matplotlib-scientific-plotting

# matplotlib ## Overview Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. It provides both a MATLAB-style pyplot interface and an object-oriented API for full control over figures, axes, and artists. Essential for generating publication-quality scientific figures. ## When to Use - Creating publication-quality plots with precise control over every element (fonts, ticks, colors, spacing) - Building multi-panel figures with complex subplot layouts for papers - Generating standard scientific plot types: line, scatter, bar, histogram, heatmap, box, violin, contour - Exporting figures to vector formats (PDF, SVG) for journal submission - Creating 3D surface, scatter, or wireframe plots - Customizing colormaps and color schemes for accessibility (colorblind-friendly) - Integrating plots with NumPy arrays and pandas DataFrames - For quick statistical visualizations (distributions, regressions), use `seaborn` instead - For interactive/web-based plots with hover and zoom, use `plotly` instead ## Prerequisites - **Python packages**: `matplotlib`, `numpy` - **Optional**: `pandas` (for DataFrame plotting), `seaborn` (for style presets) - **Environment**: Works in scripts, Jupyter notebooks (`%matplotlib inline`), and GUI apps ```bash pip install matplotlib numpy ``` ## Quick Start ```python import matplotlib.pyplot as plt import numpy as np # Publication-ready figure template: set size, plot, label, save as PDF fig, ...