#!/usr/bin/env python
# coding: utf-8

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

# In[1]:


import pandas as pd
from lets_plot import *
from lets_plot.mapping import as_discrete


# In[2]:


LetsPlot.setup_html()


# In[3]:


mpg = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')
mpg.head(3)


# In[4]:


p = (ggplot(mpg, aes(y='hwy')) 
     + scale_color_viridis(option="magma", end=0.8) 
     + ggsize(700, 400))


# In[5]:


# 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)


# In[6]:


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


# #### 1. Show Just Outliers
# 
# Use `stat="boxplot_outlier"`.

# In[7]:


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

p + outliers


# #### 2. Add Ribbons and Mid-points

# In[8]:


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")