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

MultiIndex.asi8 = None

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.argmin(axis=None) [source] return a ndarray of the minimum argument indexer See also numpy.ndarray.argmin