Matplotlib - Data Visualization

The most widely used library for 2D (and basic 3D) plotting. It provides full control over every element of a figure, from line styles to axis spines.

When to Use

• Creating publication-quality 2D plots (Line, Scatter, Bar, Hist)
• Visualizing scientific data (Heatmaps, Contours, Vector fields)
• Generating complex multi-panel figures
• Fine-tuning plots for papers/reports (LaTeX support)
• Building custom visualization tools and dashboards
• Plotting data directly from NumPy arrays or Pandas DataFrames

Reference Documentation

Official docs: https://matplotlib.org/stable/index.html
Gallery: https://matplotlib.org/stable/gallery/index.html (Essential for finding examples)
Search patterns: plt.subplots, ax.set_title, ax.legend, plt.savefig, matplotlib.colors