Row slicing of a sparse dataframe is too slow
#### Code Sample

```python
df = Dataframe(numpy.zeros(10000,10000))
random_fill_df(df, num_elements=20)
df = df.to_sparse(fill_value=0)
timeit.timeit('df.loc[[23, 45, 65, 67],:]', globals=globals(), number=10)
```
#### Problem description

The reason why row slicing takes so long is because a sparse dataframe a bunch of sparse series. Column slicing is several order of magnitude faster but row slicing is very poor. The sparse dataframe doesn't take advantage of the scipy sparse matrix library which is even faster (both column and row).

#### Expected Output

In case data is stored as a scipy sparse matrix (as well) inside dataframe object, the slicing operations can be improved, by several orders of magnitude.

I propose that data be stored as a sparse matrix as well in the dataframe object.

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

<details>

pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 36.2.0
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
