/pandas-dev/pandas/issues/29141
|
||||
|
||||
data = make_data()[:5]
|
||||
df = pd.DataFrame(
|
||||
{"id1": [0, 0, 0, 1, 1], "id2": [0, 1, 0, 1, 1], "decimals": DecimalArray(data)}
|
||||
)
|
||||
|
||||
# single key, selected column
|
||||
expected = pd.Series(to_decimal([data[0], data[3]]))
|
||||
result = df.groupby("id1")["decimals"].agg(lambda x: x.iloc[0])
|
||||
tm.assert_series_equal(result, expected, check_names=False)
|
||||
result = df["decimals"].groupby(df["id1"]).agg(lambda x: x.iloc[0])
|
||||
tm.assert_series_equal(result, expected, check_names=False)
|
||||
|
||||
# multiple keys, selected column
|
||||
expected = pd.Series(
|
||||
to_decimal([data[0], data[1], data[3]]),
|
||||
index=pd.MultiIndex.from_tuples([(0, 0), (0, 1), (1, 1)]),
|
||||
)
|
||||
result = df.groupby(["id1", "id2"])["decimals"].agg(lambda x: x.iloc[0])
|
||||
tm.assert_series_equal(result, expected, check_names=False)
|
||||
result = df["decimals"].groupby([df["id1"], df["id2"]]).agg(lambda x: x.iloc[0])
|
||||
tm.assert_series_equal(result, expected, check_names=False)
|
||||
|
||||
# multiple columns
|
||||
expected = pd.DataFrame({"id2": [0, 1], "decimals": to_decimal([data[0], data[3]])})
|
||||
result = df.groupby("id1").agg(lambda x: x.iloc[0])
|
||||
tm.assert_frame_equal(result, expected, check_names=False)
|
||||
|
||||
|
||||
def test_groupby_agg_ea_method(monkeypatch):
|
||||
# Ensure that the result of agg is inferred to be decimal dtype
|
||||
# https://github.com/pandas-dev/pandas/issues/29141
|
||||
|
||||
def DecimalArray__my_sum(self):
|
||||
return np.sum(np.array(self))
|
||||
|
||||
monkeypatch.setattr(DecimalArray, "my_sum", DecimalArray__my_sum, raising=False)
|
||||
|
||||
data = make_data()[:5]
|
||||
df = pd.DataFrame({"id": [0, 0, 0, 1, 1], "decimals": DecimalArray(data)})
|
||||
expected = pd.Series(to_decimal([data[0] + data[1] + data[2], data[3] + data[4]]))
|
||||
|
||||
result = df.groupby("id")["decimals"].agg(lambda x: x.values.my_sum())
|
||||
tm.assert_series_equal(result, expected, check_names=False)
|
||||
s = pd.Series(DecimalArray(data))
|
||||
grouper = np.array([0, 0, 0, 1, 1], dtype=np.int64)
|
||||
result = s.groupby(grouper).agg(lambda x: x.values.my_sum())
|
||||
tm.assert_series_equal(result, expected, check_names=False)
|
||||
|
||||
|
||||
def test_indexing_no_materialize(monkeypatch):
|
||||
# See https://github.com/pandas-dev/pandas/issues/29708
|
||||
# Ensure that indexing operations do not materialize (convert to a numpy
|
||||
# array) the ExtensionArray unnecessary
|
||||
|
||||
def DecimalArray__array__(self, dtype=None):
|
||||
raise Exception("tried to convert a DecimalArray to a numpy array")
|
||||
|
||||
monkeypatch.setattr(DecimalArray, "__array__", DecimalArray__array__, raising=False)
|
||||
|
||||
data = make_data()
|
||||
s = pd.Series(DecimalArray(data))
|
||||
df = pd.DataFrame({"a": s, "b": range(len(s))})
|
||||
|
||||
# ensure the following operations do not raise an error
|
||||
s[s > 0.5]
|
||||
df[s > 0.5]
|
||||
s.at[0]
|
||||
df.at[0, "a"]
|
||||
|
||||
|
||||
def test_to_numpy_keyword():
|
||||
# test the extra keyword
|
||||
values = [decimal.Decimal("1.1111"), decimal.Decimal("2.2222")]
|
||||
expected = np.array(
|
||||
[decimal.Decimal("1.11"), decimal.Decimal("2.22")], dtype="object"
|
||||
)
|
||||
a = pd.array(values, dtype="decimal")
|
||||
result = a.to_numpy(decimals=2)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
result = pd.Series(a).to_numpy(decimals=2)
|
||||
tm.assert_numpy_array_equal(result, expected)
|
||||
|
||||
|
||||
def test_array_copy_on_write(using_copy_on_write):
|
||||
df = pd.DataFrame({"a": [decimal.Decimal(2), decimal.Decimal(3)]}, dtype="object")
|
||||
df2 = df.astype(DecimalDtype())
|
||||
df.iloc[0, 0] = 0
|
||||
if using_copy_on_write:
|
||||
expected = pd.DataFrame(
|
||||
{"a": [decimal.Decimal(2), decimal.Decimal(3)]}, dtype=DecimalDtype()
|
||||
)
|
||||
tm.assert_equal(df2.values, expected.values)
|
||||
Reference in New Issue
Block a user