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.codes

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.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

CategoricalIndex.categories

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