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I have this code in R:

boxplot(ToothGrowth$len ~ ToothGrowth$supp, main = "Tooth growth in Guinea Pig", xlab = "Delivery method", ylab = "Tooth length")

I know that the tilde operator in R means the relationship between the dependent and independent variables, in other words: tooth growth length depends on supply. Is there any possible methods to 'translate' this R code to Python Pandas?

This is my dataset in R:

"len","supp","dose" 4.2,"VC",0.5 11.5,"VC",0.5 7.3,"VC",0.5 5.8,"VC",0.5 6.4,"VC",0.5 10,"VC",0.5 11.2,"VC",0.5 11.2,"VC",0.5 5.2,"VC",0.5 7,"VC",0.5 16.5,"VC",1 16.5,"VC",1 15.2,"VC",1 17.3,"VC",1 22.5,"VC",1 17.3,"VC",1 13.6,"VC",1 14.5,"VC",1 18.8,"VC",1 15.5,"VC",1 23.6,"VC",2 18.5,"VC",2 33.9,"VC",2 25.5,"VC",2 26.4,"VC",2 32.5,"VC",2 26.7,"VC",2 21.5,"VC",2 23.3,"VC",2 29.5,"VC",2 15.2,"OJ",0.5 21.5,"OJ",0.5 17.6,"OJ",0.5 9.7,"OJ",0.5 14.5,"OJ",0.5 10,"OJ",0.5 8.2,"OJ",0.5 9.4,"OJ",0.5 16.5,"OJ",0.5 9.7,"OJ",0.5 19.7,"OJ",1 23.3,"OJ",1 23.6,"OJ",1 26.4,"OJ",1 20,"OJ",1 25.2,"OJ",1 25.8,"OJ",1 21.2,"OJ",1 14.5,"OJ",1 27.3,"OJ",1 25.5,"OJ",2 26.4,"OJ",2 22.4,"OJ",2 24.5,"OJ",2 24.8,"OJ",2 30.9,"OJ",2 26.4,"OJ",2 27.3,"OJ",2 29.4,"OJ",2 23,"OJ",2
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You could make use of matplotlib and seaborn packages for this:

import matplotlib.pyplot as plt import seaborn as sns plt.figure(figsize=(12, 9)) sns.boxplot(x = 'supp', y = 'len', data = df, palette = 'rainbow') plt.title("Tooth growth in Guinea Pig") plt.xlabel('Delivery method') plt.ylabel('Tooth length') plt.savefig('my_plot.png')

enter image description here

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To mimic the ~ relationship-builder from R in python, you can use the by= argument of pd.boxplot:

import pandas as pd import matplotlib df.boxplot(column=['len'], by=['supp','dose']) matplotlib.pyplot.show() # if needed

enter image description here

Data

from io import StringIO import pandas as pd String = """"len","supp","dose" 4.2,"VC",0.5 11.5,"VC",0.5 7.3,"VC",0.5 5.8,"VC",0.5 6.4,"VC",0.5 10,"VC",0.5 11.2,"VC",0.5 11.2,"VC",0.5 5.2,"VC",0.5 7,"VC",0.5 16.5,"VC",1 16.5,"VC",1 15.2,"VC",1 17.3,"VC",1 22.5,"VC",1 17.3,"VC",1 13.6,"VC",1 14.5,"VC",1 18.8,"VC",1 15.5,"VC",1 23.6,"VC",2 18.5,"VC",2 33.9,"VC",2 25.5,"VC",2 26.4,"VC",2 32.5,"VC",2 26.7,"VC",2 21.5,"VC",2 23.3,"VC",2 29.5,"VC",2 15.2,"OJ",0.5 21.5,"OJ",0.5 17.6,"OJ",0.5 9.7,"OJ",0.5 14.5,"OJ",0.5 10,"OJ",0.5 8.2,"OJ",0.5 9.4,"OJ",0.5 16.5,"OJ",0.5 9.7,"OJ",0.5 19.7,"OJ",1 23.3,"OJ",1 23.6,"OJ",1 26.4,"OJ",1 20,"OJ",1 25.2,"OJ",1 25.8,"OJ",1 21.2,"OJ",1 14.5,"OJ",1 27.3,"OJ",1 25.5,"OJ",2 26.4,"OJ",2 22.4,"OJ",2 24.5,"OJ",2 24.8,"OJ",2 30.9,"OJ",2 26.4,"OJ",2 27.3,"OJ",2 29.4,"OJ",2 23,"OJ",2""" csvStringIO = StringIO(csvString) df = pd.read_csv(csvStringIO, sep=",", header=None)
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