Matplotlib Python

Matplotlib figure construction

Use the explicit object interface for reusable code:

Figure -> one or more Axes -> Axis objects and data/annotation Artists

pyplot may create the Figure and Axes, but pass ax through helpers rather than relying on hidden current-figure state.

Workflow

1. Define the perceptual question, audience, output dimensions, medium, units, accessibility needs, and whether axes are shared.
2. Inspect data shape, missing values, category order, datetime timezone, uncertainty, and scale. Do not let implicit string categories or date parsing silently determine order.
3. Create fig, ax = plt.subplots(...); use Axes methods for marks, labels, limits, scales, annotations, and legends. Return Figure/Axes or save the artifact; do not bury global display side effects inside helpers.
4. Use data coordinates for values, axes coordinates for annotations tied to the panel, and figure coordinates only for figure-level layout.
5. Set layout and export deliberately. For automation, select a noninteractive backend before importing pyplot, save with explicit size/DPI/format, and close figures created in loops or tests.
6. Verify semantics and rendering: series count, data values, labels, limits, scale, legend, accessible colors, output dimensions, and nonempty image.

Invariants

• A Figure is the complete canvas; an Axes is one plotting region; an Axis owns ticks and scale; almost every visible item is an Artist.
• Do not mix stateful current-Axes calls with explicit Axes ownership in a reusable function.
• Do not truncate a quantitative bar baseline without explicit disclosure and a defensible reason.
• Use one shared normalization for color values that must be comparable across panels.
• Use Seaborn only as a statistical plotting layer when its aggregation and uncertainty semantics are intended; verify the resulting Matplotlib objects.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(7, 4), layout="constrained")
ax.plot(months, values, marker="o", label="Observed")
ax.set(title="Monthly throughput", xlabel="Month", ylabel="Orders")
ax.legend()
fig.savefig("throughput.svg", metadata={"Title": "Monthly throughput"})
plt.close(fig)

Read the object and coordinate model, chart and scale decisions, and render verification.