The loc attribute is one of several indexers, see the pandas.core.indexing module, specifically the get_indexers_list() function:
# the supported indexers
def get_indexers_list():
return [
('ix', _IXIndexer),
('iloc', _iLocIndexer),
('loc', _LocIndexer),
('at', _AtIndexer),
('iat', _iAtIndexer),
]
Each of those classes is defined in the same module.
That function is used to add attributes to the NDFrame class, which is a base class of pandas.DataFrame. Each of the classes in the get_indexers_list() result is added as a property object.
So to re-use the object type, you could add your properties, using the same code if necessary; add the same class method to your class
@classmethod
def _create_indexer(cls, name, indexer):
"""Create an indexer like _name in the class."""
if getattr(cls, name, None) is None:
_indexer = functools.partial(indexer, name)
setattr(cls, name, property(_indexer, doc=indexer.__doc__))
then add the indexers with
# install the indexes
for _name, _indexer in indexing.get_indexers_list():
DataFrameCollection._create_indexer(_name, _indexer)
Given a dfcollection instance of your DataFrameCollection class, dfcollection.loc would then result in _LocIndexer('loc', dfcollection) being called and returned.
Do study the remaining code in pandas.core.indexing to see how each indexer then expects to find information on your DataFrameCollection instance; it's the self.obj reference in the indexer methods.
For example, dfcollection.loc[...] is translated to _LocationIndexer.__getitem__(), which delegates to _LocIndexer._is_scalar_access(), _LocIndexer._getitem_scalar(), _NDFrameIndexer._getitem_tuple() and _LocIndexer._getitem_axis(), which together with methods these delegate to, need access to at least the .axes, .ndim ._get_value(), ._get_axis_name(), ._get_axis_number(), ._get_axis(), ._reindex_with_indexers() and ._take() attributes and methods on the dataframe.
