Row/Column operations performance differences under win
I've noticed performance differences between column and row wise function applications under windows 8.1. The problem most likely roots back the memory storage of the numpy arrays, which are per default 'C' like, hence causing for all column based pandas operations signifcant performance differences vs row wise operations.Under linux the problem is not present (or beeing taken care of explicity).  If this is hard to fix in windows, I would argue to change the default behaviour to 'Fortran' like memory storaging (column by column), as arguably most time series data is rather of type many rows vs few columns.
#### Code Sample, a copy-pastable example if possible

```
import numpy as np
import pandas as pd
num_sim = 1000
f = np.array(np.random.standard_normal((num_sim,num_sim)), order='F')
c = np.array(np.random.standard_normal((num_sim,num_sim)), order='C')
df_f = pd.DataFrame(f)
df_c = pd.DataFrame(c)
```
#### Expected Output

I expect similiar performance of colum or row wise operations.

```
%timeit df_f.mean()
100 loops, best of 3: 1.9 ms per loop
%timeit df_f.mean(axis=1)
100 loops, best of 3: 10.3 ms per loop
%timeit df_c.mean()
100 loops, best of 3: 10.2 ms per loop
%timeit df_c.mean(axis=1)
100 loops, best of 3: 1.86 ms per loop
```
#### output of `pd.show_versions()`

commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.18.1
nose: None
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.6.2.None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.0
