pd.DataFrame.and_ is not exposed
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
data = pd.DataFrame.from_dict({'a': [1, 0, 0, 1], 'b': [0, 0, 1, 1]}, dtype=bool)
flags = pd.Series([1, 0, 0, 1], dtype=bool)

# This raises a warning 
data.mul(flags, axis=0)
#/home/joncrall/venv2/local/lib/python2.7/site-packages/pandas/computation/expressions.py:182: #UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the #bool dtype, use '&' instead

# But we can't do data & mask because we need to specify axis=0, instead we have to do
data.__and__(mask, axis=0)
# Why is there no data.and_(mask, axis=0)?
```
#### Problem description

I have a use case where I have a data frame of where rows are samples and columns are 
sample properties. I also have a Series with associated flags that indicate which rows should have
all values set to false. 

At first I thought I could just the `&` operator, but I ran into a broadcasting problem `ValueError: operands could not be broadcast together with shapes (16,) (4,) `. So, I started to look for another way.  I don't want to do this inplace so `data[flags] = False` wont work unless I use copy (but that's ugly).

Instead I want to use an `and` function, but I couldn't find one (I'm still a bit new to the pandas API). So, I settled for using multiplication. However, I got a warning when I used `data.mul`. It told me to use `&` which tipped me off that there was some way to call a `and` function with the kwargs  `axis=0`. I found that using `data.__and__(flags, axis=0)` will work.

I'm wondering why `data.mul` is exposed as a public version of `data.__mul__` but why is there no `data.and_` version of `data.__and__`?

#### Expected Output

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-106-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 25.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.0
statsmodels: 0.8.0rc1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.1
boto: 2.42.0
pandas_datareader: None
</details>
