How to iterate over pandas multiindex dataframe using index

Late to the party, I found that the following works, too:

for date in df.index.unique("date"):
    print(df.loc[date])

It uses the level optional parameter of the Index.unique method introduced in version 0.23.0.

You can specify either the level number or label.

One easy way would be to groupby the first level of the index - iterating over the groupby object will return the group keys and a subframe containing each group.

In [136]: for date, new_df in df.groupby(level=0):
     ...:     print(new_df)
     ...:     
                    observation1  observation2
date       Time                               
2012-11-02 9:15:00     79.373668           224
           9:16:00    130.841316           477

                    observation1  observation2
date       Time                               
2012-11-03 9:15:00     45.312814           835
           9:16:00    123.776946           623
           9:17:00    153.766460           624
           9:18:00    463.276946           626
           9:19:00    663.176934           622
           9:20:00    763.773330           621

                    observation1  observation2
date       Time                               
2012-11-04 9:15:00    115.449437           122
           9:16:00    123.776946           555
           9:17:00    153.766460           344
           9:18:00    463.276946           212

You can also use droplevel to remove the first index (the useless date index):

In [136]: for date, new_df in df.groupby(level=0):
     ...:     print(new_df.droplevel(0))
     ...:
         observation1  observation2
Time
9:15:00     79.373668           224
9:16:00    130.841316           477
...

Tagging off of @psorenson answer, we can get unique level indices and its related data frame slices without numpy as follows:

for date in df.index.get_level_values('date').unique():
    print(df.loc[date])

What about this?

for idate in df.index.get_level_values('date'):
    complex_process(df.ix[idate], idate)