Yaroslav,
Those figures look great. Seaborn has some similar functionality (scroll
down a bit):
http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb#Comparing-distributions:-boxplot-and-violinplot
The main point of the most recent overhaul of boxplots was to allow users
to just what you describe. The methods plt.boxplot and ax.boxplot now do
very little on their own. Input data are passed to
matplotlib.cbook.boxplot_stats, that function returns a list of
dictionaries of statistics, and then ax.bxp actually does the drawing. All
of this is to say that you can write your own function to modify
boxplot_stats' output or generate independently the list of dictionaries
expected by ax.bxp.
The keys of those dictionaries can include:
- label -> tick label for the boxplot
- mean -> mean value (can plot as a line or point)
- median -> 50th percentile
- q1 -> first quartile (25th pctl)
- q3 -> third quartile (75 (pctl)
- cilo -> lower notch around the median
- ciho -> upper notch around the median
- whislo -> end of the lower whisker
- whishi -> end of the upper whisker
- fliers -> outliers
Basically, you can set the appropriate values to whatever you want to draw
boxplots however you wish (like open/close diagrams for pandas).
Also, the `whis` kwarg accepted by boxplot and cbook.boxplot_stats can
either be a float (1.5 by default), a list of integer percentiles (like 5,
95), or the strings 'range', 'limits', or 'min/max', all of which will
extend the whiskers to over all of the data.
Since you're running off of master, you should access to this new
functionality.
Here's a link to the PR that overhauled ax.boxplot and created ax.bxp:
https://github.com/matplotlib/matplotlib/pull/2643
Looking at it now -- it looks like cbook.boxplot_stats' docstring got
cutoff. I'll pull together a PR to fix that soon.
Feel free to hit me up with any other questions!
-paul
On Sat, Feb 15, 2014 at 2:20 PM, Yaroslav Halchenko <[email protected]>wrote:
> Hi Paul,
>
> On Sat, 15 Feb 2014, Paul Hobson wrote:
> > As the author of the fix and the recent overhaul to boxplots
>
> Thanks for that!
>
> > I can say with certainty that R is wrong! ;-)
>
> phew -- thanks ;)
>
> > More seriously, the main thing that I take away from Tukey's paper
> about
> > boxplots, is that there are many valid ways to draw them. I
> personally set
> > up the new boxplot functionality to take the most basic boxplot
> definition
> > very literally. My guess is that R is fudging those rules a bit for
> the
> > purpose of completeness, or aesthetics, or ...(?)
>
> well -- I was trying to figure out why the divergence from R's boxplot
> help, but so far it seemed to match description/definition for boxplot
> as in matplotlib. I guess the next step would be to look "inside"
> (running apt-get source r-base now ;-) )
>
> > Perhaps one can look at the purpose of boxplots in two different
> fashions:
> > 1) Matplotlib: show some of the data and some basic stats
> > 2) R (I'm guession): show how the data are /probably/ distributed.�
> > Obviously, I prefer #1. But I'm not going to say that #2 is wrong just
> > yet.
>
> would you may be interested to adopt (or just do independently) an
> option to e.g. plot the data point? once I shared this one
> http://nbviewer.ipython.org/url/www.onerussian.com/tmp/run_plots.ipynb
> and the actual code https://gist.github.com/yarikoptic/9023331
>
> I just never got to formalize it into mpl pull request :-/
> --
> Yaroslav O. Halchenko, Ph.D.
> http://neuro.debian.net http://www.pymvpa.org http://www.fail2ban.org
> Senior Research Associate, Psychological and Brain Sciences Dept.
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419
> WWW: http://www.linkedin.com/in/yarik
>
>
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