plotters-statistical

Statistical chart primitives for plotters, packaged as reusable series types that plug into chart.draw_series(...) exactly like plotters' own built-in Histogram / LineSeries / CandleStick.

Each chart type is a composite element implementing plotters' Drawable + PointCollection traits — the same pattern plotters uses internally for CandleStick — so these feel native rather than bolted on.

plotters version: pinned to =0.3.7. This crate mirrors plotters' element/series extension pattern, so it depends on the exact trait signatures of one release. Bumping plotters here is a deliberate, verified step, not an automatic caret upgrade.

Chart types

Series (plug into chart.draw_series(...)):

This crate	Replaces (Python)	Notes
`BoxPlot` / `BoxPlotSeries`	`matplotlib.pyplot.boxplot`, `seaborn.boxplot`	Tukey 1.5×IQR whiskers + outliers; vertical or horizontal
`ViolinPlot` / `ViolinPlotSeries`	`seaborn.violinplot`	Gaussian KDE outline, optional embedded box
`RocCurve`	`sklearn.metrics.RocCurveDisplay`	AUC in legend, opt-in chance diagonal + AUC shading
`PrecisionRecallCurve`	`sklearn.metrics.PrecisionRecallDisplay`	AP in legend, prevalence baseline (not a diagonal)
`RegularizationPath`	scikit-learn coefficient-path plots	Color-cycled lines, log-x friendly, zero-crossing markers
`ResidualPlot`	`seaborn.residplot`	Zero line + binned moving-average trend
`Ecdf`	`statsmodels` ECDF, `seaborn.ecdfplot`	Step curve, optional DKW confidence band, complementary mode
`QqPlot`	`statsmodels.qqplot`, `scipy.stats.probplot`	Normal quantiles + robust reference line
`CalibrationCurve`	`sklearn.calibration.CalibrationDisplay`	Reliability diagram vs `y = x`
`GainChart`	cumulative-gain / lift charts	`Gain` or `Lift` mode with chance baseline
`Heatmap`	`seaborn.heatmap` (cells)	Any matrix; configurable colormap, normalization, annotations

Figures (own their axes / multi-panel layout; render onto a DrawingArea):

This crate	Replaces (Python)	Notes
`CorrelationHeatmap`	`seaborn.heatmap` on `df.corr()`	Pearson/Spearman, colorbar, cell annotations, diverging map
`MissingnessHeatmap`	`missingno.matrix`	Present/absent map with per-column missing %
`PairPlot`	`seaborn.pairplot`	Scatterplot matrix, hist/ECDF diagonal, optional hue

Colormaps

GradientColorMap (viridis, magma, blues, reds, RdBu, coolwarm, grayscale, or custom stops) plus a Normalization (linear or symmetric/diverging) drive every value-to-color chart. Sources: matplotlib and ColorBrewer.

Design: math is separate from rendering

All numeric work lives in the stats module and has no plotters dependency, so it is unit-tested against hand-computed reference values (NumPy/scikit-learn equivalents) independently of any rendering:

stats::quartiles — type-7 quartiles, IQR, Tukey fences, outliers
stats::kde — Gaussian KDE with a Silverman-rule default bandwidth
stats::roc / stats::precision_recall — threshold sweeps, AUC / average precision
stats::correlation — Pearson & Spearman, correlation matrices
stats::ecdf — ECDF + DKW band; stats::normal — inverse-normal quantiles
stats::histogram — Sturges / Freedman–Diaconis / Scott / fixed binning
stats::calibration / stats::gain — reliability bins, cumulative gain & lift

The series module turns those already-computed values into plotters draw calls.

Quick start

use plotters::prelude::*;
use plotters_statistical::BoxPlotSeries;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let root = SVGBackend::new("boxes.svg", (640, 480)).into_drawing_area();
    root.fill(&WHITE)?;
    let mut chart = ChartBuilder::on(&root)
        .set_label_area_size(LabelAreaPosition::Left, 40)
        .set_label_area_size(LabelAreaPosition::Bottom, 40)
        .build_cartesian_2d(0.5f64..2.5f64, 0f64..10f64)?;
    chart.configure_mesh().draw()?;

    let groups = vec![
        (1.0, vec![1.0, 2.0, 2.5, 3.0, 9.0]),
        (2.0, vec![2.0, 3.0, 3.5, 4.0, 4.2]),
    ];
    chart.draw_series(BoxPlotSeries::from_samples(groups)?)?;
    root.present()?;
    Ok(())
}

Examples

One runnable example per chart type, plus a combined dashboard:

# series

cargo run --example box_plot

cargo run --example violin_plot

cargo run --example roc_curve

cargo run --example precision_recall_curve

cargo run --example regularization_path

cargo run --example residual_plot

cargo run --example ecdf

cargo run --example qq_plot

cargo run --example calibration_curve

cargo run --example gain_chart

cargo run --example heatmap

# figures

cargo run --example correlation_heatmap

cargo run --example missingness_heatmap

cargo run --example pair_plot

# combined

cargo run --example dashboard          # the six original panels — hero image

Each writes an .svg into the working directory.

Styling

style::palette_color(i) cycles the Okabe–Ito color-blind-safe qualitative palette (Okabe & Ito, 2008, https://jfly.uni-koeln.de/color/), shared by every multi-series chart type. Every chart type's style struct is fully overridable — see examples/box_plot.rs for a style override.

Origin

Built to replace hand-drawn chart code across the rust-ml-guide project: its EDA, model-evaluation, and regularization chapters each re-implemented these charts by hand. The v0.1 core (BoxPlot, ViolinPlot, RocCurve, PrecisionRecallCurve, RegularizationPath, ResidualPlot) covered the named gaps; the v0.2 additions (ECDF, Q–Q, calibration, gain/lift, generic heatmap, and the correlation / missingness / pair-plot figures) round the package out to a general statistical-plotting toolkit for plotters.

License

Licensed under the MIT license (LICENSE).

plotters-statistical 0.1.0