Exception during groupby pandas

I am just beginning to learn analytics with python for network analysis using the Python For Data Analysis book and I'm getting confused by an exception I get while doing some groupby's... here's my situation.

I have a CSV of NetFlow data that I've imported to pandas. The data looks something like:

dt, srcIP, srcPort, dstIP, dstPort, bytes
2013-06-06 00:00:01.123, 123.123.1.1, 12345, 234.234.1.1, 80, 75

I've imported and indexed the data as follows:

df = pd.read_csv('mycsv.csv')
df.index = pd.to_datetime(full_set.pop('dt'))

What I want is a count of unique srcIPs which visit my servers per time period (I have data over several days and I'd like time period by date,hour). I can obtain an overall traffic graph by grouping and plotting as follows:

df.groupby([lambda t: t.date(), lambda t: t.hour]).srcIP.nunique().plot()

However, I want to know how that overall traffic is split amongst my servers. My intuition was to additionally group by the 'dstIP' column (which only has 5 unique values), but I get errors when I try to aggregate on srcIP.

grouped = df.groupby([lambda t: t.date(), lambda t: t.hour, 'dstIP'])
grouped.sip.nunique()
...
Exception: Reindexing only valid with uniquely valued Index objects

So, my specific question is: How can I avoid this exception in order to create a plot where traffic is aggregated over 1 hour blocks and there is a different series for each server.

More generally, please let me know what newb errors I'm making. Also, the data does not have regular frequency timestamps and I don't want sampled data in case that makes any difference in your answer.

EDIT 1 This is my ipython session exactly as input. output ommitted except for the deepest few calls in the error.

EDIT 2 Upgrading pandas from 0.8.0 to 0.12.0 as yielded a more descriptive exception shown below

import numpy as np
import pandas as pd
import time
import datetime

full_set = pd.read_csv('june.csv', parse_dates=True, index_col=0)
full_set.sort_index(inplace=True)
gp = full_set.groupby(lambda t: (t.date(), t.hour, full_set['dip'][t]))
gp['sip'].nunique()
... 
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in _make_labels(self)
   1239             raise Exception('Should not call this method grouping by level')
   1240         else:
-> 1241             labs, uniques = algos.factorize(self.grouper, sort=self.sort)
   1242             uniques = Index(uniques, name=self.name)
   1243             self._labels = labs

/usr/local/lib/python2.7/dist-packages/pandas/core/algorithms.pyc in factorize(values, sort, order, na_sentinel)
    123     table = hash_klass(len(vals))
    124     uniques = vec_klass()
--> 125     labels = table.get_labels(vals, uniques, 0, na_sentinel)
    126 
    127     labels = com._ensure_platform_int(labels)

/usr/local/lib/python2.7/dist-packages/pandas/hashtable.so in pandas.hashtable.PyObjectHashTable.get_labels (pandas/hashtable.c:12229)()

/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc in __hash__(self)
     52     def __hash__(self):
     53         raise TypeError('{0!r} objects are mutable, thus they cannot be'
---> 54                               ' hashed'.format(self.__class__.__name__))
     55 
     56     def __unicode__(self):

TypeError: 'TimeSeries' objects are mutable, thus they cannot be hashed