Great, thanks!
Rgds
marcus
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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_
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 behaviou
I'm on python 2.
I get the same outputs after adding "from __future__ import division".
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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
n
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 wrote:
> Hi,
>
> the outliers in the boxplot do not seem to be drawn in the following
> extreme
> scenario:
>
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
