534 lines
18 KiB
Python
534 lines
18 KiB
Python
"""
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Methods that can be shared by many array-like classes or subclasses:
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Series
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Index
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ExtensionArray
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"""
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from __future__ import annotations
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import operator
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from typing import Any
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import warnings
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import numpy as np
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from pandas._libs import lib
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from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
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from pandas.util._exceptions import find_stack_level
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from pandas.core.dtypes.generic import ABCNDFrame
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from pandas.core import roperator
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from pandas.core.construction import extract_array
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from pandas.core.ops.common import unpack_zerodim_and_defer
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REDUCTION_ALIASES = {
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"maximum": "max",
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"minimum": "min",
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"add": "sum",
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"multiply": "prod",
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}
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class OpsMixin:
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# -------------------------------------------------------------
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# Comparisons
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def _cmp_method(self, other, op):
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return NotImplemented
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@unpack_zerodim_and_defer("__eq__")
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def __eq__(self, other):
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return self._cmp_method(other, operator.eq)
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@unpack_zerodim_and_defer("__ne__")
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def __ne__(self, other):
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return self._cmp_method(other, operator.ne)
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@unpack_zerodim_and_defer("__lt__")
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def __lt__(self, other):
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return self._cmp_method(other, operator.lt)
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@unpack_zerodim_and_defer("__le__")
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def __le__(self, other):
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return self._cmp_method(other, operator.le)
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@unpack_zerodim_and_defer("__gt__")
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def __gt__(self, other):
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return self._cmp_method(other, operator.gt)
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@unpack_zerodim_and_defer("__ge__")
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def __ge__(self, other):
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return self._cmp_method(other, operator.ge)
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# -------------------------------------------------------------
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# Logical Methods
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def _logical_method(self, other, op):
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return NotImplemented
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@unpack_zerodim_and_defer("__and__")
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def __and__(self, other):
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return self._logical_method(other, operator.and_)
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@unpack_zerodim_and_defer("__rand__")
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def __rand__(self, other):
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return self._logical_method(other, roperator.rand_)
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@unpack_zerodim_and_defer("__or__")
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def __or__(self, other):
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return self._logical_method(other, operator.or_)
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@unpack_zerodim_and_defer("__ror__")
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def __ror__(self, other):
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return self._logical_method(other, roperator.ror_)
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@unpack_zerodim_and_defer("__xor__")
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def __xor__(self, other):
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return self._logical_method(other, operator.xor)
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@unpack_zerodim_and_defer("__rxor__")
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def __rxor__(self, other):
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return self._logical_method(other, roperator.rxor)
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# -------------------------------------------------------------
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# Arithmetic Methods
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def _arith_method(self, other, op):
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return NotImplemented
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@unpack_zerodim_and_defer("__add__")
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def __add__(self, other):
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return self._arith_method(other, operator.add)
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@unpack_zerodim_and_defer("__radd__")
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def __radd__(self, other):
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return self._arith_method(other, roperator.radd)
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@unpack_zerodim_and_defer("__sub__")
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def __sub__(self, other):
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return self._arith_method(other, operator.sub)
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@unpack_zerodim_and_defer("__rsub__")
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def __rsub__(self, other):
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return self._arith_method(other, roperator.rsub)
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@unpack_zerodim_and_defer("__mul__")
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def __mul__(self, other):
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return self._arith_method(other, operator.mul)
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@unpack_zerodim_and_defer("__rmul__")
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def __rmul__(self, other):
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return self._arith_method(other, roperator.rmul)
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@unpack_zerodim_and_defer("__truediv__")
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def __truediv__(self, other):
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return self._arith_method(other, operator.truediv)
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@unpack_zerodim_and_defer("__rtruediv__")
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def __rtruediv__(self, other):
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return self._arith_method(other, roperator.rtruediv)
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@unpack_zerodim_and_defer("__floordiv__")
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def __floordiv__(self, other):
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return self._arith_method(other, operator.floordiv)
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@unpack_zerodim_and_defer("__rfloordiv")
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def __rfloordiv__(self, other):
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return self._arith_method(other, roperator.rfloordiv)
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@unpack_zerodim_and_defer("__mod__")
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def __mod__(self, other):
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return self._arith_method(other, operator.mod)
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@unpack_zerodim_and_defer("__rmod__")
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def __rmod__(self, other):
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return self._arith_method(other, roperator.rmod)
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@unpack_zerodim_and_defer("__divmod__")
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def __divmod__(self, other):
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return self._arith_method(other, divmod)
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@unpack_zerodim_and_defer("__rdivmod__")
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def __rdivmod__(self, other):
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return self._arith_method(other, roperator.rdivmod)
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@unpack_zerodim_and_defer("__pow__")
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def __pow__(self, other):
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return self._arith_method(other, operator.pow)
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@unpack_zerodim_and_defer("__rpow__")
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def __rpow__(self, other):
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return self._arith_method(other, roperator.rpow)
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# -----------------------------------------------------------------------------
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# Helpers to implement __array_ufunc__
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def _is_aligned(frame, other):
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"""
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Helper to check if a DataFrame is aligned with another DataFrame or Series.
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"""
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from pandas import DataFrame
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if isinstance(other, DataFrame):
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return frame._indexed_same(other)
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else:
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# Series -> match index
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return frame.columns.equals(other.index)
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def _maybe_fallback(ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
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"""
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In the future DataFrame, inputs to ufuncs will be aligned before applying
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the ufunc, but for now we ignore the index but raise a warning if behaviour
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would change in the future.
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This helper detects the case where a warning is needed and then fallbacks
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to applying the ufunc on arrays to avoid alignment.
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See https://github.com/pandas-dev/pandas/pull/39239
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"""
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from pandas import DataFrame
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from pandas.core.generic import NDFrame
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n_alignable = sum(isinstance(x, NDFrame) for x in inputs)
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n_frames = sum(isinstance(x, DataFrame) for x in inputs)
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if n_alignable >= 2 and n_frames >= 1:
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# if there are 2 alignable inputs (Series or DataFrame), of which at least 1
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# is a DataFrame -> we would have had no alignment before -> warn that this
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# will align in the future
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# the first frame is what determines the output index/columns in pandas < 1.2
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first_frame = next(x for x in inputs if isinstance(x, DataFrame))
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# check if the objects are aligned or not
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non_aligned = sum(
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not _is_aligned(first_frame, x) for x in inputs if isinstance(x, NDFrame)
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)
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# if at least one is not aligned -> warn and fallback to array behaviour
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if non_aligned:
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warnings.warn(
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"Calling a ufunc on non-aligned DataFrames (or DataFrame/Series "
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"combination). Currently, the indices are ignored and the result "
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"takes the index/columns of the first DataFrame. In the future , "
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"the DataFrames/Series will be aligned before applying the ufunc.\n"
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"Convert one of the arguments to a NumPy array "
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"(eg 'ufunc(df1, np.asarray(df2)') to keep the current behaviour, "
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"or align manually (eg 'df1, df2 = df1.align(df2)') before passing to "
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"the ufunc to obtain the future behaviour and silence this warning.",
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FutureWarning,
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stacklevel=find_stack_level(),
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)
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# keep the first dataframe of the inputs, other DataFrame/Series is
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# converted to array for fallback behaviour
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new_inputs = []
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for x in inputs:
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if x is first_frame:
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new_inputs.append(x)
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elif isinstance(x, NDFrame):
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new_inputs.append(np.asarray(x))
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else:
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new_inputs.append(x)
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# call the ufunc on those transformed inputs
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return getattr(ufunc, method)(*new_inputs, **kwargs)
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# signal that we didn't fallback / execute the ufunc yet
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return NotImplemented
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def array_ufunc(self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
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"""
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Compatibility with numpy ufuncs.
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See also
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--------
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numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__
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"""
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from pandas.core.generic import NDFrame
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from pandas.core.internals import BlockManager
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cls = type(self)
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kwargs = _standardize_out_kwarg(**kwargs)
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# for backwards compatibility check and potentially fallback for non-aligned frames
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result = _maybe_fallback(ufunc, method, *inputs, **kwargs)
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if result is not NotImplemented:
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return result
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# for binary ops, use our custom dunder methods
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result = maybe_dispatch_ufunc_to_dunder_op(self, ufunc, method, *inputs, **kwargs)
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if result is not NotImplemented:
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return result
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# Determine if we should defer.
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no_defer = (
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np.ndarray.__array_ufunc__,
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cls.__array_ufunc__,
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)
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for item in inputs:
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higher_priority = (
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hasattr(item, "__array_priority__")
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and item.__array_priority__ > self.__array_priority__
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)
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has_array_ufunc = (
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hasattr(item, "__array_ufunc__")
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and type(item).__array_ufunc__ not in no_defer
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and not isinstance(item, self._HANDLED_TYPES)
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)
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if higher_priority or has_array_ufunc:
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return NotImplemented
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# align all the inputs.
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types = tuple(type(x) for x in inputs)
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alignable = [x for x, t in zip(inputs, types) if issubclass(t, NDFrame)]
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if len(alignable) > 1:
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# This triggers alignment.
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# At the moment, there aren't any ufuncs with more than two inputs
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# so this ends up just being x1.index | x2.index, but we write
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# it to handle *args.
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if len(set(types)) > 1:
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# We currently don't handle ufunc(DataFrame, Series)
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# well. Previously this raised an internal ValueError. We might
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# support it someday, so raise a NotImplementedError.
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raise NotImplementedError(
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"Cannot apply ufunc {} to mixed DataFrame and Series "
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"inputs.".format(ufunc)
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)
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axes = self.axes
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for obj in alignable[1:]:
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# this relies on the fact that we aren't handling mixed
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# series / frame ufuncs.
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for i, (ax1, ax2) in enumerate(zip(axes, obj.axes)):
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axes[i] = ax1.union(ax2)
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reconstruct_axes = dict(zip(self._AXIS_ORDERS, axes))
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inputs = tuple(
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x.reindex(**reconstruct_axes) if issubclass(t, NDFrame) else x
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for x, t in zip(inputs, types)
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)
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else:
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reconstruct_axes = dict(zip(self._AXIS_ORDERS, self.axes))
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if self.ndim == 1:
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names = [getattr(x, "name") for x in inputs if hasattr(x, "name")]
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name = names[0] if len(set(names)) == 1 else None
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reconstruct_kwargs = {"name": name}
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else:
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reconstruct_kwargs = {}
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def reconstruct(result):
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if ufunc.nout > 1:
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# np.modf, np.frexp, np.divmod
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return tuple(_reconstruct(x) for x in result)
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return _reconstruct(result)
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def _reconstruct(result):
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if lib.is_scalar(result):
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return result
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if result.ndim != self.ndim:
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if method == "outer":
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if self.ndim == 2:
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# we already deprecated for Series
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msg = (
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"outer method for ufunc {} is not implemented on "
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"pandas objects. Returning an ndarray, but in the "
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"future this will raise a 'NotImplementedError'. "
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"Consider explicitly converting the DataFrame "
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"to an array with '.to_numpy()' first."
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)
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warnings.warn(
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msg.format(ufunc), FutureWarning, stacklevel=find_stack_level()
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)
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return result
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raise NotImplementedError
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return result
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if isinstance(result, BlockManager):
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# we went through BlockManager.apply e.g. np.sqrt
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result = self._constructor(result, **reconstruct_kwargs, copy=False)
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else:
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# we converted an array, lost our axes
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result = self._constructor(
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result, **reconstruct_axes, **reconstruct_kwargs, copy=False
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)
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# TODO: When we support multiple values in __finalize__, this
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# should pass alignable to `__finalize__` instead of self.
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# Then `np.add(a, b)` would consider attrs from both a and b
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# when a and b are NDFrames.
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if len(alignable) == 1:
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result = result.__finalize__(self)
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return result
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if "out" in kwargs:
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# e.g. test_multiindex_get_loc
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result = dispatch_ufunc_with_out(self, ufunc, method, *inputs, **kwargs)
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return reconstruct(result)
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if method == "reduce":
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# e.g. test.series.test_ufunc.test_reduce
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result = dispatch_reduction_ufunc(self, ufunc, method, *inputs, **kwargs)
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if result is not NotImplemented:
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return result
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# We still get here with kwargs `axis` for e.g. np.maximum.accumulate
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# and `dtype` and `keepdims` for np.ptp
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if self.ndim > 1 and (len(inputs) > 1 or ufunc.nout > 1):
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# Just give up on preserving types in the complex case.
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# In theory we could preserve them for them.
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# * nout>1 is doable if BlockManager.apply took nout and
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# returned a Tuple[BlockManager].
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# * len(inputs) > 1 is doable when we know that we have
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# aligned blocks / dtypes.
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# e.g. my_ufunc, modf, logaddexp, heaviside, subtract, add
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inputs = tuple(np.asarray(x) for x in inputs)
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# Note: we can't use default_array_ufunc here bc reindexing means
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# that `self` may not be among `inputs`
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result = getattr(ufunc, method)(*inputs, **kwargs)
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elif self.ndim == 1:
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# ufunc(series, ...)
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inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
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result = getattr(ufunc, method)(*inputs, **kwargs)
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else:
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# ufunc(dataframe)
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if method == "__call__" and not kwargs:
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# for np.<ufunc>(..) calls
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# kwargs cannot necessarily be handled block-by-block, so only
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# take this path if there are no kwargs
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mgr = inputs[0]._mgr
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result = mgr.apply(getattr(ufunc, method))
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else:
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# otherwise specific ufunc methods (eg np.<ufunc>.accumulate(..))
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# Those can have an axis keyword and thus can't be called block-by-block
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result = default_array_ufunc(inputs[0], ufunc, method, *inputs, **kwargs)
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# e.g. np.negative (only one reached), with "where" and "out" in kwargs
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result = reconstruct(result)
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return result
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def _standardize_out_kwarg(**kwargs) -> dict:
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"""
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If kwargs contain "out1" and "out2", replace that with a tuple "out"
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np.divmod, np.modf, np.frexp can have either `out=(out1, out2)` or
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`out1=out1, out2=out2)`
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"""
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if "out" not in kwargs and "out1" in kwargs and "out2" in kwargs:
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out1 = kwargs.pop("out1")
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out2 = kwargs.pop("out2")
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out = (out1, out2)
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kwargs["out"] = out
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return kwargs
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def dispatch_ufunc_with_out(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
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"""
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If we have an `out` keyword, then call the ufunc without `out` and then
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set the result into the given `out`.
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"""
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# Note: we assume _standardize_out_kwarg has already been called.
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out = kwargs.pop("out")
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where = kwargs.pop("where", None)
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result = getattr(ufunc, method)(*inputs, **kwargs)
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if result is NotImplemented:
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return NotImplemented
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if isinstance(result, tuple):
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# i.e. np.divmod, np.modf, np.frexp
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if not isinstance(out, tuple) or len(out) != len(result):
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raise NotImplementedError
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for arr, res in zip(out, result):
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_assign_where(arr, res, where)
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return out
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if isinstance(out, tuple):
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if len(out) == 1:
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out = out[0]
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else:
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raise NotImplementedError
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_assign_where(out, result, where)
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return out
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def _assign_where(out, result, where) -> None:
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"""
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Set a ufunc result into 'out', masking with a 'where' argument if necessary.
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"""
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if where is None:
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# no 'where' arg passed to ufunc
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out[:] = result
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else:
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np.putmask(out, where, result)
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def default_array_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
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"""
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Fallback to the behavior we would get if we did not define __array_ufunc__.
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Notes
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-----
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We are assuming that `self` is among `inputs`.
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"""
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if not any(x is self for x in inputs):
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raise NotImplementedError
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new_inputs = [x if x is not self else np.asarray(x) for x in inputs]
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return getattr(ufunc, method)(*new_inputs, **kwargs)
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def dispatch_reduction_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
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"""
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Dispatch ufunc reductions to self's reduction methods.
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"""
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assert method == "reduce"
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if len(inputs) != 1 or inputs[0] is not self:
|
|
return NotImplemented
|
|
|
|
if ufunc.__name__ not in REDUCTION_ALIASES:
|
|
return NotImplemented
|
|
|
|
method_name = REDUCTION_ALIASES[ufunc.__name__]
|
|
|
|
# NB: we are assuming that min/max represent minimum/maximum methods,
|
|
# which would not be accurate for e.g. Timestamp.min
|
|
if not hasattr(self, method_name):
|
|
return NotImplemented
|
|
|
|
if self.ndim > 1:
|
|
if isinstance(self, ABCNDFrame):
|
|
# TODO: test cases where this doesn't hold, i.e. 2D DTA/TDA
|
|
kwargs["numeric_only"] = False
|
|
|
|
if "axis" not in kwargs:
|
|
# For DataFrame reductions we don't want the default axis=0
|
|
# Note: np.min is not a ufunc, but uses array_function_dispatch,
|
|
# so calls DataFrame.min (without ever getting here) with the np.min
|
|
# default of axis=None, which DataFrame.min catches and changes to axis=0.
|
|
# np.minimum.reduce(df) gets here bc axis is not in kwargs,
|
|
# so we set axis=0 to match the behaviorof np.minimum.reduce(df.values)
|
|
kwargs["axis"] = 0
|
|
|
|
# By default, numpy's reductions do not skip NaNs, so we have to
|
|
# pass skipna=False
|
|
return getattr(self, method_name)(skipna=False, **kwargs)
|