def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
# Lightly modified version of
# https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html
# The primary modification is not boxing scalar return values
# in PandasArray, since pandas' ExtensionArrays are 1-d.
out = kwargs.get("out", ())
result = ops.maybe_dispatch_ufunc_to_dunder_op(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
if "out" in kwargs:
# e.g. test_ufunc_unary
return arraylike.dispatch_ufunc_with_out(
self, ufunc, method, *inputs, **kwargs
)
if method == "reduce":
result = arraylike.dispatch_reduction_ufunc(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
# e.g. tests.series.test_ufunc.TestNumpyReductions
return result
# Defer to the implementation of the ufunc on unwrapped values.
inputs = tuple(x._ndarray if isinstance(x, PandasArray) else x for x in inputs)
if out:
kwargs["out"] = tuple(
x._ndarray if isinstance(x, PandasArray) else x for x in out
)
result = getattr(ufunc, method)(*inputs, **kwargs)
if ufunc.nout > 1:
# multiple return values; re-box array-like results
return tuple(type(self)(x) for x in result)
elif method == "at":
# no return value
return None
elif method == "reduce":
if isinstance(result, np.ndarray):
# e.g. test_np_reduce_2d
return type(self)(result)
# e.g. test_np_max_nested_tuples
return result
else:
# one return value; re-box array-like results
return type(self)(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 DecimalArray._from_sequence(x)
if ufunc.nout > 1:
return tuple(reconstruct(x) for x in result)
else:
return reconstruct(result)
def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
# For MaskedArray inputs, we apply the ufunc to ._data
# and mask the result.
out = kwargs.get("out", ())
for x in inputs + out:
if not isinstance(x, self._HANDLED_TYPES + (BaseMaskedArray,)):
return NotImplemented
# for binary ops, use our custom dunder methods
result = ops.maybe_dispatch_ufunc_to_dunder_op(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
if "out" in kwargs:
# e.g. test_ufunc_with_out
return arraylike.dispatch_ufunc_with_out(
self, ufunc, method, *inputs, **kwargs
)
if method == "reduce":
result = arraylike.dispatch_reduction_ufunc(
self, ufunc, method, *inputs, **kwargs
)
if result is not NotImplemented:
return result
mask = np.zeros(len(self), dtype=bool)
inputs2 = []
for x in inputs:
if isinstance(x, BaseMaskedArray):
mask |= x._mask
inputs2.append(x._data)
else:
inputs2.append(x)
def reconstruct(x):
# we don't worry about scalar `x` here, since we
# raise for reduce up above.
from pandas.core.arrays import (
BooleanArray,
FloatingArray,
IntegerArray,
)
if is_bool_dtype(x.dtype):
m = mask.copy()
return BooleanArray(x, m)
elif is_integer_dtype(x.dtype):
m = mask.copy()
return IntegerArray(x, m)
elif is_float_dtype(x.dtype):
m = mask.copy()
if x.dtype == np.float16:
# reached in e.g. np.sqrt on BooleanArray
# we don't support float16
x = x.astype(np.float32)
return FloatingArray(x, m)
else:
x[mask] = np.nan
return x
result = getattr(ufunc, method)(*inputs2, **kwargs)
if ufunc.nout > 1:
# e.g. np.divmod
return tuple(reconstruct(x) for x in result)
elif method == "reduce":
# e.g. np.add.reduce; test_ufunc_reduce_raises
if self._mask.any():
return self._na_value
return result
else:
return reconstruct(result)
