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. 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