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> 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> 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 > > 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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