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Matplotlib

293
206
+ 1
5
Plotly.js

275
514
+ 1
68
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Matplotlib vs Plotly: What are the differences?

Developers describe Matplotlib as "A plotting library for the Python programming language". It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. On the other hand, Plotly is detailed as "The Web's fastest growing charting libraries". Plotly.js is the only open-source JavaScript library for data visualization in the sciences and engineering. Open-source interfaces to Plotly.js are available for Python, R, MATLAB, and React.

Matplotlib and Plotly can be primarily classified as "Charting Libraries" tools.

Plotly is an open source tool with 10.6K GitHub stars and 1.23K GitHub forks. Here's a link to Plotly's open source repository on GitHub.

Wellzesta, Algo Edge Technologies, and ADEXT are some of the popular companies that use Plotly, whereas Matplotlib is used by Opportunity Network, King's Digital Lab, and Quezx.com. Plotly has a broader approval, being mentioned in 15 company stacks & 67 developers stacks; compared to Matplotlib, which is listed in 10 company stacks and 19 developer stacks.

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Pros of Matplotlib
Pros of Plotly.js
  • 5
    The standard Swiss Army Knife of plotting
  • 16
    Bindings to popular languages like Python, Node, R, etc
  • 10
    Integrated zoom and filter-out tools in charts and maps
  • 9
    Great support for complex and multiple axes
  • 8
    Powerful out-of-the-box featureset
  • 6
    Beautiful visualizations
  • 4
    Active user base
  • 3
    Webgl chart types are extremely performant
  • 3
    Impressive support for webgl 3D charts
  • 3
    Charts are easy to share with a cloud account
  • 2
    Interactive charts
  • 2
    Publication quality image export
  • 2
    Easy to use online editor for creating plotly.js charts

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Cons of Matplotlib
Cons of Plotly.js
  • 4
    Lots of code
  • 15
    Terrible document

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- No public GitHub repository available -

What is Matplotlib?

It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

What is Plotly.js?

It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.

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What companies use Matplotlib?
What companies use Plotly.js?
See which teams inside your own company are using Matplotlib or Plotly.js.
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What tools integrate with Matplotlib?
What tools integrate with Plotly.js?

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What are some alternatives to Matplotlib and Plotly.js?
Tableau
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
MATLAB
Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
Bokeh
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.
R Language
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
ggplot2
It is a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers.
See all alternatives