Convert the following RStudio code into Jupyter-Python code: Three-way Factorial Analysis of Variance > daphnia View(daphnia) > attach(daphnia) > names(daphnia) [1] Growth.rate Water Detergent Daphnia

Convert the following RStudio code into Jupyter-Python code:

Three-way Factorial Analysis of Variance

> daphnia <- read.csv("/Course Downloadable Data Exercises-20190120/Course Data/daphnia.csv")

> View(daphnia)

> attach(daphnia)

> names(daphnia)

[1] "Growth.rate" "Water" "Detergent" "Daphnia"

> factorial<-aov(Growth.rate~Water*Detergent*Daphnia)

> summary(factorial)

Df Sum Sq Mean Sq F value Pr(>F)

Water 1 1.99 1.985 2.850 0.097838 .

Detergent 3 2.21 0.737 1.059 0.375478

Daphnia 2 39.18 19.589 28.128 8.23e-09 ***

Water:Detergent 3 0.17 0.058 0.084 0.968608

Water:Daphnia 2 13.73 6.866 9.859 0.000259 ***

Detergent:Daphnia 6 20.60 3.433 4.930 0.000532 ***

Water:Detergent:Daphnia 6 5.85 0.975 1.399 0.234324

Residuals 48 33.43 0.696

---

Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1

> tapply(Growth.rate,list(Detergent,Daphnia),mean)

Clone1 Clone2 Clone3

BrandA 2.732227 3.919002 5.003268

BrandB 2.929140 4.402931 4.698062

BrandC 3.071335 4.772805 4.019397

BrandD 2.626797 5.213745 2.834151

> interaction.plot(Detergent,Daphnia,Growth.rate)