BUG: Slicing timeseries with over 1000000 entries with time fails

[willpan]

Slicing a timeseries with time results in a TypeError if the timeseries contains over 1,000,000 entries.

import pandas as pd
from datetime import time

ser = pd.Series(index=pd.date_range('2000-1-1', periods=1000000, freq=pd.datetools.offsets.Second()))
ser.head(999999)[time(15,0)] # this is fine
ser[time(15,0)] # this fails

[jreback]

pls pd.show_versions()

[willpan]

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel 
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.15.0
nose: 1.3.3
Cython: None
numpy: 1.8.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: None                                                                                   
patsy: 0.3.0
dateutil: 2.2
pytz: 2013.8
bottleneck: None
tables: 2.4.0 
numexpr: 2.1
matplotlib: 1.4.0
openpyxl: 1.8.6
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.3.5
bs4: None
html5lib: 0.90                                                                                 
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None

[jreback]

hmm, looks like a conversion issue....welcome a pull-request to fix