ambiguous ValueError message
#### Code Sample, a copy-pastable example if possible

```
df=pd.DataFrame([(i+j,i-j,i*j) for i in range(0,4) for j in (2,6)])
df.columns=['a','b','c']
df.loc[:,['a','c']].div(df['b'],axis=0)
```

Output:
"ValueError: putmask: mask and data must be the same size"

This message is too ambiguous: what size is it referring to ? 
After some investigation, I understood that data size was referring to divisor data type,
not to the size (length) of the DataFrame and Series, which I wrongly understood was the message referring to:
type(df['b'][0]): class 'numpy.int64'  (error)
type((df['b']/1)[0]): class 'numpy.float64' (no error)

Therefore, changing the last line to:
df.loc[:,['a','c']].div(df['b']/1,axis=0)
worked:
#### Expected Output

```
      a         c
```

0 -1.000000 -0.000000
1 -1.000000 -0.000000
2 -3.000000 -2.000000
3 -1.400000 -1.200000
4       inf       inf
5 -2.000000 -3.000000
6  5.000000  6.000000
7 -3.000000 -6.000000
#### output of `pd.show_versions()`
## INSTALLED VERSIONS

commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-85-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8

pandas: 0.13.1
Cython: None
numpy: 1.8.2
scipy: 0.13.3
statsmodels: None
IPython: None
sphinx: None
patsy: None
scikits.timeseries: None
dateutil: 2.0
pytz: 2012c
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
sqlalchemy: None
lxml: 3.3.3
bs4: 4.2.1
html5lib: 0.999
bq: None
apiclient: None
