Pandas and h5py load the same data (ndarray) differently: hvl_t support.
Based off this [question](http://stackoverflow.com/q/29986826/232794). In fact more or less copied and pasted :)

I have a file in HDF5 format.  It was created using the HDF5's C++ API using these:

``` c++
    struct SignalDefH5
    {
        char  id   [128];
        char  name [ 64];
        char  units[ 16];
        float min;
        float max;
        hvl_t tags; /* This right there does not work in Pandas... */
    };

    struct TagDefH5
    {
        char  tag [ 64];
        char  desc[256];
    };
```

Note that tags is supposed to be a list of strings. If I load the file using h5py, I get this:

``` python
    >>> import h5py
    >>> hfile = h5py.File('test.h5', 'r')
    >>> signals = hfile['/signals']
    >>> signals[0]
    ('id1', 'a pressure', 'bar', 0.0, 300.0, ['Pressure'])
    >>> type(signals[0][5])
    numpy.ndarray
```

However, if I use Pandas to load the same file, I get this:

``` python
    >>> store = pd.HDFStore('test.h5')
    >>> store.root.signals
    /signals (Table(179,)) ''
      description := {
      "id": StringCol(itemsize=128, shape=(), dflt='', pos=0),
      "name": StringCol(itemsize=64, shape=(), dflt='', pos=1),
      "units": StringCol(itemsize=16, shape=(), dflt='', pos=2),
      "min": Float32Col(shape=(), dflt=0.0, pos=3),
      "max": Float32Col(shape=(), dflt=0.0, pos=4),
      "tags": StringCol(itemsize=64, shape=(), dflt='', pos=5)}
      byteorder := 'little'
      chunkshape := (234,)
    >>> store.root.signals[0]
    ('id1', 'a pressure', 'bar', 0.0, 300.0, '\x02\x00\x00\x00\x00\x00\x00\x00\xf0f\x1e\x04\x00\x00\x00\x00\xba\nVT\xd1!\xa7\xdd\xb0\xe3\x9a\x02\x00\x00\x00\x00@\xecR\x1f\xa2\x7f\x00\x00}B\x178\x96\xa4u\xe6\xb0\xdd\x7f\x02\x00\x00\x00\x00 \x01')
    >>> type(store.root.signals[0][5])
    numpy.string_
```

So, [Pandas cannot cope with this type](http://stackoverflow.com/a/29988505/232794) yet it would be nice if it could... I am quit happy to test any patch and help in as much as I can.

Here's all the versions:

``` python
pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-229.1.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8

pandas: 0.16.0
nose: 1.3.6
Cython: 0.22
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.0.0
sphinx: 1.2.3
patsy: None
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
