ggvis vs Matplotlib

Struggling to choose between ggvis and Matplotlib? Both products offer unique advantages, making it a tough decision.

ggvis is a Data Visualization solution with tags like r, ggplot2, interactive, data-visualization, graphics, web-browser.

It boasts features such as Grammar of Graphics-based visualization using the ggplot2 API, Interactivity through linking graphical elements to data, Built on top of Shiny for reactive programming, Can embed plots in R Markdown documents and Shiny apps, Supports faceting, zooming, panning, etc., Exporting plots to SVG and PNG format and pros including Leverages ggplot2 syntax for easy plotting, Interactivity enables exploration of data, Tight integration with Shiny apps, Can create standalone visualizations to embed in web pages.

On the other hand, Matplotlib is a Photos & Graphics product tagged with plotting, graphs, charts, visualization, python.

Its standout features include 2D plotting, Publication quality output, Support for many plot types (line, bar, scatter, histogram etc), Extensive customization options, IPython/Jupyter notebook integration, Animations and interactivity, LaTeX support for mathematical typesetting, and it shines with pros like Mature and feature-rich, Large user community and extensive documentation, Highly customizable, Integrates well with NumPy, Pandas and SciPy, Output can be saved to many file formats.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

ggvis

ggvis

ggvis is an R package for creating interactive data visualizations and graphics in a web browser. It builds on the popular ggplot2 package but allows users to add interactivity, make visualizations reusable, and embed them in web pages.

Categories:
Data Visualization Interactive Graphics
r ggplot2 interactive data-visualization graphics web-browser

Ggvis Features

  1. Grammar of Graphics-based visualization using the ggplot2 API
  2. Interactivity through linking graphical elements to data
  3. Built on top of Shiny for reactive programming
  4. Can embed plots in R Markdown documents and Shiny apps
  5. Supports faceting, zooming, panning, etc.
  6. Exporting plots to SVG and PNG format

Pricing

  • Open Source

Pros

Leverages ggplot2 syntax for easy plotting

Interactivity enables exploration of data

Tight integration with Shiny apps

Can create standalone visualizations to embed in web pages

Cons

Limited adoption compared to static ggplot2

Interactivity requires knowledge of reactivity in Shiny

Less customizable than D3.js for web-based graphics

Matplotlib

Matplotlib

Matplotlib is a comprehensive 2D plotting library for Python that allows users to create a wide variety of publication-quality graphs, charts, and visualizations. It integrates well with NumPy and Pandas data structures.

Categories:
Photos & Graphics Data Visualization
plotting graphs charts visualization python

Matplotlib Features

  1. 2D plotting
  2. Publication quality output
  3. Support for many plot types (line, bar, scatter, histogram etc)
  4. Extensive customization options
  5. IPython/Jupyter notebook integration
  6. Animations and interactivity
  7. LaTeX support for mathematical typesetting

Pricing

  • Open Source

Pros

Mature and feature-rich

Large user community and extensive documentation

Highly customizable

Integrates well with NumPy, Pandas and SciPy

Output can be saved to many file formats

Cons

Steep learning curve

Plotting code can be verbose

3D plotting support is limited

Cannot do web visualization (unlike Bokeh or Plotly)