ENH: serialization of categorical to HDF5 (GH7621)

This is implemented by storing the codes directly in the table. And a metadata VLArray of the categories.
Query and appending work as expected. The only quirk is that I don't allow you to append to a table unless the new data has exactly the same categories. Otherwise the codes become meaningless.

This has the nice property of drastically shrinking the storage cost compared to regular strings (which are stored as fixed width of the maximum for that particular column).

I bumped the actual HDF5 storage version to current (was 0.10.1). Its not strictly necessary as this is a completely optional feature, but I am adding the sub-group space 'meta' (which FYI we can use for other things, e.g. to store the column labels and avoid the 64KB limit in attrs, their is an issue about this somewhere)


```
In [14]: df = DataFrame({'a' : Series(list('abccdef')).astype('category'), 'b' : np.random.randn(7)})

In [15]: df
Out[15]: 
   a         b
0  a -0.094609
1  b -1.814638
2  c  0.214974
3  c -0.195395
4  d  0.206022
5  e  1.130589
6  f -0.832810

In [19]: store = pd.HDFStore('test.h5',mode='w')

In [20]: store.append('df',df,data_columns=['a'])

In [21]: store.select('df',where=["a in ['b','d']"])
Out[21]: 
   a         b
1  b -1.814638
4  d  0.206022

In [22]: store.select('df',where=["a in ['b','d']"]).dtypes
Out[22]: 
a    category
b     float64
dtype: object

In [25]: store.get_storer('df').group
Out[25]: 
/df (Group) u''
  children := ['table' (Table), 'meta' (Group)]

In [26]: store.get_storer('df').group.table
Out[26]: 
/df/table (Table(7,)) ''
  description := {
  "index": Int64Col(shape=(), dflt=0, pos=0),
  "values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
  "a": Int8Col(shape=(), dflt=0, pos=2)}
  byteorder := 'little'
  chunkshape := (3855,)
  autoindex := True
  colindexes := {
    "a": Index(6, medium, shuffle, zlib(1)).is_csi=False,
    "index": Index(6, medium, shuffle, zlib(1)).is_csi=False}

In [27]: store.get_storer('df').group.meta 
Out[27]: 
/df/meta (Group) u''
  children := ['a' (VLArray)]
```
Maybe it would be informative to explicitely show that the read-back-in column is still a categorical?