index.levels not being updated by groupby

Summary:

Input:

df.D.ix['c1','d1']
t1    0
t2    0
t3    1
t4    1
t5    1
Name: D

Operation:

grouped = df.groupby('D')
for i,j in grouped:
    print 'D:',i
    print 'Actual index[2]:',j.index[0][2]
    print 'First element of levels[2]:',j.index.levels[2][0]

Output:

D: 0.0
Actual index[2]: t1
First element of levels[2]: t1
D: 1.0
Actual index[2]: t3
First element of levels[2]: t1

Details:

http://nbviewer.ipython.org/4482106/

[jtratner]

Right now, I don’t consider this a bug. Can you help me understand why an end user needs to care about what is actually in the levels?

To be clear, if we don’t update them, we can share the levels indexes between all the views and copies of this MI, instead of allocating new ndarrays (and hash tables?) for each.

I could see adding a method to allow consolidation of a MultiIndex, but you can get the same thing now by doing:

new_index = MultiIndex.from_tuples(index.values)

[ghost]

See https://github.com/pydata/pandas/issues/2770#issuecomment-29551251. That’s not what levels is for. Not a bug. closing.

Edit: It should be

new_index = MultiIndex.from_tuples(mi.tolist())

Which does an extra copy (or two).