Confusing behavior with (multi-)assignment and _LocIndexer/_IXIndexer
``` python
df = pandas.DataFrame([[1,2,3,4,5]], columns=["A", "B", "C", "D", "E"])
# Suppose you want to set df.B to df.C when df.A ==1
# then the following both work:
df.loc[df.A== 1, "B"] = df.loc[df.A == 1, "C"]
df.ix[df.A == 1, "B"] = df.ix[df.A == 1, "C"]
# you can even mix and match them with ix on one side and loc on the other

# but maybe you have two or more columns you want to set... Its natural to think that:
df.loc[df.A== 1, ["B", "C"]] = df.loc[df.A == 1, ["D", "E"]]
df.ix[df.A == 1, ["B", "C"]] = df.ix[df.A == 1, ["D", "E"]]
# but they actually just NaN out df.B and df.C (it isn't an issue of a silent copy losing updates)
# in fact the application of NaN even happens if you have singletons
df.loc[df.A== 1, ["D"]] = df.loc[df.A == 1, ["E"]]

# presumably because
type(df.ix[df.A == 1, "B"])
# is pandas.core.series.Series but 
type(df.ix[df.A == 1, ["B"]])
# is pandas.core.frame.DataFrame
# but when printed they look really similar... 
#0    3
# Name: B, dtype: int64
# versus
#    B
#0  3
# so it is easy to get confused

# If this can't be made to work in the natural fashion it would be a lot nicer if it could just throw an error
# the same way the following does:
df.ix[df.A == 1, ["B", "C"]] = df[df.A == 1]["D", "E"] 
```
## INSTALLED VERSIONS

commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.29.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.4
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: 2.3.4
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: None
boto: None
