BUG: pandas std broken, erratic behavior
I seem to have encountered a **bug** while using 
_DataFrame.apply(np.std)_ or _DF.groupby.agg(np.std)_

**tested on:**
- debian 7.9 (wheezy), pandas version: 0.16.2.dev
- ubuntu 15.10, pandas version:  0.15.0
  - _more version details_ for the tested snippets _below_

**test code:**

``` python
import numpy as np
import pandas as pd
print("pandas version: ", pd.__version__)


# why is there a difference ?
s = pd.Series([1.11] * 10)
print(np.std(s))
#1.88486436615e-08
print(np.std(s.values))
#2.22044604925e-16

# why is it significantly != 0 ?
# why is there a difference ?
print(np.std(pd.Series([53426.7756333882,] * 50)))
#0.0011048543456
print(np.std(pd.Series([53426.7756333882,] * 123)))
#0.000704429402084

# doing that with data frames
df = pd.DataFrame([538512.198638,] * 123)
print(df.apply(np.std))
#0    0.030867
# dtype: float64
print(np.std(df))
#0    0.030867
# dtype: float64
print(np.std(df.values))
#2.32830643654e-10
```

using the following data set even **NaNs appear as result for std**
funnily this **happens if you add enough digits after the comma**

``` python
import numpy as np
import pandas as pd
print("pandas version: ", pd.__version__)

df = pd.DataFrame([538512.1986379109,]*126)
df['uid']=15
df.set_index('uid', inplace=True)

# why do I occasionally even get NaN for identical values?
# NaN appears if there are enough decimal places after the comma 
print(df.groupby(level=0).agg([np.mean, np.std]))
#                 0    
#              mean std
#uid                   
#15   538512.198638 NaN
df['bla'] = 911.
print(df.groupby(level=0).agg([np.mean, np.std]))
#                 0      bla    
#              mean std mean std
#uid                            
#15   538512.198638 NaN  911   0
df['bla'] = 911.12351171571542312243214
print(df.groupby(level=0).agg([np.mean, np.std]))
#                 0      bla    
#              mean std mean std
#uid                            
#15   538512.198638 NaN  911 NaN


```

**bug hypotheses:**
this looks like a detail-problem regarding the way pandas applies functions
is the size of the mapped-on container calculated wrong? (e.g. not the single column series length is used, but the overall container's number of elements)

**version details:** (ubuntu 15.10)
## INSTALLED VERSIONS

commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_AT.UTF-8

pandas: 0.15.0
nose: 1.3.6
Cython: 0.23.3
numpy: 1.8.2
scipy: 0.14.1
statsmodels: 0.5.0
IPython: 2.3.0
sphinx: 1.2.3
patsy: 0.4.0
dateutil: 2.2
pytz: 2014.10
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.4.2
openpyxl: 2.3.0-b1
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
rpy2: 2.6.2
sqlalchemy: 1.0.8
pymysql: 0.6.2.None
psycopg2: 2.6.1 (dt dec mx pq3 ext lo64)
