Sindbad~EG File Manager
"""
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 (
ArrayManager,
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, ArrayManager)):
# we went through BlockManager.apply e.g. np.sqrt
result = self._constructor_from_mgr(result, axes=result.axes)
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)
Sindbad File Manager Version 1.0, Coded By Sindbad EG ~ The Terrorists