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?
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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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)
>
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users at python.org
> Matplotlib-users Info Page
>
>
_______________________________________________
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Matplotlib-users at python.org Matplotlib-users Info Page
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)
>
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users@python.org
> Matplotlib-users Info Page
>
>
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Matplotlib-users@python.org Matplotlib-users Info Page
In that case, I think you should take Elan’s advice, compute the box stats from your histogram data however you feel is appropriate, and then feed that to Axes.bxp, which expects a list of dictionaries.
we split up boxplot into the cbook.boxplot_stats and Axes.bxp for uses cases that we couldn’t anticipate.