"""
|
Methods that can be shared by many array-like classes or subclasses:
|
Series
|
Index
|
ExtensionArray
|
"""
|
from __future__ import annotations
|
|
import operator
|
from typing import Any
|
|
import numpy as np
|
|
from pandas._libs import lib
|
from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
|
|
from pandas.core.dtypes.generic import ABCNDFrame
|
|
from pandas.core import roperator
|
from pandas.core.construction import extract_array
|
from pandas.core.ops.common import unpack_zerodim_and_defer
|
|
REDUCTION_ALIASES = {
|
"maximum": "max",
|
"minimum": "min",
|
"add": "sum",
|
"multiply": "prod",
|
}
|
|
|
class OpsMixin:
|
# -------------------------------------------------------------
|
# Comparisons
|
|
def _cmp_method(self, other, op):
|
return NotImplemented
|
|
@unpack_zerodim_and_defer("__eq__")
|
def __eq__(self, other):
|
return self._cmp_method(other, operator.eq)
|
|
@unpack_zerodim_and_defer("__ne__")
|
def __ne__(self, other):
|
return self._cmp_method(other, operator.ne)
|
|
@unpack_zerodim_and_defer("__lt__")
|
def __lt__(self, other):
|
return self._cmp_method(other, operator.lt)
|
|
@unpack_zerodim_and_defer("__le__")
|
def __le__(self, other):
|
return self._cmp_method(other, operator.le)
|
|
@unpack_zerodim_and_defer("__gt__")
|
def __gt__(self, other):
|
return self._cmp_method(other, operator.gt)
|
|
@unpack_zerodim_and_defer("__ge__")
|
def __ge__(self, other):
|
return self._cmp_method(other, operator.ge)
|
|
# -------------------------------------------------------------
|
# Logical Methods
|
|
def _logical_method(self, other, op):
|
return NotImplemented
|
|
@unpack_zerodim_and_defer("__and__")
|
def __and__(self, other):
|
return self._logical_method(other, operator.and_)
|
|
@unpack_zerodim_and_defer("__rand__")
|
def __rand__(self, other):
|
return self._logical_method(other, roperator.rand_)
|
|
@unpack_zerodim_and_defer("__or__")
|
def __or__(self, other):
|
return self._logical_method(other, operator.or_)
|
|
@unpack_zerodim_and_defer("__ror__")
|
def __ror__(self, other):
|
return self._logical_method(other, roperator.ror_)
|
|
@unpack_zerodim_and_defer("__xor__")
|
def __xor__(self, other):
|
return self._logical_method(other, operator.xor)
|
|
@unpack_zerodim_and_defer("__rxor__")
|
def __rxor__(self, other):
|
return self._logical_method(other, roperator.rxor)
|
|
# -------------------------------------------------------------
|
# Arithmetic Methods
|
|
def _arith_method(self, other, op):
|
return NotImplemented
|
|
@unpack_zerodim_and_defer("__add__")
|
def __add__(self, other):
|
"""
|
Get Addition of DataFrame and other, column-wise.
|
|
Equivalent to ``DataFrame.add(other)``.
|
|
Parameters
|
----------
|
other : scalar, sequence, Series, dict or DataFrame
|
Object to be added to the DataFrame.
|
|
Returns
|
-------
|
DataFrame
|
The result of adding ``other`` to DataFrame.
|
|
See Also
|
--------
|
DataFrame.add : Add a DataFrame and another object, with option for index-
|
or column-oriented addition.
|
|
Examples
|
--------
|
>>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
|
... index=['elk', 'moose'])
|
>>> df
|
height weight
|
elk 1.5 500
|
moose 2.6 800
|
|
Adding a scalar affects all rows and columns.
|
|
>>> df[['height', 'weight']] + 1.5
|
height weight
|
elk 3.0 501.5
|
moose 4.1 801.5
|
|
Each element of a list is added to a column of the DataFrame, in order.
|
|
>>> df[['height', 'weight']] + [0.5, 1.5]
|
height weight
|
elk 2.0 501.5
|
moose 3.1 801.5
|
|
Keys of a dictionary are aligned to the DataFrame, based on column names;
|
each value in the dictionary is added to the corresponding column.
|
|
>>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
|
height weight
|
elk 2.0 501.5
|
moose 3.1 801.5
|
|
When `other` is a :class:`Series`, the index of `other` is aligned with the
|
columns of the DataFrame.
|
|
>>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
|
>>> df[['height', 'weight']] + s1
|
height weight
|
elk 3.0 500.5
|
moose 4.1 800.5
|
|
Even when the index of `other` is the same as the index of the DataFrame,
|
the :class:`Series` will not be reoriented. If index-wise alignment is desired,
|
:meth:`DataFrame.add` should be used with `axis='index'`.
|
|
>>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
|
>>> df[['height', 'weight']] + s2
|
elk height moose weight
|
elk NaN NaN NaN NaN
|
moose NaN NaN NaN NaN
|
|
>>> df[['height', 'weight']].add(s2, axis='index')
|
height weight
|
elk 2.0 500.5
|
moose 4.1 801.5
|
|
When `other` is a :class:`DataFrame`, both columns names and the
|
index are aligned.
|
|
>>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
|
... index=['elk', 'moose', 'deer'])
|
>>> df[['height', 'weight']] + other
|
height weight
|
deer NaN NaN
|
elk 1.7 NaN
|
moose 3.0 NaN
|
"""
|
return self._arith_method(other, operator.add)
|
|
@unpack_zerodim_and_defer("__radd__")
|
def __radd__(self, other):
|
return self._arith_method(other, roperator.radd)
|
|
@unpack_zerodim_and_defer("__sub__")
|
def __sub__(self, other):
|
return self._arith_method(other, operator.sub)
|
|
@unpack_zerodim_and_defer("__rsub__")
|
def __rsub__(self, other):
|
return self._arith_method(other, roperator.rsub)
|
|
@unpack_zerodim_and_defer("__mul__")
|
def __mul__(self, other):
|
return self._arith_method(other, operator.mul)
|
|
@unpack_zerodim_and_defer("__rmul__")
|
def __rmul__(self, other):
|
return self._arith_method(other, roperator.rmul)
|
|
@unpack_zerodim_and_defer("__truediv__")
|
def __truediv__(self, other):
|
return self._arith_method(other, operator.truediv)
|
|
@unpack_zerodim_and_defer("__rtruediv__")
|
def __rtruediv__(self, other):
|
return self._arith_method(other, roperator.rtruediv)
|
|
@unpack_zerodim_and_defer("__floordiv__")
|
def __floordiv__(self, other):
|
return self._arith_method(other, operator.floordiv)
|
|
@unpack_zerodim_and_defer("__rfloordiv")
|
def __rfloordiv__(self, other):
|
return self._arith_method(other, roperator.rfloordiv)
|
|
@unpack_zerodim_and_defer("__mod__")
|
def __mod__(self, other):
|
return self._arith_method(other, operator.mod)
|
|
@unpack_zerodim_and_defer("__rmod__")
|
def __rmod__(self, other):
|
return self._arith_method(other, roperator.rmod)
|
|
@unpack_zerodim_and_defer("__divmod__")
|
def __divmod__(self, other):
|
return self._arith_method(other, divmod)
|
|
@unpack_zerodim_and_defer("__rdivmod__")
|
def __rdivmod__(self, other):
|
return self._arith_method(other, roperator.rdivmod)
|
|
@unpack_zerodim_and_defer("__pow__")
|
def __pow__(self, other):
|
return self._arith_method(other, operator.pow)
|
|
@unpack_zerodim_and_defer("__rpow__")
|
def __rpow__(self, other):
|
return self._arith_method(other, roperator.rpow)
|
|
|
# -----------------------------------------------------------------------------
|
# Helpers to implement __array_ufunc__
|
|
|
def array_ufunc(self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
|
"""
|
Compatibility with numpy ufuncs.
|
|
See also
|
--------
|
numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__
|
"""
|
from pandas.core.frame import (
|
DataFrame,
|
Series,
|
)
|
from pandas.core.generic import NDFrame
|
from pandas.core.internals import BlockManager
|
|
cls = type(self)
|
|
kwargs = _standardize_out_kwarg(**kwargs)
|
|
# for binary ops, use our custom dunder methods
|
result = maybe_dispatch_ufunc_to_dunder_op(self, ufunc, method, *inputs, **kwargs)
|
if result is not NotImplemented:
|
return result
|
|
# Determine if we should defer.
|
no_defer = (
|
np.ndarray.__array_ufunc__,
|
cls.__array_ufunc__,
|
)
|
|
for item in inputs:
|
higher_priority = (
|
hasattr(item, "__array_priority__")
|
and item.__array_priority__ > self.__array_priority__
|
)
|
has_array_ufunc = (
|
hasattr(item, "__array_ufunc__")
|
and type(item).__array_ufunc__ not in no_defer
|
and not isinstance(item, self._HANDLED_TYPES)
|
)
|
if higher_priority or has_array_ufunc:
|
return NotImplemented
|
|
# align all the inputs.
|
types = tuple(type(x) for x in inputs)
|
alignable = [x for x, t in zip(inputs, types) if issubclass(t, NDFrame)]
|
|
if len(alignable) > 1:
|
# This triggers alignment.
|
# At the moment, there aren't any ufuncs with more than two inputs
|
# so this ends up just being x1.index | x2.index, but we write
|
# it to handle *args.
|
set_types = set(types)
|
if len(set_types) > 1 and {DataFrame, Series}.issubset(set_types):
|
# We currently don't handle ufunc(DataFrame, Series)
|
# well. Previously this raised an internal ValueError. We might
|
# support it someday, so raise a NotImplementedError.
|
raise NotImplementedError(
|
f"Cannot apply ufunc {ufunc} to mixed DataFrame and Series inputs."
|
)
|
axes = self.axes
|
for obj in alignable[1:]:
|
# this relies on the fact that we aren't handling mixed
|
# series / frame ufuncs.
|
for i, (ax1, ax2) in enumerate(zip(axes, obj.axes)):
|
axes[i] = ax1.union(ax2)
|
|
reconstruct_axes = dict(zip(self._AXIS_ORDERS, axes))
|
inputs = tuple(
|
x.reindex(**reconstruct_axes) if issubclass(t, NDFrame) else x
|
for x, t in zip(inputs, types)
|
)
|
else:
|
reconstruct_axes = dict(zip(self._AXIS_ORDERS, self.axes))
|
|
if self.ndim == 1:
|
names = [getattr(x, "name") for x in inputs if hasattr(x, "name")]
|
name = names[0] if len(set(names)) == 1 else None
|
reconstruct_kwargs = {"name": name}
|
else:
|
reconstruct_kwargs = {}
|
|
def reconstruct(result):
|
if ufunc.nout > 1:
|
# np.modf, np.frexp, np.divmod
|
return tuple(_reconstruct(x) for x in result)
|
|
return _reconstruct(result)
|
|
def _reconstruct(result):
|
if lib.is_scalar(result):
|
return result
|
|
if result.ndim != self.ndim:
|
if method == "outer":
|
raise NotImplementedError
|
return result
|
if isinstance(result, BlockManager):
|
# we went through BlockManager.apply e.g. np.sqrt
|
result = self._constructor(result, **reconstruct_kwargs, copy=False)
|
else:
|
# we converted an array, lost our axes
|
result = self._constructor(
|
result, **reconstruct_axes, **reconstruct_kwargs, copy=False
|
)
|
# TODO: When we support multiple values in __finalize__, this
|
# should pass alignable to `__finalize__` instead of self.
|
# Then `np.add(a, b)` would consider attrs from both a and b
|
# when a and b are NDFrames.
|
if len(alignable) == 1:
|
result = result.__finalize__(self)
|
return result
|
|
if "out" in kwargs:
|
# e.g. test_multiindex_get_loc
|
result = dispatch_ufunc_with_out(self, ufunc, method, *inputs, **kwargs)
|
return reconstruct(result)
|
|
if method == "reduce":
|
# e.g. test.series.test_ufunc.test_reduce
|
result = dispatch_reduction_ufunc(self, ufunc, method, *inputs, **kwargs)
|
if result is not NotImplemented:
|
return result
|
|
# We still get here with kwargs `axis` for e.g. np.maximum.accumulate
|
# and `dtype` and `keepdims` for np.ptp
|
|
if self.ndim > 1 and (len(inputs) > 1 or ufunc.nout > 1):
|
# Just give up on preserving types in the complex case.
|
# In theory we could preserve them for them.
|
# * nout>1 is doable if BlockManager.apply took nout and
|
# returned a Tuple[BlockManager].
|
# * len(inputs) > 1 is doable when we know that we have
|
# aligned blocks / dtypes.
|
|
# e.g. my_ufunc, modf, logaddexp, heaviside, subtract, add
|
inputs = tuple(np.asarray(x) for x in inputs)
|
# Note: we can't use default_array_ufunc here bc reindexing means
|
# that `self` may not be among `inputs`
|
result = getattr(ufunc, method)(*inputs, **kwargs)
|
elif self.ndim == 1:
|
# ufunc(series, ...)
|
inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
|
result = getattr(ufunc, method)(*inputs, **kwargs)
|
else:
|
# ufunc(dataframe)
|
if method == "__call__" and not kwargs:
|
# for np.<ufunc>(..) calls
|
# kwargs cannot necessarily be handled block-by-block, so only
|
# take this path if there are no kwargs
|
mgr = inputs[0]._mgr
|
result = mgr.apply(getattr(ufunc, method))
|
else:
|
# otherwise specific ufunc methods (eg np.<ufunc>.accumulate(..))
|
# Those can have an axis keyword and thus can't be called block-by-block
|
result = default_array_ufunc(inputs[0], ufunc, method, *inputs, **kwargs)
|
# e.g. np.negative (only one reached), with "where" and "out" in kwargs
|
|
result = reconstruct(result)
|
return result
|
|
|
def _standardize_out_kwarg(**kwargs) -> dict:
|
"""
|
If kwargs contain "out1" and "out2", replace that with a tuple "out"
|
|
np.divmod, np.modf, np.frexp can have either `out=(out1, out2)` or
|
`out1=out1, out2=out2)`
|
"""
|
if "out" not in kwargs and "out1" in kwargs and "out2" in kwargs:
|
out1 = kwargs.pop("out1")
|
out2 = kwargs.pop("out2")
|
out = (out1, out2)
|
kwargs["out"] = out
|
return kwargs
|
|
|
def dispatch_ufunc_with_out(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
|
"""
|
If we have an `out` keyword, then call the ufunc without `out` and then
|
set the result into the given `out`.
|
"""
|
|
# Note: we assume _standardize_out_kwarg has already been called.
|
out = kwargs.pop("out")
|
where = kwargs.pop("where", None)
|
|
result = getattr(ufunc, method)(*inputs, **kwargs)
|
|
if result is NotImplemented:
|
return NotImplemented
|
|
if isinstance(result, tuple):
|
# i.e. np.divmod, np.modf, np.frexp
|
if not isinstance(out, tuple) or len(out) != len(result):
|
raise NotImplementedError
|
|
for arr, res in zip(out, result):
|
_assign_where(arr, res, where)
|
|
return out
|
|
if isinstance(out, tuple):
|
if len(out) == 1:
|
out = out[0]
|
else:
|
raise NotImplementedError
|
|
_assign_where(out, result, where)
|
return out
|
|
|
def _assign_where(out, result, where) -> None:
|
"""
|
Set a ufunc result into 'out', masking with a 'where' argument if necessary.
|
"""
|
if where is None:
|
# no 'where' arg passed to ufunc
|
out[:] = result
|
else:
|
np.putmask(out, where, result)
|
|
|
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):
|
raise NotImplementedError
|
|
new_inputs = [x if x is not self else np.asarray(x) for x in inputs]
|
|
return getattr(ufunc, method)(*new_inputs, **kwargs)
|
|
|
def dispatch_reduction_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
|
"""
|
Dispatch ufunc reductions to self's reduction methods.
|
"""
|
assert method == "reduce"
|
|
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)
|
