http://nbviewer.ipython.org/github/nayyarv/matplotlib/blob/master/examples/sta tistics/A utomating%20Binwidth%20Choice%20for%20Histogram.ipynb
Long story short, histogram visualisations that depend on numpy (such as matplotlib, or nearly all of them) have poor default behaviour as I have to constantly play around with the number of bins to get a good idea of what I'm looking at. The bins=10 works ok for up to 1000 points or very normal data, but has poor performance for anything else, and doesn't account for variability either. I don't have a method easily available to scale the number of bins given the data. R doesn't suffer from these problems and provides methods for use with it's hist method. I would like to provide similar functionality for matplotlib, to at least provide some kind of good starting point, as histograms are very useful for initial data discovery. The notebook above provides an explanation of the problem as well as some proposed alternatives. Use different datasets (type and size) to see the performance of the suggestions. All of the methods proposed exist in R and literature. I've put together an implementation to add this new functionality, but am hesitant to make a pull request as I would like some feedback from a maintainer before doing so. https://github.com/numpy/numpy/compare/master...nayyarv:master I've provided them as functions for easy refactoring, as it can be argued that it should be in it's own function/file/class, or alternatively can be turned into simple if, elif statements. I believe this belongs in numpy as it is where the functionality exists for histogram methods that most libraries build on, and it would useful for them to not require scipy for example. I will update the documentation accordingly before making a pull request, and add in more tests to show it's functionality. I can adapt my ipython notebook into a quick tutorial/help file if need be. I've already attempted to add this into matplotlib before being redirected here https://github.com/matplotlib/matplotlib/issues/4316 _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
