[Matplotlib-users] plotting distributions, direct input of histogram

[Paul Hobson]

I don't see how a binned histogram results are compatible with a boxplot,
which directly computes the quartiles and fences from raw data.

I don't understand how we'd begin to infer what those value are.
-paul

On Fri, Aug 2, 2019 at 1:36 PM Elan Ernest <elch.rz at ruetz-online.de> wrote:

> For boxplots with predefined statistics consider the `ax.bxp` function,
>
> https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.bxp.html
>
> For violinplots, one can use `ax.violin`,
>
> https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.violin.html
>
> however, you would need to have calculated the kernel density estimate
> yourself, which is in general impossible with already aggregated
> statistics.
>
>
> Am 02.08.2019 um 13:32 schrieb Neal Becker:
> > I'm frequently plotting distributions using e.g., boxplot, violinplot.
>  But
> > I've already binned my data using my own histogram class.  So I already
> have
> > an array of bins, and array of counts for each bin.
> >
> > I don't see any way to directly input this data to plotting routines
> such as
> > boxplot or violinplot.  What I've been doing is using
> collections.Counter to
> > convert this into a single array, for example if the value '10' occurs
> > '1000' times, I produce an array with [10]*1000.  Obviously, this doesn't
> > scale to 10's of millions of samples.
> >
> > Is there any way to input my data that already has been binned and
> counted?
> >
> > Thanks,
> > Neal
> >
> > (Also, I really wish the same for seaborn)
> >
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> >
> >
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