Hdf5 and spatial indexes
Yes, r-trees can be stored on disk easily. (It's much harder with KD-trees and quad-trees)
That is why the index is block oriented - the block size is meant to be chosen to match hour drive.
I don't use pandas, and will not give a library recommendation.
I have a large dataset, 11 million rows, and I loaded the data into pandas.  I want to then build a spatial index, like rtree or quad tree, but when I push it into memory, it consumes a ton of RAM along with the already reading the large file.  
To help reduce the memory footprint, I was thinking of trying to push the index to disk.
Can you store the tree in a table? Or even a dataframe and store it in hdf table?  Is there a better strategy?
Thanks