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Showcase: What You Can Build

Well-known chart designs from the matplotlib, seaborn, Altair, and D3 traditions — built in Ferrum's Rust engine. Every image below was produced entirely in Python using Ferrum's grammar-of-graphics API; no matplotlib, no browser, no external renderer. These designs lean on Ferrum features like categorical color ranges, temporal axis auto-inference, size and shape legends, stroke routing on line marks, sort specs, and annotation date coordinates.

Where the Gallery catalogs each individual mark and helper, the Showcase assembles those primitives into complete, recognizable chart designs. Each card shows the concise API call alongside the rendered output.

Four-encoding charts

Encode x, y, size, and color simultaneously for maximum information density.

  • Gapminder Bubble

    showcase_gapminder_bubble

    fm.Chart(df).mark_point().encode(x="gdp_per_capita", y="life_expectancy", size=fm.Size("population"), color="region")

    Four simultaneous encodings — position, size, and color — in one readable chart. The size legend uses Ferrum's new multi-legend stacking; both legends render side by side.

Comparison and annotation

Charts built for the "story in the data" — where one series, one event, or one gap is the point.

  • Highlight Line

    showcase_highlight_line

    ctx = fm.Chart(ctx_df).mark_line(stroke="#cccccc", opacity=0.55).encode(x="x", y="y", detail="series") + fm.Chart(hi_df).mark_line(stroke="#e4572e")

    Nine gray context series recede; one accent series commands attention. detail= groups the context lines without adding a color legend.

  • Dumbbell Chart

    showcase_dumbbell

    rule = fm.Chart(df).mark_rule(stroke="#cccccc").encode(x="score_before", x2=fm.X2("score_after"), y="category") + fm.Chart(df_pts).mark_point().encode(x="score", color="period")

    Before-and-after per category. Connecting rules use x + x2 encoding; a categorical color range pins "Before" and "After" to consistent hues.

  • Time Series with Events

    showcase_time_series

    fm.annotate_rect(x1="2023-06-01", x2="2023-09-01", y1=70, y2=125, fill="#fbbf24") + fm.Chart(df).mark_line().encode(x=fm.X("date", axis=fm.Axis(label_format="%b"))) + fm.annotate_vline(x="2023-09-01")

    Temporal columns auto-infer their scale. Annotation coordinates accept ISO date strings. The yellow band is annotate_rect; the rule is annotate_vline.

Ranked and sorted

Sorted layouts for ranked comparisons — sorted along the axis, not alphabetically.

  • Cleveland Dot Plot

    showcase_cleveland_dot

    stem = fm.Chart(df).mark_rule(stroke="#d1d5db").encode(x=fm.X("score_zero", axis=fm.Axis(title="score")), x2=fm.X2("score"), y=fm.Y("skill", scale=ordinal)) + fm.Chart(df).mark_point(fill="#3b82f6")

    Lollipop composition: a mark_rule stem anchored to zero plus a mark_point dot. Category order is explicit via OrdinalScale(domain=sorted_domain).

Small multiples

Faceted panels with shared scales and consistent encodings across categories.

  • Faceted Small Multiples

    showcase_faceted_scatter

    fm.Chart(df).mark_point(opacity=0.65).encode(x="petal_length", y="sepal_width", color=fm.Color("species", scale=fm.OrdinalScale(domain=species_order, range=colors))).facet(col="species")

    Three scatter panels sharing a common axis range. An explicit OrdinalScale(domain=..., range=...) keeps each species consistently colored across facet panels.