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graph-design

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Economist-style data-visualisation system for matplotlib/seaborn — theme, finalize() title-stack, direct labels, and 20+ chart helpers with a when-to-load index of 39 worked examples. Use whenever creating, restyling or reviewing charts/plots/figures in Python: it owns figure sizing, typography, titles/descriptors/sources, annotation conventions and chart-type selection. Not for non-matplotlib charting (d3, plotly, spreadsheets).

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DJRHails

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المهن ذات الصلةSOC

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علماء البياناتمهن الحاسوب والرياضيات·SOC 15-2051

SKILL.md

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name	graph-design
description	Economist-style data-visualisation system for matplotlib/seaborn — theme, finalize() title-stack, direct labels, and 20+ chart helpers with a when-to-load index of 39 worked examples. Use whenever creating, restyling or reviewing charts/plots/figures in Python: it owns figure sizing, typography, titles/descriptors/sources, annotation conventions and chart-type selection. Not for non-matplotlib charting (d3, plotly, spreadsheets).
license	MIT
version	0.12.0
author	Daniel Hails <graphs@hails.info>
tags	["matplotlib","seaborn","data-visualisation","charts","economist"]
metadata	{"source":"https://github.com/DJRHails/graphs","package":"djrhails-graphs (PyPI)"}

graph-design

Economist-style data-visualisation system for matplotlib and seaborn.

This repo is the skill (standard agent-skill layout: SKILL.md + references/ + examples/) and the Python package that powers it. Install the library with pip install djrhails-graphs; install the skill by submoduling/vendoring this repo as a skill directory (e.g. skills/graph-design). Import as graphs.

Quick start

A two-series line chart with direct labels, a footnote marker, and a packed footnote-plus-source row — the conventions the library is built for.

import matplotlib.pyplot as plt
import numpy as np

from graphs import finalize, footnotes, label_lines, save_chart, set_theme, subplots

set_theme()

months = np.arange(24)
us = 2.0 + 4.0 * np.exp(-months / 9) + np.random.default_rng(0).normal(0, 0.25, 24)
eu = 1.8 + 4.5 * np.exp(-months / 11) + np.random.default_rng(7).normal(0, 0.30, 24)

fig, ax = subplots("wide")
ax.plot(months, us, label="America")
ax.plot(months, eu, label="Euro area")
label_lines(ax)

finalize(
    ax,
    title="Cooling off",
    descriptor="Headline CPI*, % change on a year earlier, monthly",
)
footnotes(
    fig,
    "*All-items consumer price index",
    source="Sources: [US Bureau of Labor Statistics](https://www.bls.gov/); "
           "[Eurostat](https://ec.europa.eu/eurostat)",
)

save_chart(__file__)  # writes quick.png beside the script

Save to quick.py and run; the output sits next to it. finalize() auto-sizes margins, and footnotes() sizes its own bottom band: a single short note packs on the source row, while several notes (or one that can't pack beside the source) stack as one definition per row above it — leave source= off finalize() so footnotes() owns the whole block. save_chart is the standard epilogue: tight bbox, 150 dpi, close, one-line confirmation.

Rules at a glance

A 30-second pre-flight before you ship a chart. Each line links to its full treatment below; the ✗ is the mistake the rule prevents.

The title/descriptor IS the y-axis label — leave ax.set_ylabel(""). Put the measured quantity + units in finalize(descriptor=...), never in an axis label. ✗ ax.set_ylabel("share of attempts"). (For a horizontal Economist-style title above the axis use y_axis_label(ax, ...), not set_ylabel.) Exceptions: a twinx() secondary axis, or a coordinate plot (ROC, scatter) where both axes are dimensions — there, label the axes and pass finalize(..., allow_ylabel=True). finalize() raises on a non-empty y-label (the ast-grep rule catches it too), so this isn't a style nicety you can skip. → Headline conventions
Title states one plain claim, quantified, no wink. ✗ "Outclassing the guardians" → ✓ "Claude Opus 4.8 outscores the specialised guard models"; ✗ "a few categories" → ✓ "9 categories". If the intervals can't back a claim, title the question instead. → Headline conventions
Descriptor names the quantity + axis mapping, one sentence — nothing else. ✗ "…F1, n=543; official protocol, CoT mode, temperature 0.6" → ✓ "DynaBench policy-violation F1 (FAIL = violation)". Model, conditions → footnotes; N → source; empty is correct when an on-chart label carries the metric (name it exactly once).
Never caption statistics machinery. Draw whiskers and ci_fill bands; "95% Wilson CI" / "bootstrap CI band" never appears in figure text. ✗ footnote "Whiskers: 95% Wilson CI on the total rate".
Source = Source: <dataset> (N=…); <script> — dataset named by entity, its computed N attached, generating script last. ✗ a bare crossfire_correlates.py. → Headline conventions
Size with subplots("daily"|"wide"), never figsize=. Fixed widths keep a set consistent; ad-hoc widths make a gallery ragged. → Default conventions
Call finalize(ax, title, descriptor, source) last — it auto-sizes every margin, seats numeric y-labels on the grid, and draws the delta marker. Don't hand-set subplots_adjust before it.
Read the rendered PNG back and check it tells its story at a glance, every element earns its place, and it's the right chart type. → Review
Line plots show uncertainty as a ci_fill band, never error-bar whiskers. A connected series over a continuous/ordinal x (a sweep, a trend, a depth curve) gets a translucent band via ci_fill(ax, x, lo, hi, color=line_col), not per-point errorbar(yerr=…) caps. ✗ ax.errorbar(x, y, yerr=…) for a trend → ✓ ax.plot(x, y, color=c); ci_fill(ax, x, lo, hi, color=c). Whiskers belong on categorical comparisons (bars, dot plots) where there's no line to fill under. → Uncertainty bands

Default visual conventions

Behaviour that's automatic unless you override it:

Charts come in two widths — size with subplots(), never a raw figsize=. Create figures with subplots("daily") (4.6in column, portrait-leaning) or subplots("wide") (7.0in article format) — like a newspaper's column formats, the width is fixed by the medium and only the height is the per-chart choice (height=). Fixed widths keep the type-to-chart ratio consistent across a set; ad-hoc figsize= widths are what make a gallery look ragged. Extra plt.subplots kwargs pass through (ncols=, sharex=, …).
Title marker is the favicon triangle (marker="delta"). The hollow red triangle is drawn inline at the first title line's baseline, sized to the cap height. Pass marker="rule" for the legacy short red rule above the title, or marker="none" to suppress entirely.
Titles and descriptors auto-wrap to the figure width. Pass them as single lines — finalize() measures with the renderer and breaks where this figure needs it (with a widow fix, so no single-word last lines). Never copy a reference chart's line breaks; explicit \n is reserved for semantic breaks (a descriptor's subject / unit split).
Explicit descriptor breaks get a semibold lead. An explicit \n splits the descriptor into a semibold subject lead over regular scope/unit lines; the lead stays semibold even if it wraps. Breaks added by the auto-wrap alone never trigger the styling.
Numeric y labels sit on their gridlines (y_labels="on_grid", the finalize() default): each gridline extends into the label gutter and ends flush with the labels' outer edge; the label rests just above its line (a little breathing room so glyphs don't touch the rule), and the bottom baseline extends into the gutter too — meeting the gridlines rather than stopping at the data edge — whether or not the floor is a tick. Applied only when the axes has visible y gridlines, so categorical axes are untouched; works on either side (e.g. a left latitude axis). Opt out with y_labels="ticks", or call y_labels_on_grid(ax) manually on extra facet panels.
A dark zero centreline is automatic when the y-range straddles 0 (finalize default; zero_rule=True): a strong C_SPINE rule on the zero baseline the data crosses, kept under every data line (so lines cross over it) and running the full gutter width so "0" sits on it. Skipped when the caller already drew their own zero rule (e.g. a bar chart's on-top axhline(0)). Set zero_rule=False for horizontal-value charts whose y-axis is merely a coordinate spanning 0 (a latitude axis through the equator). On a faceted figure call dark_zero_line(ax) per panel — finalize only finishes the primary axes.
Annotations default to 9pt — callout, highlight_label, direction_label, threshold_arrows match direct-label size (the print spec's 7.5pt reads too small at daily-chart scale).
Footnote markers auto-superscript. *, †, ‡, §, **, ††, ‡‡, §§ render as superscripts anywhere they appear in titles, descriptors, source lines, footnote bodies, or legend entry texts (finalize() post-processes every legend on the figure) — write plain text, the renderer handles the typography.
Uncertainty on a line is a filled band, not whiskers. For a connected series over a continuous or ordinal x — a sweep, a trend, a depth/budget curve — draw the confidence interval as a translucent ci_fill(ax, x, lo, hi, color=line_col) band behind the line (step 3 of Build, so it sits under the data), passing the line's own colour so the band reads as its range. Per-point errorbar(yerr=…) caps clutter a trend, fight the eye across the series, and break down once two lines overlap; a band shows trend and range together. Reserve error-bar whiskers for categorical estimates — bars and dot plots, where there is no line to fill beneath and each point is an independent comparison (a binomial proportion's Wilson CI on a bar stays a whisker). ci_fill defaults to the Economist salmon for a single series; pass color= per line on a multi-series chart so each band matches its line.
Frameless legends are default for both smart_legend() and top_legend(). Boxed legends are opt-in.
Source + footnotes use C_SOURCE (#404040, the styleguide's 75% black) — darker than the muted C_LABEL_MUTED grey, so attribution stays legible while reading as metadata.
finalize always auto-layouts every margin. It sizes all four subplots_adjust margins AND the inter-panel wspace/hspace from the renderer — there is no opt-out and, in the common cases, nothing to set by hand. Top/bottom fit the title-stack and the source/footnote band over the measured x-tick labels; left/right are measured from the actual y-axis text (a chart with long left-hung category labels gets a wide left, a plain right-axis chart keeps a tight left + a measured right); wspace/hspace size a grid of panels (wspace from the inter-column y-label width, hspace from the x-tick band + panel_label height — pass finalize(panel_labels=True) for a multi-row faceted layout that adds panel_label headings after finalize). On a non-Agg backend the side margins and spacing fall back to fixed constants. A caller may still fig.subplots_adjust(...) after finalize to override a specific value, but the everyday faceted / wide-left charts no longer need to. Don't restore top= after finalize — it anchors the title to its own auto top, so overriding top detaches the title; raise y_start instead to reserve more headroom.
Multi-note footnotes stack by default. footnotes(stack=None) (the default) renders more than one note — or a single note that can't pack beside the source row — as a term-definition stack: one row per note, source on the bottom row, and the bottom band grows to fit the measured rows, wrapped continuation lines included (no finalize(footnote_lines=) needed on single-row figures; a multi-row grid still reserves up front with it). A single short note keeps the packed note-beside-source row (the Economist age-gap pattern). Force either layout with stack=True / stack=False.
Long footnotes word-wrap automatically. footnotes(wrap=True) is the default — an overflowing row breaks to continuation lines, and the chart shifts up to reserve room. No need to hand-break with \n.
Orphan footnote markers warn. footnotes(check_anchors=True) (default) raises a UserWarning when a note starts with * / † / ‡ / § that isn't found in the title, descriptor, axis labels, legend entries, or any in-chart text. Anchor the marker by adding it after a word in the descriptor (e.g. descriptor="Verified contracts* per TVL bucket") or a legend label (label="Self-attributed*").

Vertical bar charts (plug-and-play)

For the common "one bar per category" layout, the three-helper combo collapses ~30 lines of per-script spine/grid/tick boilerplate:

bars = bar_v(ax, labels, values, log=True)               # spines, grid, ticks
bar_value_labels(ax, bars, fmt="{:,}", skip_zero=True)   # value above each bar
bar_sublabels(ax, bars, [f"n={n}" for n in counts])      # denominator below
finalize(ax, title="…", descriptor="…", source="…")

bar_v highlights the max in C_RED by default; pass highlight_idx= to spotlight a specific bar, or highlight_max=False to skip. bar_sublabels auto-pads the x-tick labels down so the sublabel band doesn't collide.

Core design principles

Rules the helpers were built to enforce. When in doubt, satisfy the most.

Title does the talking — state the finding, not the topic. Subtitle carries units, geography, time range.
Every element earns its place — if removing it doesn't hurt comprehension, remove it.
Put information where the reader's eye is already going — labels next to what they label.
Direct labelling beats legends. (See label_lines.)
Each chart type has its own conventions. Pick the type first, then apply its rules. (See cycle_for.)
Hue communicates kind; saturation communicates importance. Same-kind differentiation uses saturation or opacity, not a new hue.
Brand colour and data colour are separated — C_RED is reserved for emphasis, not as the default fill.
Four categories is a working ceiling for thermometer / bubble / pie / doughnut. The fix is usually a different chart, not more colours.
Don't truncate scales to fit the comparison.
The chart type carries assumptions — lines imply continuity, bars imply discrete categories.
Axis scaling is editorial, not neutral.
Double scales need discipline — align zero lines, match axis colour to its line. Otherwise split or index.
One chart, one baseline. If you can't combine cleanly, split.
Signal non-zero baselines visually — broken_axis() on line / thermometer / scatter. Never on bar/column (use a thermometer).
Annotations are first-class elements — callouts, highlight panels, event markers have their own typography and geometry.
Mobile is a redesign, not a resize.
Negative space is engineered — the paddings and axis ends are deliberate.
Sometimes the right answer is less data — cut or show an average.
The chart is responsible to the reader, not to the data. Misleading, confusing, pointless — three failure modes every chart has to pass.

Headline conventions

The strings passed to finalize(title=, descriptor=, source=) and footnotes(...) form an inverted pyramid — each row answers the reader's next question, and every fact sits on the lowest row that still serves it. Get them wrong and a technically correct chart still reads as a draft. The essentials:

Title: one plain claim the chart can defend — "Claude Opus 4.8 outscores the specialised guard models", not "Outclassing the guardians". Quantify what the chart quantifies ("9 categories", "96% of cross-fires*"); star coined terms and define them in a footnote, or rewrite them away; match claim strength to the drawn evidence (weak intervals → title the question). Don't write winks — an existing wink survives only if it decodes on sight into the exact mechanism ("Marking your own homework" for self-review); the replica gallery keeps its Economist winks, research figures don't get new ones.
Descriptor: the measured quantity + axis mapping, one sentence — or empty when an on-chart label already names the metric (name it exactly once). Model ids, protocol knobs and condition tags go to footnotes; sample sizes go to the source line; anything the legend already says is cut. Because the descriptor carries the quantity, leave the matplotlib axis label empty (ax.set_ylabel("")); for a horizontal Economist-style title above the axis use y_axis_label(ax, ...), never a hardcoded ax.set_ylabel("text"). The exception is a coordinate plot (ROC, scatter) or a twinx() secondary axis, where an axis genuinely needs its own label — there, pass finalize(..., allow_ylabel=True) to opt out. (Enforced two ways: finalize() raises ValueError on a non-empty y-label at runtime, and enforcement/rules/no-hardcoded-ylabel.yml flags it at lint time.)
Footnotes: starred definitions first, then conditions. Each coined term gets *term: plain-words definition with a concrete example; anchor the star wherever the reader meets the term (title, descriptor, legend entry, panel label). Conditions + model follow, blank-line separated, with every count computed ("unparseable outputs (N=0) scored wrong"). Never: CI/whisker mechanics, epistemic status tags, project-internal commentary.
Source: Source: <dataset> (N=…); <script> — dataset named by entity with its computed N attached, generating script last, Sources: for several. Markdown links survive into SVG/PDF output.

Read references/headline-conventions.md before writing the strings for a publishable chart — it carries the worked examples, footnote categories, hyperlink rules and a before/after table.

Read references/relative-framing.md before choosing a metric — it covers when to plot data relative to a baseline (index, anomaly, share, per-capita, vs a reference group) instead of absolute values, which technique fits which comparison, and how to mark and label the baseline so the framing stays honest.

Palette

Nine main colours in PALETTE: red, blue, cyan, green, yellow, olive, purple, gold, grey. Default cycle leads with red. Pull a chart-type-specific order with cycle_for("bar" | "bar_stacked" | "line" | "scatter" | "bubble" | "thermometer"). snapshot_palette(n, *, accent=None) returns a chronological slate→accent ramp for the "snapshots of the same series over time" pattern.

Structural greys (all automatic in the theme): C_SPINE (zero-baseline only), C_GRID, C_LABEL, C_LABEL_MUTED, C_SOURCE (75% black — source + footnotes), C_CI, C_BOX_FILL, C_BG, C_BG_TINT, C_BG_TRANSPARENT, C_HIGHLIGHT_PANEL, C_HIGHLIGHT_PANEL_RED.

Style overrides to apply on top:

Chronological categories → light-to-dark tints of one colour, not the cycle. Use snapshot_palette().
"Other" / "Don't know" → C_OTHER (slate grey).
Positive vs. negative → only differentiate by colour for meaningful pairs (imports/exports, gain/loss).

API

Theme, finalisation, layout

Function	Purpose
`set_theme(bg=None, transparent=False)`	Apply theme globally. Call once. White background by default; pass `C_BG_TRANSPARENT` + `transparent=True` for transparent output.
`subplots(format="daily", , height=None, *kwargs)`	`plt.subplots` at a standard chart width — `"daily"` 4.6in / `"wide"` 7.0in; height is the per-chart choice.
`finalize(ax, title, descriptor, source, *, marker="delta", y_labels="on_grid", panel_labels=False, zero_rule=True, …)`	Title stack (auto-wrapped), optional marker, source line, y-axis right, on-grid y labels, and a dark zero centreline when the y-range straddles 0 (`zero_rule`). Auto-sizes ALL margins + inter-panel `wspace`/`hspace` from the renderer (left/right from the y-axis text, spacing from a grid); `panel_labels=True` for multi-row facets that add `panel_label` after. Override a specific value with `subplots_adjust` after if ever needed.
`dark_zero_line(ax)`	Dark `C_SPINE` rule on the zero baseline the data straddles, under the data lines. Auto-applied by `finalize`; call per panel on facets.
`panel_label(ax, label)`	Bold sub-heading + dark rule (faceted charts).
`footnotes(fig, *notes, source=None, wrap=True, stack=None, check_anchors=True)`	Footnote strip + optional source line. `stack=None` (default) auto-picks the layout: several notes — or one that can't pack beside the source — stack one row per note with the source on the bottom row, and the bottom band self-sizes to the measured rows; a single short note packs beside the source. Auto-superscripts `, †, ‡, §, *, ††, ‡‡, §§`. Long rows word-wrap to fit the figure (`wrap=True`, default). Warns when a leading marker has no anchor in the title/descriptor/axis labels/legend entries (axes or figure-level)/in-chart text (`check_anchors=True`).
`y_axis_label(ax, text, *, unit=None)`	Horizontal title above the y-axis; `unit=` renders below in muted colour. Call BEFORE `finalize` — it reserves a band under the descriptor and re-anchors the label to the final axes top, clear of in-axes text poking past the axes edge and (for `side="left"`) above the `panel_label` band.
`x_axis_label(ax, text, *, labelpad=None)`	Project-styled `set_xlabel` (`C_SPINE`, 8.5pt); footnote markers superscripted by `finalize`.
`year_axis(ax, *, abbreviate=True)`	Date x-axis formatter: first year full, subsequent two-digit.
`year_ticks(ax, years, *, inset=True)`	Same convention for numeric year axes: full first/century years, two-digit otherwise, inset ends.
`x_axis_top(ax)`	Move the value axis to the top (horizontal-chart convention); call after `finalize()` when a legend row intervenes.
`save_chart(__file__, *, deck=False)`	Standard save epilogue: `<script>.png` beside the script, tight bbox, 150 dpi, close. `deck=True` also writes `<script>_deck.png`.
`save_deck_variant(fig, path, *, dpi=150)`	Deck variant beside the full chart: strips the tagged headline (marker, title, descriptor, source, footnotes, any `suptitle`), keeps `y_axis_label` blocks / legends / `panel_label` headings; writes `<stem>_deck.png`. Call after saving the full chart.

Chart helpers

Function	Purpose
`cycle_for(chart_type) → list[str]`	Recommended colour order for a chart type.
`snapshot_palette(n, *, accent=None)`	Chronological slate→accent ramp for snapshot lines.
`bar_h(ax, categories, values, *, highlight_max=True)`	Horizontal bars; max in `C_RED` by default.
`bar_v(ax, categories, values, *, highlight_max=True, log=False, headroom=1.25, y_formatter=None)`	Vertical bars; handles spine/grid/tick boilerplate. `log=True` gives symlog + auto decade ticks.
`bar_value_labels(ax, bars, *, fmt="{:,.0f}", formatter=None, fontsize=9)`	Annotate each bar with its value above the bar. Pass `formatter` for currency / mixed units.
`bar_sublabels(ax, bars, labels, *, fontsize=8, offset_pt=4)`	Per-bar secondary text below the baseline (denominators, "n=…"). Auto-pads x-tick labels down to clear the band.
`dumbbell(ax, categories, start, end, *, label_start, label_end)`	Before/after dot-and-line. Defaults red→blue.
`thermometer(ax, categories, values, *, series_labels, dot=True)`	Tick-and-dot ranked categories. Warns above 4 series.
`threshold_lollipop(ax, categories, values, *, threshold=1.0)`	Horizontal lollipop with fixed centre + leader lines.
`bump_chart(ax, ranks, *, highlight, aspect=…, max_rank=…)`	Rank-over-time PCHIP-smoothed lines with white halo at crossings. `max_rank` crops the rank axis so a large backdrop can't compress the story band.
`dot_plot(ax, categories, series, *, size=150)`	Cleveland dot plot; same-value dots in a row merge into pie-split markers. Returns legend handles.
`pie_marker(ax, x, y, wedge_colors, *, size=150)`	One marker split into N equal wedges so overlapping points stay visible.
`scatter_standard(ax, x, y)`	General-trend scatter, 50% opacity, no stroke.
`scatter_highlight(ax, x, y)`	Outlier / labelled scatter, 100% opacity.
`scatter_category(ax, x, y)`	Bubble dot, 50% fill + 0.3px stroke for overlap.
`trend_line(ax, x, y)`	Dashed 1px trend line.
`smoothed_line(ax, x, y, *, color)`	Three-layer scatter + CI band + smoothed line.
`ci_fill(ax, x, lo, hi, *, color=None)`	CI band for a line — the way to show uncertainty on a sweep/trend, not `errorbar` whiskers. Salmon by default; pass the line colour to match.

Annotations

Function	Purpose
`callout(ax, xy, text, *, xytext, arrow=True)`	Pale-fill text callout with optional arrow.
`highlight_panel(ax, x_start, x_end, *, label=None)`	Vertical event-period band.
`highlight_label(ax, xy, text, *, role="primary")`	Single-point label. `"secondary"` = grey/caps/light.
`index_marker(ax, x, *, y=100)`	Red rule + black dot for index charts.
`broken_axis(ax, *, axis="y", side="auto")`	Non-zero-baseline heartbeat mark. Line/scatter/thermometer. `side="auto"` follows the y-label side.
`direction_label(ax, text, xy, *, arrow="↑")`	One-sided directional cue ("↑ Older husband") in axes-fraction coords.
`number_box(ax, xy, n)`	Numbered cross-reference box.
`threshold_arrows(ax, threshold, *, left_text, right_text)`	Directional label pair straddling a threshold.

Labels, axes, legends

Function	Purpose
`label_lines(ax, *, stroke=True)`	Direct labels at line ends with collision avoidance; white halo on by default.
`inset_tick_labels(ax, *, axis="x")`	First tick label `ha="left"`, last `ha="right"`.
`y_labels_on_grid(ax)`	Sit y tick labels on gridlines extended under them (the `finalize()` default; call manually on extra facet panels).
`italicize_labels(ax, labels)`	Italicise specific tick labels in place.
`style_labels(ax, *, italic=(), bold=())`	Per-label italic/bold preserving tick colour.
`color_axis(ax, side, color, *, spine=True, ticks=True)`	Colour a spine + ticks + labels to match a series.
`right_axis(ax)`	Apply right-axis convention to a panel.
`smart_legend(ax)`	Frameless legend in the emptiest corner.
`top_legend(ax_or_fig, handles?, labels?, *, x=0.02)`	Frameless top-anchored legend under the title-stack. `top_legend(ax, ncol=2)` derives handles/labels from the axes. Call BEFORE `finalize` (auto-`y`) and it reserves its own band — no `subplots_adjust`.

Number formatting

Function	Purpose
`format_count(n, *, sig=2)`	Compact count label: 2 s.f. + k/M/B/T unit (`1234 → "1.2k"`, `500 → "500"`).
`scale_axis(ax, *, axis="y", by=1000)`	Divide an axis's tick labels by a shared magnitude (`20,000 → 20`); name the magnitude in the descriptor.
`magnitude_word(by)`	English name for the divisor (`1000 → "thousand"`) — pairs with `scale_axis` for the descriptor unit.

Verification

Function	Purpose
`verify_layout(fig, *, tolerance=0.005)`	Warn when any text artist (tick labels, titles, legends, footnotes) extends past the figure bounds. Catches the class of bug where `savefig(bbox_inches="tight")` silently expands the saved canvas to fit overflow. Auto-called by `footnotes()`.
`finalize(..., allow_ylabel=False)`	Runtime guard: `finalize()` raises `ValueError` when the axes carries a hardcoded non-empty `ax.set_ylabel(...)` (the descriptor should carry the quantity). Pass `allow_ylabel=True` for a coordinate plot (ROC/scatter) or a `twinx()` secondary axis.
`enforcement/rules/no-hardcoded-ylabel.yml`	Lint-time twin of the above: ast-grep rule (`severity: error`) that flags a hardcoded non-empty `ax.set_ylabel(...)`, with a `# ast-grep-ignore: no-hardcoded-ylabel` escape for twin/coordinate axes. See `enforcement/README.md`.

Workflow

Build

set_theme().
(If not the default cycle) ax.set_prop_cycle(color=cycle_for("…")).
Plot — ci_fill and highlight_panel first so they sit behind the data.
Annotate — callout, highlight_label, index_marker, broken_axis.
Direct label — label_lines(ax) over smart_legend(ax) for line charts.
finalize(ax, title, descriptor, source) last — auto-sizes margins.
Faceted: finalize() on the first axes with title_x pinned and y_start≈0.075 (reserves the title-stack room) — it auto-sizes the inter-panel wspace/hspace from the panels' y-labels and x-tick band, so you usually set NO subplots_adjust. Pass panel_labels=True for a multi-row layout, then call panel_label() per axes (they anchor to the final positions). Only reach for a post-finalize subplots_adjust if a bespoke layout (hand-placed gutter artists) still needs one.

Review

Building the chart is half the job. Reading it back is the other half. Every figure should be opened (with the Read tool inline, or in a viewer) and evaluated against the story it was made to tell. Don't ship a draft.

For each rendered figure, answer three questions before moving on:

Does the chart tell its intended story at a glance? If the reader needs body text to know what to look at, the title is doing too little. Re-read the title — does it state the finding, or only the topic? "Claude Opus 4.8 outscores the specialised guard models" tells you what to see; "GDP growth, 2010–2024" doesn't. Iterate until the title carries the story alone.
Is every element earning its place? Bars at the rounding-noise threshold, legends that duplicate direct labels, gridlines that don't anchor anything, sub-labels nobody will read, decimal places past the data's precision — cut them. Less data often makes the point sharper. A six-row table reduced to its three meaningful rows is a better chart, not a smaller one. Apply the "Every element earns its place" core principle ruthlessly.
Is this the right chart type? Switching costs nothing — the script is ten lines.
- Six-bucket bar chart trying to show "climbs then dips"? → line chart.
- Two-series grouped bars comparing the same metric at two points in time? → dumbbell.
- Scatter with 200 points and a single highlight? → histogram + callout (or scatter_standard + scatter_highlight).
- Pie / doughnut with more than four slices? → bar_h ranked.
- "How does X depend on Y" with strong trend? → smoothed_line, not raw scatter.
- Long-form ranking change over time? → bump_chart, not stacked bars.
The chart-type table in cycle_for() and the per-type rules in the core design principles are the reference.

If any answer is "no" or "kind of", iterate — re-title, cut elements, or switch chart type. The first render is a draft. verify_layout() (auto- called from footnotes()) catches mechanical overflow bugs, but it cannot tell you whether the chart is good — that part is editorial, not mechanical.

Examples

Runnable scripts in examples/. Each entry leads with the situation that should make you open it — match your data shape and story shape against the Load when clause, then read the script as the worked reference for that pattern.

bar_chart.py — Load when: one value per category, ranked, and the biggest (or one chosen) value is the point. Minimal bar_h demo with the default highlight_max=True.
dumbbell_chart.py — Load when: each category has a before and an after value and the change is the message. dumbbell plus a right-aligned top_legend fed from ax._dumbbell_handles.
faceted_chart.py — Load when: several series share an axis but overlap so badly that one panel buries the story — split into small multiples. The faceting workflow: right_axis + ci_fill per axes, finalize(y_start=0.075) (auto-sizes the inter-panel wspace from the per-panel labels — no manual subplots_adjust), then panel_label per axes (anchored to the final positions).
faceted_top_legend.py — Load when: faceted panels share a colour key that must sit above the row of panels, hands-off. top_legend is called BEFORE finalize; finalize measures it, reserves a band between the two-line descriptor and the panels, and re-anchors it to the final axes top — no subplots_adjust, no hand-tuned y= / y_start padding.
scatter_chart.py — Load when: a relationship cloud where a few named outliers carry the story. scatter_standard + scatter_highlight two-layer treatment, trend_line, callout on the outliers.
thermometer_chart.py — Load when: 2–4 series compared across the same ranked categories, where grouped bars would clutter. thermometer(dot=False) three-series variant, x-axis on top, frameless top_legend.
index_chart.py — Load when: series of different magnitudes must be compared as growth since a common moment, with an event window to flag. index_marker + highlight_panel band + secondary highlight_label + broken_axis + label_lines.
line_chart.py — Load when: noisy point estimates over time with uncertainty — the reader should see trend and range, not individual points. smoothed_line (scatter + CI band + trend), custom _LineBandHandler legend, year_axis(set_locator=False).
bump_chart.py — Load when: the story is rank changes over time in a large field, with only a few entities highlighted against a faded backdrop. bump_chart with highlight=, colors=, right_labels=True, x_labels_top=True, max_rank cropping; real data via _data.py.
corbyn.py — Load when: ranked bars where one dominant value tempts you to truncate the scale (don't — show it whole), or specific row labels need bold/italic emphasis. bar_h + style_labels(italic=, bold=).
dogs.py — Load when: two different units genuinely must share one panel and the twin axes have to be honest — both ranges sized so 1% of the midpoint spans equal distance. color_axis(spine=False, ticks=False), manual series titles via render_text_with_superscripts, a two-note footnotes(source=) definition stack.
brexit.py — Load when: irregular poll/survey readings over time — connecting the dots would look erratic, so show points plus a smoothed trend on a truncated axis. scatter_standard + Savitzky-Golay smoothing, manual year ticks + year_axis(set_locator=False), inset_tick_labels, broken_axis(side="right").
us_trade.py — Load when: two related time series with incompatible units or baselines tempt you toward a double axis — split into stacked sharex panels instead. panel_label, right_axis, inset_tick_labels, source via footnotes(source=).
pensions.py — Load when: a scatter where labelled points need emphasis but a second colour would falsely read as a category — use opacity within one hue. Same-hue dots with opacity-for-emphasis, italic average label via FontProperties, y_axis_label(unit=).
eu_balance.py — Load when: a stacked composition has too many members to colour — keep the named few plus an "Others" bucket, and show two measures as side-by-side panels. Positive+negative stacked bars per year, shared top_legend, right_axis.
affordability_chart.py — Load when: categories are measured against a meaningful threshold (above = fine, below = not) and span a wide value range. threshold_lollipop(threshold=1.0) on a log x-axis + threshold_arrows straddling the threshold + two-note footnotes.
age_gap_chart.py — Load when: the same curve measured at several historical moments, and the story is how its shape shifted — earlier years fade, the present pops. Chronological lines via snapshot_palette(4), in-chart series labels, broken_axis(side="right"), right-anchored footnotes(y=, x=).
australia_heat.py — Load when: a time series of signed deviations around zero where direction is the story. Diverging annual bar_v bars, positive red / negative blue on a zero baseline.
malaria.py — Load when: observed history fans out into multiple scenario paths and the reader must not confuse projection with fact. Solid history line splitting into three dashed forecasts inside a highlight_panel FORECAST band.
co2_emissions.py — Load when: each category has two multiplicative dimensions (rate × size = total) and the totals matter as much as the rates. Variable-width Mekko bars with on-bar totals + dashed global-average rule.
christianity.py — Load when: many categories each have exactly two time points and the trajectories — who's rising, who's flat — are the message. Two-point slope chart with endpoint dots, stacked value labels, one accent line.
graduate_pay.py — Load when: a dense dot cloud with a strong nonlinear trend, where the x variable reads better reversed ("more selective →"). scatter_standard cloud, reversed x-axis, solid trend curve.
generational_politics.py — Load when: cohort or panel-survey waves — several groups tracked over irregular waves, with gaps in coverage and an overall average. PCHIP-smoothed lines, gap segments in lighter tint, dotted average, end-of-line labels.
uber_tips.py — Load when: paired group values per category, expressed relative to an explicit reference point that needs marking. Vertical dumbbell/lollipop pairs with a ringed reference marker and leader line.
us_refugees.py — Load when: an actual annual quantity tracked against an administrative limit — the gap between allowed and delivered is the story. Neutral grey bars + stepped red policy-cap line (real published data).
polluted_cities.py — Load when: the ranking itself is the chart — a top-N list where membership patterns matter more than precise values, and >~10 rows need splitting into blocks. Two-block ranked table with colour-graded value chips and selective row bands.
arctic_warming.py — Load when: a value profiled across an ordered spatial dimension (latitude, depth, distance), with a per-point range across sources. Dot-line over translucent range bars, x-axis on top, banded regions of interest.
trump_sanctions.py — Load when: annual-count bars where eras or regimes are the comparison frame. bar_v time series on tinted presidential-era highlight_panel bands, inset_tick_labels.
populist_votes.py — Load when: a two-part composition over a long period where both the total and the mix change — the flip is the story. Two-series stacked bars across 40 years, top_legend.
plastic_bottles.py — Load when: composition shares across a few snapshots, and the reader also needs geographic orientation for an obscure location. Stacked 100% bars + orthographic locator-globe inset under the legend.
alcohol_drinkers.py — Load when: the same group breakdown applied across several different denominators (population vs revenue vs consumption) — the mismatch is the point. Three stacked 100% horizontal bars under a shared pictogram legend strip.
language_speed.py — Load when: comparing full distributions per category, not summary statistics — especially when a second panel shows the distributions collapsing together. Two-panel ridgeline densities (plain fill_between), direction_label reading cues, panel_label.
london_roads.py — Load when: one metric over two cyclic dimensions (day × hour) where the pattern lives in the grid, not in any single series. Heatmap with a discrete sampled colour scale and hand-built legend.
elderly_screens.py — Load when: composition totals over time compared between two groups — both the level and the mix matter, so neither lines nor 100% bars suffice. Two stacked-area panels on a shared scale, right_axis.
wework.py — Load when: comparing two entities across several metrics of wildly different scales — per-panel normalisation so every comparison spans its band. Per-metric banded panels with paired bars, per-panel x-scales, value labels inside or beyond the bar tip.
millennial_parents.py — Load when: chronological snapshot lines (as age_gap_chart.py) but specific snapshots must pin to exact colours from a reference. snapshot_palette with per-line colour overrides.
bad_bunny.py — Load when: response shares for a single survey question — ranked bar_h (as bar_chart.py) but with the survey convention of the x-axis on top. bar_h + footnotes.
spending_convergence.py — Load when: two lines converging or crossing, where the meaningful window forces a truncated y-axis that must be signalled, not hidden. Crossing line pair with the broken_axis heartbeat mark beside the right tick column.
gold_rally.py — Load when: comparing returns over a sub-year window — rebased to 100 (as index_chart.py) but with dense daily data and month-resolution ticks. index_marker + month-letter ticks, direct line labels.
nuclear_warheads.py — Load when: ranked totals per entity with an internal status breakdown, plus a forecast for one entity that must read as projection. Stacked barh inventories, top axis + top_legend, dashed forecast box.
european_warming.py — Load when: many same-unit time series where one entity is the story and the rest are context — highlight two lines against a faded backdrop of the others, no legend. A wink title carried by a y-axis-labelling descriptor; direct labels on the two highlighted lines; real Berkeley Earth decadal anomalies.

name	graph-design
description	Economist-style data-visualisation system for matplotlib/seaborn — theme, finalize() title-stack, direct labels, and 20+ chart helpers with a when-to-load index of 39 worked examples. Use whenever creating, restyling or reviewing charts/plots/figures in Python: it owns figure sizing, typography, titles/descriptors/sources, annotation conventions and chart-type selection. Not for non-matplotlib charting (d3, plotly, spreadsheets).
license	MIT
version	0.12.0
author	Daniel Hails <graphs@hails.info>
tags	["matplotlib","seaborn","data-visualisation","charts","economist"]
metadata	{"source":"https://github.com/DJRHails/graphs","package":"djrhails-graphs (PyPI)"}

graph-design

Economist-style data-visualisation system for matplotlib and seaborn.

This repo is the skill (standard agent-skill layout: SKILL.md + references/ + examples/) and the Python package that powers it. Install the library with pip install djrhails-graphs; install the skill by submoduling/vendoring this repo as a skill directory (e.g. skills/graph-design). Import as graphs.

Quick start

A two-series line chart with direct labels, a footnote marker, and a packed footnote-plus-source row — the conventions the library is built for.

import matplotlib.pyplot as plt
import numpy as np

from graphs import finalize, footnotes, label_lines, save_chart, set_theme, subplots

set_theme()

months = np.arange(24)
us = 2.0 + 4.0 * np.exp(-months / 9) + np.random.default_rng(0).normal(0, 0.25, 24)
eu = 1.8 + 4.5 * np.exp(-months / 11) + np.random.default_rng(7).normal(0, 0.30, 24)

fig, ax = subplots("wide")
ax.plot(months, us, label="America")
ax.plot(months, eu, label="Euro area")
label_lines(ax)

finalize(
    ax,
    title="Cooling off",
    descriptor="Headline CPI*, % change on a year earlier, monthly",
)
footnotes(
    fig,
    "*All-items consumer price index",
    source="Sources: [US Bureau of Labor Statistics](https://www.bls.gov/); "
           "[Eurostat](https://ec.europa.eu/eurostat)",
)

save_chart(__file__)  # writes quick.png beside the script

Rules at a glance

A 30-second pre-flight before you ship a chart. Each line links to its full treatment below; the ✗ is the mistake the rule prevents.

The title/descriptor IS the y-axis label — leave ax.set_ylabel(""). Put the measured quantity + units in finalize(descriptor=...), never in an axis label. ✗ ax.set_ylabel("share of attempts"). (For a horizontal Economist-style title above the axis use y_axis_label(ax, ...), not set_ylabel.) Exceptions: a twinx() secondary axis, or a coordinate plot (ROC, scatter) where both axes are dimensions — there, label the axes and pass finalize(..., allow_ylabel=True). finalize() raises on a non-empty y-label (the ast-grep rule catches it too), so this isn't a style nicety you can skip. → Headline conventions
Title states one plain claim, quantified, no wink. ✗ "Outclassing the guardians" → ✓ "Claude Opus 4.8 outscores the specialised guard models"; ✗ "a few categories" → ✓ "9 categories". If the intervals can't back a claim, title the question instead. → Headline conventions
Descriptor names the quantity + axis mapping, one sentence — nothing else. ✗ "…F1, n=543; official protocol, CoT mode, temperature 0.6" → ✓ "DynaBench policy-violation F1 (FAIL = violation)". Model, conditions → footnotes; N → source; empty is correct when an on-chart label carries the metric (name it exactly once).
Never caption statistics machinery. Draw whiskers and ci_fill bands; "95% Wilson CI" / "bootstrap CI band" never appears in figure text. ✗ footnote "Whiskers: 95% Wilson CI on the total rate".
Source = Source: <dataset> (N=…); <script> — dataset named by entity, its computed N attached, generating script last. ✗ a bare crossfire_correlates.py. → Headline conventions
Size with subplots("daily"|"wide"), never figsize=. Fixed widths keep a set consistent; ad-hoc widths make a gallery ragged. → Default conventions
Call finalize(ax, title, descriptor, source) last — it auto-sizes every margin, seats numeric y-labels on the grid, and draws the delta marker. Don't hand-set subplots_adjust before it.
Read the rendered PNG back and check it tells its story at a glance, every element earns its place, and it's the right chart type. → Review
Line plots show uncertainty as a ci_fill band, never error-bar whiskers. A connected series over a continuous/ordinal x (a sweep, a trend, a depth curve) gets a translucent band via ci_fill(ax, x, lo, hi, color=line_col), not per-point errorbar(yerr=…) caps. ✗ ax.errorbar(x, y, yerr=…) for a trend → ✓ ax.plot(x, y, color=c); ci_fill(ax, x, lo, hi, color=c). Whiskers belong on categorical comparisons (bars, dot plots) where there's no line to fill under. → Uncertainty bands

Default visual conventions

Behaviour that's automatic unless you override it:

Charts come in two widths — size with subplots(), never a raw figsize=. Create figures with subplots("daily") (4.6in column, portrait-leaning) or subplots("wide") (7.0in article format) — like a newspaper's column formats, the width is fixed by the medium and only the height is the per-chart choice (height=). Fixed widths keep the type-to-chart ratio consistent across a set; ad-hoc figsize= widths are what make a gallery look ragged. Extra plt.subplots kwargs pass through (ncols=, sharex=, …).
Title marker is the favicon triangle (marker="delta"). The hollow red triangle is drawn inline at the first title line's baseline, sized to the cap height. Pass marker="rule" for the legacy short red rule above the title, or marker="none" to suppress entirely.
Titles and descriptors auto-wrap to the figure width. Pass them as single lines — finalize() measures with the renderer and breaks where this figure needs it (with a widow fix, so no single-word last lines). Never copy a reference chart's line breaks; explicit \n is reserved for semantic breaks (a descriptor's subject / unit split).
Explicit descriptor breaks get a semibold lead. An explicit \n splits the descriptor into a semibold subject lead over regular scope/unit lines; the lead stays semibold even if it wraps. Breaks added by the auto-wrap alone never trigger the styling.
Numeric y labels sit on their gridlines (y_labels="on_grid", the finalize() default): each gridline extends into the label gutter and ends flush with the labels' outer edge; the label rests just above its line (a little breathing room so glyphs don't touch the rule), and the bottom baseline extends into the gutter too — meeting the gridlines rather than stopping at the data edge — whether or not the floor is a tick. Applied only when the axes has visible y gridlines, so categorical axes are untouched; works on either side (e.g. a left latitude axis). Opt out with y_labels="ticks", or call y_labels_on_grid(ax) manually on extra facet panels.
A dark zero centreline is automatic when the y-range straddles 0 (finalize default; zero_rule=True): a strong C_SPINE rule on the zero baseline the data crosses, kept under every data line (so lines cross over it) and running the full gutter width so "0" sits on it. Skipped when the caller already drew their own zero rule (e.g. a bar chart's on-top axhline(0)). Set zero_rule=False for horizontal-value charts whose y-axis is merely a coordinate spanning 0 (a latitude axis through the equator). On a faceted figure call dark_zero_line(ax) per panel — finalize only finishes the primary axes.
Annotations default to 9pt — callout, highlight_label, direction_label, threshold_arrows match direct-label size (the print spec's 7.5pt reads too small at daily-chart scale).
Footnote markers auto-superscript. *, †, ‡, §, **, ††, ‡‡, §§ render as superscripts anywhere they appear in titles, descriptors, source lines, footnote bodies, or legend entry texts (finalize() post-processes every legend on the figure) — write plain text, the renderer handles the typography.
Uncertainty on a line is a filled band, not whiskers. For a connected series over a continuous or ordinal x — a sweep, a trend, a depth/budget curve — draw the confidence interval as a translucent ci_fill(ax, x, lo, hi, color=line_col) band behind the line (step 3 of Build, so it sits under the data), passing the line's own colour so the band reads as its range. Per-point errorbar(yerr=…) caps clutter a trend, fight the eye across the series, and break down once two lines overlap; a band shows trend and range together. Reserve error-bar whiskers for categorical estimates — bars and dot plots, where there is no line to fill beneath and each point is an independent comparison (a binomial proportion's Wilson CI on a bar stays a whisker). ci_fill defaults to the Economist salmon for a single series; pass color= per line on a multi-series chart so each band matches its line.
Frameless legends are default for both smart_legend() and top_legend(). Boxed legends are opt-in.
Source + footnotes use C_SOURCE (#404040, the styleguide's 75% black) — darker than the muted C_LABEL_MUTED grey, so attribution stays legible while reading as metadata.
finalize always auto-layouts every margin. It sizes all four subplots_adjust margins AND the inter-panel wspace/hspace from the renderer — there is no opt-out and, in the common cases, nothing to set by hand. Top/bottom fit the title-stack and the source/footnote band over the measured x-tick labels; left/right are measured from the actual y-axis text (a chart with long left-hung category labels gets a wide left, a plain right-axis chart keeps a tight left + a measured right); wspace/hspace size a grid of panels (wspace from the inter-column y-label width, hspace from the x-tick band + panel_label height — pass finalize(panel_labels=True) for a multi-row faceted layout that adds panel_label headings after finalize). On a non-Agg backend the side margins and spacing fall back to fixed constants. A caller may still fig.subplots_adjust(...) after finalize to override a specific value, but the everyday faceted / wide-left charts no longer need to. Don't restore top= after finalize — it anchors the title to its own auto top, so overriding top detaches the title; raise y_start instead to reserve more headroom.
Multi-note footnotes stack by default. footnotes(stack=None) (the default) renders more than one note — or a single note that can't pack beside the source row — as a term-definition stack: one row per note, source on the bottom row, and the bottom band grows to fit the measured rows, wrapped continuation lines included (no finalize(footnote_lines=) needed on single-row figures; a multi-row grid still reserves up front with it). A single short note keeps the packed note-beside-source row (the Economist age-gap pattern). Force either layout with stack=True / stack=False.
Long footnotes word-wrap automatically. footnotes(wrap=True) is the default — an overflowing row breaks to continuation lines, and the chart shifts up to reserve room. No need to hand-break with \n.
Orphan footnote markers warn. footnotes(check_anchors=True) (default) raises a UserWarning when a note starts with * / † / ‡ / § that isn't found in the title, descriptor, axis labels, legend entries, or any in-chart text. Anchor the marker by adding it after a word in the descriptor (e.g. descriptor="Verified contracts* per TVL bucket") or a legend label (label="Self-attributed*").

Vertical bar charts (plug-and-play)

For the common "one bar per category" layout, the three-helper combo collapses ~30 lines of per-script spine/grid/tick boilerplate:

bars = bar_v(ax, labels, values, log=True)               # spines, grid, ticks
bar_value_labels(ax, bars, fmt="{:,}", skip_zero=True)   # value above each bar
bar_sublabels(ax, bars, [f"n={n}" for n in counts])      # denominator below
finalize(ax, title="…", descriptor="…", source="…")

Core design principles

Rules the helpers were built to enforce. When in doubt, satisfy the most.

Title does the talking — state the finding, not the topic. Subtitle carries units, geography, time range.
Every element earns its place — if removing it doesn't hurt comprehension, remove it.
Put information where the reader's eye is already going — labels next to what they label.
Direct labelling beats legends. (See label_lines.)
Each chart type has its own conventions. Pick the type first, then apply its rules. (See cycle_for.)
Hue communicates kind; saturation communicates importance. Same-kind differentiation uses saturation or opacity, not a new hue.
Brand colour and data colour are separated — C_RED is reserved for emphasis, not as the default fill.
Four categories is a working ceiling for thermometer / bubble / pie / doughnut. The fix is usually a different chart, not more colours.
Don't truncate scales to fit the comparison.
The chart type carries assumptions — lines imply continuity, bars imply discrete categories.
Axis scaling is editorial, not neutral.
Double scales need discipline — align zero lines, match axis colour to its line. Otherwise split or index.
One chart, one baseline. If you can't combine cleanly, split.
Signal non-zero baselines visually — broken_axis() on line / thermometer / scatter. Never on bar/column (use a thermometer).
Annotations are first-class elements — callouts, highlight panels, event markers have their own typography and geometry.
Mobile is a redesign, not a resize.
Negative space is engineered — the paddings and axis ends are deliberate.
Sometimes the right answer is less data — cut or show an average.
The chart is responsible to the reader, not to the data. Misleading, confusing, pointless — three failure modes every chart has to pass.

Headline conventions

Title: one plain claim the chart can defend — "Claude Opus 4.8 outscores the specialised guard models", not "Outclassing the guardians". Quantify what the chart quantifies ("9 categories", "96% of cross-fires*"); star coined terms and define them in a footnote, or rewrite them away; match claim strength to the drawn evidence (weak intervals → title the question). Don't write winks — an existing wink survives only if it decodes on sight into the exact mechanism ("Marking your own homework" for self-review); the replica gallery keeps its Economist winks, research figures don't get new ones.
Descriptor: the measured quantity + axis mapping, one sentence — or empty when an on-chart label already names the metric (name it exactly once). Model ids, protocol knobs and condition tags go to footnotes; sample sizes go to the source line; anything the legend already says is cut. Because the descriptor carries the quantity, leave the matplotlib axis label empty (ax.set_ylabel("")); for a horizontal Economist-style title above the axis use y_axis_label(ax, ...), never a hardcoded ax.set_ylabel("text"). The exception is a coordinate plot (ROC, scatter) or a twinx() secondary axis, where an axis genuinely needs its own label — there, pass finalize(..., allow_ylabel=True) to opt out. (Enforced two ways: finalize() raises ValueError on a non-empty y-label at runtime, and enforcement/rules/no-hardcoded-ylabel.yml flags it at lint time.)
Footnotes: starred definitions first, then conditions. Each coined term gets *term: plain-words definition with a concrete example; anchor the star wherever the reader meets the term (title, descriptor, legend entry, panel label). Conditions + model follow, blank-line separated, with every count computed ("unparseable outputs (N=0) scored wrong"). Never: CI/whisker mechanics, epistemic status tags, project-internal commentary.
Source: Source: <dataset> (N=…); <script> — dataset named by entity with its computed N attached, generating script last, Sources: for several. Markdown links survive into SVG/PDF output.

Read references/headline-conventions.md before writing the strings for a publishable chart — it carries the worked examples, footnote categories, hyperlink rules and a before/after table.

Palette

Style overrides to apply on top:

Chronological categories → light-to-dark tints of one colour, not the cycle. Use snapshot_palette().
"Other" / "Don't know" → C_OTHER (slate grey).
Positive vs. negative → only differentiate by colour for meaningful pairs (imports/exports, gain/loss).

API

Theme, finalisation, layout

Function	Purpose
`set_theme(bg=None, transparent=False)`	Apply theme globally. Call once. White background by default; pass `C_BG_TRANSPARENT` + `transparent=True` for transparent output.
`subplots(format="daily", , height=None, *kwargs)`	`plt.subplots` at a standard chart width — `"daily"` 4.6in / `"wide"` 7.0in; height is the per-chart choice.
`finalize(ax, title, descriptor, source, *, marker="delta", y_labels="on_grid", panel_labels=False, zero_rule=True, …)`	Title stack (auto-wrapped), optional marker, source line, y-axis right, on-grid y labels, and a dark zero centreline when the y-range straddles 0 (`zero_rule`). Auto-sizes ALL margins + inter-panel `wspace`/`hspace` from the renderer (left/right from the y-axis text, spacing from a grid); `panel_labels=True` for multi-row facets that add `panel_label` after. Override a specific value with `subplots_adjust` after if ever needed.
`dark_zero_line(ax)`	Dark `C_SPINE` rule on the zero baseline the data straddles, under the data lines. Auto-applied by `finalize`; call per panel on facets.
`panel_label(ax, label)`	Bold sub-heading + dark rule (faceted charts).
`footnotes(fig, *notes, source=None, wrap=True, stack=None, check_anchors=True)`	Footnote strip + optional source line. `stack=None` (default) auto-picks the layout: several notes — or one that can't pack beside the source — stack one row per note with the source on the bottom row, and the bottom band self-sizes to the measured rows; a single short note packs beside the source. Auto-superscripts `, †, ‡, §, *, ††, ‡‡, §§`. Long rows word-wrap to fit the figure (`wrap=True`, default). Warns when a leading marker has no anchor in the title/descriptor/axis labels/legend entries (axes or figure-level)/in-chart text (`check_anchors=True`).
`y_axis_label(ax, text, *, unit=None)`	Horizontal title above the y-axis; `unit=` renders below in muted colour. Call BEFORE `finalize` — it reserves a band under the descriptor and re-anchors the label to the final axes top, clear of in-axes text poking past the axes edge and (for `side="left"`) above the `panel_label` band.
`x_axis_label(ax, text, *, labelpad=None)`	Project-styled `set_xlabel` (`C_SPINE`, 8.5pt); footnote markers superscripted by `finalize`.
`year_axis(ax, *, abbreviate=True)`	Date x-axis formatter: first year full, subsequent two-digit.
`year_ticks(ax, years, *, inset=True)`	Same convention for numeric year axes: full first/century years, two-digit otherwise, inset ends.
`x_axis_top(ax)`	Move the value axis to the top (horizontal-chart convention); call after `finalize()` when a legend row intervenes.
`save_chart(__file__, *, deck=False)`	Standard save epilogue: `<script>.png` beside the script, tight bbox, 150 dpi, close. `deck=True` also writes `<script>_deck.png`.
`save_deck_variant(fig, path, *, dpi=150)`	Deck variant beside the full chart: strips the tagged headline (marker, title, descriptor, source, footnotes, any `suptitle`), keeps `y_axis_label` blocks / legends / `panel_label` headings; writes `<stem>_deck.png`. Call after saving the full chart.

Chart helpers

Function	Purpose
`cycle_for(chart_type) → list[str]`	Recommended colour order for a chart type.
`snapshot_palette(n, *, accent=None)`	Chronological slate→accent ramp for snapshot lines.
`bar_h(ax, categories, values, *, highlight_max=True)`	Horizontal bars; max in `C_RED` by default.
`bar_v(ax, categories, values, *, highlight_max=True, log=False, headroom=1.25, y_formatter=None)`	Vertical bars; handles spine/grid/tick boilerplate. `log=True` gives symlog + auto decade ticks.
`bar_value_labels(ax, bars, *, fmt="{:,.0f}", formatter=None, fontsize=9)`	Annotate each bar with its value above the bar. Pass `formatter` for currency / mixed units.
`bar_sublabels(ax, bars, labels, *, fontsize=8, offset_pt=4)`	Per-bar secondary text below the baseline (denominators, "n=…"). Auto-pads x-tick labels down to clear the band.
`dumbbell(ax, categories, start, end, *, label_start, label_end)`	Before/after dot-and-line. Defaults red→blue.
`thermometer(ax, categories, values, *, series_labels, dot=True)`	Tick-and-dot ranked categories. Warns above 4 series.
`threshold_lollipop(ax, categories, values, *, threshold=1.0)`	Horizontal lollipop with fixed centre + leader lines.
`bump_chart(ax, ranks, *, highlight, aspect=…, max_rank=…)`	Rank-over-time PCHIP-smoothed lines with white halo at crossings. `max_rank` crops the rank axis so a large backdrop can't compress the story band.
`dot_plot(ax, categories, series, *, size=150)`	Cleveland dot plot; same-value dots in a row merge into pie-split markers. Returns legend handles.
`pie_marker(ax, x, y, wedge_colors, *, size=150)`	One marker split into N equal wedges so overlapping points stay visible.
`scatter_standard(ax, x, y)`	General-trend scatter, 50% opacity, no stroke.
`scatter_highlight(ax, x, y)`	Outlier / labelled scatter, 100% opacity.
`scatter_category(ax, x, y)`	Bubble dot, 50% fill + 0.3px stroke for overlap.
`trend_line(ax, x, y)`	Dashed 1px trend line.
`smoothed_line(ax, x, y, *, color)`	Three-layer scatter + CI band + smoothed line.
`ci_fill(ax, x, lo, hi, *, color=None)`	CI band for a line — the way to show uncertainty on a sweep/trend, not `errorbar` whiskers. Salmon by default; pass the line colour to match.

Annotations

Function	Purpose
`callout(ax, xy, text, *, xytext, arrow=True)`	Pale-fill text callout with optional arrow.
`highlight_panel(ax, x_start, x_end, *, label=None)`	Vertical event-period band.
`highlight_label(ax, xy, text, *, role="primary")`	Single-point label. `"secondary"` = grey/caps/light.
`index_marker(ax, x, *, y=100)`	Red rule + black dot for index charts.
`broken_axis(ax, *, axis="y", side="auto")`	Non-zero-baseline heartbeat mark. Line/scatter/thermometer. `side="auto"` follows the y-label side.
`direction_label(ax, text, xy, *, arrow="↑")`	One-sided directional cue ("↑ Older husband") in axes-fraction coords.
`number_box(ax, xy, n)`	Numbered cross-reference box.
`threshold_arrows(ax, threshold, *, left_text, right_text)`	Directional label pair straddling a threshold.

Labels, axes, legends

Function	Purpose
`label_lines(ax, *, stroke=True)`	Direct labels at line ends with collision avoidance; white halo on by default.
`inset_tick_labels(ax, *, axis="x")`	First tick label `ha="left"`, last `ha="right"`.
`y_labels_on_grid(ax)`	Sit y tick labels on gridlines extended under them (the `finalize()` default; call manually on extra facet panels).
`italicize_labels(ax, labels)`	Italicise specific tick labels in place.
`style_labels(ax, *, italic=(), bold=())`	Per-label italic/bold preserving tick colour.
`color_axis(ax, side, color, *, spine=True, ticks=True)`	Colour a spine + ticks + labels to match a series.
`right_axis(ax)`	Apply right-axis convention to a panel.
`smart_legend(ax)`	Frameless legend in the emptiest corner.
`top_legend(ax_or_fig, handles?, labels?, *, x=0.02)`	Frameless top-anchored legend under the title-stack. `top_legend(ax, ncol=2)` derives handles/labels from the axes. Call BEFORE `finalize` (auto-`y`) and it reserves its own band — no `subplots_adjust`.

Number formatting

Function	Purpose
`format_count(n, *, sig=2)`	Compact count label: 2 s.f. + k/M/B/T unit (`1234 → "1.2k"`, `500 → "500"`).
`scale_axis(ax, *, axis="y", by=1000)`	Divide an axis's tick labels by a shared magnitude (`20,000 → 20`); name the magnitude in the descriptor.
`magnitude_word(by)`	English name for the divisor (`1000 → "thousand"`) — pairs with `scale_axis` for the descriptor unit.

Verification

Function	Purpose
`verify_layout(fig, *, tolerance=0.005)`	Warn when any text artist (tick labels, titles, legends, footnotes) extends past the figure bounds. Catches the class of bug where `savefig(bbox_inches="tight")` silently expands the saved canvas to fit overflow. Auto-called by `footnotes()`.
`finalize(..., allow_ylabel=False)`	Runtime guard: `finalize()` raises `ValueError` when the axes carries a hardcoded non-empty `ax.set_ylabel(...)` (the descriptor should carry the quantity). Pass `allow_ylabel=True` for a coordinate plot (ROC/scatter) or a `twinx()` secondary axis.
`enforcement/rules/no-hardcoded-ylabel.yml`	Lint-time twin of the above: ast-grep rule (`severity: error`) that flags a hardcoded non-empty `ax.set_ylabel(...)`, with a `# ast-grep-ignore: no-hardcoded-ylabel` escape for twin/coordinate axes. See `enforcement/README.md`.

Workflow

Build

set_theme().
(If not the default cycle) ax.set_prop_cycle(color=cycle_for("…")).
Plot — ci_fill and highlight_panel first so they sit behind the data.
Annotate — callout, highlight_label, index_marker, broken_axis.
Direct label — label_lines(ax) over smart_legend(ax) for line charts.
finalize(ax, title, descriptor, source) last — auto-sizes margins.
Faceted: finalize() on the first axes with title_x pinned and y_start≈0.075 (reserves the title-stack room) — it auto-sizes the inter-panel wspace/hspace from the panels' y-labels and x-tick band, so you usually set NO subplots_adjust. Pass panel_labels=True for a multi-row layout, then call panel_label() per axes (they anchor to the final positions). Only reach for a post-finalize subplots_adjust if a bespoke layout (hand-placed gutter artists) still needs one.

Review

For each rendered figure, answer three questions before moving on:

Does the chart tell its intended story at a glance? If the reader needs body text to know what to look at, the title is doing too little. Re-read the title — does it state the finding, or only the topic? "Claude Opus 4.8 outscores the specialised guard models" tells you what to see; "GDP growth, 2010–2024" doesn't. Iterate until the title carries the story alone.
Is every element earning its place? Bars at the rounding-noise threshold, legends that duplicate direct labels, gridlines that don't anchor anything, sub-labels nobody will read, decimal places past the data's precision — cut them. Less data often makes the point sharper. A six-row table reduced to its three meaningful rows is a better chart, not a smaller one. Apply the "Every element earns its place" core principle ruthlessly.
Is this the right chart type? Switching costs nothing — the script is ten lines.
- Six-bucket bar chart trying to show "climbs then dips"? → line chart.
- Two-series grouped bars comparing the same metric at two points in time? → dumbbell.
- Scatter with 200 points and a single highlight? → histogram + callout (or scatter_standard + scatter_highlight).
- Pie / doughnut with more than four slices? → bar_h ranked.
- "How does X depend on Y" with strong trend? → smoothed_line, not raw scatter.
- Long-form ranking change over time? → bump_chart, not stacked bars.
The chart-type table in cycle_for() and the per-type rules in the core design principles are the reference.

Examples

bar_chart.py — Load when: one value per category, ranked, and the biggest (or one chosen) value is the point. Minimal bar_h demo with the default highlight_max=True.
dumbbell_chart.py — Load when: each category has a before and an after value and the change is the message. dumbbell plus a right-aligned top_legend fed from ax._dumbbell_handles.
faceted_chart.py — Load when: several series share an axis but overlap so badly that one panel buries the story — split into small multiples. The faceting workflow: right_axis + ci_fill per axes, finalize(y_start=0.075) (auto-sizes the inter-panel wspace from the per-panel labels — no manual subplots_adjust), then panel_label per axes (anchored to the final positions).
faceted_top_legend.py — Load when: faceted panels share a colour key that must sit above the row of panels, hands-off. top_legend is called BEFORE finalize; finalize measures it, reserves a band between the two-line descriptor and the panels, and re-anchors it to the final axes top — no subplots_adjust, no hand-tuned y= / y_start padding.
scatter_chart.py — Load when: a relationship cloud where a few named outliers carry the story. scatter_standard + scatter_highlight two-layer treatment, trend_line, callout on the outliers.
thermometer_chart.py — Load when: 2–4 series compared across the same ranked categories, where grouped bars would clutter. thermometer(dot=False) three-series variant, x-axis on top, frameless top_legend.
index_chart.py — Load when: series of different magnitudes must be compared as growth since a common moment, with an event window to flag. index_marker + highlight_panel band + secondary highlight_label + broken_axis + label_lines.
line_chart.py — Load when: noisy point estimates over time with uncertainty — the reader should see trend and range, not individual points. smoothed_line (scatter + CI band + trend), custom _LineBandHandler legend, year_axis(set_locator=False).
bump_chart.py — Load when: the story is rank changes over time in a large field, with only a few entities highlighted against a faded backdrop. bump_chart with highlight=, colors=, right_labels=True, x_labels_top=True, max_rank cropping; real data via _data.py.
corbyn.py — Load when: ranked bars where one dominant value tempts you to truncate the scale (don't — show it whole), or specific row labels need bold/italic emphasis. bar_h + style_labels(italic=, bold=).
dogs.py — Load when: two different units genuinely must share one panel and the twin axes have to be honest — both ranges sized so 1% of the midpoint spans equal distance. color_axis(spine=False, ticks=False), manual series titles via render_text_with_superscripts, a two-note footnotes(source=) definition stack.
brexit.py — Load when: irregular poll/survey readings over time — connecting the dots would look erratic, so show points plus a smoothed trend on a truncated axis. scatter_standard + Savitzky-Golay smoothing, manual year ticks + year_axis(set_locator=False), inset_tick_labels, broken_axis(side="right").
us_trade.py — Load when: two related time series with incompatible units or baselines tempt you toward a double axis — split into stacked sharex panels instead. panel_label, right_axis, inset_tick_labels, source via footnotes(source=).
pensions.py — Load when: a scatter where labelled points need emphasis but a second colour would falsely read as a category — use opacity within one hue. Same-hue dots with opacity-for-emphasis, italic average label via FontProperties, y_axis_label(unit=).
eu_balance.py — Load when: a stacked composition has too many members to colour — keep the named few plus an "Others" bucket, and show two measures as side-by-side panels. Positive+negative stacked bars per year, shared top_legend, right_axis.
affordability_chart.py — Load when: categories are measured against a meaningful threshold (above = fine, below = not) and span a wide value range. threshold_lollipop(threshold=1.0) on a log x-axis + threshold_arrows straddling the threshold + two-note footnotes.
age_gap_chart.py — Load when: the same curve measured at several historical moments, and the story is how its shape shifted — earlier years fade, the present pops. Chronological lines via snapshot_palette(4), in-chart series labels, broken_axis(side="right"), right-anchored footnotes(y=, x=).
australia_heat.py — Load when: a time series of signed deviations around zero where direction is the story. Diverging annual bar_v bars, positive red / negative blue on a zero baseline.
malaria.py — Load when: observed history fans out into multiple scenario paths and the reader must not confuse projection with fact. Solid history line splitting into three dashed forecasts inside a highlight_panel FORECAST band.
co2_emissions.py — Load when: each category has two multiplicative dimensions (rate × size = total) and the totals matter as much as the rates. Variable-width Mekko bars with on-bar totals + dashed global-average rule.
christianity.py — Load when: many categories each have exactly two time points and the trajectories — who's rising, who's flat — are the message. Two-point slope chart with endpoint dots, stacked value labels, one accent line.
graduate_pay.py — Load when: a dense dot cloud with a strong nonlinear trend, where the x variable reads better reversed ("more selective →"). scatter_standard cloud, reversed x-axis, solid trend curve.
generational_politics.py — Load when: cohort or panel-survey waves — several groups tracked over irregular waves, with gaps in coverage and an overall average. PCHIP-smoothed lines, gap segments in lighter tint, dotted average, end-of-line labels.
uber_tips.py — Load when: paired group values per category, expressed relative to an explicit reference point that needs marking. Vertical dumbbell/lollipop pairs with a ringed reference marker and leader line.
us_refugees.py — Load when: an actual annual quantity tracked against an administrative limit — the gap between allowed and delivered is the story. Neutral grey bars + stepped red policy-cap line (real published data).
polluted_cities.py — Load when: the ranking itself is the chart — a top-N list where membership patterns matter more than precise values, and >~10 rows need splitting into blocks. Two-block ranked table with colour-graded value chips and selective row bands.
arctic_warming.py — Load when: a value profiled across an ordered spatial dimension (latitude, depth, distance), with a per-point range across sources. Dot-line over translucent range bars, x-axis on top, banded regions of interest.
trump_sanctions.py — Load when: annual-count bars where eras or regimes are the comparison frame. bar_v time series on tinted presidential-era highlight_panel bands, inset_tick_labels.
populist_votes.py — Load when: a two-part composition over a long period where both the total and the mix change — the flip is the story. Two-series stacked bars across 40 years, top_legend.
plastic_bottles.py — Load when: composition shares across a few snapshots, and the reader also needs geographic orientation for an obscure location. Stacked 100% bars + orthographic locator-globe inset under the legend.
alcohol_drinkers.py — Load when: the same group breakdown applied across several different denominators (population vs revenue vs consumption) — the mismatch is the point. Three stacked 100% horizontal bars under a shared pictogram legend strip.
language_speed.py — Load when: comparing full distributions per category, not summary statistics — especially when a second panel shows the distributions collapsing together. Two-panel ridgeline densities (plain fill_between), direction_label reading cues, panel_label.
london_roads.py — Load when: one metric over two cyclic dimensions (day × hour) where the pattern lives in the grid, not in any single series. Heatmap with a discrete sampled colour scale and hand-built legend.
elderly_screens.py — Load when: composition totals over time compared between two groups — both the level and the mix matter, so neither lines nor 100% bars suffice. Two stacked-area panels on a shared scale, right_axis.
wework.py — Load when: comparing two entities across several metrics of wildly different scales — per-panel normalisation so every comparison spans its band. Per-metric banded panels with paired bars, per-panel x-scales, value labels inside or beyond the bar tip.
millennial_parents.py — Load when: chronological snapshot lines (as age_gap_chart.py) but specific snapshots must pin to exact colours from a reference. snapshot_palette with per-line colour overrides.
bad_bunny.py — Load when: response shares for a single survey question — ranked bar_h (as bar_chart.py) but with the survey convention of the x-axis on top. bar_h + footnotes.
spending_convergence.py — Load when: two lines converging or crossing, where the meaningful window forces a truncated y-axis that must be signalled, not hidden. Crossing line pair with the broken_axis heartbeat mark beside the right tick column.
gold_rally.py — Load when: comparing returns over a sub-year window — rebased to 100 (as index_chart.py) but with dense daily data and month-resolution ticks. index_marker + month-letter ticks, direct line labels.
nuclear_warheads.py — Load when: ranked totals per entity with an internal status breakdown, plus a forecast for one entity that must read as projection. Stacked barh inventories, top axis + top_legend, dashed forecast box.
european_warming.py — Load when: many same-unit time series where one entity is the story and the rest are context — highlight two lines against a faded backdrop of the others, no legend. A wink title carried by a y-axis-labelling descriptor; direct labels on the two highlighted lines; real Berkeley Earth decadal anomalies.