Parallel Coordinates
Signature
sp.build_parallel(
title: str,
axes: list[str],
series: list[list[float]],
*,
series_names: list[str] | None = None,
color_groups: list[str] | None = None,
palette: list[int] | None = None,
width: int = 1000,
height: int = 480,
background: str | None = None,
line_opacity: float = 0.6,
) -> Chart
Aliases: sp.parallel
Description
Parallel coordinates chart — each axis is a dimension, each line is an observation. Ideal for detecting patterns in high-dimensional data.
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
title | str | required | Chart title |
axes | list[str] | required | Axis labels (one per dimension) |
series | list[list[float]] | required | One inner list per observation (must match len(axes)) |
series_names | list[str] | None | None | Label per observation |
color_groups | list[str] | None | None | Group names for coloring lines |
palette | list[int] | None | None | Custom group colors |
width | int | 1000 | Canvas width |
height | int | 480 | Canvas height |
background | str | None | None | Background color |
line_opacity | float | 0.6 | Line opacity (0.0–1.0) |
Returns
Chart
Examples
Iris dataset parallel coordinates
import seraplot as sp
axes = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"]
data = [
[5.1, 3.5, 1.4, 0.2],
[6.7, 3.1, 4.7, 1.5],
[6.3, 3.3, 6.0, 2.5],
]
groups = ["Setosa", "Versicolor", "Virginica"]
chart = sp.build_parallel(
"Iris Parallel Coordinates",
axes=axes,
series_values=data,
palette=[0x6366f1, 0x22d3ee, 0xf43f5e],
)const sp = require('seraplot');
const axes = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"]
const data = [
[5.1, 3.5, 1.4, 0.2],
[6.7, 3.1, 4.7, 1.5],
[6.3, 3.3, 6.0, 2.5],
]
const groups = ["Setosa", "Versicolor", "Virginica"]
const chart = sp.build_parallel("Iris Parallel Coordinates",
axes,
{
series_values: data,
palette: [0x6366f1, 0x22d3ee, 0xf43f5e]
})import * as sp from 'seraplot';
const axes: string[] = ["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"]
const data: number[] = [
[5.1, 3.5, 1.4, 0.2],
[6.7, 3.1, 4.7, 1.5],
[6.3, 3.3, 6.0, 2.5],
]
const groups: string[] = ["Setosa", "Versicolor", "Virginica"]
const chart = sp.build_parallel("Iris Parallel Coordinates",
axes,
{
series_values: data,
palette: [0x6366f1, 0x22d3ee, 0xf43f5e]
})▶ Live Preview
See also
Signature
sp.build_parallel(
title: str,
axes: list[str],
series: list[list[float]],
*,
series_names: list[str] | None = None,
color_groups: list[str] | None = None,
palette: list[int] | None = None,
width: int = 1000,
height: int = 480,
background: str | None = None,
line_opacity: float = 0.6,
) -> Chart
Aliases: sp.parallel
Description
Coordonnées parallèles — chaque axe est une dimension, chaque ligne est une observation. Idéal pour détecter des motifs dans des données multidimensionnelles.
Paramètres
| Paramètre | Type | Défaut | Description |
|---|---|---|---|
title | str | requis | Titre du graphique |
axes | list[str] | requis | Étiquettes des axes (une par dimension) |
series | list[list[float]] | requis | Une liste par observation (même longueur que axes) |
series_names | list[str] | None | None | Étiquette par observation |
color_groups | list[str] | None | None | Noms de groupe pour la coloration des lignes |
palette | list[int] | None | None | Couleurs personnalisées par groupe |
width | int | 1000 | Largeur du canvas |
height | int | 480 | Hauteur du canvas |
background | str | None | None | Couleur de fond |
line_opacity | float | 0.6 | Opacité des lignes (0.0–1.0) |
Retourne
Chart
Exemples
import seraplot as sp
axes = ["Long. sépale", "Larg. sépale", "Long. pétale", "Larg. pétale"]
data = [
[5.1, 3.5, 1.4, 0.2],
[6.7, 3.1, 4.7, 1.5],
[6.3, 3.3, 6.0, 2.5],
]
groupes = ["Setosa", "Versicolor", "Virginica"]
chart = sp.build_parallel(
"Coordonnées parallèles — Iris",
axes=axes,
series=data,
color_groups=groupes,
palette=[0x6366f1, 0x22d3ee, 0xf43f5e],
)