HDF5 taking more space than CSV
Consider the following example:
## Prepare the data:

```
import string
import random
import pandas as pd

matrix = np.random.random((100, 3000))
my_cols = [random.choice(string.ascii_uppercase) for x in range(matrix.shape[1])]
mydf = pd.DataFrame(matrix, columns=my_cols)
mydf['something'] = 'hello_world'
```
## Set highest compression possible for HDF5:

```
store = pd.HDFStore('myfile.h5',complevel=9, complib='bzip2')
store['mydf'] = mydf
store.close()
```
## Save also to CSV:

```
mydf.to_csv('myfile.csv', sep=':')
```

The result is:
- `myfile.h5` is 11 MB big
- `myfile.csv` is 5.6 MB big

I have tried with other compression methods and levels. Is this a bug? (I am using Pandas 0.11 and the latest stable version of HDF5 and Python).
## Summary

I am surprised that even though: 
- The majority of the DataFrame (300K entries) is of type `float64`, 
- There are only 100 strings, and **all** of them hold the exact same value (`hello_world`)
- I am using the highest possible compression scheme.

the HDF5 takes _more_ space than the CSV file. Is this normal/expected?

Note: I have posted this issue also in PyTables, since I don't know where exactly it fits: 
https://github.com/PyTables/PyTables/issues/257

Note: This issue is based on the following thread at StackOverflow: 
http://stackoverflow.com/questions/16639877/hdf5-taking-more-space-than-csv
