Is there is an equivalent of R's GGally::ggpairs function in python?

Question

GGally::ggpairs function provides support for both numeric and categorical datatypes, making it possible to see the interactions between all variables in one place, in a few lines of code but with high flexibility.
Here is an example :

cols = c("#4c86ad", "#f5dfb3")
insurance_data %>%
  dplyr::select(age, bmi, smoker, charges) %>%
  GGally::ggpairs(
    lower = list(
      continuous = GGally::wrap("points", col = cols[1],alpha=0.6),
      combo = GGally::wrap("box", fill = "white", col ="black")
    ),
    upper = list(
      continuous = GGally::wrap("cor", col = cols[1]),
      combo = GGally::wrap("facetdensity", col = "black")
    ),
    diag = list(
      continuous = GGally::wrap("barDiag", fill = cols[2], col ="black", bins = 18),
      discrete = GGally::wrap("barDiag", fill = cols[2], col ="black"))
  )

Is there is any way of reproducing this in python ?

Answer 1

I love using ggpairs in R, and found seaborn's PairGrid which is similar.

You can specify the layout style and type of plot to place in each "layout section"

You can check out more examples in the docs, but here is one example.

import seaborn as sns
penguins = sns.load_dataset("penguins")
g = sns.PairGrid(penguins, hue="species")
g.map_diag(sns.histplot)
g.map_offdiag(sns.scatterplot)
g.add_legend()

Seaborn PairGrid Example with Penguin Data