plotting distributions, direct input of histogram

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)

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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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)
>
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users at python.org
> https://mail.python.org/mailman/listinfo/matplotlib-users
>
>
_______________________________________________
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Matplotlib-users at python.org
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Binning the data will of course results in some quantization error, but if
the bins are small enough that would be acceptable in my application.

Paul Hobson wrote:

···

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@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)
>
> _______________________________________________
> Matplotlib-users mailing list
> Matplotlib-users@python.org
> https://mail.python.org/mailman/listinfo/matplotlib-users
>
>
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@python.org
https://mail.python.org/mailman/listinfo/matplotlib-users

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Matplotlib-users@python.org
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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.

-paul

···

On Mon, Aug 5, 2019 at 12:00 PM Neal Becker ndbecker2@gmail.com wrote:

Binning the data will of course results in some quantization error, but if

the bins are small enough that would be acceptable in my application.

Paul Hobson wrote:

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@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)


Matplotlib-users mailing list

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