ENH: support for msgpack serialization/deserialization
extension of #3828

ToDo
- [x] remove use of `pytest` in test_msgpack
- [ ] PERF!

```
msgpack serialization/deserialization

support all pandas objects: Timestamp,Period,all index types,Series,DataFrame,Panel,Sparse suite
docs included (in io.rst)
iterator support
top-level api support

compression and direct calls to in-line msgpack (enabled via #3828) will wait for 0.13+

closes #686
```

```
Benchmarking: 50k rows of 2x columns of floats with a datetime index

In [4]: %timeit df.to_msgpack('foo')
100 loops, best of 3: 16.5 ms per loop

In [2]: %timeit df.to_pickle('foo')
10 loops, best of 3: 12.9 ms per loop

In [6]: %timeit df.to_csv('foo')
1 loops, best of 3: 470 ms per loop

In [11]: %timeit df.to_hdf('foo2','df',mode='w')
10 loops, best of 3: 20.1 ms per loop

In [13]: %timeit df.to_hdf('foo2','df',mode='w',table=True)
10 loops, best of 3: 81.4 ms per loop
```

```
In [5]: %timeit pd.read_msgpack('foo')
100 loops, best of 3: 16.3 ms per loop

In [3]: %timeit pd.read_pickle('foo')
1000 loops, best of 3: 1.28 ms per loop

In [7]: %timeit pd.read_csv('foo')
10 loops, best of 3: 46.1 ms per loop

In [12]: %timeit pd.read_hdf('foo2','df')
100 loops, best of 3: 5.64 ms per loop

In [14]: %timeit pd.read_hdf('foo2','df')
100 loops, best of 3: 9.39 ms per loop
```

```
In [1]: df = DataFrame(randn(10,2),
   ...:                      columns=list('AB'),
   ...:                      index=date_range('20130101',periods=10))

In [2]: pd.to_msgpack('foo.msg',df)

In [3]: pd.read_msgpack('foo.msg')
Out[3]: 
                   A         B
2013-01-01  0.676700 -1.702599
2013-01-02 -0.070164 -1.368716
2013-01-03 -0.877145 -1.427964
2013-01-04 -0.295715 -0.176954
2013-01-05  0.566986  0.588918
2013-01-06 -0.307070  1.541773
2013-01-07  1.302388  0.689701
2013-01-08  0.165292  0.273496
2013-01-09 -3.492113 -1.178075
2013-01-10 -1.069521  0.848614
```
