matplotlib

Use plt.subplots(nrows, ncols) to create a figure with a grid of axes, then index into the returned axes array to plot on each one. For irregular layouts, use plt.subplot_mosaic or GridSpec to span rows and columns.

Call plt.savefig with dpi=300 and bbox_inches='tight' for publication-quality PNG output. For scalable vector output suitable for journals, save to PDF or SVG format instead.

Use the object-oriented interface (fig, ax = plt.subplots()) for production code and multi-panel figures because it gives explicit control over figures and axes. Reserve the pyplot state-machine interface for quick interactive exploration.

Overlapping labels happen when matplotlib's default spacing cannot fit titles, ticks, and legends. Fix it by creating the figure with constrained_layout=True, calling tight_layout(), or saving with bbox_inches='tight'.

Use perceptually uniform colormaps like viridis, plasma, or cividis for sequential data, and diverging colormaps like coolwarm for data centered on a meaningful midpoint. Avoid jet because it is not perceptually uniform and misleads interpretation.

Use seaborn for quick statistical plots with sensible defaults and plotly for interactive web-based visualizations. Matplotlib is the right choice when you need fine-grained control over every plot element or custom plot types.