AttributeError: 'Hour' object has no attribute 'normalize' when masking a time series
Hi All,

I have a strange bug when I want to mask values of a time series (that I get from a pickled Panel).

I have the following Series:

``` python

In[30]: ts=pd.read_pickle('df_issue.pkl')['wind_speed']

In [31]:ts
Out[31]: 
2013-12-31 16:00:00         NaN
2013-12-31 17:00:00         NaN
2013-12-31 18:00:00    9.845031
2013-12-31 19:00:00         NaN
2013-12-31 20:00:00         NaN
2013-12-31 21:00:00         NaN
2013-12-31 22:00:00         NaN
2013-12-31 23:00:00         NaN
Freq: H, Name: wind_speed, dtype: float64
```

And I have an exception when I try to mask it:

``` python
In [32]: ts<0.
Traceback (most recent call last):

  File "<ipython-input-32-534147d368f7>", line 1, in <module>
    ts<0.

  File "/usr/local/lib/python2.7/dist-packages/pandas/core/ops.py", line 585, in wrapper
    res[mask] = masker

  File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 637, in __setitem__
    self.where(~key, value, inplace=True)

  File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 3238, in where
    inplace=True)

  File "/usr/local/lib/python2.7/dist-packages/pandas/core/internals.py", line 2219, in putmask
    return self.apply('putmask', **kwargs)

  File "/usr/local/lib/python2.7/dist-packages/pandas/core/internals.py", line 2185, in apply
    b_items = self.items[b.mgr_locs.indexer]

  File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.py", line 1387, in __getitem__
    new_offset = key.step * self.offset

  File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/offsets.py", line 265, in __rmul__
    return self.__mul__(someInt)

  File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/offsets.py", line 262, in __mul__
    return self.__class__(n=someInt * self.n, normalize=self.normalize, **self.kwds)

AttributeError: 'Hour' object has no attribute 'normalize'
```

If I make the same Series manually, it is working:

``` python

ts1=pd.Series(data=[np.nan,np.nan,9.845031]+[np.nan]*5,index=pd.date_range('2013-12-31 16:00:00',periods=8,freq='H'))

In [36]: ts1
Out[36]: 
2013-12-31 16:00:00         NaN
2013-12-31 17:00:00         NaN
2013-12-31 18:00:00    9.845031
2013-12-31 19:00:00         NaN
2013-12-31 20:00:00         NaN
2013-12-31 21:00:00         NaN
2013-12-31 22:00:00         NaN
2013-12-31 23:00:00         NaN
Freq: H, dtype: float64

In [37]: ts1<0.
Out[37]: 
2013-12-31 16:00:00    False
2013-12-31 17:00:00    False
2013-12-31 18:00:00    False
2013-12-31 19:00:00    False
2013-12-31 20:00:00    False
2013-12-31 21:00:00    False
2013-12-31 22:00:00    False
2013-12-31 23:00:00    False
Freq: H, dtype: bool
```

I really don't understand why...
You can find the pickle file here: http://we.tl/lrsFvmanVl

Thanks,
Greg

Here is the show_version output:
## INSTALLED VERSIONS

commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.8.0-37-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.14.1
nose: 1.3.0
Cython: 0.20.1
numpy: 1.8.1
scipy: 0.13.3
statsmodels: 0.6.0.dev-Unknown
IPython: 1.1.0
sphinx: 1.1.3
patsy: 0.2.1
scikits.timeseries: None
dateutil: 2.2
pytz: 2014.4
bottleneck: 0.6.0
tables: 3.1.1
numexpr: 2.0.1
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.2
xlsxwriter: None
lxml: 3.0.0
bs4: 4.3.2
html5lib: None
httplib2: 0.7.2
apiclient: None
rpy2: None
sqlalchemy: None
pymysql: None
psycopg2: None
