It would take a function, similar to the 'estimator' parameter in barplot, and scale the counts according to that function.
#SNS DISTPLOT RENAME X AXIS CODE#
Would it be worth including the code snippet above as an example in countplot? I guess I might just write some wrapper function that performs as desired, but I have to think that something like this would interest more people than just me.Įdit: Another idea might be to include something like 'scaling' as a passed parameter in countplot and factorplot. So why include countplot? This is part of what I really like about seaborn.Īnyway, It's possible that this "quality of life" handling of percentages out of the box is not worth the effort. I've only had a glance at the code for countplot and haven't fully wrapped my head around it, but am I right in my understanding that countplot is basically a special case function implementing the same underlying plotting functionality as barplot? This is what confuses me: surely it would be even more trivial to pass counts into barplot than it is to pass percentages or normalized values. But I find this line of reasoning a little strange, because of the inclusion of countplot in the first place. I can also appreciate the argument that this can be done in basically a one-liner in pandas. My inexperience with ggplot may mean that there's something important I'm missing. I posted because the ggplot inclusion of this functionality was also suggestive to me that it is of general use. I may be completely wrong in my idea that this is a reasonably generalized desire, and I'm not sure if there's a good way to find out, though this thread and stackexchange are suggestive at least. Or am I misunderstanding how you propose that normalized values are obtained?) I doubt it throws anyone for too big of a loop. (I notice that ggplot outputs these values withīut still gives normalized values on the graph. But It looks to me like having some kind of normalized rendition could be a pretty generalized need. Had I not seen the R snippet above and also stumbled across this discussion thread, I would probably not have bothered to say anything. And I can also appreciate the difficulty in finding where to draw the line for a suitably general API. And naturally it's not the only way to do it.
If hue is not specified, then the y axis is labeled as percent (as if sns.barplot(x="x", y="x", data=df, estimator=lambda x: len(x) / len(df) * 100) had been called).Pass a value into countplot, something like, 'percent=True' I'd like to propose the possibility that the most headache-free way to do this might be:Ġ. I based this off of observations with distplot, but there was a little bit of guesswork in the exact cutoff lines and when I looked at various graphs using countplot, it would have been really convenient to be able to stretch them into normalized values as the R output does above, without having to figure out the best way to do it myself from the bottom up. The first set of images was from my efforts to divide the ages up into discrete categories based on their different survival rates in Kaggle's Titanic dataset. The stacked bars might be overkill, but the general point remains that seeing these makes it easier to evaluate percentages between categories at a glance. Labs(x = 'Training data only', y= "Percent") + facet_grid(.~Sex) + Geom_bar(stat='count', position='fill') + P6 <- ggplot(all, aes(x = Pclass, fill = Survived)) + Labs(x = 'Training data only', y= "Count") + facet_grid(.~Sex) + Geom_bar(stat='count', position='stack') + P5 <- ggplot(all, aes(x = Pclass, fill = Survived)) + The proposed trivial solution, when "hue" is added, does not perform as I would naturally hope: Honestly, I think some way to handle percentages well would be an excellent quality of life addition.
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