Objects.Plot.Pair - Seaborn

python

import seaborn.objects as so
from seaborn import load_dataset
mpg = load_dataset("mpg")

Plot one dependent variable against multiple independent variables by assigning y and pairing on x:

python

(
    so.Plot(mpg, y="acceleration")
    .pair(x=["displacement", "weight"])
    .add(so.Dots())
)

Show multiple pairwise relationships by passing lists to both x and y:

python

(
    so.Plot(mpg)
    .pair(x=["displacement", "weight"], y=["horsepower", "acceleration"])
    .add(so.Dots())
)

When providing lists for both x and y, pass cross=False to pair each position in the list rather than showing all pairwise relationships:

python

(
    so.Plot(mpg)
    .pair(
        x=["weight", "acceleration"],
        y=["displacement", "horsepower"],
        cross=False,
    )
    .add(so.Dots())
)

When plotting against several x or y variables, it is possible to wrap the subplots to produce a two-dimensional grid:

python

(
    so.Plot(mpg, y="mpg")
    .pair(x=["displacement", "weight", "horsepower", "cylinders"], wrap=2)
    .add(so.Dots())
)

Pairing can be combined with faceting, either pairing on y and faceting on col or pairing on x and faceting on row:

python

(
    so.Plot(mpg, x="weight")
    .pair(y=["horsepower", "acceleration"])
    .facet(col="origin")
    .add(so.Dots())
)

While typically convenient to assign pairing variables as references to the common data, it's also possible to pass a list of vectors:

python

(
    so.Plot(mpg["weight"])
    .pair(y=[mpg["horsepower"], mpg["acceleration"]])
    .add(so.Dots())
)

python

(
    so.Plot(mpg, y="mpg")
    .pair(x=["weight", "displacement"])
    .label(x0="Weight (lb)", x1="Displacement (cu in)", y="MPG")
    .add(so.Dots())
)