Skip to main content

h5py and pandas for large array storage

I've actually gone back to pure hdf5 (via the h5py interface) for storing and accessing numerical data. Pandas via PyTables started to get too complicated and started to get in the way of my analysis (I was spending too much time on the docs, and testing out cases etc.).

My application is simple. There is a rather large array of numbers that I would like to store on disk and load subsets of to perform operations on cells/subsets. For this I found pandas to be a bad compromise. Either I had to load all the data all at once into memory, or I had to go through a really slow disk interface (which probably WAS loading everything into memory at the same time). I just don't have the luxury to fight with it so long.

I'm seeing that pandas has a (kind of) proper way of doing what I'm doing, but in h5py it just seems more natural and less encumbering :(

UPDATE: So, as previously mentioned, Pandas shines as a database substitute, where you want to select subsets of data based on some criterion. Pandas has a method (to_hdf) that will save a dataframe as a PyTables table that DOES allow you to do efficient sub-sampling without loading everything onto disk using the 'select' method, but even that is pretty slow compared to directly pulling things using h5py (and cumbersome). But it works really nicely for actual conditional select statements. Code updated to reflect this.

Timing information for randomly accessing 1000 individual cells from a 1000x1000 array of floats 
h5py                - 0.295 s
pandas frame        - 14.8 s   (reloaded table on each lookup probably)
pandas frame_table  - 3.943 s  

python test.py | grep 'function calls'
         95023 function calls in 0.295 seconds
         711312 function calls (707157 primitive calls) in 14.808 seconds
         1331709 function calls (1269472 primitive calls) in 3.943 seconds


import h5py, pandas as pd, numpy, cProfile

def create_data_files():
  r = numpy.empty((1000,1000),dtype=float)
  df = pd.DataFrame(r)

  with pd.get_store('pandas.h5','w') as f:
    f.append('data', df)

  with h5py.File('h5py.h5','w') as f:
    f.create_dataset('data', data=r)

def access_h5py(idx):
  with h5py.File('h5py.h5') as f:
    for n in range(idx.shape[0]):
      f['/data'][idx[n][0],idx[n][1]]

def access_pandas(idx):
  with pd.get_store('pandas.h5') as f:
    for n in range(idx.shape[0]):
      f['data'].iloc[idx[n][0],idx[n][1]]

def slice_pandas(idx):
  with pd.get_store('pandas.h5') as f:
    for n in range(idx.shape[0]):
      f.select('data', [('index', '=', idx[n][0]), ('columns', '=', idx[n][1])])

#create_data_files()
idx = numpy.random.randint(1000,size=(1000,2))
cProfile.run('access_h5py(idx)')
cProfile.run('access_pandas(idx)')
cProfile.run('slice_pandas(idx)')
Labels:

Comments

Post a Comment

Popular posts from this blog

A note on Python's __exit__() and errors

Python's context managers are a very neat way of handling code that needs a teardown once you are done. Python objects have do have a destructor method ( __del__ ) called right before the last instance of the object is about to be destroyed. You can do a teardown there. However there is a lot of fine print to the __del__ method. A cleaner way of doing tear-downs is through Python's context manager , manifested as the with keyword. class CrushMe: def __init__(self): self.f = open('test.txt', 'w') def foo(self, a, b): self.f.write(str(a - b)) def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.f.close() return True with CrushMe() as c: c.foo(2, 3) One thing that is important, and that got me just now, is error handling. I made the mistake of ignoring all those 'junk' arguments ( exc_type, exc_val, exc_tb ). I just skimmed the docs and what popped out is that you need to return True or...
Post a Comment
Read more

Remove field code from Word document

e.g. before submitting a MS, or hand manipulating some formatting because Word does things (like cross-references) so half-assed [from here ] Select all the text (CTRL-A) Press Ctrl+Shift+F9 Editing to remove anonymous comments that only contain thanks. I really appreciate the thanks, but it makes it harder to find comments that carry pertinent information. I'm also going to try and paste informative comments in the body of the post to make them easier to find.
24 comments
Read more

h5py and multiprocessing

The HDF5 format has been working awesome for me, but I ran into danger when I started to mix it with multiprocessing. It was the worst kind of danger: the intermittent error. Here are the dangers/issues in order of escalation (TL;DR is use a generator to feed data from your file into the child processes as they spawn. It's the easiest way. Read on for harder ways.) An h5py file handle can't be pickled and therefore can't be passed as an argument using pool.map() If you set the handle as a global and access it from the child processes you run the risk of racing which leads to corrupted reads. My personal runin was that my code sometimes ran fine but sometimes would complain that there are NaNs or Infinity in the data. This wasted some time tracking down. Other people have had this kind of problem [ 1 ]. Same problem if you pass the filename and have the different processes open individual instances of the file separately. The hard way to solve this problem is to sw...
Post a Comment
Read more