MultiIndex.difference(other) [source] Compute sorted set difference of two MultiIndex objects Returns: diff : MultiIndex

MultiIndex.delete(loc) [source] Make new index with passed location deleted Returns: new_index : MultiIndex

MultiIndex.data return the data pointer of the underlying data

MultiIndex.copy(names=None, dtype=None, levels=None, labels=None, deep=False, _set_identity=False, **kwargs) [source] Make a copy of this object. Names, dtype, levels and labels can be passed and will be set on new copy. Parameters: names : sequence, optional dtype : numpy dtype or pandas type, optional levels : sequence, optional labels : sequence, optional Returns: copy : MultiIndex Notes In most cases, there should be no functional difference from using deep, but if deep is passed it

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

MultiIndex.astype(dtype, copy=True) [source] Create an Index with values cast to dtypes. The class of a new Index is determined by dtype. When conversion is impossible, a ValueError exception is raised. Parameters: dtype : numpy dtype or pandas type copy : bool, default True By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is returned.

MultiIndex.asof_locs(where, mask) [source] where : array of timestamps mask : array of booleans where data is not NA

MultiIndex.asof(label) [source] For a sorted index, return the most recent label up to and including the passed label. Return NaN if not found. See also get_loc asof is a thin wrapper around get_loc with method=?pad?

MultiIndex.asi8 = None

MultiIndex.argsort(*args, **kwargs) [source]