Hi, the outliers in the boxplot do not seem to be drawn in the following extreme scenario: Data Value: 1, Frequency: 5 Data Value: 2, Frequency: 100 Data Value: 3, Frequency: 5 Here, Q1 = Q2 = Q3, so IQR = 0. Data values 1 and 3 are therefore outliers according to the definition in the api (Refer to parameter "whis" under "boxplot": http://matplotlib.org/api/pyplot_api.html <http://matplotlib.org/api/pyplot_api.html> ) But the code below produces a boxplot that shows them as max-min whiskers (rather than fliers): import matplotlib.pyplot as plt data = 100 * [2] + 5 * [1] + 5 * [3] ax = plt.gca() bp = ax.boxplot(data, showfliers=True) for flier in bp['fliers']: flier.set(marker='o', color='gray') <http://matplotlib.1069221.n5.nabble.com/file/n46027/figure_1.png> What I though it would look like is obtained by perturbing half of the data points 2 to 2.000001: <http://matplotlib.1069221.n5.nabble.com/file/n46027/figure_2.png> Is this a bug or I'm not getting something right? rgds marcus -- View this message in context: http://matplotlib.1069221.n5.nabble.com/boxplot-behaviour-in-an-extreme-scenario-tp46027.html Sent from the matplotlib - users mailing list archive at Nabble.com.
Are you running python 2 or python 3? If you're on python 2, what happens if you add "from __future__ import division" to the top of your script? On Tue, Aug 25, 2015 at 10:31 PM, chtan <ch...@un...> wrote: > Hi, > > the outliers in the boxplot do not seem to be drawn in the following > extreme > scenario: > Data Value: 1, Frequency: 5 > Data Value: 2, Frequency: 100 > Data Value: 3, Frequency: 5 > > Here, Q1 = Q2 = Q3, so IQR = 0. > Data values 1 and 3 are therefore outliers according to the definition in > the api > (Refer to parameter "whis" under "boxplot": > http://matplotlib.org/api/pyplot_api.html > <http://matplotlib.org/api/pyplot_api.html> ) > > But the code below produces a boxplot that shows them as max-min whiskers > (rather than fliers): > > import matplotlib.pyplot as plt > data = 100 * [2] + 5 * [1] + 5 * [3] > ax = plt.gca() > bp = ax.boxplot(data, showfliers=True) > for flier in bp['fliers']: > flier.set(marker='o', color='gray') > > <http://matplotlib.1069221.n5.nabble.com/file/n46027/figure_1.png> > > > What I though it would look like is obtained by perturbing half of the data > points 2 to 2.000001: > > <http://matplotlib.1069221.n5.nabble.com/file/n46027/figure_2.png> > > > Is this a bug or I'm not getting something right? > > rgds > marcus > > > > -- > View this message in context: > http://matplotlib.1069221.n5.nabble.com/boxplot-behaviour-in-an-extreme-scenario-tp46027.html > Sent from the matplotlib - users mailing list archive at Nabble.com. > > > ------------------------------------------------------------------------------ > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >
I'm on python 2. I get the same outputs after adding "from __future__ import division". -- View this message in context: http://matplotlib.1069221.n5.nabble.com/boxplot-behaviour-in-an-extreme-scenario-tp46027p46031.html Sent from the matplotlib - users mailing list archive at Nabble.com.
Your perturbed and unperturbed scenarios draw the same figure on my machine (mpl v1.4.1). The reason why you don't get any outliers is the following: Boxplot uses matplotlib.cbook.boxplot_stats under the hood to compute where everything will be drawn. If you look in there, you'll see this little nugget: # interquartile range stats['iqr'] = q3 - q1 if stats['iqr'] == 0: whis = 'range' When whis = 'range', the whiskers fall back to extending to the min an max. So that is at least the intent of the code. Open to a different interpretation of what should be happening, though. On Wed, Aug 26, 2015 at 1:08 AM, Paul Hobson <pmh...@gm...> wrote: > Are you running python 2 or python 3? If you're on python 2, what happens > if you add "from __future__ import division" to the top of your script? > > On Tue, Aug 25, 2015 at 10:31 PM, chtan <ch...@un...> wrote: > >> Hi, >> >> the outliers in the boxplot do not seem to be drawn in the following >> extreme >> scenario: >> Data Value: 1, Frequency: 5 >> Data Value: 2, Frequency: 100 >> Data Value: 3, Frequency: 5 >> >> Here, Q1 = Q2 = Q3, so IQR = 0. >> Data values 1 and 3 are therefore outliers according to the definition in >> the api >> (Refer to parameter "whis" under "boxplot": >> http://matplotlib.org/api/pyplot_api.html >> <http://matplotlib.org/api/pyplot_api.html> ) >> >> But the code below produces a boxplot that shows them as max-min whiskers >> (rather than fliers): >> >> import matplotlib.pyplot as plt >> data = 100 * [2] + 5 * [1] + 5 * [3] >> ax = plt.gca() >> bp = ax.boxplot(data, showfliers=True) >> for flier in bp['fliers']: >> flier.set(marker='o', color='gray') >> >> <http://matplotlib.1069221.n5.nabble.com/file/n46027/figure_1.png> >> >> >> What I though it would look like is obtained by perturbing half of the >> data >> points 2 to 2.000001: >> >> <http://matplotlib.1069221.n5.nabble.com/file/n46027/figure_2.png> >> >> >> Is this a bug or I'm not getting something right? >> >> rgds >> marcus >> >> >> >> -- >> View this message in context: >> http://matplotlib.1069221.n5.nabble.com/boxplot-behaviour-in-an-extreme-scenario-tp46027.html >> Sent from the matplotlib - users mailing list archive at Nabble.com. >> >> >> ------------------------------------------------------------------------------ >> _______________________________________________ >> Matplotlib-users mailing list >> Mat...@li... >> https://lists.sourceforge.net/lists/listinfo/matplotlib-users >> > >
Uh, now I understand why it's behaving this way. Tx Paul. >From the documentation, it seems natural to expect the behaviour to be uniform throughout the meaningful range for IQR. How may I go about searching for the responsible code on my own in situations like this? >From the perplexing behaviour to the little nugget in matplotlib.cbook.boxplot_stats, the path isn't clear to me. Any general advice? -- View this message in context: http://matplotlib.1069221.n5.nabble.com/boxplot-behaviour-in-an-extreme-scenario-tp46027p46032.html Sent from the matplotlib - users mailing list archive at Nabble.com.
Even though I'm familiar with the boxplot source code, I largely use IPython for quick investigations like this. In IPython, doing something like "matplotlib.Axes.boxplot??" shows the full source code for that functions\. Then I saw/remembered that boxplot now just calls matplotlib.cbook.boxplot_stats and passes the results to matplotlib.Axes.bxp. So then I did "matplotlib.cbook.boxplot_stats" to see how the whiskers were computed. -paul On Wed, Aug 26, 2015 at 8:43 PM, chtan <ch...@un...> wrote: > Uh, now I understand why it's behaving this way. Tx Paul. > > >From the documentation, it seems natural to expect the behaviour to be > uniform throughout the meaningful range for IQR. > > How may I go about searching for the responsible code on my own in > situations like this? > >From the perplexing behaviour to the little nugget in > matplotlib.cbook.boxplot_stats, the path isn't clear to me. > > Any general advice? > > > > -- > View this message in context: > http://matplotlib.1069221.n5.nabble.com/boxplot-behaviour-in-an-extreme-scenario-tp46027p46032.html > Sent from the matplotlib - users mailing list archive at Nabble.com. > > > ------------------------------------------------------------------------------ > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >
Great, thanks! Rgds marcus -- View this message in context: http://matplotlib.1069221.n5.nabble.com/boxplot-behaviour-in-an-extreme-scenario-tp46027p46034.html Sent from the matplotlib - users mailing list archive at Nabble.com.
