Data viz

Overview of Data Visualization Tools (R, Python, and Linux)

Data visualization is essential for exploratory analysis, model diagnostics, and communication of results. This section provides a quick overview of common visualization libraries used in scientific computing, organized by programming environment.


Python

Python offers a wide range of libraries for both static and interactive plotting:

  • Matplotlib: The foundational 2D plotting library for Python. Offers granular control and supports high-quality export for publication.
  • Seaborn: Built on top of Matplotlib, Seaborn simplifies statistical plotting with intuitive syntax and attractive defaults.
  • Plotly: Enables interactive plots in the browser. Supports scatter plots, choropleths, 3D plots, and more.
  • Altair: A declarative library based on Vega-Lite. Best suited for tidy datasets and exploratory visualization.
  • Bokeh: Ideal for building interactive visualizations and dashboards. Integrates well with web applications.
  • PyVista / Mayavi: Used for advanced 3D and volumetric visualizations, often in engineering and geoscience contexts.

R

R is known for its expressive plotting ecosystem and is particularly well suited for statistical graphics:


Linux & Command-Line Tools

Command-line tools are efficient for automation and quick diagnostics, especially in headless or remote environments:

  • gnuplot: A terminal-based plotting tool that supports 2D/3D plotting. Scriptable and highly customizable.
  • Graphviz: Visualizes graphs from .dot files. Excellent for network diagrams, flowcharts, and dependency trees.
  • ImageMagick: Not a plotting tool per se, but widely used for converting, resizing, and compositing images (e.g., building figures programmatically).


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