[Feature Request] Add method to pd.Series that collapses redundant items into list-like objects
This may be out of scope but it is a monkey patch that I've made that has been very useful for me that I wanted to share.  What this function does is take a bunch of indicies that have the same value and group them together in a similar way to `groupby`.

I have a version where there is an `into` argument that changes the `type` of each object (e.g. `set`, `list`, etc.).

I am aware this may be out of scope but I use it quite often and wanted to see what the community thought.


```
y_iris = pd.Series({'iris_0': 0, 'iris_1': 0, 'iris_2': 0, 'iris_3': 0, 'iris_4': 0, 'iris_5': 0, 'iris_6': 0, 'iris_7': 0, 'iris_8': 0, 'iris_9': 0, 'iris_10': 0, 'iris_11': 0, 'iris_12': 0, 'iris_13': 0, 'iris_14': 0, 'iris_15': 0, 'iris_16': 0, 'iris_17': 0, 'iris_18': 0, 'iris_19': 0, 'iris_20': 0, 'iris_21': 0, 'iris_22': 0, 'iris_23': 0, 'iris_24': 0, 'iris_25': 0, 'iris_26': 0, 'iris_27': 0, 'iris_28': 0, 'iris_29': 0, 'iris_30': 0, 'iris_31': 0, 'iris_32': 0, 'iris_33': 0, 'iris_34': 0, 'iris_35': 0, 'iris_36': 0, 'iris_37': 0, 'iris_38': 0, 'iris_39': 0, 'iris_40': 0, 'iris_41': 0, 'iris_42': 0, 'iris_43': 0, 'iris_44': 0, 'iris_45': 0, 'iris_46': 0, 'iris_47': 0, 'iris_48': 0, 'iris_49': 0, 'iris_50': 1, 'iris_51': 1, 'iris_52': 1, 'iris_53': 1, 'iris_54': 1, 'iris_55': 1, 'iris_56': 1, 'iris_57': 1, 'iris_58': 1, 'iris_59': 1, 'iris_60': 1, 'iris_61': 1, 'iris_62': 1, 'iris_63': 1, 'iris_64': 1, 'iris_65': 1, 'iris_66': 1, 'iris_67': 1, 'iris_68': 1, 'iris_69': 1, 'iris_70': 1, 'iris_71': 1, 'iris_72': 1, 'iris_73': 1, 'iris_74': 1, 'iris_75': 1, 'iris_76': 1, 'iris_77': 1, 'iris_78': 1, 'iris_79': 1, 'iris_80': 1, 'iris_81': 1, 'iris_82': 1, 'iris_83': 1, 'iris_84': 1, 'iris_85': 1, 'iris_86': 1, 'iris_87': 1, 'iris_88': 1, 'iris_89': 1, 'iris_90': 1, 'iris_91': 1, 'iris_92': 1, 'iris_93': 1, 'iris_94': 1, 'iris_95': 1, 'iris_96': 1, 'iris_97': 1, 'iris_98': 1, 'iris_99': 1, 'iris_100': 2, 'iris_101': 2, 'iris_102': 2, 'iris_103': 2, 'iris_104': 2, 'iris_105': 2, 'iris_106': 2, 'iris_107': 2, 'iris_108': 2, 'iris_109': 2, 'iris_110': 2, 'iris_111': 2, 'iris_112': 2, 'iris_113': 2, 'iris_114': 2, 'iris_115': 2, 'iris_116': 2, 'iris_117': 2, 'iris_118': 2, 'iris_119': 2, 'iris_120': 2, 'iris_121': 2, 'iris_122': 2, 'iris_123': 2, 'iris_124': 2, 'iris_125': 2, 'iris_126': 2, 'iris_127': 2, 'iris_128': 2, 'iris_129': 2, 'iris_130': 2, 'iris_131': 2, 'iris_132': 2, 'iris_133': 2, 'iris_134': 2, 'iris_135': 2, 'iris_136': 2, 'iris_137': 2, 'iris_138': 2, 'iris_139': 2, 'iris_140': 2, 'iris_141': 2, 'iris_142': 2, 'iris_143': 2, 'iris_144': 2, 'iris_145': 2, 'iris_146': 2, 'iris_147': 2, 'iris_148': 2, 'iris_149': 2}, name="iris_targets")

def collapse(self):
    """
    pd.Series mapping of a:b to {b:[x,y,z]}
    """
    d_group_elements = OrderedDict()
    for target_id in sorted(self.unique()):
        d_group_elements[target_id] = self.compress(lambda x: x == target_id).index
    return pd.Series(d_group_elements, name=self.name)
pd.Series.collapse = collapse

y_iris.collapse()
# 0    Index(['iris_0', 'iris_1', 'iris_10', 'iris_11', 'iris_12', 'iris_13',
#        'iris_14', 'iris_15', 'iris_16', 'iris_17', 'iris_18', 'iris_19',
#        'iris_2', 'iris_20', 'iris_21', 'iris_22', 'iris_23', 'iris_24',
#        'iris_25', 'iris_26', 'iris_27', 'iris_28', 'iris_29', 'iris_3',
#        '...
# 1    Index(['iris_50', 'iris_51', 'iris_52', 'iris_53', 'iris_54', 'iris_55',
#        'iris_56', 'iris_57', 'iris_58', 'iris_59', 'iris_60', 'iris_61',
#        'iris_62', 'iris_63', 'iris_64', 'iris_65', 'iris_66', 'iris_67',
#        'iris_68', 'iris_69', 'iris_70', 'iris_71', 'iris_72', 'iris_73',
#     ...
# 2    Index(['iris_100', 'iris_101', 'iris_102', 'iris_103', 'iris_104', 'iris_105',
#        'iris_106', 'iris_107', 'iris_108', 'iris_109', 'iris_110', 'iris_111',
#        'iris_112', 'iris_113', 'iris_114', 'iris_115', 'iris_116', 'iris_117',
#        'iris_118', 'iris_119', 'iris_120', 'iris_121', 'iri...
# Name: iris_targets, dtype: object
```