Matplotlib ion(): Interactive Mode, pause(), and Live Plot Updates
Use Matplotlib ion(), ioff(), isinteractive(), and pause() correctly for interactive shells, live updates, and non-blocking plot workflows.
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Use Matplotlib ion(), ioff(), isinteractive(), and pause() correctly for interactive shells, live updates, and non-blocking plot workflows.
Use Matplotlib savefig to export PNG, PDF, and SVG files with correct dpi, bbox_inches, transparent backgrounds, and predictable paths.
Use Matplotlib errorbar() with yerr, xerr, asymmetric uncertainty, capsize, log scales, and readable error-bar styling.
Use Matplotlib barh() to create readable horizontal charts, sort categories, add labels and error bars, and build stacked comparisons.
Draw Matplotlib vertical markers with axvline(), data-space segments with vlines(), and ordinary line data with plot().
Use NumPy hstack() to join vectors and matrices horizontally, understand one- and two-dimensional shape rules, and avoid mismatched layouts.
Use NumPy vstack() to combine arrays by rows, understand one-dimensional promotion, validate shapes, and choose concatenate or stack when the operation differs.
Repeat NumPy array patterns with tile(), understand reps and shape promotion, compare repeat(), and avoid unnecessary copies for arithmetic.
Use NumPy squeeze() to remove axes of length one, select an axis deliberately, understand shape changes, and avoid dimension errors.
Create Matplotlib tables with cellText, row and column labels, widths, colors, font size, and readable layout control.