Python pandas.core.dtypes.generic.ABCCategorical() Examples
The following are 18
code examples of pandas.core.dtypes.generic.ABCCategorical().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
pandas.core.dtypes.generic
, or try the search function
.
.
Example #1
| Source File: category.py From recruit with Apache License 2.0 | 5 votes |
|
def _create_categorical(cls, data, dtype=None):
"""
*this is an internal non-public method*
create the correct categorical from data and the properties
Parameters
----------
data : data for new Categorical
dtype : CategoricalDtype, defaults to existing
Returns
-------
Categorical
"""
if (isinstance(data, (cls, ABCSeries)) and
is_categorical_dtype(data)):
data = data.values
if not isinstance(data, ABCCategorical):
return Categorical(data, dtype=dtype)
if isinstance(dtype, CategoricalDtype) and dtype != data.dtype:
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
return data
Example #2
| Source File: test_generic.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
Example #3
| Source File: category.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def _add_comparison_methods(cls):
""" add in comparison methods """
def _make_compare(op):
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, op)(other)
return _evaluate_compare
cls.__eq__ = _make_compare('__eq__')
cls.__ne__ = _make_compare('__ne__')
cls.__lt__ = _make_compare('__lt__')
cls.__gt__ = _make_compare('__gt__')
cls.__le__ = _make_compare('__le__')
cls.__ge__ = _make_compare('__ge__')
Example #4
| Source File: category.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def _create_categorical(self, data, categories=None, ordered=None,
dtype=None):
"""
*this is an internal non-public method*
create the correct categorical from data and the properties
Parameters
----------
data : data for new Categorical
categories : optional categories, defaults to existing
ordered : optional ordered attribute, defaults to existing
dtype : CategoricalDtype, defaults to existing
Returns
-------
Categorical
"""
if (isinstance(data, (ABCSeries, type(self))) and
is_categorical_dtype(data)):
data = data.values
if not isinstance(data, ABCCategorical):
if ordered is None and dtype is None:
ordered = False
from pandas.core.categorical import Categorical
data = Categorical(data, categories=categories, ordered=ordered,
dtype=dtype)
else:
from pandas.core.dtypes.dtypes import CategoricalDtype
if categories is not None:
data = data.set_categories(categories, ordered=ordered)
elif ordered is not None and ordered != data.ordered:
data = data.set_ordered(ordered)
if isinstance(dtype, CategoricalDtype):
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
return data
Example #5
| Source File: category.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def __new__(cls, data=None, categories=None, ordered=None, dtype=None,
copy=False, name=None, fastpath=False, **kwargs):
if fastpath:
return cls._simple_new(data, name=name, dtype=dtype)
if name is None and hasattr(data, 'name'):
name = data.name
if isinstance(data, ABCCategorical):
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
elif isinstance(data, CategoricalIndex):
data = data._data
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
else:
# don't allow scalars
# if data is None, then categories must be provided
if is_scalar(data):
if data is not None or categories is None:
cls._scalar_data_error(data)
data = []
data = cls._create_categorical(cls, data, categories, ordered)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
Example #6
| Source File: test_generic.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
Example #7
| Source File: category.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _add_comparison_methods(cls):
""" add in comparison methods """
def _make_compare(op):
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, op)(other)
return _evaluate_compare
cls.__eq__ = _make_compare('__eq__')
cls.__ne__ = _make_compare('__ne__')
cls.__lt__ = _make_compare('__lt__')
cls.__gt__ = _make_compare('__gt__')
cls.__le__ = _make_compare('__le__')
cls.__ge__ = _make_compare('__ge__')
Example #8
| Source File: category.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _create_categorical(self, data, categories=None, ordered=None,
dtype=None):
"""
*this is an internal non-public method*
create the correct categorical from data and the properties
Parameters
----------
data : data for new Categorical
categories : optional categories, defaults to existing
ordered : optional ordered attribute, defaults to existing
dtype : CategoricalDtype, defaults to existing
Returns
-------
Categorical
"""
if (isinstance(data, (ABCSeries, type(self))) and
is_categorical_dtype(data)):
data = data.values
if not isinstance(data, ABCCategorical):
if ordered is None and dtype is None:
ordered = False
from pandas.core.categorical import Categorical
data = Categorical(data, categories=categories, ordered=ordered,
dtype=dtype)
else:
from pandas.core.dtypes.dtypes import CategoricalDtype
if categories is not None:
data = data.set_categories(categories, ordered=ordered)
elif ordered is not None and ordered != data.ordered:
data = data.set_ordered(ordered)
if isinstance(dtype, CategoricalDtype):
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
return data
Example #9
| Source File: category.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def __new__(cls, data=None, categories=None, ordered=None, dtype=None,
copy=False, name=None, fastpath=False, **kwargs):
if fastpath:
return cls._simple_new(data, name=name, dtype=dtype)
if name is None and hasattr(data, 'name'):
name = data.name
if isinstance(data, ABCCategorical):
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
elif isinstance(data, CategoricalIndex):
data = data._data
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
else:
# don't allow scalars
# if data is None, then categories must be provided
if is_scalar(data):
if data is not None or categories is None:
cls._scalar_data_error(data)
data = []
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
Example #10
| Source File: category.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def _create_categorical(cls, data, dtype=None):
"""
*this is an internal non-public method*
create the correct categorical from data and the properties
Parameters
----------
data : data for new Categorical
dtype : CategoricalDtype, defaults to existing
Returns
-------
Categorical
"""
if (isinstance(data, (cls, ABCSeries)) and
is_categorical_dtype(data)):
data = data.values
if not isinstance(data, ABCCategorical):
return Categorical(data, dtype=dtype)
if isinstance(dtype, CategoricalDtype) and dtype != data.dtype:
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
return data
Example #11
| Source File: test_generic.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
simplefilter('ignore', FutureWarning)
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
assert isinstance(self.datetime_array, gt.ABCDatetimeArray)
assert not isinstance(self.datetime_index, gt.ABCDatetimeArray)
assert isinstance(self.timedelta_array, gt.ABCTimedeltaArray)
assert not isinstance(self.timedelta_index, gt.ABCTimedeltaArray)
Example #12
| Source File: category.py From vnpy_crypto with MIT License | 5 votes |
|
def _create_categorical(self, data, categories=None, ordered=None,
dtype=None):
"""
*this is an internal non-public method*
create the correct categorical from data and the properties
Parameters
----------
data : data for new Categorical
categories : optional categories, defaults to existing
ordered : optional ordered attribute, defaults to existing
dtype : CategoricalDtype, defaults to existing
Returns
-------
Categorical
"""
if (isinstance(data, (ABCSeries, type(self))) and
is_categorical_dtype(data)):
data = data.values
if not isinstance(data, ABCCategorical):
if ordered is None and dtype is None:
ordered = False
from pandas.core.arrays import Categorical
data = Categorical(data, categories=categories, ordered=ordered,
dtype=dtype)
else:
if categories is not None:
data = data.set_categories(categories, ordered=ordered)
elif ordered is not None and ordered != data.ordered:
data = data.set_ordered(ordered)
if isinstance(dtype, CategoricalDtype):
# we want to silently ignore dtype='category'
data = data._set_dtype(dtype)
return data
Example #13
| Source File: category.py From vnpy_crypto with MIT License | 5 votes |
|
def __new__(cls, data=None, categories=None, ordered=None, dtype=None,
copy=False, name=None, fastpath=False):
if fastpath:
return cls._simple_new(data, name=name, dtype=dtype)
if name is None and hasattr(data, 'name'):
name = data.name
if isinstance(data, ABCCategorical):
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
elif isinstance(data, CategoricalIndex):
data = data._data
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
else:
# don't allow scalars
# if data is None, then categories must be provided
if is_scalar(data):
if data is not None or categories is None:
cls._scalar_data_error(data)
data = []
data = cls._create_categorical(cls, data, categories, ordered,
dtype)
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
Example #14
| Source File: test_generic.py From vnpy_crypto with MIT License | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
Example #15
| Source File: test_generic.py From recruit with Apache License 2.0 | 5 votes |
|
def test_abc_types(self):
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndex)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCInt64Index)
assert isinstance(pd.UInt64Index([1, 2, 3]), gt.ABCUInt64Index)
assert isinstance(pd.Float64Index([1, 2, 3]), gt.ABCFloat64Index)
assert isinstance(self.multi_index, gt.ABCMultiIndex)
assert isinstance(self.datetime_index, gt.ABCDatetimeIndex)
assert isinstance(self.timedelta_index, gt.ABCTimedeltaIndex)
assert isinstance(self.period_index, gt.ABCPeriodIndex)
assert isinstance(self.categorical_df.index, gt.ABCCategoricalIndex)
assert isinstance(pd.Index(['a', 'b', 'c']), gt.ABCIndexClass)
assert isinstance(pd.Int64Index([1, 2, 3]), gt.ABCIndexClass)
assert isinstance(pd.Series([1, 2, 3]), gt.ABCSeries)
assert isinstance(self.df, gt.ABCDataFrame)
with catch_warnings(record=True):
simplefilter('ignore', FutureWarning)
assert isinstance(self.df.to_panel(), gt.ABCPanel)
assert isinstance(self.sparse_series, gt.ABCSparseSeries)
assert isinstance(self.sparse_array, gt.ABCSparseArray)
assert isinstance(self.sparse_frame, gt.ABCSparseDataFrame)
assert isinstance(self.categorical, gt.ABCCategorical)
assert isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
assert isinstance(pd.DateOffset(), gt.ABCDateOffset)
assert isinstance(pd.Period('2012', freq='A-DEC').freq,
gt.ABCDateOffset)
assert not isinstance(pd.Period('2012', freq='A-DEC'),
gt.ABCDateOffset)
assert isinstance(pd.Interval(0, 1.5), gt.ABCInterval)
assert not isinstance(pd.Period('2012', freq='A-DEC'), gt.ABCInterval)
assert isinstance(self.datetime_array, gt.ABCDatetimeArray)
assert not isinstance(self.datetime_index, gt.ABCDatetimeArray)
assert isinstance(self.timedelta_array, gt.ABCTimedeltaArray)
assert not isinstance(self.timedelta_index, gt.ABCTimedeltaArray)
Example #16
| Source File: category.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def _add_comparison_methods(cls):
""" add in comparison methods """
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(
other._values, dtype=self.dtype)
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)
cls.__eq__ = _make_compare(operator.eq)
cls.__ne__ = _make_compare(operator.ne)
cls.__lt__ = _make_compare(operator.lt)
cls.__gt__ = _make_compare(operator.gt)
cls.__le__ = _make_compare(operator.le)
cls.__ge__ = _make_compare(operator.ge)
Example #17
| Source File: category.py From vnpy_crypto with MIT License | 4 votes |
|
def _add_comparison_methods(cls):
""" add in comparison methods """
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)
cls.__eq__ = _make_compare(operator.eq)
cls.__ne__ = _make_compare(operator.ne)
cls.__lt__ = _make_compare(operator.lt)
cls.__gt__ = _make_compare(operator.gt)
cls.__le__ = _make_compare(operator.le)
cls.__ge__ = _make_compare(operator.ge)
Example #18
| Source File: category.py From recruit with Apache License 2.0 | 4 votes |
|
def _add_comparison_methods(cls):
""" add in comparison methods """
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(
other._values, dtype=self.dtype)
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)
cls.__eq__ = _make_compare(operator.eq)
cls.__ne__ = _make_compare(operator.ne)
cls.__lt__ = _make_compare(operator.lt)
cls.__gt__ = _make_compare(operator.gt)
cls.__le__ = _make_compare(operator.le)
cls.__ge__ = _make_compare(operator.ge)
