Disabling the outliers in a geom_boxplots() hides the markers but keeps the original scale (as if the outliers were present). Should the axis be rescaled (eg. how seaborn behaves)?
If not, is there a clean way to achieve this?
later edit: I know it can be done manualy, just wondering if there's an automated way of recomputing the limits:
(
p9.ggplot(df)
+ p9.geom_boxplot(p9.aes(x="factor(map)", y="fft_max", color="objective"), outlier_shape='')
+ p9.coord_flip()
+ p9.ylim(0.3, 0.5)
)
Disabling the outliers in a
geom_boxplots()hides the markers but keeps the original scale (as if the outliers were present). Should the axis be rescaled (eg. how seaborn behaves)?If not, is there a clean way to achieve this?
later edit: I know it can be done manualy, just wondering if there's an automated way of recomputing the limits:
( p9.ggplot(df) + p9.geom_boxplot(p9.aes(x="factor(map)", y="fft_max", color="objective"), outlier_shape='') + p9.coord_flip() + p9.ylim(0.3, 0.5) )