Section: Relplot | Data Visualization on Seaborn

Section outline

The relplot() function can be used to create scatter plots and line plots.

Here is the function signature:

Signature relplot

There is, of course, documentation available online, so we will only cover the most essential elements needed to display what we need as quickly as possible, namely:

Parameter name	Explanations	Required type	Example
data	Table you are planning to use	DataFrame, Series, dict, array, or list of arrays	data=table
x	Variable of the table for the x-axis	string corresponding to a variable of the table	x="weight"
y	Variable of the table for the y-axis	string corresponding to a variable of the table	y=”height”
hue	Variable of the table to distinguish with colors	string corresponding to a variable of the table	hue=”age”
size	Variable of the table to distinguish with different sizes	string corresponding to a variable of the table	size=”money”
style	Variable of the table to distinguish with different styles	string corresponding to a variable of the table	style=”sex”
row	Variable of the table that will be used to make different plots, in rows	string corresponding to a variable of the table	row=”category”
col	Variable of the table that will be used to make different plots, in columns	string corresponding to a variable of the table	col=”job”
kind	Type of plot desired	string, 2 choices	kind=”scatter” or kind=”line”

Here is an example using the following code, which you can run:

 Importation Code
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from pyodide.http import open_url

url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv"
data = pd.read_csv(open_url(url))
← Running

 Cell 2
sns.relplot(data=data,x="bill_length_mm",y="bill_depth_mm",hue="species",style="sex",size="body_mass_g",col="island")← Running

It can be observed that col allows you to create different plots within the same figure, hue uses colors to distinguish one variable, and style uses the shape of the markers to distinguish another variable. You can experiment with different parameters and rerun the code.

Ellipses can be added to relplot scatter plots. To draw on the plot, you first need to retrieve the ax object:

 Cell 3
from matplotlib.patches import Ellipse

g = sns.relplot(
    data=data,
    x="bill_length_mm",
    y="body_mass_g",
    hue="species",
    kind="scatter",
    height=5
)
ax = g.ax

def add_confidence_ellipse(x, y, ax, n_std=2.0, **kwargs):
    # Delete NaN before calculation
    mask = ~(np.isnan(x) | np.isnan(y))
    x, y = x[mask], y[mask]
    if len(x) < 2:
        return
    cov = np.cov(x, y)
    mean = np.mean(x), np.mean(y)
    eigvals, eigvecs = np.linalg.eigh(cov)
    order = eigvals.argsort()[::-1]
    eigvals, eigvecs = eigvals[order], eigvecs[:, order]
    angle = np.degrees(np.arctan2(*eigvecs[:, 0][::-1]))
    width, height = 2 * n_std * np.sqrt(eigvals)
    ellipse = Ellipse(
        xy=mean,
        width=width,
        height=height,
        angle=angle,
        fill=False,
        **kwargs
    )
    ax.add_patch(ellipse)

palette = sns.color_palette()

for i, species in enumerate(data["species"].unique()):
    subset = data[data["species"] == species]
    add_confidence_ellipse(
        subset["bill_length_mm"].values,
        subset["body_mass_g"].values,
        ax,
        edgecolor=palette[i],
        linewidth=2
    )

ax.set_xlabel("Bill length (mm)")
ax.set_ylabel("Body mass (g)")
ax.set_title("95% Confidence Ellipse by Species — Penguins")← Running

Ellipse comes from matplotlib.patches.

You can also display line plots by setting kind to "line":

 Cell 4
sns.relplot(data=data,x="bill_length_mm",y="bill_depth_mm",hue="species",style="sex",col="island",kind="scatter")
← Running

size cannot be used with a plot of kind "line".
This time, style changes the line style according to the value of the "sex" variable.