On Fri, Feb 19, 2016 at 12:08 PM, Allan Haldane <allanhald...@gmail.com> wrote:
> I also want to add a historical note here, that 'groupby' has been > discussed a couple times before. > > Travis Oliphant even made an NEP for it, and Wes McKinney lightly hinted > at adding it to numpy. > > http://thread.gmane.org/gmane.comp.python.numeric.general/37480/focus=37480 > http://thread.gmane.org/gmane.comp.python.numeric.general/38272/focus=38299 > http://docs.scipy.org/doc/numpy-1.10.1/neps/groupby_additions.html > > Travis's idea for a ufunc method 'reduceby' is more along the lines of > what I was originally thinking. Just musing about it, it might cover few > small cases pandas groupby might not: It could work on arbitrary ufuncs, > and over particular axes of multidimensional data. Eg, to sum over > pixels from NxNx3 image data. But maybe pandas can cover the > multidimensional case through additional index columns or with Panel. > xarray is now covering that area. There are also recfunctions in numpy.lib that never got a lot of attention and expansion. There were plans to cover more of the matplotlib versions in numpy, but I have no idea and didn't check what happened to it.. Josef > > Cheers, > Allan > > On 02/15/2016 05:31 PM, Paul Hobson wrote: > > Just for posterity -- any future readers to this thread who need to do > > pandas-like on record arrays should look at matplotlib's mlab submodule. > > > > I've been in situations (::cough:: Esri production ::cough::) where I've > > had one hand tied behind my back and unable to install pandas. mlab was > > a big help there. > > > > https://goo.gl/M7Mi8B > > > > -paul > > > > > > > > On Mon, Feb 15, 2016 at 1:28 PM, Lluís Vilanova <vilan...@ac.upc.edu > > <mailto:vilan...@ac.upc.edu>> wrote: > > > > Benjamin Root writes: > > > > > Seems like you are talking about xarray: > https://github.com/pydata/xarray > > > > Oh, I wasn't aware of xarray, but there's also this: > > > > > > > https://people.gso.ac.upc.edu/vilanova/doc/sciexp2/user_guide/data.html#basic-indexing > > > > > https://people.gso.ac.upc.edu/vilanova/doc/sciexp2/user_guide/data.html#dimension-oblivious-indexing > > > > > > Cheers, > > Lluis > > > > > > > > > Cheers! > > > Ben Root > > > > > On Fri, Feb 12, 2016 at 9:40 AM, Sérgio <filab...@gmail.com > > <mailto:filab...@gmail.com>> wrote: > > > > > Hello, > > > > > > > This is my first e-mail, I will try to make the idea simple. > > > > > > > Similar to masked array it would be interesting to use a label > > array to > > > guide operations. > > > > > > > Ex.: > > >>>> x > > > labelled_array(data = > > > > > [[0 1 2] > > > [3 4 5] > > > [6 7 8]], > > > label = > > > [[0 1 2] > > > [0 1 2] > > > [0 1 2]]) > > > > > > >>>> sum(x) > > > array([9, 12, 15]) > > > > > > > The operations would create a new axis for label indexing. > > > > > > > You could think of it as a collection of masks, one for each > > label. > > > > > > > I don't know a way to make something like this efficiently > > without a loop. > > > Just wondering... > > > > > > > Sérgio. > > > > > _______________________________________________ > > > NumPy-Discussion mailing list > > > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > > > > _______________________________________________ > > > NumPy-Discussion mailing list > > > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion >
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