Shared Axes & Outer Labels

When a subplot grid shares an axis, drawing tick labels and axis labels on every panel is redundant — the interior labels just repeat the shared scale. publiplots handles this the way seaborn’s FacetGrid does: pass sharex / sharey to pp.subplots() and the interior labels are hidden automatically, leaving labels only on the bottom row (x) and the left column (y).

This is the publiplots equivalent of matplotlib’s label_outer(). Because publiplots builds its grid with fig.add_axes (no SubplotSpec), the standard ax.label_outer() is unavailable — the outer edge is computed from publiplots’ own axes matrix instead, and exposed through two surfaces:

1. ``pp.subplots(…, label_outer=True)`` — the default. Hiding is applied automatically when sharex / sharey is active.

2. ``pp.label_outer(axes, *, sharex, sharey)`` — a standalone helper mirroring ax.label_outer(). Use it to re-apply outer-only labeling after late set_xlabel / set_ylabel calls, or on a grid built elsewhere.

A welcome side effect: hidden interior labels free their reserved space, so the auto-layout engine tightens the canvas — the panels pack closer together without changing their declared millimetre sizes.

import publiplots as pp
import pandas as pd
import numpy as np

# Shared fixture --------------------------------------------------------------

rng = np.random.default_rng(42)
_df = pd.DataFrame({
    "x": rng.normal(size=320),
    "y": rng.normal(size=320),
    "group": np.tile(["Control", "Low", "High"], 320 // 3 + 1)[:320],
    "panel": np.repeat(["A", "B", "C", "D", "E", "F"], 320 // 6 + 1)[:320],
})

1. The default: shared axes hide interior labels

With sharex=True, sharey=True (and the default label_outer=True), a 2x3 grid keeps x labels only on the bottom row and y labels only on the left column. The interior panels are clean, and the grid tightens to reclaim the space the hidden labels would have taken.

fig, axes = pp.subplots(2, 3, axes_size=(40, 30), sharex=True, sharey=True)
for (r, c), panel in zip(
    [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)], "ABCDEF"
):
    pp.scatterplot(
        data=_df[_df["panel"] == panel], x="x", y="y",
        hue="group", palette="pastel",
        title=f"Panel {panel}", ax=axes[r, c],
    )
pp.show()

2. Opt out with label_outer="all"

Pass label_outer="all" (or False) to draw every label on every panel — the pre-existing behaviour. Compare the duplicated x / y labels and tick labels here against the clean grid above.

fig, axes = pp.subplots(
    2, 3, axes_size=(40, 30), sharex=True, sharey=True, label_outer="all",
)
for (r, c), panel in zip(
    [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)], "ABCDEF"
):
    pp.scatterplot(
        data=_df[_df["panel"] == panel], x="x", y="y",
        hue="group", palette="pastel",
        title=f"Panel {panel}", ax=axes[r, c],
    )
pp.show()

3. Share one axis only

sharex and sharey are independent. Sharing only the x-axis (sharex="col") hides x labels on the non-bottom rows but leaves every y label in place — each column still reads its own vertical scale. Use sharey="row" for the mirror case.

fig, axes = pp.subplots(2, 3, axes_size=(40, 30), sharex="col")
for (r, c), panel in zip(
    [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)], "ABCDEF"
):
    pp.scatterplot(
        data=_df[_df["panel"] == panel], x="x", y="y",
        hue="group", palette="pastel",
        title=f"Panel {panel}", ax=axes[r, c],
    )
pp.show()

4. The standalone pp.label_outer helper

pp.subplots applies outer-only labeling at construction. If you set axis labels after that — or build the grid without sharing and decide later — call pp.label_outer(axes, sharex=..., sharey=...) to (re)apply it. It operates in place, just like ax.label_outer().

fig, axes = pp.subplots(2, 2, axes_size=(45, 32), label_outer="all")
for (r, c), panel in zip([(0, 0), (0, 1), (1, 0), (1, 1)], "ABCD"):
    pp.scatterplot(
        data=_df[_df["panel"] == panel], x="x", y="y",
        hue="group", palette="pastel",
        title=f"Panel {panel}", ax=axes[r, c],
    )
    axes[r, c].set_xlabel("x position")
    axes[r, c].set_ylabel("y position")

# Hide interior labels post-hoc — keeps x on the bottom row, y on the left.
pp.label_outer(axes, sharex=True, sharey=True)
pp.show()

5. Outer labels + a shared legend band

Outer-only labeling composes with the legend bands from the legend placement gallery. Here a shared side="bottom" legend collects the per-panel entries into a single band below a grid whose interior labels are hidden — the bottom row keeps its x label, and the band tucks in below it without fighting for space.

fig, axes = pp.subplots(2, 3, axes_size=(40, 28), sharex=True, sharey=True)
pp.legend(side="bottom")
for (r, c), panel in zip(
    [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)], "ABCDEF"
):
    pp.scatterplot(
        data=_df[_df["panel"] == panel], x="x", y="y",
        hue="group", palette="pastel",
        title=f"Panel {panel}", ax=axes[r, c],
    )
pp.show()