As a rule of thumb, if you run out of memory in python, you will also run out of memory in matlab. I've done a lot of work in both and found that while python's

As a rule of thumb, if you run out of memory in python, you will also run out of memory in matlab. I've done a lot of work in both and found that while python's memory performance may not be ideal, at least when you run into trouble in python you have options. With matlab I found that when I got that dreaded "Out of memory" message the prompt, there was little I could do. The internals are completely opaque, shipping my code to C is a pain in the ass, and there are very few language constructs to help you control how you use memory. In most cases running out of memory in matlab meant either making the problem smaller or running it on a beefier machine. I think this is the reason why you see a lot of labs at universities with machines that have 96GB of memory, even though their datasets seem to be much smaller. FWIW, as far as processing lots of data is concerned, python is not without issues. If you do it naively you will run out of memory really quickly. But by picking your tools correctly you can go a long way. Use Pandas and/or Sparse arrays whenever possible. Learn how numpy broadcasting operations contribute to memory explosions. Take a gander at the source of that sklearn method you're using, since it's often quite obvious that the particular implementation will choke. I've found that these days I try my best to avoid loading datasets into memory. This is second nature for people who work with 'big data', but it's an m.o. that takes some getting used to. That is, blocking and/or streaming your data, and appropriately subdividing your problem for distributed computation. It's worth mentioning that this problem with python is under active research and development. The guys at continuum developed IOpro to deal with the issue of memory efficiency when loading data, and to make streaming data from flat files/S3/mongodb/whatever easier and more stable. Also, their (very young) project called Blaze is meant to be a drop-in replacement for numpy, but is designed for efficiency and specifically for dealing with out-of-core computation. We'll see...