CategoricalIndex.to_series(**kwargs) [source] Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index Returns: Series : dtype will be based on the type of the Index values.

CategoricalIndex.to_native_types(slicer=None, **kwargs) [source] slice and dice then format

CategoricalIndex.to_datetime(dayfirst=False) [source] DEPRECATED: use pandas.to_datetime() instead. For an Index containing strings or datetime.datetime objects, attempt conversion to DatetimeIndex

CategoricalIndex.tolist() [source] return a list of the Index values

CategoricalIndex.take(indices, axis=0, allow_fill=True, fill_value=None, **kwargs) [source] return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. Parameters: indices : list Indices to be taken axis : int, optional The axis over which to select values, always 0. allow_fill : bool, default True fill_value : bool, default None If allow_fill=True and fill_value is not None, indices specified by -1 is regarded as NA. If Index doesn?t hol

CategoricalIndex.T return the transpose, which is by definition self

CategoricalIndex.sym_diff(*args, **kwargs) [source]

CategoricalIndex.symmetric_difference(other, result_name=None) [source] Compute the symmetric difference of two Index objects. It?s sorted if sorting is possible. Parameters: other : Index or array-like result_name : str Returns: symmetric_difference : Index Notes symmetric_difference contains elements that appear in either idx1 or idx2 but not both. Equivalent to the Index created by idx1.difference(idx2) | idx2.difference(idx1) with duplicates dropped. Examples >>> idx1 = Ind

CategoricalIndex.summary(name=None) [source]

CategoricalIndex.strides return the strides of the underlying data