Hi all,

I am trying to use plotly for creating inline boxplots in jupyter notebooks running on a python kernel. My problem is that all example code I was able to find requires to define a trace for each box explicitly, e.g.:

import plotly.plotly as py

import plotly.graph_objs as go

import numpy as np

y0 = np.random.randn(50)-1

y1 = np.random.randn(50)+1

trace0 = go.Box(

y=y0

)

trace1 = go.Box(

y=y1

)

data = [trace0, trace1]

py.iplot(data)

This is fine if I only have a few boxplots, but I generally work with multifactorial data. I am looking for a way to use grouping variables to generate boxplots. This is reasonably straightforward with plotly in R:

p <- plot_ly(diamonds, y = ~price, color = I(â€śblackâ€ť),

alpha = 0.1, boxpoints = â€śsuspectedoutliersâ€ť)

p1 <- p %>% add_boxplot(x = â€śOverallâ€ť)

p2 <- p %>% add_boxplot(x = ~cut)

subplot(

p1, p2, shareY = TRUE,

widths = c(0.2, 0.8), margin = 0

) %>% hide_legend()

However, I am still not sure this has the same flexibility as R base plotting would allow via the formula syntax, where I can easily plot multiple boxplots arranged by factorial levels (y~x*u*v etc). Is there any way to achieve this with plotly in python without handily defining a trace for each individual box (I tried using the R-code in my python kernel with rpy2 but the plot doesnâ€™t show - it works on an IRkernel, though)?

Thanks

D