ENH: allow column oriented table storage in HDFStore
renamed `carray` package: https://github.com/Blosc/bcolz

Soliciting any comments on this proposal to create a columnar access table in `HDFStore`.

This is actually very straightforward to do.

need a new kw argument to describe the type of format for
storage: ``format='s|t|c'` (also allows expansion in the future to other formats)
- `s` is the `Storer` format (e.g. `store['df'] = value`), implied currently
  with `put``
- `t` is the `Table` format (e.g. `store.append('df',value)`, created
  with `table=True` when using `put` or using `append`
- `c` is a `CTable` format (new), which is a column oriented table

so will essentially deprecate `append=,table=` keywords (or just translate them)
to a `format=` kw.

```
df.to_hdf('test.h5','df',format='c')
```

Will have a master node which holds the structure.
Will store a format with a single column from a `DataFrame` in a sub-node of the
master.

advantages:
- index(s) are kept in their own columns (this is true with `Table` now)
- allows easy delete/add of columns (somewhat tricky in the `Table` format)
- allows appends (interesting twist is that have to keep the indices in sync)
- selection is straightforward as everything is indexed the same
- selecting a small number of columns relative to the total should be faster than an equivalent `Table`
- API will be the same as current. This is essentially an extension of the `append_as_multiple / select_as_multiple` multi-table accssors.
- can be included/coexist alongside existing `Table/Storer`s

disadvantages:
- selecting lots of columns will be somewhat slower that an equivalent `Table`
- requires syncing of all the indices (the coordinates of all rows)
- delete operations will be somewhat slower than an equivalent `Table`

There are actually 2 different formats that could be used here, I propose just the single-file for now. However, The sub-nodes could be spread out in a directory and stored as separate files. This allows concurrent access with some concurrent reads allowed (this is pretty tricky, so hold off on this for now).

This `CTable` format will use the existing `PyTables` infrastructure under the hood; it is possible to use the `ctable` module however http://carray.pytables.org/docs/manual/ (this is basically what BLAZE uses under the hood for its storage backend)
