Python generic.ABCIndex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 ABCIndex [as 別名]
def _join_i8_wrapper(joinf, dtype, with_indexers=True):
        """
        Create the join wrapper methods.
        """
        from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin

        @staticmethod
        def wrapper(left, right):
            if isinstance(left, (np.ndarray, ABCIndex, ABCSeries,
                                 DatetimeLikeArrayMixin)):
                left = left.view('i8')
            if isinstance(right, (np.ndarray, ABCIndex, ABCSeries,
                                  DatetimeLikeArrayMixin)):
                right = right.view('i8')
            results = joinf(left, right)
            if with_indexers:
                join_index, left_indexer, right_indexer = results
                join_index = join_index.view(dtype)
                return join_index, left_indexer, right_indexer
            return results

        return wrapper 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCIndex [as 別名]
def is_bool_indexer(key):
    if isinstance(key, (ABCSeries, np.ndarray, ABCIndex)):
        if key.dtype == np.object_:
            key = np.asarray(_values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError('cannot index with vector containing '
                                     'NA / NaN values')
                return False
            return True
        elif key.dtype == np.bool_:
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCIndex [as 別名]
def test_generated_op_names(opname, indices):
    index = indices
    if isinstance(index, ABCIndex) and opname == 'rsub':
        # pd.Index.__rsub__ does not exist; though the method does exist
        # for subclasses.  see GH#19723
        return
    opname = '__{name}__'.format(name=opname)
    method = getattr(index, opname)
    assert method.__name__ == opname 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCIndex [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 ABCIndex [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 ABCIndex [as 別名]
def _validate(data):
        from pandas.core.index import Index

        if (isinstance(data, ABCSeries) and
                not ((is_categorical_dtype(data.dtype) and
                      is_object_dtype(data.values.categories)) or
                     (is_object_dtype(data.dtype)))):
            # it's neither a string series not a categorical series with
            # strings inside the categories.
            # this really should exclude all series with any non-string values
            # (instead of test for object dtype), but that isn't practical for
            # performance reasons until we have a str dtype (GH 9343)
            raise AttributeError("Can only use .str accessor with string "
                                 "values, which use np.object_ dtype in "
                                 "pandas")
        elif isinstance(data, Index):
            # can't use ABCIndex to exclude non-str

            # see src/inference.pyx which can contain string values
            allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
            if is_categorical_dtype(data.dtype):
                inf_type = data.categories.inferred_type
            else:
                inf_type = data.inferred_type
            if inf_type not in allowed_types:
                message = ("Can only use .str accessor with string values "
                           "(i.e. inferred_type is 'string', 'unicode' or "
                           "'mixed')")
                raise AttributeError(message)
            if data.nlevels > 1:
                message = ("Can only use .str accessor with Index, not "
                           "MultiIndex")
                raise AttributeError(message) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCIndex [as 別名]
def _make_accessor(cls, data):
        from pandas.core.index import Index

        if (isinstance(data, ABCSeries) and
                not ((is_categorical_dtype(data.dtype) and
                      is_object_dtype(data.values.categories)) or
                     (is_object_dtype(data.dtype)))):
            # it's neither a string series not a categorical series with
            # strings inside the categories.
            # this really should exclude all series with any non-string values
            # (instead of test for object dtype), but that isn't practical for
            # performance reasons until we have a str dtype (GH 9343)
            raise AttributeError("Can only use .str accessor with string "
                                 "values, which use np.object_ dtype in "
                                 "pandas")
        elif isinstance(data, Index):
            # can't use ABCIndex to exclude non-str

            # see scc/inferrence.pyx which can contain string values
            allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
            if data.inferred_type not in allowed_types:
                message = ("Can only use .str accessor with string values "
                           "(i.e. inferred_type is 'string', 'unicode' or "
                           "'mixed')")
                raise AttributeError(message)
            if data.nlevels > 1:
                message = ("Can only use .str accessor with Index, not "
                           "MultiIndex")
                raise AttributeError(message)
        return cls(data) 

# 需要導入模塊: from pandas.core.dtypes import generic [as 別名]
# 或者: from pandas.core.dtypes.generic import ABCIndex [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 ABCIndex [as 別名]
def is_bool_indexer(key):
    # type: (Any) -> bool
    """
    Check whether `key` is a valid boolean indexer.

    Parameters
    ----------
    key : Any
        Only list-likes may be considered boolean indexers.
        All other types are not considered a boolean indexer.
        For array-like input, boolean ndarrays or ExtensionArrays
        with ``_is_boolean`` set are considered boolean indexers.

    Returns
    -------
    bool

    Raises
    ------
    ValueError
        When the array is an object-dtype ndarray or ExtensionArray
        and contains missing values.
    """
    na_msg = 'cannot index with vector containing NA / NaN values'
    if (isinstance(key, (ABCSeries, np.ndarray, ABCIndex)) or
            (is_array_like(key) and is_extension_array_dtype(key.dtype))):
        if key.dtype == np.object_:
            key = np.asarray(values_from_object(key))

            if not lib.is_bool_array(key):
                if isna(key).any():
                    raise ValueError(na_msg)
                return False
            return True
        elif is_bool_dtype(key.dtype):
            # an ndarray with bool-dtype by definition has no missing values.
            # So we only need to check for NAs in ExtensionArrays
            if is_extension_array_dtype(key.dtype):
                if np.any(key.isna()):
                    raise ValueError(na_msg)
            return True
    elif isinstance(key, list):
        try:
            arr = np.asarray(key)
            return arr.dtype == np.bool_ and len(arr) == len(key)
        except TypeError:  # pragma: no cover
            return False

    return False