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"boxplot_outlier" Statistics#

Computes outlier values on "box-plot" chart but can be used in alternative visualizations as well.

import pandas as pd

from lets_plot import *

LetsPlot.setup_html()

mpg = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')
mpg.head(3)
Unnamed: 0 manufacturer model displ year cyl trans drv cty hwy fl class
0 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compact
1 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compact
2 3 audi a4 2.0 2008 4 manual(m6) f 20 31 p compact 
p = (ggplot(mpg, aes(y='hwy')) 
     + scale_color_viridis(option="magma", end=0.8) 
     + ggsize(700, 400))

# Ordering by variable "..middle.." when using stat "boxplot" or "boxplot_outlier".
class_by_middle=as_discrete('class', order_by='..middle..', order=1)

# Equivalent ordering by variable "..y.." when using `stat_summary()`.
class_by_y=as_discrete('class', order_by='..y..', order=1)

p + geom_boxplot(aes(x=class_by_middle, color='..middle..'))

1. Show Just Outliers#

Use stat="boxplot_outlier".

outliers = geom_point(aes(x=class_by_middle, color='..middle..'), stat="boxplot_outlier")

p + outliers

2. Add Ribbons and Mid-points#

ribbon1 = geom_ribbon(aes(
                    x=class_by_middle, 
                    ymin="..ymin..",
                    ymax="..ymax.."), stat="boxplot")

ribbon2 = geom_ribbon(aes(
                    x=class_by_middle, 
                    ymin="..lower..",
                    ymax="..upper.."), stat="boxplot")

mid_points = stat_summary(aes(x=class_by_y, color="..y.."), 
                          fun="mq", 
                          geom="point", shape=15, size=6)

p + ribbon1 + ribbon2 + mid_points + outliers + labs(color="Middle")