CategoricalIndex.name = None

CategoricalIndex.min(*args, **kwargs) [source] The minimum value of the object. Only ordered Categoricals have a minimum! Returns: min : the minimum of this Categorical Raises: TypeError If the Categorical is not ordered.

CategoricalIndex.memory_usage(deep=False) [source] Memory usage of my values Parameters: deep : bool Introspect the data deeply, interrogate object dtypes for system-level memory consumption Returns: bytes used See also numpy.ndarray.nbytes Notes Memory usage does not include memory consumed by elements that are not components of the array if deep=False

CategoricalIndex.max(*args, **kwargs) [source] The maximum value of the object. Only ordered Categoricals have a maximum! Returns: max : the maximum of this Categorical Raises: TypeError If the Categorical is not ordered.

CategoricalIndex.map(mapper) [source] Apply mapper function to its categories (not codes). Parameters: mapper : callable Function to be applied. When all categories are mapped to different categories, the result will be Categorical which has the same order property as the original. Otherwise, the result will be np.ndarray. Returns: applied : Categorical or np.ndarray.

CategoricalIndex.join(other, how='left', level=None, return_indexers=False) [source] this is an internal non-public method Compute join_index and indexers to conform data structures to the new index. Parameters: other : Index how : {?left?, ?right?, ?inner?, ?outer?} level : int or level name, default None return_indexers : boolean, default False Returns: join_index, (left_indexer, right_indexer)

CategoricalIndex.itemsize return the size of the dtype of the item of the underlying data

CategoricalIndex.item() [source] return the first element of the underlying data as a python scalar

CategoricalIndex.is_unique = None

CategoricalIndex.is_type_compatible(kind) [source]