Yes, the same order is guaranteed if it is not modified. See the docs here. Edit: Regarding if changing the value (but not adding/removing a key) will affect the order, this is what the comments in the C-source says: /* CAUTION: PyDict_SetItem() must guarantee that it won’t resize the * dictionary if it’s merely replacing … Read more
What languages/environments have you used for developing scientific software, esp. data analysis? What libraries? (E.g., what do you use for plotting?) I used to work for Enthought, the primary corporate sponsor of SciPy. We collaborated with scientists from the companies that contracted Enthought for custom software development. Python/SciPy seemed to be a comfortable environment for … Read more
So your data isn’t that big, and the fact that you’re having trouble plotting it points to issues with the tools. Matplotlib has lots of options and the output is fine, but it’s a huge memory hog and it fundamentally assumes your data is small. But there are other options out there. So as an … Read more
I already posted this in another thread but I think it fits better in this one: UPDATE (30.07.2014): I re-run the the benchmark on our new HPC. Both the hardware as well as the software stack changed from the setup in the original answer. I put the results in a google spreadsheet (contains also the … Read more
It’s probably faster and easier to use numpy.digitize(): import numpy data = numpy.random.random(100) bins = numpy.linspace(0, 1, 10) digitized = numpy.digitize(data, bins) bin_means = [data[digitized == i].mean() for i in range(1, len(bins))] An alternative to this is to use numpy.histogram(): bin_means = (numpy.histogram(data, bins, weights=data)[0] / numpy.histogram(data, bins)[0]) Try for yourself which one is faster… … Read more