531 lines
17 KiB
Python
531 lines
17 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 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.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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"""
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Get Addition of DataFrame and other, column-wise.
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Equivalent to ``DataFrame.add(other)``.
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Parameters
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----------
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other : scalar, sequence, Series, dict or DataFrame
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Object to be added to the DataFrame.
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Returns
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-------
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DataFrame
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The result of adding ``other`` to DataFrame.
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See Also
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--------
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DataFrame.add : Add a DataFrame and another object, with option for index-
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or column-oriented addition.
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Examples
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--------
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>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
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... index=['elk', 'moose'])
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>>> df
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height weight
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elk 1.5 500
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moose 2.6 800
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Adding a scalar affects all rows and columns.
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>>> df[['height', 'weight']] + 1.5
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height weight
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elk 3.0 501.5
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moose 4.1 801.5
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Each element of a list is added to a column of the DataFrame, in order.
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>>> df[['height', 'weight']] + [0.5, 1.5]
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height weight
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elk 2.0 501.5
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moose 3.1 801.5
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Keys of a dictionary are aligned to the DataFrame, based on column names;
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each value in the dictionary is added to the corresponding column.
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>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
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height weight
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elk 2.0 501.5
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moose 3.1 801.5
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When `other` is a :class:`Series`, the index of `other` is aligned with the
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columns of the DataFrame.
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>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
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>>> df[['height', 'weight']] + s1
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height weight
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elk 3.0 500.5
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moose 4.1 800.5
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Even when the index of `other` is the same as the index of the DataFrame,
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the :class:`Series` will not be reoriented. If index-wise alignment is desired,
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:meth:`DataFrame.add` should be used with `axis='index'`.
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>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
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>>> df[['height', 'weight']] + s2
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elk height moose weight
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elk NaN NaN NaN NaN
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moose NaN NaN NaN NaN
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>>> df[['height', 'weight']].add(s2, axis='index')
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height weight
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elk 2.0 500.5
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moose 4.1 801.5
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When `other` is a :class:`DataFrame`, both columns names and the
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index are aligned.
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>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
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... index=['elk', 'moose', 'deer'])
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>>> df[['height', 'weight']] + other
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height weight
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deer NaN NaN
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elk 1.7 NaN
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moose 3.0 NaN
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"""
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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 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.frame import (
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DataFrame,
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Series,
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)
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from pandas.core.generic import NDFrame
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from pandas.core.internals import (
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ArrayManager,
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BlockManager,
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)
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cls = type(self)
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kwargs = _standardize_out_kwarg(**kwargs)
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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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set_types = set(types)
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if len(set_types) > 1 and {DataFrame, Series}.issubset(set_types):
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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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f"Cannot apply ufunc {ufunc} to mixed DataFrame and Series inputs."
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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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raise NotImplementedError
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return result
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if isinstance(result, (BlockManager, ArrayManager)):
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# we went through BlockManager.apply e.g. np.sqrt
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result = self._constructor_from_mgr(result, axes=result.axes)
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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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|
|
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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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|
|
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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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|
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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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|
|
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def _assign_where(out, result, where) -> None:
|
|
"""
|
|
Set a ufunc result into 'out', masking with a 'where' argument if necessary.
|
|
"""
|
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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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|
|
|
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def default_array_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
|
|
"""
|
|
Fallback to the behavior we would get if we did not define __array_ufunc__.
|
|
|
|
Notes
|
|
-----
|
|
We are assuming that `self` is among `inputs`.
|
|
"""
|
|
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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|
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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):
|
|
"""
|
|
Dispatch ufunc reductions to self's reduction methods.
|
|
"""
|
|
assert method == "reduce"
|
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|
|
if len(inputs) != 1 or inputs[0] is not self:
|
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return NotImplemented
|
|
|
|
if ufunc.__name__ not in REDUCTION_ALIASES:
|
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return NotImplemented
|
|
|
|
method_name = REDUCTION_ALIASES[ufunc.__name__]
|
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|
|
# NB: we are assuming that min/max represent minimum/maximum methods,
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|
# which would not be accurate for e.g. Timestamp.min
|
|
if not hasattr(self, method_name):
|
|
return NotImplemented
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if self.ndim > 1:
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if isinstance(self, ABCNDFrame):
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# TODO: test cases where this doesn't hold, i.e. 2D DTA/TDA
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kwargs["numeric_only"] = False
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if "axis" not in kwargs:
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# For DataFrame reductions we don't want the default axis=0
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# Note: np.min is not a ufunc, but uses array_function_dispatch,
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# so calls DataFrame.min (without ever getting here) with the np.min
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# default of axis=None, which DataFrame.min catches and changes to axis=0.
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# np.minimum.reduce(df) gets here bc axis is not in kwargs,
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# so we set axis=0 to match the behaviorof np.minimum.reduce(df.values)
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kwargs["axis"] = 0
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# By default, numpy's reductions do not skip NaNs, so we have to
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# pass skipna=False
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return getattr(self, method_name)(skipna=False, **kwargs)
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