Python generic.ABCCategorical方法代碼示例

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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__') 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCCategorical [as 別名]
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)