Numpy str on integers extremely slow?

I found a weird performance difference today: create a numpy array of integers, and then convert it to an array of strings in a list comprehension. It's weirdly slow. Cast the numpy integers to python int's before calling str(), and you get nearly a 10x speedup. Observe:

$ python -m timeit 'import numpy as np; newX = [x for x in np.arange(4096)]; [str(x) for x in newX]'

100 loops, best of 3: 10.5 msec per loop

$ python -m timeit 'import numpy as np; newX = [x for x in np.arange(4096)]; [str(int(x)) for x in newX]'

1000 loops, best of 3: 1.23 msec per loop

Line by line python performance profiling (in particular, about where that @profile decorator comes from)

Here's the thing I didn't understand about rkern's line-by-line performance profiler: the kernprof executable contains information about the @profile decorator - no import is necessary. When you run your script without kernprof, you will get an error. But if you run kernprof -l [original python call], it works. Happy profiling!

Things you should be wary of: switching to on-disk strategies so you can fit multiple jobs in memory...

I recently made it possible to use pytables with my pylearn2 code, and I was excited because, in addition to allowing me to work with larger datasets, I thought it would also allow me to kick off a large number of jobs with medium-sized datasets in parallel. So, I had two such jobs running on the server, and then, unrelatedly, I tried doing some line-by-line processing of a large file...and noticed that it was taking FOREVER, and that the bottleneck seemed to be I/O. To my chagrin, I'd forgotten that when you switch to I/O heavy jobs, parallel processing becomes limited by the number of read-write heads you have...and according to the internets, most hard disks can only have one active head at a time. What's worse, if you're parallelising, you're requiring that head to bounce around between very different regions of the disk, incurring significant overhead. It makes me cringe just thinking about it. [stupid stupid stupid]