Large speed downgrade in function from 0.12 to 0.13
I only just upgraded to 0.13 so noticing this now.  The function below was run on the setup below twice, once with pandas 0.13.1 and another time with 0.12.  There is large speed difference as shown in the output.  Note that I know that the function as written can be simplified as pd.rolling_std(df1,trail_num, trail_num)*df2, but the below is an example and the actual function I use requires the below approach.

``` python
import pandas as pd
import numpy as np

def main_func2(df1, df2, trail_num):
    df3 = pd.DataFrame(index=df1.index, columns=df2.columns)
    for inum in range(trail_num, len(df1)):
        columns_subset = df2.iloc[inum].valid().index
        dfslice = df1.filter(columns_subset)[inum - trail_num: inum]
        slicestd = dfslice.std()
        df3.iloc[inum] =  slicestd
    return df3

np.random.seed(1234)
r2 = pd.DataFrame(np.random.randn(1000,100))
l2 = pd.DataFrame(np.random.randn(1000,100))

print pd.__version__
%timeit main_func2(r2,l2,3)

Output 1:
0.13.1
1 loops, best of 3: 6.44 s per loop

Output 2:
0.12.0
1 loops, best of 3: 1.53 s per loop

```

In [3]: pd.show_versions()
## INSTALLED VERSIONS

commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 2012Server
machine: AMD64
processor: Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.13.1
Cython: None
numpy: 1.8.0
scipy: None
statsmodels: None
IPython: 1.1.0
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.2
pytz: None
bottleneck: 0.7.0
tables: None
numexpr: 2.2.2
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: None
bs4: None
html5lib: None
bq: None
apiclient: None
