Matplotlib vs ggvis

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

Matplotlib is a Photos & Graphics solution with tags like plotting, graphs, charts, visualization, python.

It boasts features such as 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 pros including 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.

On the other hand, ggvis is a Data Visualization product tagged with r, ggplot2, interactive, data-visualization, graphics, web-browser.

Its standout features include 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 it shines with pros like 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.

Feature	Matplotlib	ggvis
Overall Score	N/A	N/A
Primary Category	Photos & Graphics	Data Visualization

Product Overview

Matplotlib

Description: 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.

ggvis

Description: 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.

Key Features Comparison

Matplotlib Features

• 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

ggvis Features

• 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

Pros & Cons Analysis

Matplotlib

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)

ggvis

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

Pricing Comparison

Matplotlib

• Open Source

ggvis

• Open Source