Yes I like the name. The primary use-case for Matplotlib is that our `hist` method can take in a list of arrays and produces N histograms in one shot. Currently with 'auto' we only use the first data set to sort out what the bins should be and then re-use those for the rest of the data sets. This will let us get the bins on the merged input, but I take Josef's point that this is not actually what we want....
Tom On Mon, Mar 12, 2018 at 11:35 PM <josef.p...@gmail.com> wrote: > On Mon, Mar 12, 2018 at 11:20 PM, Eric Wieser > <wieser.eric+nu...@gmail.com> wrote: > >> Given that the bin selection are data driven, transferring them across > datasets might not be so useful. > > > > The main application would be to compute bins across the union of all > > datasets. This is already possibly by using `np.histogram` and > > discarding the first result, but that's super wasteful. > > assuming "union" means a combined dataset. > > If you stack datasets, then the number of observations will not be > correct for individual datasets. > > In that case an additional keyword like nobs, or whatever name would > be appropriate for numpy, would be useful, e.g. use the average number > of observations across datasets. > Auxiliary statistic like std could then be computed on the total > dataset (if that makes sense, which would not be the case if the > variance across datasets is larger than the variance within datasets. > > Josef > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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