def _make_compare(op):
opname = '__{op}__'.format(op=op.__name__)
def _evaluate_compare(self, other):
# if we have a Categorical type, then must have the same
# categories
if isinstance(other, CategoricalIndex):
other = other._values
elif isinstance(other, Index):
other = self._create_categorical(
self, other._values, categories=self.categories,
ordered=self.ordered)
if isinstance(other, (ABCCategorical, np.ndarray,
ABCSeries)):
if len(self.values) != len(other):
raise ValueError("Lengths must match to compare")
if isinstance(other, ABCCategorical):
if not self.values.is_dtype_equal(other):
raise TypeError("categorical index comparisons must "
"have the same categories and ordered "
"attributes")
result = op(self.values, other)
if isinstance(result, ABCSeries):
# Dispatch to pd.Categorical returned NotImplemented
# and we got a Series back; down-cast to ndarray
result = result.values
return result
return compat.set_function_name(_evaluate_compare, opname, cls)
def _make_comparison_op(op, cls):
# TODO: share code with indexes.base version? Main difference is that
# the block for MultiIndex was removed here.
def cmp_method(self, other):
if isinstance(other, ABCDataFrame):
return NotImplemented
if isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
if other.ndim > 0 and len(self) != len(other):
raise ValueError('Lengths must match to compare')
if needs_i8_conversion(self) and needs_i8_conversion(other):
# we may need to directly compare underlying
# representations
return self._evaluate_compare(other, op)
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings(record=True):
with np.errstate(all='ignore'):
result = op(self.values, np.asarray(other))
return result
name = '__{name}__'.format(name=op.__name__)
# TODO: docstring?
return compat.set_function_name(cmp_method, name, cls)
def _create_comparison_method(cls, op):
def cmp_method(self, other):
op_name = op.__name__
mask = None
if isinstance(other, IntegerArray):
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other)
if other.ndim > 0 and len(self) != len(other):
raise ValueError('Lengths must match to compare')
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings(record=True):
with np.errstate(all='ignore'):
result = op(self._data, other)
# nans propagate
if mask is None:
mask = self._mask
else:
mask = self._mask | mask
result[mask] = True if op_name == 'ne' else False
return result
name = '__{name}__'.format(name=op.__name__)
return set_function_name(cmp_method, name, cls)
def _make_compare(opname):
def _evaluate_compare(self, other):
# if we have a Categorical type, then must have the same
# categories
if isinstance(other, CategoricalIndex):
other = other._values
elif isinstance(other, Index):
other = self._create_categorical(
self, other._values, categories=self.categories,
ordered=self.ordered)
if isinstance(other, (ABCCategorical, np.ndarray,
ABCSeries)):
if len(self.values) != len(other):
raise ValueError("Lengths must match to compare")
if isinstance(other, ABCCategorical):
if not self.values.is_dtype_equal(other):
raise TypeError("categorical index comparisions must "
"have the same categories and ordered "
"attributes")
return getattr(self.values, opname)(other)
return compat.set_function_name(_evaluate_compare, opname, cls)
def _dt_array_cmp(cls, op):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
meth = getattr(dtl.DatetimeLikeArrayMixin, opname)
if isinstance(other, (datetime, np.datetime64, compat.string_types)):
if isinstance(other, (datetime, np.datetime64)):
# GH#18435 strings get a pass from tzawareness compat
self._assert_tzawareness_compat(other)
try:
other = _to_m8(other, tz=self.tz)
except ValueError:
# string that cannot be parsed to Timestamp
return ops.invalid_comparison(self, other, op)
result = meth(self, other)
if isna(other):
result.fill(nat_result)
elif lib.is_scalar(other):
return ops.invalid_comparison(self, other, op)
else:
if isinstance(other, list):
# FIXME: This can break for object-dtype with mixed types
other = type(self)(other)
elif not isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
# Following Timestamp convention, __eq__ is all-False
# and __ne__ is all True, others raise TypeError.
return ops.invalid_comparison(self, other, op)
if is_object_dtype(other):
result = op(self.astype('O'), np.array(other))
elif not (is_datetime64_dtype(other) or
is_datetime64tz_dtype(other)):
# e.g. is_timedelta64_dtype(other)
return ops.invalid_comparison(self, other, op)
else:
self._assert_tzawareness_compat(other)
result = meth(self, np.asarray(other))
result = com.values_from_object(result)
# Make sure to pass an array to result[...]; indexing with
# Series breaks with older version of numpy
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _create_arithmetic_method(cls, op):
def integer_arithmetic_method(self, other):
op_name = op.__name__
mask = None
if isinstance(other, (ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
if getattr(other, 'ndim', 0) > 1:
raise NotImplementedError(
"can only perform ops with 1-d structures")
if isinstance(other, IntegerArray):
other, mask = other._data, other._mask
elif getattr(other, 'ndim', None) == 0:
other = other.item()
elif is_list_like(other):
other = np.asarray(other)
if not other.ndim:
other = other.item()
elif other.ndim == 1:
if not (is_float_dtype(other) or is_integer_dtype(other)):
raise TypeError(
"can only perform ops with numeric values")
else:
if not (is_float(other) or is_integer(other)):
raise TypeError("can only perform ops with numeric values")
# nans propagate
if mask is None:
mask = self._mask
else:
mask = self._mask | mask
# 1 ** np.nan is 1. So we have to unmask those.
if op_name == 'pow':
mask = np.where(self == 1, False, mask)
elif op_name == 'rpow':
mask = np.where(other == 1, False, mask)
with np.errstate(all='ignore'):
result = op(self._data, other)
# divmod returns a tuple
if op_name == 'divmod':
div, mod = result
return (self._maybe_mask_result(div, mask, other, 'floordiv'),
self._maybe_mask_result(mod, mask, other, 'mod'))
return self._maybe_mask_result(result, mask, other, op_name)
name = '__{name}__'.format(name=op.__name__)
return set_function_name(integer_arithmetic_method, name, cls)
def _create_unary_method(cls, op):
def sparse_unary_method(self):
fill_value = op(np.array(self.fill_value)).item()
values = op(self.sp_values)
dtype = SparseDtype(values.dtype, fill_value)
return cls._simple_new(values, self.sp_index, dtype)
name = '__{name}__'.format(name=op.__name__)
return compat.set_function_name(sparse_unary_method, name, cls)
def _create_method(cls, op, coerce_to_dtype=True):
"""
A class method that returns a method that will correspond to an
operator for an ExtensionArray subclass, by dispatching to the
relevant operator defined on the individual elements of the
ExtensionArray.
Parameters
----------
op : function
An operator that takes arguments op(a, b)
coerce_to_dtype : bool
boolean indicating whether to attempt to convert
the result to the underlying ExtensionArray dtype
(default True)
Returns
-------
A method that can be bound to a method of a class
Example
-------
Given an ExtensionArray subclass called MyExtensionArray, use
>>> __add__ = cls._create_method(operator.add)
in the class definition of MyExtensionArray to create the operator
for addition, that will be based on the operator implementation
of the underlying elements of the ExtensionArray
"""
def _binop(self, other):
def convert_values(param):
if isinstance(param, ExtensionArray) or is_list_like(param):
ovalues = param
else: # Assume its an object
ovalues = [param] * len(self)
return ovalues
lvalues = self
rvalues = convert_values(other)
# If the operator is not defined for the underlying objects,
# a TypeError should be raised
res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
if coerce_to_dtype:
try:
res = self._from_sequence(res)
except TypeError:
pass
return res
op_name = ops._get_op_name(op, True)
return set_function_name(_binop, op_name, cls)
def _td_index_cmp(opname, cls):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
def wrapper(self, other):
result = getattr(TimedeltaArrayMixin, opname)(self, other)
if is_bool_dtype(result):
# support of bool dtype indexers
return result
return Index(result)
return compat.set_function_name(wrapper, opname, cls)
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
op = getattr(self.asi8, opname)
# We want to eventually defer to the Series or PeriodIndex (which will
# return here with an unboxed PeriodArray). But before we do that,
# we do a bit of validation on type (Period) and freq, so that our
# error messages are sensible
not_implemented = isinstance(other, (ABCSeries, ABCIndexClass))
if not_implemented:
other = other._values
if isinstance(other, Period):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other.ordinal)
elif isinstance(other, cls):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
if not_implemented:
return NotImplemented
result = op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _dt_array_cmp(opname, cls):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
meth = getattr(dtl.DatetimeLikeArrayMixin, opname)
if isinstance(other, (datetime, np.datetime64, compat.string_types)):
if isinstance(other, datetime):
# GH#18435 strings get a pass from tzawareness compat
self._assert_tzawareness_compat(other)
other = _to_m8(other, tz=self.tz)
result = meth(self, other)
if isna(other):
result.fill(nat_result)
else:
if isinstance(other, list):
other = type(self)(other)
elif not isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries)):
# Following Timestamp convention, __eq__ is all-False
# and __ne__ is all True, others raise TypeError.
if opname == '__eq__':
return np.zeros(shape=self.shape, dtype=bool)
elif opname == '__ne__':
return np.ones(shape=self.shape, dtype=bool)
raise TypeError('%s type object %s' %
(type(other), str(other)))
if is_datetimelike(other):
self._assert_tzawareness_compat(other)
result = meth(self, np.asarray(other))
result = com._values_from_object(result)
# Make sure to pass an array to result[...]; indexing with
# Series breaks with older version of numpy
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _td_array_cmp(cls, op):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = opname == '__ne__'
def wrapper(self, other):
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
return NotImplemented
if _is_convertible_to_td(other) or other is NaT:
try:
other = Timedelta(other)
except ValueError:
# failed to parse as timedelta
return ops.invalid_comparison(self, other, op)
result = op(self.view('i8'), other.value)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
return ops.invalid_comparison(self, other, op)
elif len(other) != len(self):
raise ValueError("Lengths must match")
else:
try:
other = type(self)._from_sequence(other)._data
except (ValueError, TypeError):
return ops.invalid_comparison(self, other, op)
result = op(self.view('i8'), other.view('i8'))
result = com.values_from_object(result)
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _td_array_cmp(cls, op):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
meth = getattr(dtl.DatetimeLikeArrayMixin, opname)
def wrapper(self, other):
if _is_convertible_to_td(other) or other is NaT:
try:
other = _to_m8(other)
except ValueError:
# failed to parse as timedelta
return ops.invalid_comparison(self, other, op)
result = meth(self, other)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
return ops.invalid_comparison(self, other, op)
elif len(other) != len(self):
raise ValueError("Lengths must match")
else:
try:
other = type(self)._from_sequence(other)._data
except (ValueError, TypeError):
return ops.invalid_comparison(self, other, op)
result = meth(self, other)
result = com.values_from_object(result)
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = opname == '__ne__'
def wrapper(self, other):
op = getattr(self.asi8, opname)
other = lib.item_from_zerodim(other)
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
return NotImplemented
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
if isinstance(other, Period):
self._check_compatible_with(other)
result = op(other.ordinal)
elif isinstance(other, cls):
self._check_compatible_with(other)
result = op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _create_comparison_method(cls, op):
op_name = op.__name__
def cmp_method(self, other):
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
other = lib.item_from_zerodim(other)
mask = None
if isinstance(other, BooleanArray):
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other)
if other.ndim > 1:
raise NotImplementedError(
"can only perform ops with 1-d structures")
if len(self) != len(other):
raise ValueError("Lengths must match to compare")
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "elementwise", FutureWarning)
with np.errstate(all="ignore"):
result = op(self._data, other)
# nans propagate
if mask is None:
mask = self._mask
else:
mask = self._mask | mask
result[mask] = op_name == "ne"
return BooleanArray(result,
np.zeros(len(result), dtype=bool),
copy=False)
name = "__{name}__".format(name=op.__name__)
return set_function_name(cmp_method, name, cls)
def _td_index_cmp(opname, cls):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
msg = "cannot compare a TimedeltaIndex with type {0}"
func = getattr(super(TimedeltaIndex, self), opname)
if _is_convertible_to_td(other) or other is NaT:
try:
other = _to_m8(other)
except ValueError:
# failed to parse as timedelta
raise TypeError(msg.format(type(other)))
result = func(other)
if isna(other):
result.fill(nat_result)
else:
if not is_list_like(other):
raise TypeError(msg.format(type(other)))
other = TimedeltaIndex(other).values
result = func(other)
result = com._values_from_object(result)
if isinstance(other, Index):
o_mask = other.values.view('i8') == iNaT
else:
o_mask = other.view('i8') == iNaT
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
# support of bool dtype indexers
if is_bool_dtype(result):
return result
return Index(result)
return compat.set_function_name(wrapper, opname, cls)
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
op = getattr(self.asi8, opname)
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
return NotImplemented
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
if isinstance(other, Period):
self._check_compatible_with(other)
result = op(other.ordinal)
elif isinstance(other, cls):
self._check_compatible_with(other)
result = op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _create_arithmetic_method(cls, op):
def arithmetic_method(self, other):
if isinstance(other, (ABCIndexClass, ABCSeries)):
return NotImplemented
elif isinstance(other, cls):
other = other._ndarray
with np.errstate(all="ignore"):
result = op(self._ndarray, other)
if op is divmod:
a, b = result
return cls(a), cls(b)
return cls(result)
return compat.set_function_name(arithmetic_method,
f"__{op.__name__}__", cls)
def _td_array_cmp(cls, op):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
meth = getattr(dtl.DatetimeLikeArrayMixin, opname)
def wrapper(self, other):
if _is_convertible_to_td(other) or other is NaT:
try:
other = _to_m8(other)
except ValueError:
# failed to parse as timedelta
return ops.invalid_comparison(self, other, op)
result = meth(self, other)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
return ops.invalid_comparison(self, other, op)
else:
try:
other = type(self)._from_sequence(other)._data
except (ValueError, TypeError):
return ops.invalid_comparison(self, other, op)
result = meth(self, other)
result = com.values_from_object(result)
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _td_index_cmp(opname, cls, nat_result=False):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
def wrapper(self, other):
msg = "cannot compare a TimedeltaIndex with type {0}"
func = getattr(super(TimedeltaIndex, self), opname)
if _is_convertible_to_td(other) or other is NaT:
try:
other = _to_m8(other)
except ValueError:
# failed to parse as timedelta
raise TypeError(msg.format(type(other)))
result = func(other)
if isna(other):
result.fill(nat_result)
else:
if not is_list_like(other):
raise TypeError(msg.format(type(other)))
other = TimedeltaIndex(other).values
result = func(other)
result = _values_from_object(result)
if isinstance(other, Index):
o_mask = other.values.view('i8') == iNaT
else:
o_mask = other.view('i8') == iNaT
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
# support of bool dtype indexers
if is_bool_dtype(result):
return result
return Index(result)
return compat.set_function_name(wrapper, opname, cls)
def _create_arithmetic_method(cls, op):
def arithmetic_method(self, other):
if isinstance(other, (ABCIndexClass, ABCSeries)):
return NotImplemented
elif isinstance(other, cls):
other = other._ndarray
with np.errstate(all="ignore"):
result = op(self._ndarray, other)
if op is divmod:
a, b = result
return cls(a), cls(b)
return cls(result)
return compat.set_function_name(arithmetic_method,
"__{}__".format(op.__name__),
cls)
def _create_comparison_method(cls, op):
op_name = op.__name__
if op_name in {"and_", "or_"}:
op_name = op_name[:-1]
def cmp_method(self, other):
if isinstance(other, (ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
if not is_scalar(other) and not isinstance(other, type(self)):
# convert list-like to ndarray
other = np.asarray(other)
if isinstance(other, np.ndarray):
# TODO: make this more flexible than just ndarray...
if len(self) != len(other):
raise AssertionError(
"length mismatch: {self} vs. {other}".format(
self=len(self), other=len(other)
)
)
other = SparseArray(other, fill_value=self.fill_value)
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
else:
with np.errstate(all="ignore"):
fill_value = op(self.fill_value, other)
result = op(self.sp_values, other)
return type(self)(
result,
sparse_index=self.sp_index,
fill_value=fill_value,
dtype=np.bool_,
)
name = "__{name}__".format(name=op.__name__)
return compat.set_function_name(cmp_method, name, cls)
def _td_index_cmp(opname, cls):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
msg = "cannot compare a {cls} with type {typ}"
func = getattr(super(TimedeltaIndex, self), opname)
if _is_convertible_to_td(other) or other is NaT:
try:
other = _to_m8(other)
except ValueError:
# failed to parse as timedelta
raise TypeError(msg.format(cls=type(self).__name__,
typ=type(other).__name__))
result = func(other)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
raise TypeError(msg.format(cls=type(self).__name__,
typ=type(other).__name__))
else:
other = TimedeltaIndex(other).values
result = func(other)
result = com._values_from_object(result)
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
# support of bool dtype indexers
if is_bool_dtype(result):
return result
return Index(result)
return compat.set_function_name(wrapper, opname, cls)
def _create_arithmetic_method(cls, op):
op_name = op.__name__
@unpack_zerodim_and_defer(op_name)
def sparse_arithmetic_method(self, other):
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all="ignore"):
fill = op(_get_fill(self), np.asarray(other))
result = op(self.sp_values, other)
if op_name == "divmod":
left, right = result
lfill, rfill = fill
return (
_wrap_result(op_name, left, self.sp_index, lfill),
_wrap_result(op_name, right, self.sp_index, rfill),
)
return _wrap_result(op_name, result, self.sp_index, fill)
else:
other = np.asarray(other)
with np.errstate(all="ignore"):
# TODO: look into _wrap_result
if len(self) != len(other):
raise AssertionError(
(f"length mismatch: {len(self)} vs. {len(other)}")
)
if not isinstance(other, SparseArray):
dtype = getattr(other, "dtype", None)
other = SparseArray(
other, fill_value=self.fill_value, dtype=dtype
)
return _sparse_array_op(self, other, op, op_name)
name = f"__{op.__name__}__"
return compat.set_function_name(sparse_arithmetic_method, name, cls)
def _period_index_cmp(opname, cls, nat_result=False):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
def wrapper(self, other):
if isinstance(other, Period):
func = getattr(self._ndarray_values, opname)
other_base, _ = _gfc(other.freq)
if other.freq != self.freq:
msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = func(other.ordinal)
elif isinstance(other, PeriodIndex):
if other.freq != self.freq:
msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
op = getattr(self._ndarray_values, opname)
result = op(other._ndarray_values)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is tslib.NaT:
result = np.empty(len(self._ndarray_values), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
func = getattr(self._ndarray_values, opname)
result = func(other.ordinal)
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _period_index_cmp(opname, cls, nat_result=False):
"""
Wrap comparison operations to convert datetime-like to datetime64
"""
def wrapper(self, other):
if isinstance(other, Period):
func = getattr(self._ndarray_values, opname)
other_base, _ = _gfc(other.freq)
if other.freq != self.freq:
msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = func(other.ordinal)
elif isinstance(other, PeriodIndex):
if other.freq != self.freq:
msg = _DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
op = getattr(self._ndarray_values, opname)
result = op(other._ndarray_values)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is tslib.NaT:
result = np.empty(len(self._ndarray_values), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
func = getattr(self._ndarray_values, opname)
result = func(other.ordinal)
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _create_arithmetic_method(cls, op):
def sparse_arithmetic_method(self, other):
op_name = op.__name__
if isinstance(other, (ABCSeries, ABCIndexClass)):
other = getattr(other, 'values', other)
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
fill = op(_get_fill(self), np.asarray(other))
result = op(self.sp_values, other)
if op_name == 'divmod':
left, right = result
lfill, rfill = fill
return (_wrap_result(op_name, left, self.sp_index, lfill),
_wrap_result(op_name, right, self.sp_index, rfill))
return _wrap_result(op_name, result, self.sp_index, fill)
else:
other = np.asarray(other)
with np.errstate(all='ignore'):
# TODO: delete sparse stuff in core/ops.py
# TODO: look into _wrap_result
if len(self) != len(other):
raise AssertionError(
("length mismatch: {self} vs. {other}".format(
self=len(self), other=len(other))))
if not isinstance(other, SparseArray):
dtype = getattr(other, 'dtype', None)
other = SparseArray(other,
fill_value=self.fill_value,
dtype=dtype)
return _sparse_array_op(self, other, op, op_name)
name = '__{name}__'.format(name=op.__name__)
return compat.set_function_name(sparse_arithmetic_method, name, cls)
def _create_logical_method(cls, op):
@ops.unpack_zerodim_and_defer(op.__name__)
def logical_method(self, other):
assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}
other_is_booleanarray = isinstance(other, BooleanArray)
other_is_scalar = lib.is_scalar(other)
mask = None
if other_is_booleanarray:
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other, dtype="bool")
if other.ndim > 1:
raise NotImplementedError(
"can only perform ops with 1-d structures"
)
other, mask = coerce_to_array(other, copy=False)
elif isinstance(other, np.bool_):
other = other.item()
if other_is_scalar and not (other is libmissing.NA or lib.is_bool(other)):
raise TypeError(
"'other' should be pandas.NA or a bool. "
f"Got {type(other).__name__} instead."
)
if not other_is_scalar and len(self) != len(other):
raise ValueError("Lengths must match to compare")
if op.__name__ in {"or_", "ror_"}:
result, mask = ops.kleene_or(self._data, other, self._mask, mask)
elif op.__name__ in {"and_", "rand_"}:
result, mask = ops.kleene_and(self._data, other, self._mask, mask)
elif op.__name__ in {"xor", "rxor"}:
result, mask = ops.kleene_xor(self._data, other, self._mask, mask)
return BooleanArray(result, mask)
name = f"__{op.__name__}__"
return set_function_name(logical_method, name, cls)
def _create_comparison_method(cls, op):
op_name = op.__name__
@unpack_zerodim_and_defer(op.__name__)
def cmp_method(self, other):
mask = None
if isinstance(other, IntegerArray):
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other)
if other.ndim > 1:
raise NotImplementedError(
"can only perform ops with 1-d structures"
)
if len(self) != len(other):
raise ValueError("Lengths must match to compare")
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "elementwise", FutureWarning)
with np.errstate(all="ignore"):
method = getattr(self._data, f"__{op_name}__")
result = method(other)
if result is NotImplemented:
result = invalid_comparison(self._data, other, op)
# nans propagate
if mask is None:
mask = self._mask
else:
mask = self._mask | mask
result[mask] = op_name == "ne"
return result
name = f"__{op.__name__}__"
return set_function_name(cmp_method, name, cls)
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = "__{name}__".format(name=op.__name__)
nat_result = opname == "__ne__"
@unpack_zerodim_and_defer(opname)
def wrapper(self, other):
ordinal_op = getattr(self.asi8, opname)
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
if isinstance(other, Period):
self._check_compatible_with(other)
result = ordinal_op(other.ordinal)
elif isinstance(other, cls):
self._check_compatible_with(other)
result = ordinal_op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = ordinal_op(other.ordinal)
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _create_method(cls, op, coerce_to_dtype=True, result_dtype=None):
"""
Add support for binary operators by unwrapping, applying, and
rewrapping.
"""
# NOTE(Clark): This overrides, but coerce_to_dtype, result_dtype might
# not be needed
def _binop(self, other):
lvalues = self._tensor
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndex)):
# Rely on Pandas to unbox and dispatch to us.
return NotImplemented
# divmod returns a tuple
if op_name in ["__divmod__", "__rdivmod__"]:
# TODO(Clark): Add support for divmod and rdivmod.
# div, mod = result
raise NotImplementedError
if isinstance(other, (TensorArray, TensorArrayElement)):
rvalues = other._tensor
else:
rvalues = other
result = op(lvalues, rvalues)
# Force a TensorArray if rvalue is not a scalar.
if (isinstance(self, TensorArrayElement)
and (not isinstance(other, TensorArrayElement)
or not np.isscalar(other))):
result_wrapped = TensorArray(result)
else:
result_wrapped = cls(result)
return result_wrapped
op_name = f"__{op.__name__}__"
return set_function_name(_binop, op_name, cls)
def _create_arithmetic_method(cls, op):
def sparse_arithmetic_method(self, other):
op_name = op.__name__
if isinstance(other, (ABCSeries, ABCIndexClass)):
other = getattr(other, 'values', other)
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
elif is_scalar(other):
with np.errstate(all='ignore'):
fill = op(_get_fill(self), np.asarray(other))
result = op(self.sp_values, other)
if op_name == 'divmod':
left, right = result
lfill, rfill = fill
return (_wrap_result(op_name, left, self.sp_index, lfill),
_wrap_result(op_name, right, self.sp_index, rfill))
return _wrap_result(op_name, result, self.sp_index, fill)
else:
other = np.asarray(other)
with np.errstate(all='ignore'):
# TODO: delete sparse stuff in core/ops.py
# TODO: look into _wrap_result
if len(self) != len(other):
raise AssertionError(
("length mismatch: {self} vs. {other}".format(
self=len(self), other=len(other))))
if not isinstance(other, SparseArray):
dtype = getattr(other, 'dtype', None)
other = SparseArray(other, fill_value=self.fill_value,
dtype=dtype)
return _sparse_array_op(self, other, op, op_name)
name = '__{name}__'.format(name=op.__name__)
return compat.set_function_name(sparse_arithmetic_method, name, cls)
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
op = getattr(self._ndarray_values, opname)
if isinstance(other, Period):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other.ordinal)
elif isinstance(other, PeriodArrayMixin):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other._ndarray_values)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self._ndarray_values), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
op = getattr(self._ndarray_values, opname)
if isinstance(other, Period):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other.ordinal)
elif isinstance(other, PeriodArrayMixin):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other._ndarray_values)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self._ndarray_values), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _td_array_cmp(cls, op):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
msg = "cannot compare a {cls} with type {typ}"
meth = getattr(dtl.DatetimeLikeArrayMixin, opname)
if _is_convertible_to_td(other) or other is NaT:
try:
other = _to_m8(other)
except ValueError:
# failed to parse as timedelta
raise TypeError(
msg.format(cls=type(self).__name__,
typ=type(other).__name__))
result = meth(self, other)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
raise TypeError(
msg.format(cls=type(self).__name__, typ=type(other).__name__))
else:
other = type(self)(other).values
result = meth(self, other)
result = com._values_from_object(result)
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _td_array_cmp(cls, op):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
msg = "cannot compare a {cls} with type {typ}"
meth = getattr(dtl.DatetimeLikeArrayMixin, opname)
if _is_convertible_to_td(other) or other is NaT:
try:
other = _to_m8(other)
except ValueError:
# failed to parse as timedelta
raise TypeError(msg.format(cls=type(self).__name__,
typ=type(other).__name__))
result = meth(self, other)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
raise TypeError(msg.format(cls=type(self).__name__,
typ=type(other).__name__))
else:
other = type(self)(other)._data
result = meth(self, other)
result = com.values_from_object(result)
o_mask = np.array(isna(other))
if o_mask.any():
result[o_mask] = nat_result
if self.hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _create_comparison_method(cls, op):
def cmp_method(self, other):
op_name = op.__name__
mask = None
if isinstance(other, (ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
if isinstance(other, IntegerArray):
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other)
if other.ndim > 0 and len(self) != len(other):
raise ValueError('Lengths must match to compare')
other = lib.item_from_zerodim(other)
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "elementwise", FutureWarning)
with np.errstate(all='ignore'):
result = op(self._data, other)
# nans propagate
if mask is None:
mask = self._mask
else:
mask = self._mask | mask
result[mask] = op_name == 'ne'
return result
name = '__{name}__'.format(name=op.__name__)
return set_function_name(cmp_method, name, cls)
def _create_comparison_method(cls, op):
def cmp_method(self, other):
op_name = op.__name__
mask = None
if isinstance(other, (ABCSeries, ABCIndexClass)):
# Rely on pandas to unbox and dispatch to us.
return NotImplemented
if isinstance(other, IntegerArray):
other, mask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other)
if other.ndim > 0 and len(self) != len(other):
raise ValueError('Lengths must match to compare')
other = lib.item_from_zerodim(other)
# numpy will show a DeprecationWarning on invalid elementwise
# comparisons, this will raise in the future
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "elementwise", FutureWarning)
with np.errstate(all='ignore'):
result = op(self._data, other)
# nans propagate
if mask is None:
mask = self._mask
else:
mask = self._mask | mask
result[mask] = True if op_name == 'ne' else False
return result
name = '__{name}__'.format(name=op.__name__)
return set_function_name(cmp_method, name, cls)
def _create_arithmetic_method(cls, op):
# Note: this handles both arithmetic and comparison methods.
def method(self, other):
from pandas.arrays import BooleanArray
assert op.__name__ in ops.ARITHMETIC_BINOPS | ops.COMPARISON_BINOPS
if isinstance(other, (ABCIndexClass, ABCSeries, ABCDataFrame)):
return NotImplemented
elif isinstance(other, cls):
other = other._ndarray
mask = isna(self) | isna(other)
valid = ~mask
if not lib.is_scalar(other):
if len(other) != len(self):
# prevent improper broadcasting when other is 2D
raise ValueError(
f"Lengths of operands do not match: {len(self)} != {len(other)}"
)
other = np.asarray(other)
other = other[valid]
if op.__name__ in ops.ARITHMETIC_BINOPS:
result = np.empty_like(self._ndarray, dtype="object")
result[mask] = StringDtype.na_value
result[valid] = op(self._ndarray[valid], other)
return StringArray(result)
else:
# logical
result = np.zeros(len(self._ndarray), dtype="bool")
result[valid] = op(self._ndarray[valid], other)
return BooleanArray(result, mask)
return compat.set_function_name(method, f"__{op.__name__}__", cls)
def _create_comparison_method(cls, op):
def cmp_method(self, other):
op_name = op.__name__
if op_name in {'and_', 'or_'}:
op_name = op_name[:-1]
if isinstance(other, (ABCSeries, ABCIndexClass)):
other = getattr(other, 'values', other)
if not is_scalar(other) and not isinstance(other, type(self)):
# convert list-like to ndarary
other = np.asarray(other)
if isinstance(other, np.ndarray):
# TODO: make this more flexible than just ndarray...
if len(self) != len(other):
raise AssertionError(
"length mismatch: {self} vs. {other}".format(
self=len(self), other=len(other)))
other = SparseArray(other, fill_value=self.fill_value)
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
else:
with np.errstate(all='ignore'):
fill_value = op(self.fill_value, other)
result = op(self.sp_values, other)
return type(self)(result,
sparse_index=self.sp_index,
fill_value=fill_value,
dtype=np.bool_)
name = '__{name}__'.format(name=op.__name__)
return compat.set_function_name(cmp_method, name, cls)
def _create_comparison_method(cls, op):
def cmp_method(self, other):
op_name = op.__name__
if op_name in {'and_', 'or_'}:
op_name = op_name[:-1]
if isinstance(other, (ABCSeries, ABCIndexClass)):
other = getattr(other, 'values', other)
if not is_scalar(other) and not isinstance(other, type(self)):
# convert list-like to ndarary
other = np.asarray(other)
if isinstance(other, np.ndarray):
# TODO: make this more flexible than just ndarray...
if len(self) != len(other):
raise AssertionError("length mismatch: {self} vs. {other}"
.format(self=len(self),
other=len(other)))
other = SparseArray(other, fill_value=self.fill_value)
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
else:
with np.errstate(all='ignore'):
fill_value = op(self.fill_value, other)
result = op(self.sp_values, other)
return type(self)(result,
sparse_index=self.sp_index,
fill_value=fill_value,
dtype=np.bool_)
name = '__{name}__'.format(name=op.__name__)
return compat.set_function_name(cmp_method, name, cls)
def _create_comparison_method(cls, op):
op_name = op.__name__
if op_name in {"and_", "or_"}:
op_name = op_name[:-1]
@unpack_zerodim_and_defer(op_name)
def cmp_method(self, other):
if not is_scalar(other) and not isinstance(other, type(self)):
# convert list-like to ndarray
other = np.asarray(other)
if isinstance(other, np.ndarray):
# TODO: make this more flexible than just ndarray...
if len(self) != len(other):
raise AssertionError(
f"length mismatch: {len(self)} vs. {len(other)}"
)
other = SparseArray(other, fill_value=self.fill_value)
if isinstance(other, SparseArray):
return _sparse_array_op(self, other, op, op_name)
else:
with np.errstate(all="ignore"):
fill_value = op(self.fill_value, other)
result = op(self.sp_values, other)
return type(self)(
result,
sparse_index=self.sp_index,
fill_value=fill_value,
dtype=np.bool_,
)
name = f"__{op.__name__}__"
return compat.set_function_name(cmp_method, name, cls)
def _create_arithmetic_method(cls, op):
op_name = op.__name__
@unpack_zerodim_and_defer(op.__name__)
def integer_arithmetic_method(self, other):
omask = None
if getattr(other, "ndim", 0) > 1:
raise NotImplementedError("can only perform ops with 1-d structures")
if isinstance(other, IntegerArray):
other, omask = other._data, other._mask
elif is_list_like(other):
other = np.asarray(other)
if other.ndim > 1:
raise NotImplementedError(
"can only perform ops with 1-d structures"
)
if len(self) != len(other):
raise ValueError("Lengths must match")
if not (is_float_dtype(other) or is_integer_dtype(other)):
raise TypeError("can only perform ops with numeric values")
else:
if not (is_float(other) or is_integer(other) or other is libmissing.NA):
raise TypeError("can only perform ops with numeric values")
if omask is None:
mask = self._mask.copy()
if other is libmissing.NA:
mask |= True
else:
mask = self._mask | omask
if op_name == "pow":
# 1 ** x is 1.
mask = np.where((self._data == 1) & ~self._mask, False, mask)
# x ** 0 is 1.
if omask is not None:
mask = np.where((other == 0) & ~omask, False, mask)
elif other is not libmissing.NA:
mask = np.where(other == 0, False, mask)
elif op_name == "rpow":
# 1 ** x is 1.
if omask is not None:
mask = np.where((other == 1) & ~omask, False, mask)
elif other is not libmissing.NA:
mask = np.where(other == 1, False, mask)
# x ** 0 is 1.
mask = np.where((self._data == 0) & ~self._mask, False, mask)
if other is libmissing.NA:
result = np.ones_like(self._data)
else:
with np.errstate(all="ignore"):
result = op(self._data, other)
# divmod returns a tuple
if op_name == "divmod":
div, mod = result
return (
self._maybe_mask_result(div, mask, other, "floordiv"),
self._maybe_mask_result(mod, mask, other, "mod"),
)
return self._maybe_mask_result(result, mask, other, op_name)
name = f"__{op.__name__}__"
return set_function_name(integer_arithmetic_method, name, cls)
def _make_evaluate_binop(op, step=False):
"""
Parameters
----------
op : callable that accepts 2 params
perform the binary op
step : callable, optional, default to False
op to apply to the step parm if not None
if False, use the existing step
"""
@unpack_zerodim_and_defer(op.__name__)
def _evaluate_numeric_binop(self, other):
if isinstance(other, ABCTimedeltaIndex):
# Defer to TimedeltaIndex implementation
return NotImplemented
elif isinstance(other, (timedelta, np.timedelta64)):
# GH#19333 is_integer evaluated True on timedelta64,
# so we need to catch these explicitly
return op(self._int64index, other)
elif is_timedelta64_dtype(other):
# Must be an np.ndarray; GH#22390
return op(self._int64index, other)
other = extract_array(other, extract_numpy=True)
attrs = self._get_attributes_dict()
left, right = self, other
try:
# apply if we have an override
if step:
with np.errstate(all="ignore"):
rstep = step(left.step, right)
# we don't have a representable op
# so return a base index
if not is_integer(rstep) or not rstep:
raise ValueError
else:
rstep = left.step
with np.errstate(all="ignore"):
rstart = op(left.start, right)
rstop = op(left.stop, right)
result = type(self)(rstart, rstop, rstep, **attrs)
# for compat with numpy / Int64Index
# even if we can represent as a RangeIndex, return
# as a Float64Index if we have float-like descriptors
if not all(is_integer(x) for x in [rstart, rstop, rstep]):
result = result.astype("float64")
return result
except (ValueError, TypeError, ZeroDivisionError):
# Defer to Int64Index implementation
return op(self._int64index, other)
# TODO: Do attrs get handled reliably?
name = f"__{op.__name__}__"
return compat.set_function_name(_evaluate_numeric_binop, name, cls)
def _td_array_cmp(cls, op):
"""
Wrap comparison operations to convert timedelta-like to timedelta64
"""
opname = f"__{op.__name__}__"
nat_result = opname == "__ne__"
@unpack_zerodim_and_defer(opname)
def wrapper(self, other):
if isinstance(other, str):
try:
other = self._scalar_from_string(other)
except ValueError:
# failed to parse as timedelta
return invalid_comparison(self, other, op)
if isinstance(other, self._recognized_scalars) or other is NaT:
other = self._scalar_type(other)
self._check_compatible_with(other)
other_i8 = self._unbox_scalar(other)
result = op(self.view("i8"), other_i8)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
return invalid_comparison(self, other, op)
elif len(other) != len(self):
raise ValueError("Lengths must match")
else:
if isinstance(other, list):
other = np.array(other)
if not isinstance(other, (np.ndarray, cls)):
return invalid_comparison(self, other, op)
if is_object_dtype(other):
with np.errstate(all="ignore"):
result = ops.comp_method_OBJECT_ARRAY(
op, self.astype(object), other
)
o_mask = isna(other)
elif not cls._is_recognized_dtype(other.dtype):
# e.g. other is datetimearray
return invalid_comparison(self, other, op)
else:
other = type(self)._from_sequence(other)
self._check_compatible_with(other)
result = op(self.view("i8"), other.view("i8"))
o_mask = other._isnan
if o_mask.any():
result[o_mask] = nat_result
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
def _create_method(cls, op, coerce_to_dtype=True):
"""
A class method that returns a method that will correspond to an
operator for an ExtensionArray subclass, by dispatching to the
relevant operator defined on the individual elements of the
ExtensionArray.
Parameters
----------
op : function
An operator that takes arguments op(a, b)
coerce_to_dtype : bool
boolean indicating whether to attempt to convert
the result to the underlying ExtensionArray dtype
(default True)
Returns
-------
A method that can be bound to a method of a class
Example
-------
Given an ExtensionArray subclass called MyExtensionArray, use
>>> __add__ = cls._create_method(operator.add)
in the class definition of MyExtensionArray to create the operator
for addition, that will be based on the operator implementation
of the underlying elements of the ExtensionArray
"""
def _binop(self, other):
def validate_length(obj1, obj2):
# validates length and converts to listlike
try:
if len(obj1) == len(obj2):
return obj2
else:
raise ValueError("Lengths must match")
except TypeError:
return [obj2] * len(obj1)
def convert_values(param):
# convert to a quantity or listlike
if isinstance(param, cls):
return param.quantity
elif isinstance(param, _Quantity):
return param
elif is_list_like(param) and isinstance(param[0], _Quantity):
return type(param[0])([p.magnitude for p in param], param[0].units)
else:
return param
if isinstance(other, Series):
return NotImplemented
lvalues = self.quantity
other = validate_length(lvalues, other)
rvalues = convert_values(other)
# Pint quantities may only be exponented by single values, not arrays.
# Reduce single value arrays to single value to allow power ops
if isinstance(rvalues, _Quantity):
if len(set(np.array(rvalues.data))) == 1:
rvalues = rvalues[0]
elif len(set(np.array(rvalues))) == 1:
rvalues = rvalues[0]
# If the operator is not defined for the underlying objects,
# a TypeError should be raised
res = op(lvalues, rvalues)
if op.__name__ == "divmod":
return (
cls.from_1darray_quantity(res[0]),
cls.from_1darray_quantity(res[1]),
)
if coerce_to_dtype:
try:
res = cls.from_1darray_quantity(res)
except TypeError:
pass
return res
op_name = ops._get_op_name(op, True)
return set_function_name(_binop, op_name, cls)
def _create_method(cls, op, coerce_to_dtype=True):
"""
A class method that returns a method that will correspond to an
operator for an ExtensionArray subclass, by dispatching to the
relevant operator defined on the individual elements of the
ExtensionArray.
Parameters
----------
op : function
An operator that takes arguments op(a, b)
coerce_to_dtype : bool
boolean indicating whether to attempt to convert
the result to the underlying ExtensionArray dtype
(default True)
Returns
-------
A method that can be bound to a method of a class
Example
-------
Given an ExtensionArray subclass called MyExtensionArray, use
>>> __add__ = cls._create_method(operator.add)
in the class definition of MyExtensionArray to create the operator
for addition, that will be based on the operator implementation
of the underlying elements of the ExtensionArray
"""
def _binop(self, other):
def validate_length(obj1, obj2):
# validates length
# CHANGED: do not convert to listlike (why should we? pint.Quantity is perfecty able to handle that...)
try:
if len(obj1) != len(obj2):
raise ValueError("Lengths must match")
except TypeError:
pass
def convert_values(param):
# convert to a quantity or listlike
if isinstance(param, cls):
return param.quantity
elif isinstance(param, (_Quantity, _Unit)):
return param
elif (is_list_like(param) and len(param) > 0
and isinstance(param[0], _Quantity)):
units = param[0].units
return type(param[0])([p.m_as(units) for p in param],
units)
else:
return param
if isinstance(other, (Series, DataFrame)):
return NotImplemented
lvalues = self.quantity
validate_length(lvalues, other)
rvalues = convert_values(other)
# If the operator is not defined for the underlying objects,
# a TypeError should be raised
res = op(lvalues, rvalues)
if op.__name__ == "divmod":
return (
cls.from_1darray_quantity(res[0]),
cls.from_1darray_quantity(res[1]),
)
if coerce_to_dtype:
try:
res = cls.from_1darray_quantity(res)
except TypeError:
pass
return res
op_name = f"__{op}__"
return set_function_name(_binop, op_name, cls)
def _make_compare(op):
opname = f"__{op.__name__}__"
_evaluate_compare = make_wrapped_comparison_op(opname)
return compat.set_function_name(_evaluate_compare, opname, cls)
def _create_method(cls, op, coerce_to_dtype=True):
"""
A class method that returns a method that will correspond to an
operator for an ExtensionArray subclass, by dispatching to the
relevant operator defined on the individual elements of the
ExtensionArray.
Parameters
----------
op : function
An operator that takes arguments op(a, b)
coerce_to_dtype : bool, default True
boolean indicating whether to attempt to convert
the result to the underlying ExtensionArray dtype.
If it's not possible to create a new ExtensionArray with the
values, an ndarray is returned instead.
Returns
-------
Callable[[Any, Any], Union[ndarray, ExtensionArray]]
A method that can be bound to a class. When used, the method
receives the two arguments, one of which is the instance of
this class, and should return an ExtensionArray or an ndarray.
Returning an ndarray may be necessary when the result of the
`op` cannot be stored in the ExtensionArray. The dtype of the
ndarray uses NumPy's normal inference rules.
Example
-------
Given an ExtensionArray subclass called MyExtensionArray, use
>>> __add__ = cls._create_method(operator.add)
in the class definition of MyExtensionArray to create the operator
for addition, that will be based on the operator implementation
of the underlying elements of the ExtensionArray
"""
def _binop(self, other):
def convert_values(param):
if isinstance(param, ExtensionArray) or is_list_like(param):
ovalues = param
else: # Assume its an object
ovalues = [param] * len(self)
return ovalues
lvalues = self
rvalues = convert_values(other)
# If the operator is not defined for the underlying objects,
# a TypeError should be raised
res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
def _maybe_convert(arr):
if coerce_to_dtype:
# https://github.com/pandas-dev/pandas/issues/22850
# We catch all regular exceptions here, and fall back
# to an ndarray.
try:
res = self._from_sequence(arr)
except Exception:
res = np.asarray(arr)
else:
res = np.asarray(arr)
return res
if op.__name__ in {'divmod', 'rdivmod'}:
a, b = zip(*res)
res = _maybe_convert(a), _maybe_convert(b)
else:
res = _maybe_convert(res)
return res
op_name = ops._get_op_name(op, True)
return set_function_name(_binop, op_name, cls)
def _make_evaluate_binop(op, step=False):
"""
Parameters
----------
op : callable that accepts 2 parms
perform the binary op
step : callable, optional, default to False
op to apply to the step parm if not None
if False, use the existing step
"""
def _evaluate_numeric_binop(self, other):
if isinstance(other, (ABCSeries, ABCDataFrame)):
return NotImplemented
elif isinstance(other, ABCTimedeltaIndex):
# Defer to TimedeltaIndex implementation
return NotImplemented
elif isinstance(other, (timedelta, np.timedelta64)):
# GH#19333 is_integer evaluated True on timedelta64,
# so we need to catch these explicitly
return op(self._int64index, other)
elif is_timedelta64_dtype(other):
# Must be an np.ndarray; GH#22390
return op(self._int64index, other)
other = self._validate_for_numeric_binop(other, op)
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
left, right = self, other
try:
# apply if we have an override
if step:
with np.errstate(all='ignore'):
rstep = step(left._step, right)
# we don't have a representable op
# so return a base index
if not is_integer(rstep) or not rstep:
raise ValueError
else:
rstep = left._step
with np.errstate(all='ignore'):
rstart = op(left._start, right)
rstop = op(left._stop, right)
result = RangeIndex(rstart,
rstop,
rstep,
**attrs)
# for compat with numpy / Int64Index
# even if we can represent as a RangeIndex, return
# as a Float64Index if we have float-like descriptors
if not all(is_integer(x) for x in
[rstart, rstop, rstep]):
result = result.astype('float64')
return result
except (ValueError, TypeError, ZeroDivisionError):
# Defer to Int64Index implementation
return op(self._int64index, other)
# TODO: Do attrs get handled reliably?
name = '__{name}__'.format(name=op.__name__)
return compat.set_function_name(_evaluate_numeric_binop, name, cls)
def _create_arithmetic_method(cls, op):
# Note: this handles both arithmetic and comparison methods.
def method(self, other):
is_arithmetic = \
True if op.__name__ in ops.ARITHMETIC_BINOPS else False
pandas_only = cls._pandas_only()
is_other_array = False
if not is_scalar(other):
is_other_array = True
other = np.asarray(other)
self_is_na = self.isna()
other_is_na = pd.isna(other)
mask = self_is_na | other_is_na
if pa is None or pandas_only:
if is_arithmetic:
ret = np.empty(self.shape, dtype=object)
else:
ret = np.zeros(self.shape, dtype=bool)
valid = ~mask
arr = self._arrow_array.to_pandas().to_numpy() \
if self._use_arrow else self._ndarray
o = other[valid] if is_other_array else other
ret[valid] = op(arr[valid], o)
if is_arithmetic:
return ArrowStringArray(ret)
else:
return pd.arrays.BooleanArray(ret, mask)
chunks = []
mask_chunks = []
start = 0
for chunk_array in self._arrow_array.chunks:
chunk_array = np.asarray(chunk_array.to_pandas())
end = start + len(chunk_array)
chunk_mask = mask[start:end]
chunk_valid = ~chunk_mask
if is_arithmetic:
result = np.empty(chunk_array.shape, dtype=object)
else:
result = np.zeros(chunk_array.shape, dtype=bool)
chunk_other = other
if is_other_array:
chunk_other = other[start:end]
chunk_other = chunk_other[chunk_valid]
# calculate only for both not None
result[chunk_valid] = op(chunk_array[chunk_valid], chunk_other)
if is_arithmetic:
chunks.append(
pa.array(result, type=pa.string(), from_pandas=True))
else:
chunks.append(result)
mask_chunks.append(chunk_mask)
if is_arithmetic:
return ArrowStringArray(pa.chunked_array(chunks))
else:
return pd.arrays.BooleanArray(np.concatenate(chunks),
np.concatenate(mask_chunks))
return set_function_name(method, f"__{op.__name__}__", cls)
def _create_method(cls, op, coerce_to_dtype=True):
"""
A class method that returns a method that will correspond to an
operator for an ExtensionArray subclass, by dispatching to the
relevant operator defined on the individual elements of the
ExtensionArray.
Parameters
----------
op : function
An operator that takes arguments op(a, b)
coerce_to_dtype : bool, default True
boolean indicating whether to attempt to convert
the result to the underlying ExtensionArray dtype.
If it's not possible to create a new ExtensionArray with the
values, an ndarray is returned instead.
Returns
-------
Callable[[Any, Any], Union[ndarray, ExtensionArray]]
A method that can be bound to a class. When used, the method
receives the two arguments, one of which is the instance of
this class, and should return an ExtensionArray or an ndarray.
Returning an ndarray may be necessary when the result of the
`op` cannot be stored in the ExtensionArray. The dtype of the
ndarray uses NumPy's normal inference rules.
Examples
--------
Given an ExtensionArray subclass called MyExtensionArray, use
>>> __add__ = cls._create_method(operator.add)
in the class definition of MyExtensionArray to create the operator
for addition, that will be based on the operator implementation
of the underlying elements of the ExtensionArray
"""
def _binop(self, other):
def convert_values(param):
if isinstance(param, ExtensionArray) or is_list_like(param):
ovalues = param
else: # Assume its an object
ovalues = [param] * len(self)
return ovalues
if isinstance(other, (ABCSeries, ABCIndexClass)):
# rely on qq_pandas to unbox and dispatch to us
return NotImplemented
lvalues = self
rvalues = convert_values(other)
# If the operator is not defined for the underlying objects,
# a TypeError should be raised
res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]
def _maybe_convert(arr):
if coerce_to_dtype:
# https://github.com/pandas-dev/pandas/issues/22850
# We catch all regular exceptions here, and fall back
# to an ndarray.
try:
res = self._from_sequence(arr)
except Exception:
res = np.asarray(arr)
else:
res = np.asarray(arr)
return res
if op.__name__ in {"divmod", "rdivmod"}:
a, b = zip(*res)
res = _maybe_convert(a), _maybe_convert(b)
else:
res = _maybe_convert(res)
return res
op_name = ops._get_op_name(op, True)
return set_function_name(_binop, op_name, cls)
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = f"__{op.__name__}__"
nat_result = opname == "__ne__"
@unpack_zerodim_and_defer(opname)
def wrapper(self, other):
ordinal_op = getattr(self.asi8, opname)
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
if isinstance(other, str):
try:
other = self._scalar_from_string(other)
except ValueError:
# string that can't be parsed as Period
return invalid_comparison(self, other, op)
elif isinstance(other, int):
# TODO: sure we want to allow this? we dont for DTA/TDA
# 2 tests rely on this
other = Period(other, freq=self.freq)
result = ordinal_op(other.ordinal)
if isinstance(other, Period):
self._check_compatible_with(other)
result = ordinal_op(other.ordinal)
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
elif not is_list_like(other):
return invalid_comparison(self, other, op)
else:
if isinstance(other, list):
# TODO: could use pd.Index to do inference?
other = np.array(other)
if not isinstance(other, (np.ndarray, cls)):
return invalid_comparison(self, other, op)
if is_object_dtype(other):
with np.errstate(all="ignore"):
result = ops.comp_method_OBJECT_ARRAY(
op, self.astype(object), other
)
o_mask = isna(other)
elif not is_period_dtype(other):
# e.g. is_timedelta64_dtype(other)
return invalid_comparison(self, other, op)
else:
assert isinstance(other, cls), type(other)
self._check_compatible_with(other)
result = ordinal_op(other.asi8)
o_mask = other._isnan
if o_mask.any():
result[o_mask] = nat_result
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)