CategoricalIndex.dropna(how='any') [source] Return Index without NA/NaN values Parameters: how : {?any?, ?all?}, default ?any? If the Index is a MultiIndex, drop the value when any or all levels are NaN. Returns: valid : Index

CategoricalIndex.drop(labels, errors='raise') [source] Make new Index with passed list of labels deleted Parameters: labels : array-like errors : {?ignore?, ?raise?}, default ?raise? If ?ignore?, suppress error and existing labels are dropped. Returns: dropped : Index

CategoricalIndex.difference(other) [source] Return a new Index with elements from the index that are not in other. This is the set difference of two Index objects. It?s sorted if sorting is possible. Parameters: other : Index or array-like Returns: difference : Index Examples >>> idx1 = pd.Index([1, 2, 3, 4]) >>> idx2 = pd.Index([3, 4, 5, 6]) >>> idx1.difference(idx2) Int64Index([1, 2], dtype='int64')

CategoricalIndex.delete(loc) [source] Make new Index with passed location(-s) deleted Returns: new_index : Index

CategoricalIndex.data return the data pointer of the underlying data

CategoricalIndex.copy(name=None, deep=False, dtype=None, **kwargs) [source] Make a copy of this object. Name and dtype sets those attributes on the new object. Parameters: name : string, optional deep : boolean, default False dtype : numpy dtype or pandas type Returns: copy : Index Notes In most cases, there should be no functional difference from using deep, but if deep is passed it will attempt to deepcopy.

CategoricalIndex.codes

CategoricalIndex.categories

CategoricalIndex.base return the base object if the memory of the underlying data is shared

CategoricalIndex.as_unordered(*args, **kwargs) [source] Sets the Categorical to be unordered Parameters: inplace : boolean (default: False) Whether or not to set the ordered attribute inplace or return a copy of this categorical with ordered set to False