Appendix C: Python Plotting & Chemical Structure Visualization

One-sentence takeaway. Python’s visualization ecosystem spans general-purpose plotting and domain-specific 3D chemical viewing; choose tools based on interactivity, dimensionality (2D/3D), and data modality.

General plotting libraries (Python)¶

Use this quick catalog to select a plotting stack based on needs such as interactivity, grammar-of-graphics, or 3D/volumetric data.

• Matplotlib: Foundational 2D plotting library for static, animated, and interactive figures; fine control for publication formatting; base for many higher-level APIs.
• Seaborn: High-level statistical visualization on top of Matplotlib with consistent themes and concise APIs for distributions, regression, and categorical plots.
• Plotly (Plotly.py): Interactive 2D/3D graphing in notebooks and the web (scatter, contour, surface, maps); exportable to static images.
• Bokeh: Interactive browser-based plotting with a Python server for streaming data and linked brushing.
• Altair: Declarative “grammar of graphics” backed by Vega-Lite; great for tidy (long-form) data with automatic interactivity.
• plotnine: A Python implementation of ggplot2’s grammar of graphics; static output with concise layered syntax.
• Pandas plotting API: DataFrame.plot(...) convenience wrappers over Matplotlib for quick exploratory analysis.
• pygal: Lightweight SVG charts; ideal for static sites where crisp vector output is preferred.
• missingno: Visual diagnostics for missing-data patterns (nullity matrix/heatmap/dendrogram).
• Leather: Minimal charting that favors correctness and SVG output over exhaustive features.
• Mayavi: 3D scientific visualization (VTK-based) for isosurfaces, volume rendering, and vector fields.
• NetworkX: Graph creation/analysis with 2D/3D drawing (via Matplotlib, Graphviz, or VTK backends).
• python-igraph: High-performance network analysis with robust layouts and scalable plotting.
• pymatviz: “A toolkit for visualizations in materials informatics.”

Pick-by-task (plotting)¶

Chemical & crystallographic visualization (Python)¶

Tools below span high-quality rendering, interactive model building, and crystallographic data workflows.

• Beautiful Atoms: Python control over Blender for high-quality static/animated renders of atomistic structures.
• PyMOL: Mature, scriptable 3D molecular viewer (ball-and-stick, ribbons, surfaces) suitable for publication figures.
• Jmol/JSmol: Scriptable Java/Web viewer (via JSmol) for interactive 3D structures; embeddable in teaching pages.
• NGLView: Jupyter widget for interactive 3D molecular visualization; integrates with MDAnalysis and Py3Dmol.
• 3Dmol.js: WebGL-based interactive 3D viewer embeddable in notebooks and web pages; supports various molecular formats.
• ASE: Atomic Simulation Environment, a set of tools for setting up, running, and analyzing atomistic simulations.
• Ovito: Open-source software for visualizing and analyzing atomistic simulation data.
• MatterViz: “A toolkit for building interactive web UIs for materials science.”

Pick-by-task (chemical/crystallographic)¶

References & further reading¶

Plotting¶

• The 7 most popular ways to plot data in Python: https://opensource.com/article/20/4/plot-data-python
• A curated list of awesome data visualization libraries and tools: https://github.com/hal9ai/awesome-dataviz
• Plotting in Python: Comparing the Options: https://anvil.works/blog/plotting-in-python
• Python Graph Gallery: https://python-graph-gallery.com/

Chemical & crystallographic visualization¶

• What are the freely available crystal-structure visualization ...: https://mattermodeling.stackexchange.com/questions/467/what-are-the-freely-available-crystal-structure-visualization-softwares
• Python for Crystallographic Computing: https://www.numberanalytics.com/blog/crystallographic-computing-python