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).