Hey, On 05/05/2015 04:34 PM, Lucas Nussbaum wrote: > Hi, > > On 04/05/15 at 23:11 +0200, Orestis Ioannou wrote: [...] >> >> - Do not create any summary table with these values and keep only the >> boxplot. Lucas what do you think on that? You mentioned generating a >> graph so does this mean that a summary table wouldn't be of much use? > > I think that the summary table is also useful, especially given that the > first quartile value is really small. It would be hard to say from the > graph if it's increasing or decreasing. > Ok good :) >> - hack into boxplot to generate custom boxplots. Already done by >> somebody [1]. IMHO it looks pretty clean since it just overwrites some >> values and keeps all the functionalities of the boxplot intact. > > I have no particular comment about the best strategy here, but given the > SLOC values are pre-computed, I wouldn't worry too much about the > performance for computing/generating those stats. > > Lucas > I did some more digging mostly since i discovered that my computation for quartiles was giving sometimes different results for the Q1 and Q3 than the one in pyplot. Long story short I compared them with the help of R and i found out in the documentation [1] that there are many ways to calculate them. I used the type 2 and pyplot, if i am correct (results coincide), uses type 7. If i understand properly the doc type 7 makes the assumption that the sample is continuous whereas type 2 considered to be discontinuous. Since my knowledge in statistics is fairly minimal i am not really sure what we have in this case :p In any case if we consider that the sample is discontinuous then i think the best option, in order to have same results both in the table and the graph is to hack into pyplot to insert pre - calculated quartiles in boxplot. I am thinking this since i couldn't find any way to tell pyplot to calculate quartiles in another way. If however the sample is considered continuous then i ll have to implement another algorithm to calculate quartiles so that the results are the same. Orestis [1] https://stat.ethz.ch/R-manual/R-patched/library/stats/html/quantile.html
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