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ops.py
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"""
Arithmetic operations for PandasObjects
This is not a public API.
"""
# necessary to enforce truediv in Python 2.X
from __future__ import division
import operator
import numpy as np
import pandas as pd
from pandas._libs import (lib, index as libindex,
algos as libalgos)
from pandas import compat
from pandas.util._decorators import Appender
from pandas.compat import bind_method
import pandas.core.missing as missing
import pandas.core.common as com
from pandas.errors import NullFrequencyError
from pandas.core.dtypes.missing import notna, isna
from pandas.core.dtypes.common import (
needs_i8_conversion,
is_datetimelike_v_numeric,
is_integer_dtype, is_categorical_dtype,
is_object_dtype, is_timedelta64_dtype,
is_datetime64_dtype, is_datetime64tz_dtype,
is_bool_dtype,
is_list_like,
is_scalar,
_ensure_object)
from pandas.core.dtypes.cast import (
maybe_upcast_putmask, find_common_type,
construct_1d_object_array_from_listlike)
from pandas.core.dtypes.generic import (
ABCSeries,
ABCDataFrame,
ABCIndex,
ABCSparseSeries, ABCSparseArray)
# -----------------------------------------------------------------------------
# Reversed Operations not available in the stdlib operator module.
# Defining these instead of using lambdas allows us to reference them by name.
def radd(left, right):
return right + left
def rsub(left, right):
return right - left
def rmul(left, right):
return right * left
def rdiv(left, right):
return right / left
def rtruediv(left, right):
return right / left
def rfloordiv(left, right):
return right // left
def rmod(left, right):
return right % left
def rdivmod(left, right):
return divmod(right, left)
def rpow(left, right):
return right ** left
def rand_(left, right):
return operator.and_(right, left)
def ror_(left, right):
return operator.or_(right, left)
def rxor(left, right):
return operator.xor(right, left)
# -----------------------------------------------------------------------------
def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{"reversed": True, "truediv": True}
"""
kwargs = {}
# Series and Panel appear to only pass __add__, __radd__, ...
# but DataFrame gets both these dunder names _and_ non-dunder names
# add, radd, ...
name = name.replace('__', '')
if name.startswith('r'):
if name not in ['radd', 'rand', 'ror', 'rxor']:
# Exclude commutative operations
kwargs['reversed'] = True
if name in ['truediv', 'rtruediv']:
kwargs['truediv'] = True
if name in ['ne']:
kwargs['masker'] = True
return kwargs
def _gen_fill_zeros(name):
"""
Find the appropriate fill value to use when filling in undefined values
in the results of the given operation caused by operating on
(generally dividing by) zero.
Parameters
----------
name : str
Returns
-------
fill_value : {None, np.nan, np.inf}
"""
name = name.strip('__')
if 'div' in name:
# truediv, floordiv, div, and reversed variants
fill_value = np.inf
elif 'mod' in name:
# mod, rmod
fill_value = np.nan
else:
fill_value = None
return fill_value
def _get_frame_op_default_axis(name):
"""
Only DataFrame cares about default_axis, specifically:
special methods have default_axis=None and flex methods
have default_axis='columns'.
Parameters
----------
name : str
Returns
-------
default_axis: str or None
"""
if name.replace('__r', '__') in ['__and__', '__or__', '__xor__']:
# bool methods
return 'columns'
elif name.startswith('__'):
# __add__, __mul__, ...
return None
else:
# add, mul, ...
return 'columns'
# -----------------------------------------------------------------------------
# Docstring Generation and Templates
_op_descriptions = {
'add': {'op': '+',
'desc': 'Addition',
'reverse': 'radd'},
'sub': {'op': '-',
'desc': 'Subtraction',
'reverse': 'rsub'},
'mul': {'op': '*',
'desc': 'Multiplication',
'reverse': 'rmul'},
'mod': {'op': '%',
'desc': 'Modulo',
'reverse': 'rmod'},
'pow': {'op': '**',
'desc': 'Exponential power',
'reverse': 'rpow'},
'truediv': {'op': '/',
'desc': 'Floating division',
'reverse': 'rtruediv'},
'floordiv': {'op': '//',
'desc': 'Integer division',
'reverse': 'rfloordiv'},
'divmod': {'op': 'divmod',
'desc': 'Integer division and modulo',
'reverse': None},
'eq': {'op': '==',
'desc': 'Equal to',
'reverse': None},
'ne': {'op': '!=',
'desc': 'Not equal to',
'reverse': None},
'lt': {'op': '<',
'desc': 'Less than',
'reverse': None},
'le': {'op': '<=',
'desc': 'Less than or equal to',
'reverse': None},
'gt': {'op': '>',
'desc': 'Greater than',
'reverse': None},
'ge': {'op': '>=',
'desc': 'Greater than or equal to',
'reverse': None}}
_op_names = list(_op_descriptions.keys())
for key in _op_names:
_op_descriptions[key]['reversed'] = False
reverse_op = _op_descriptions[key]['reverse']
if reverse_op is not None:
_op_descriptions[reverse_op] = _op_descriptions[key].copy()
_op_descriptions[reverse_op]['reversed'] = True
_op_descriptions[reverse_op]['reverse'] = key
_flex_doc_SERIES = """
{desc} of series and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value for
missing data in one of the inputs.
Parameters
----------
other : Series or scalar value
fill_value : None or float value, default None (NaN)
Fill existing missing (NaN) values, and any new element needed for
successful array alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
Returns
-------
result : Series
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'c_', 'd'])
>>> a.add(b, fill_value=0)
a 2.0
b 1.0
c 1.0
c_ 1.0
d NaN
dtype: float64
See also
--------
Series.{reverse}
"""
_arith_doc_FRAME = """
Binary operator %s with support to substitute a fill_value for missing data in
one of the inputs
Parameters
----------
other : Series, DataFrame, or constant
axis : {0, 1, 'index', 'columns'}
For Series input, axis to match Series index on
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful array alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
Notes
-----
Mismatched indices will be unioned together
Returns
-------
result : DataFrame
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'c_', 'd'])
>>> a.add(b, fill_value=0)
a 2.0
b 1.0
c 1.0
c_ 1.0
d NaN
dtype: float64
"""
_flex_doc_FRAME = """
{desc} of dataframe and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``, but with support to substitute a fill_value for
missing data in one of the inputs.
Parameters
----------
other : Series, DataFrame, or constant
axis : {{0, 1, 'index', 'columns'}}
For Series input, axis to match Series index on
fill_value : None or float value, default None
Fill existing missing (NaN) values, and any new element needed for
successful array alignment, with this value before computation.
If data in both corresponding DataFrame locations is missing
the result will be missing
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level
Notes
-----
Mismatched indices will be unioned together
Returns
-------
result : DataFrame
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'c_', 'd'])
>>> a.add(b, fill_value=0)
0
a 2.0
b 1.0
c 1.0
c_ 1.0
d NaN
See also
--------
DataFrame.{reverse}
"""
_flex_doc_PANEL = """
{desc} of series and other, element-wise (binary operator `{op_name}`).
Equivalent to ``{equiv}``.
Parameters
----------
other : DataFrame or Panel
axis : {{items, major_axis, minor_axis}}
Axis to broadcast over
Returns
-------
Panel
See also
--------
Panel.{reverse}
"""
_agg_doc_PANEL = """
Wrapper method for {op_name}
Parameters
----------
other : DataFrame or Panel
axis : {{items, major_axis, minor_axis}}
Axis to broadcast over
Returns
-------
Panel
"""
def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring
to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...}
typ : str {series, 'dataframe']}
Returns
-------
doc : str
"""
op_name = op_name.replace('__', '')
op_desc = _op_descriptions[op_name]
if op_desc['reversed']:
equiv = 'other ' + op_desc['op'] + ' ' + typ
else:
equiv = typ + ' ' + op_desc['op'] + ' other'
if typ == 'series':
base_doc = _flex_doc_SERIES
elif typ == 'dataframe':
base_doc = _flex_doc_FRAME
elif typ == 'panel':
base_doc = _flex_doc_PANEL
else:
raise AssertionError('Invalid typ argument.')
doc = base_doc.format(desc=op_desc['desc'], op_name=op_name,
equiv=equiv, reverse=op_desc['reverse'])
return doc
# -----------------------------------------------------------------------------
# Masking NA values and fallbacks for operations numpy does not support
def fill_binop(left, right, fill_value):
"""
If a non-None fill_value is given, replace null entries in left and right
with this value, but only in positions where _one_ of left/right is null,
not both.
Parameters
----------
left : array-like
right : array-like
fill_value : object
Returns
-------
left : array-like
right : array-like
Notes
-----
Makes copies if fill_value is not None
"""
# TODO: can we make a no-copy implementation?
if fill_value is not None:
left_mask = isna(left)
right_mask = isna(right)
left = left.copy()
right = right.copy()
# one but not both
mask = left_mask ^ right_mask
left[left_mask & mask] = fill_value
right[right_mask & mask] = fill_value
return left, right
def mask_cmp_op(x, y, op, allowed_types):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
allowed_types : class or tuple of classes
Returns
-------
result : ndarray[bool]
"""
# TODO: Can we make the allowed_types arg unnecessary?
xrav = x.ravel()
result = np.empty(x.size, dtype=bool)
if isinstance(y, allowed_types):
yrav = y.ravel()
mask = notna(xrav) & notna(yrav)
result[mask] = op(np.array(list(xrav[mask])),
np.array(list(yrav[mask])))
else:
mask = notna(xrav)
result[mask] = op(np.array(list(xrav[mask])), y)
if op == operator.ne: # pragma: no cover
np.putmask(result, ~mask, True)
else:
np.putmask(result, ~mask, False)
result = result.reshape(x.shape)
return result
# -----------------------------------------------------------------------------
# Functions that add arithmetic methods to objects, given arithmetic factory
# methods
def _create_methods(cls, arith_method, comp_method, bool_method,
special=False):
# creates actual methods based upon arithmetic, comp and bool method
# constructors.
# numexpr is available for non-sparse classes
subtyp = getattr(cls, '_subtyp', '')
use_numexpr = 'sparse' not in subtyp
have_divmod = issubclass(cls, ABCSeries)
# divmod is available for Series and SparseSeries
# if we're not using numexpr, then don't pass a str_rep
if use_numexpr:
op = lambda x: x
else:
op = lambda x: None
if special:
def names(x):
if x[-1] == "_":
return "__{name}_".format(name=x)
else:
return "__{name}__".format(name=x)
else:
names = lambda x: x
# yapf: disable
new_methods = dict(
add=arith_method(operator.add, names('add'), op('+')),
radd=arith_method(radd, names('radd'), op('+')),
sub=arith_method(operator.sub, names('sub'), op('-')),
mul=arith_method(operator.mul, names('mul'), op('*')),
truediv=arith_method(operator.truediv, names('truediv'), op('/')),
floordiv=arith_method(operator.floordiv, names('floordiv'), op('//')),
# Causes a floating point exception in the tests when numexpr enabled,
# so for now no speedup
mod=arith_method(operator.mod, names('mod'), None),
pow=arith_method(operator.pow, names('pow'), op('**')),
# not entirely sure why this is necessary, but previously was included
# so it's here to maintain compatibility
rmul=arith_method(operator.mul, names('rmul'), op('*')),
rsub=arith_method(rsub, names('rsub'), op('-')),
rtruediv=arith_method(rtruediv, names('rtruediv'), op('/')),
rfloordiv=arith_method(rfloordiv, names('rfloordiv'), op('//')),
rpow=arith_method(rpow, names('rpow'), op('**')),
rmod=arith_method(rmod, names('rmod'), op('%')))
# yapf: enable
new_methods['div'] = new_methods['truediv']
new_methods['rdiv'] = new_methods['rtruediv']
# Comp methods never had a default axis set
if comp_method:
new_methods.update(dict(
eq=comp_method(operator.eq, names('eq'), op('==')),
ne=comp_method(operator.ne, names('ne'), op('!=')),
lt=comp_method(operator.lt, names('lt'), op('<')),
gt=comp_method(operator.gt, names('gt'), op('>')),
le=comp_method(operator.le, names('le'), op('<=')),
ge=comp_method(operator.ge, names('ge'), op('>='))))
if bool_method:
new_methods.update(
dict(and_=bool_method(operator.and_, names('and_'), op('&')),
or_=bool_method(operator.or_, names('or_'), op('|')),
# For some reason ``^`` wasn't used in original.
xor=bool_method(operator.xor, names('xor'), op('^')),
rand_=bool_method(rand_, names('rand_'), op('&')),
ror_=bool_method(ror_, names('ror_'), op('|')),
rxor=bool_method(rxor, names('rxor'), op('^'))))
if have_divmod:
# divmod doesn't have an op that is supported by numexpr
new_methods['divmod'] = arith_method(divmod, names('divmod'), None)
new_methods = {names(k): v for k, v in new_methods.items()}
return new_methods
def add_methods(cls, new_methods):
for name, method in new_methods.items():
# For most methods, if we find that the class already has a method
# of the same name, it is OK to over-write it. The exception is
# inplace methods (__iadd__, __isub__, ...) for SparseArray, which
# retain the np.ndarray versions.
force = not (issubclass(cls, ABCSparseArray) and
name.startswith('__i'))
if force or name not in cls.__dict__:
bind_method(cls, name, method)
# ----------------------------------------------------------------------
# Arithmetic
def add_special_arithmetic_methods(cls, arith_method=None,
comp_method=None, bool_method=None):
"""
Adds the full suite of special arithmetic methods (``__add__``,
``__sub__``, etc.) to the class.
Parameters
----------
arith_method : function (optional)
factory for special arithmetic methods, with op string:
f(op, name, str_rep)
comp_method : function (optional)
factory for rich comparison - signature: f(op, name, str_rep)
bool_method : function (optional)
factory for boolean methods - signature: f(op, name, str_rep)
"""
new_methods = _create_methods(cls, arith_method, comp_method, bool_method,
special=True)
# inplace operators (I feel like these should get passed an `inplace=True`
# or just be removed
def _wrap_inplace_method(method):
"""
return an inplace wrapper for this method
"""
def f(self, other):
result = method(self, other)
# this makes sure that we are aligned like the input
# we are updating inplace so we want to ignore is_copy
self._update_inplace(result.reindex_like(self, copy=False)._data,
verify_is_copy=False)
return self
return f
new_methods.update(
dict(__iadd__=_wrap_inplace_method(new_methods["__add__"]),
__isub__=_wrap_inplace_method(new_methods["__sub__"]),
__imul__=_wrap_inplace_method(new_methods["__mul__"]),
__itruediv__=_wrap_inplace_method(new_methods["__truediv__"]),
__ifloordiv__=_wrap_inplace_method(new_methods["__floordiv__"]),
__imod__=_wrap_inplace_method(new_methods["__mod__"]),
__ipow__=_wrap_inplace_method(new_methods["__pow__"])))
if not compat.PY3:
new_methods["__idiv__"] = _wrap_inplace_method(new_methods["__div__"])
if bool_method:
new_methods.update(
dict(__iand__=_wrap_inplace_method(new_methods["__and__"]),
__ior__=_wrap_inplace_method(new_methods["__or__"]),
__ixor__=_wrap_inplace_method(new_methods["__xor__"])))
add_methods(cls, new_methods=new_methods)
def add_flex_arithmetic_methods(cls, flex_arith_method,
flex_comp_method=None, flex_bool_method=None):
"""
Adds the full suite of flex arithmetic methods (``pow``, ``mul``, ``add``)
to the class.
Parameters
----------
flex_arith_method : function
factory for flex arithmetic methods, with op string:
f(op, name, str_rep)
flex_comp_method : function, optional,
factory for rich comparison - signature: f(op, name, str_rep)
"""
new_methods = _create_methods(cls, flex_arith_method,
flex_comp_method, flex_bool_method,
special=False)
new_methods.update(dict(multiply=new_methods['mul'],
subtract=new_methods['sub'],
divide=new_methods['div']))
# opt out of bool flex methods for now
for k in ('ror_', 'rxor', 'rand_'):
if k in new_methods:
new_methods.pop(k)
add_methods(cls, new_methods=new_methods)
# -----------------------------------------------------------------------------
# Series
def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeated alignment
if not left.index.equals(right.index):
if align_asobject:
# to keep original value's dtype for bool ops
left = left.astype(object)
right = right.astype(object)
left, right = left.align(right, copy=False)
return left, right
def _construct_result(left, result, index, name, dtype):
"""
If the raw op result has a non-None name (e.g. it is an Index object) and
the name argument is None, then passing name to the constructor will
not be enough; we still need to override the name attribute.
"""
out = left._constructor(result, index=index, dtype=dtype)
out.name = name
return out
def _construct_divmod_result(left, result, index, name, dtype):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
constructor = left._constructor
return (
constructor(result[0], index=index, name=name, dtype=dtype),
constructor(result[1], index=index, name=name, dtype=dtype),
)
def _arith_method_SERIES(op, name, str_rep):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
eval_kwargs = _gen_eval_kwargs(name)
fill_zeros = _gen_fill_zeros(name)
construct_result = (_construct_divmod_result
if op is divmod else _construct_result)
def na_op(x, y):
import pandas.core.computation.expressions as expressions
try:
result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
except TypeError:
if isinstance(y, (np.ndarray, ABCSeries, pd.Index)):
dtype = find_common_type([x.dtype, y.dtype])
result = np.empty(x.size, dtype=dtype)
mask = notna(x) & notna(y)
result[mask] = op(x[mask], com._values_from_object(y[mask]))
else:
assert isinstance(x, np.ndarray)
result = np.empty(len(x), dtype=x.dtype)
mask = notna(x)
result[mask] = op(x[mask], y)
result, changed = maybe_upcast_putmask(result, ~mask, np.nan)
result = missing.fill_zeros(result, x, y, name, fill_zeros)
return result
def safe_na_op(lvalues, rvalues):
try:
with np.errstate(all='ignore'):
return na_op(lvalues, rvalues)
except Exception:
if is_object_dtype(lvalues):
return libalgos.arrmap_object(lvalues,
lambda x: op(x, rvalues))
raise
def wrapper(left, right, name=name, na_op=na_op):
if isinstance(right, ABCDataFrame):
return NotImplemented
left, right = _align_method_SERIES(left, right)
res_name = _get_series_op_result_name(left, right)
if is_datetime64_dtype(left) or is_datetime64tz_dtype(left):
result = dispatch_to_index_op(op, left, right, pd.DatetimeIndex)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
elif is_timedelta64_dtype(left):
result = dispatch_to_index_op(op, left, right, pd.TimedeltaIndex)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
elif is_categorical_dtype(left):
raise TypeError("{typ} cannot perform the operation "
"{op}".format(typ=type(left).__name__, op=str_rep))
lvalues = left.values
rvalues = right
if isinstance(rvalues, ABCSeries):
rvalues = rvalues.values
result = safe_na_op(lvalues, rvalues)
return construct_result(left, result,
index=left.index, name=res_name, dtype=None)
return wrapper
def dispatch_to_index_op(op, left, right, index_class):
"""
Wrap Series left in the given index_class to delegate the operation op
to the index implementation. DatetimeIndex and TimedeltaIndex perform
type checking, timezone handling, overflow checks, etc.
Parameters
----------
op : binary operator (operator.add, operator.sub, ...)
left : Series
right : object
index_class : DatetimeIndex or TimedeltaIndex
Returns
-------
result : object, usually DatetimeIndex, TimedeltaIndex, or Series
"""
left_idx = index_class(left)
# avoid accidentally allowing integer add/sub. For datetime64[tz] dtypes,
# left_idx may inherit a freq from a cached DatetimeIndex.
# See discussion in GH#19147.
if left_idx.freq is not None:
left_idx = left_idx._shallow_copy(freq=None)
try:
result = op(left_idx, right)
except NullFrequencyError:
# DatetimeIndex and TimedeltaIndex with freq == None raise ValueError
# on add/sub of integers (or int-like). We re-raise as a TypeError.
raise TypeError('incompatible type for a datetime/timedelta '
'operation [{name}]'.format(name=op.__name__))
return result
def _get_series_op_result_name(left, right):
# `left` is always a pd.Series
if isinstance(right, (ABCSeries, pd.Index)):
name = com._maybe_match_name(left, right)
else:
name = left.name
return name
def _comp_method_OBJECT_ARRAY(op, x, y):
if isinstance(y, list):
y = construct_1d_object_array_from_listlike(y)
if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)):
if not is_object_dtype(y.dtype):
y = y.astype(np.object_)
if isinstance(y, (ABCSeries, ABCIndex)):
y = y.values
result = lib.vec_compare(x, y, op)
else:
result = lib.scalar_compare(x, y, op)
return result
def _comp_method_SERIES(op, name, str_rep):
"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
masker = _gen_eval_kwargs(name).get('masker', False)
def na_op(x, y):
# dispatch to the categorical if we have a categorical
# in either operand
if is_categorical_dtype(x):
return op(x, y)
elif is_categorical_dtype(y) and not is_scalar(y):
return op(y, x)
elif is_object_dtype(x.dtype):
result = _comp_method_OBJECT_ARRAY(op, x, y)
elif is_datetimelike_v_numeric(x, y):
raise TypeError("invalid type comparison")
else:
# we want to compare like types
# we only want to convert to integer like if
# we are not NotImplemented, otherwise
# we would allow datetime64 (but viewed as i8) against
# integer comparisons
# we have a datetime/timedelta and may need to convert
mask = None
if (needs_i8_conversion(x) or
(not is_scalar(y) and needs_i8_conversion(y))):
if is_scalar(y):
mask = isna(x)
y = libindex.convert_scalar(x, com._values_from_object(y))
else:
mask = isna(x) | isna(y)
y = y.view('i8')
x = x.view('i8')
try:
with np.errstate(all='ignore'):
result = getattr(x, name)(y)
if result is NotImplemented:
raise TypeError("invalid type comparison")
except AttributeError:
result = op(x, y)
if mask is not None and mask.any():
result[mask] = masker
return result
def wrapper(self, other, axis=None):
# Validate the axis parameter
if axis is not None:
self._get_axis_number(axis)
if isinstance(other, ABCDataFrame): # pragma: no cover
# Defer to DataFrame implementation; fail early
return NotImplemented
elif isinstance(other, ABCSeries):
name = com._maybe_match_name(self, other)
if not self._indexed_same(other):
msg = 'Can only compare identically-labeled Series objects'
raise ValueError(msg)
res_values = na_op(self.values, other.values)
return self._constructor(res_values, index=self.index, name=name)
elif isinstance(other, (np.ndarray, pd.Index)):
# do not check length of zerodim array
# as it will broadcast
if (not is_scalar(lib.item_from_zerodim(other)) and
len(self) != len(other)):
raise ValueError('Lengths must match to compare')
res_values = na_op(self.values, np.asarray(other))
return self._constructor(res_values,
index=self.index).__finalize__(self)
elif (isinstance(other, pd.Categorical) and
not is_categorical_dtype(self)):
raise TypeError("Cannot compare a Categorical for op {op} with "
"Series of dtype {typ}.\nIf you want to compare "
"values, use 'series <op> np.asarray(other)'."
.format(op=op, typ=self.dtype))
elif is_scalar(other) and isna(other):
# numpy does not like comparisons vs None
if op is operator.ne:
res_values = np.ones(len(self), dtype=bool)
else:
res_values = np.zeros(len(self), dtype=bool)
return self._constructor(res_values, index=self.index,
name=self.name, dtype='bool')
if is_categorical_dtype(self):
# cats are a special case as get_values() would return an ndarray,
# which would then not take categories ordering into account
# we can go directly to op, as the na_op would just test again and
# dispatch to it.
with np.errstate(all='ignore'):
res = op(self.values, other)
else:
values = self.get_values()
if isinstance(other, (list, np.ndarray)):
other = np.asarray(other)
with np.errstate(all='ignore'):
res = na_op(values, other)
if is_scalar(res):
raise TypeError('Could not compare {typ} type with Series'
.format(typ=type(other)))
# always return a full value series here
res = com._values_from_object(res)