How to store wide tables in pytables / hdf5
This might not, in fact, be possible to do in a naive way.  HDF5 allocates 64 kb of space for meta-data for every data set.  This meta data includes the types of the columns.  So while the number of columns is a soft limit, somewhere in the 2-3 thousand range you typically run out of space to store the meta data (depending on the length of the column names, etc).  
Furthermore, doesn't numpy limit the number of columns to 32?  How are you representing the data with numpy now?  Anything that you can get into a numpy array should correspond to a pytables Array class.
I have data coming from a csv which has a few thousand columns and ten thousand (or so) rows. Within each column the data is of the same type, but different columns have data of different type*. Previously I have been pickling the data from numpy and storing on disk, but it's quite slow, especially because usually I want to load some subset of the columns rather than all of them.
I want to put the data into hdf5 using pytables, and my first approach was to put the data in a single table, with one hdf5 column per csv column. Unfortunately this didn't work, I assume because of the 512 (soft) column limit.
What is a sensible way to store this data?
* I mean, the type of the data after it has been converted from text.