ferrum-viz 0.10.0

Ferrum is a statistical visualization library for Python: a grammar-first charting system that unifies exploratory plots, statistical graphics, interactive views, and model diagnostics, backed by a Rust engine.

Ferrum

Grammar-of-graphics statistical visualization for Python, with a Rust core.

Ferrum builds every chart — scatter plot, faceted histogram, ROC curve, SHAP beeswarm — from the same grammar of data, encodings, marks, scales, and statistical transforms. The declaration layer is Python; the computation engine is Rust compiled via PyO3.

Install

For 'batteries included'-- ML diagnostic plots and interactive Jupyter rendering:

pip install ferrum-viz[all]   # recommended — includes scikit-learn, SHAP, Jupyter interactive

For a lean install without optional extras:

pip install ferrum-viz

Quickstart

import ferrum as fm
import polars as pl

df = pl.DataFrame({"x": [1, 2, 3, 4], "y": [2, 4, 3, 5], "group": ["a", "a", "b", "b"]})

chart = fm.Chart(df).mark_point().encode(x="x", y="y", color="group:N")
chart.save("scatter.svg")
chart.show_png()  # raster output, no display server needed

Key features

• One chart model — scatter plots, statistical graphics, and ML diagnostics share the same grammar and compose with +, |, &.
• Stat transforms in the pipeline — KDE, LOESS, bootstrap CIs, binning, and aggregations are declared in the chart spec and computed in Rust.
• Model diagnostics as charts — fm.roc_chart(model, X, y), fm.confusion_matrix_chart(...), fm.shap_chart(...) return regular Chart objects.
• Zero system dependencies — no Cairo, no X11, no display server. Renders anywhere pip install works.
• DataFrame pluralism — polars, pandas, modin, cuDF, dask, ibis, and pyarrow all work through Chart(data).
• Interactive rendering — chart.interactive() switches to a GPU-backed WASM renderer with selections, zoom/pan, linked views, and tooltips. Backed by anywidget for Jupyter.

Themes

Twelve built-in themes — from warm cream (Paper Ink, the default) to dark, publication, and editorial styles.

Performance

200,000-point scatter benchmark (median of 3 runs, Apple M-series):

Metric	Ferrum	plotnine	seaborn	Altair	Plotly
SVG render	27 ms	7.56 s	1.95 s	2.86 s	2.51 s
SVG file size	590 KB	137 MB	32.6 MB	57.8 MB	267 KB
PNG render	78 ms	2.35 s	119 ms	—	2.50 s
Interactive HTML	4.9 MB	—	—	14.3 MB	9.8 MB

At 1M points, Altair OOMs and plotnine takes 39 s. Ferrum renders in 57 ms. Full benchmarks →

How Ferrum compares

	Ferrum	plotnine	seaborn	Altair	Plotly
Grammar	Layered, typed encodings, faceting	ggplot2 port, full GoG	Convenience helpers	Vega-Lite declarative	Imperative traces
Diagnostics	44 helpers, 28 visualizers	—	—	—	—
Composition	+ | & operators	+ layers, facets only	Manual subplots	| & (Vega-Lite)	make_subplots
Interactivity	WASM/GPU, selections, linked views	matplotlib backends	matplotlib backends	Vega-Lite selections	Plotly.js
Scale ceiling	10M+ rows	~100k marks	~100k marks	~5k rows	~500k marks
DataFrames	polars, pandas, modin, cuDF, dask, ibis	pandas only	pandas only	pandas, polars	pandas, polars
System deps	None	matplotlib	matplotlib	None	None
Backend	Rust (SVG, raster, WASM)	matplotlib	matplotlib	Vega-Lite (V8)	Plotly.js (kaleido)

Detailed migration guides → for plotnine, seaborn, yellowbrick, and scikit-plot.

Examples

# Layer a LOESS trend on a scatter plot
points = fm.Chart(df).mark_point(opacity=0.6).encode(x="x", y="y", color="group:N")
trend = fm.Chart(df).mark_smooth(method="loess").encode(x="x", y="y", color="group:N")
chart = points + trend

# Compose diagnostics into a model report
source = fm.ModelSource(model, X_test, y_test)
report = (fm.roc_chart(source) | fm.confusion_matrix_chart(source)) & fm.importance_chart(source)
report.save("model_report.svg")

# Figure-level helpers
fm.displot(df, x="value", hue="group", kind="kde")
fm.catplot(df, x="species", y="measurement", kind="violin")
fm.pairplot(df, vars=["a", "b", "c"], hue="label")

Architecture

Layer	Role
src/ferrum/	Python declaration API — Chart, encodings, marks, themes, plots
crates/ferrum-core/	Rust computation engine — transforms, scales, rendering
Arrow CDI	Zero-copy data transport between Python and Rust via pyo3-arrow

Development

Requires Python 3.10+, Rust toolchain, and maturin.

uv sync
unset CONDA_PREFIX && uv run --no-sync maturin develop   # build Rust extension
uv run pytest                                            # run tests

How this was built

[!NOTE] Built in 10 days by one human and an agentic Claude framework. 975 commits · 103k lines · 3,829 tests · 12 phases · 13 agents · 16 skills

Read the retrospective →

Documentation

Full docs at ferrumviz.com.

License

See LICENSE for details.