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DistributionPlot

Planned · @experimental · Requires faststrap[chartjs]

Planned Component

This component is part of the faststrap[plots] / faststrap[ml] optional extra and has not been implemented yet. The documentation is a preview of the planned API.

Renders a histogram with optional KDE (kernel density estimation) overlay from a pandas Series or numpy array. Designed for exploratory data analysis dashboards.

Quick Start

from faststrap.components.advance import DistributionPlot
import pandas as pd

data = pd.Series([1, 2, 2, 3, 3, 3, 4, 4, 5])

DistributionPlot(
    data,
    bins=10,
    kde=True,
    title="Score Distribution",
)

Features

  • Histogram with configurable bin count
  • Optional KDE overlay line
  • Theme-aware colors (adapts to light/dark mode)
  • Responsive sizing via Bootstrap utilities
  • Accepts pandas.Series, pandas.DataFrame column, or numpy.ndarray

Parameters

Parameter Type Default Description
data pd.Series \| np.ndarray required Numeric data to visualize
bins int 10 Number of histogram bins
kde bool False Show KDE overlay curve
title str \| None None Optional chart title
responsive bool True Full-width responsive wrapper
height int \| str 300 Chart height in pixels or CSS value
theme_colors dict \| None None Override bar and line colors
**kwargs Any Extra wrapper attributes

Notes

  • Requires faststrap[chartjs] for the Chart.js rendering backend.
  • For server-side rendering without JS, consider wrapping matplotlib output with Chart().
  • Marked @experimental — API may change before stable release.