I spotted this a little late, but I work with a similar problem (mortgage prepayment models). My solution has been to skip the pandas HDFStore layer and use straight pytables. I save each column as an individual HDF5 array in my final file.
My basic workflow is to first get a CSV file from the database. I gzip it, so it's not as huge. Then I convert that to a row-oriented HDF5 file, by iterating over it in python, converting each row to a real data type, and writing it to a HDF5 file. That takes some tens of minutes, but it doesn't use any memory, since it's only operating row-by-row. Then I "transpose" the row-oriented HDF5 file into a column-oriented HDF5 file.
The table transpose looks like:
def transpose_table(h_in, table_path, h_out, group_name="data", group_path="/"):
# Get a reference to the input data.
tb = h_in.getNode(table_path)
# Create the output group to hold the columns.
grp = h_out.createGroup(group_path, group_name, filters=tables.Filters(complevel=1))
for col_name in tb.colnames:
logger.debug("Processing %s", col_name)
# Get the data.
col_data = tb.col(col_name)
# Create the output array.
arr = h_out.createCArray(grp,
col_name,
tables.Atom.from_dtype(col_data.dtype),
col_data.shape)
# Store the data.
arr[:] = col_data
h_out.flush()
Reading it back in then looks like:
def read_hdf5(hdf5_path, group_path="/data", columns=None):
"""Read a transposed data set from a HDF5 file."""
if isinstance(hdf5_path, tables.file.File):
hf = hdf5_path
else:
hf = tables.openFile(hdf5_path)
grp = hf.getNode(group_path)
if columns is None:
data = [(child.name, child[:]) for child in grp]
else:
data = [(child.name, child[:]) for child in grp if child.name in columns]
# Convert any float32 columns to float64 for processing.
for i in range(len(data)):
name, vec = data[i]
if vec.dtype == np.float32:
data[i] = (name, vec.astype(np.float64))
if not isinstance(hdf5_path, tables.file.File):
hf.close()
return pd.DataFrame.from_items(data)
Now, I generally run this on a machine with a ton of memory, so I may not be careful enough with my memory usage. For example, by default the load operation reads the whole data set.
This generally works for me, but it's a bit clunky, and I can't use the fancy pytables magic.
Edit: The real advantage of this approach, over the array-of-records pytables default, is that I can then load the data into R using h5r, which can't handle tables. Or, at least, I've been unable to get it to load heterogeneous tables.