[Python] How to define groups in `plotnine` using interactions of features

I cannot find how to do the equivalent of ggplot's interaction feature.

What I'd like to do


This site has an example that has the sort of thing I am looking to reproduce: I have two discrete factors (in the example, store and promotion), and I would like to produce a line plot with each unique combination making a "group" to connect the lines with.

Example in ggplot


Taking it from the example above:

#create line plot with values grouped by store and promo
ggplot(df, aes(x=week, y=sales, color=store, shape=promo,
group=interaction(store, promo))) +
geom_point(size=3) +
geom_line()

What I'd expect to work in plotnine


import plotnine as p9
import pandas as pd

df = pd.DataFrame({
'week': [1,2,3,4]*4,
'store': (["A"]*8 + ["B"]*8),
'promo': (["promo1"]*4 + ["promo2"]*4)*2,
'sales': [1, 2, 6, 7, 2, 3, 5, 6, 3, 4, 7, 8, 3, 5, 8, 9]
})

#create line plot with values grouped by store and promo
(
p9.ggplot(df, p9.aes(x="week", y="sales", color="store", shape="promo",
group="interaction(store, promo)")) +
p9.geom_point(size=3) +
p9.geom_line()
)


This breaks because it doesn't know what interaction is. If I want the plot, I have to create a column in the data solely for defininte groups:

# ... as before

df['group_col'] = df.apply(lambda row: (row.store, row.promo), axis=1)
(
p9.ggplot(df, p9.aes(x="week", y="sales", color="store", shape="promo",
group="group_col")) +
p9.geom_point(size=3) +
p9.geom_line()
)


This isn't great, because it means I am manually creating (and the tidying columns) for every plot grouping that makes sense, rather than the R-style approach where the interaction happens inside p9 but doesn't alter the actual data frame.

Addendum for interaction comment


An answer below stated I wanted to use interaction which was a R feature, and wasn't in Python (and thus wasn't part of a plotnine question). That wasn't what was being asked for; in the expectation section I was expecting to use interaction in a similar way I use factor.

In more detail: The expectation part didn't expect to use interaction as a function, it was part of string interpolation:

.... group="interaction(store, promo)"))


The python interpreter never sees a call to interaction, it only sees a string. This is similar to how factor works in plotnine -- factor is a base R function, but we can use

.... color="factor(numeric_id)"


in a plotnine geom, and it will interpret the numeric_id as a discrete variable, rather than a continuous one.

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