Default iteration behavior of the DataFrame
#### Code Sample, a copy-pastable example if possible

```python
for x in my_data_frame:
    print rep(x)

```
#### Problem description

The current behavior is to print the columns during the normal iteration of a data frame.  However, most of the time when actually using a DataFrame we want to iterate over the rows.  How useful in data analysis is it to iterate over the columns?  In my nearly 2 years of using pandas, not once have I had to do this.   If one needs this, ```my_data_frame.columns``` works just fine. 

In addition, there is no ```my_data_frame.rows``` to work on.  The only good way to iterate over rows is to use iterrows:

```python

for index, row in my_data_frame.iterrows():
    print row

```

This seems like something that should be much more obvious than it is, and to even find that answer, one would pretty much need to google and find the stack overflow answer. 

Is there any way we could iterate over the rows when using the python ```in``` syntax?  I think this is a much more obvious thing to do during iteration than going through the columns for most users. 

#### Expected Output

Row output using the python ```in``` keyword.

#### Output of ``pd.show_versions()``

<details>
# Paste the output here pd.show_versions() here

loaded rc file /Users/jadolfbr/.matplotlib/matplotlibrc
matplotlib version 1.5.1
verbose.level helpful
interactive is False
platform is darwin

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 20.3.1
Cython: None
numpy: 1.11.1
scipy: 0.13.0b1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None

</details>
