KDE Chart
Signature
sp.build_kde_chart(
title: str,
values: list[float],
*,
color_hex: int = 0x6366F1,
bandwidth: float = 1.0,
fill: bool = True,
width: int = 900,
height: int = 480,
x_label: str = "",
y_label: str = "Density",
gridlines: bool = True,
background: str | None = None,
palette: list[int] | None = None,
series_names: list[str] | None = None,
) -> Chart
Aliases: sp.kde
Description
Kernel Density Estimation (KDE) curve — a smooth, continuous estimate of a probability distribution. Better than histograms for identifying the underlying shape of data.
When multiple series are provided via a flat values list with matching series_names, several overlaid density curves are drawn.
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
title | str | required | Chart title |
values | list[float] | required | Sample data points |
color_hex | int | 0x6366F1 | Curve color |
bandwidth | float | 1.0 | Smoothing bandwidth scale factor |
fill | bool | True | Fill area under curve |
width | int | 900 | Canvas width |
height | int | 480 | Canvas height |
x_label | str | "" | X-axis label |
y_label | str | "Density" | Y-axis label |
gridlines | bool | True | Horizontal gridlines |
palette | list[int] | None | None | Multi-series color palette |
series_names | list[str] | None | None | Multi-series legend names |
Returns
Chart
Examples
Single distribution
import seraplot as sp
import random
values = [random.gauss(50, 10) for _ in range(500)]
chart = sp.build_kde_chart(
"Score Distribution",
values=values,
x_label="Score",
filled=True,
bandwidth=1.0,
)const sp = require('seraplot');
import random
const values = [random.gauss(50, 10) for _ in range(500)]
const chart = sp.build_kde_chart("Score Distribution",
{
values: values,
x_label: "Score",
filled: true,
bandwidth: 1.0
})import * as sp from 'seraplot';
import random
const values: number[] = [random.gauss(50, 10) for _ in range(500)]
const chart = sp.build_kde_chart("Score Distribution",
{
values: values,
x_label: "Score",
filled: true,
bandwidth: 1.0
})▶ Live Preview
See also
Signature
sp.build_kde_chart(
title: str,
values: list[float],
*,
color_hex: int = 0x6366F1,
bandwidth: float = 1.0,
fill: bool = True,
width: int = 900,
height: int = 480,
x_label: str = "",
y_label: str = "Density",
gridlines: bool = True,
background: str | None = None,
palette: list[int] | None = None,
series_names: list[str] | None = None,
) -> Chart
Aliases: sp.kde
Description
Courbe d'estimation par noyau (KDE) — estimation lissée et continue d'une distribution de probabilité. Plus informative qu'un histogramme pour identifier la forme sous-jacente des données.
Plusieurs séries peuvent être superposées via series_names.
Paramètres
| Paramètre | Type | Défaut | Description |
|---|---|---|---|
title | str | requis | Titre du graphique |
values | list[float] | requis | Échantillons de données |
color_hex | int | 0x6366F1 | Couleur de la courbe |
bandwidth | float | 1.0 | Facteur de lissage de la bande passante |
fill | bool | True | Remplir l'aire sous la courbe |
width | int | 900 | Largeur du canvas |
height | int | 480 | Hauteur du canvas |
x_label | str | "" | Étiquette de l'axe X |
y_label | str | "Density" | Étiquette de l'axe Y |
gridlines | bool | True | Lignes de grille horizontales |
palette | list[int] | None | None | Palette multi-séries |
series_names | list[str] | None | None | Noms des séries pour la légende |
Retourne
Chart
Exemples
Distribution simple
import seraplot as sp
import random
valeurs = [random.gauss(50, 10) for _ in range(500)]
chart = sp.build_kde_chart(
"Distribution des scores",
values=valeurs,
x_label="Score",
bandwidth=1.0,
)