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  1. Stackups
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  3. UI Components
  4. Charting Libraries
  5. Matplotlib vs Plotly

Matplotlib vs Plotly

OverviewComparisonAlternatives

Overview

Plotly.js
Plotly.js
Stacks399
Followers694
Votes69
GitHub Stars17.9K
Forks1.9K
Matplotlib
Matplotlib
Stacks1.6K
Followers336
Votes11

Matplotlib vs Plotly: What are the differences?

Introduction

Matplotlib and Plotly are both popular libraries used for data visualization in Python. While they serve the same purpose, there are several key differences between the two. In this article, we will explore these differences and compare the features and functionalities of Matplotlib and Plotly.

  1. Plotting Style and Interactivity: Matplotlib is a static library that produces basic visualizations with a simple syntax. It uses a simple procedural approach to create static images of plots. On the other hand, Plotly is an interactive library that supports highly interactive and dynamic visualizations with a more complex syntax. It allows users to create animated and interactive plots that can be embedded in websites and shared online.

  2. Backend and Rendering: Matplotlib uses a backend system that determines how the plots are rendered. It supports various backends including the default "agg" backend for non-interactive rendering and "TkAgg" backend for interactive rendering in GUI environments. Plotly, on the other hand, uses its own WebGL-based graphics engine that allows for smoother and faster rendering of interactive plots in web browsers.

  3. Ease of Use: Matplotlib is known for its simplicity and ease of use. It provides a simple and intuitive API for creating basic plots quickly. However, creating complex and interactive plots may require more coding and customization. On the other hand, Plotly provides a higher-level API that makes it easier to create complex plots with less code. It also offers a wide range of pre-built charts and templates that can be customized with ease.

  4. Online Collaboration and Sharing: Plotly has a strong focus on online collaboration and sharing. It provides an online platform called plotly.py where users can create, edit, and share their plots with others. This platform also supports version control, collaboration, and sharing of plots through social media or embedded code. Matplotlib, on the other hand, lacks these online collaboration features and is mainly used for offline plotting and visualization.

  5. Support for Web Technologies: Plotly is built on web technologies such as HTML, CSS, and JavaScript, which makes it more suited for web-based applications and dashboards. It supports interactive features such as zooming, panning, and hover tooltips, which are not readily available in Matplotlib. Matplotlib, on the other hand, is better suited for generating static images for publications and offline analysis.

  6. Integration with Jupyter Notebooks: Both Matplotlib and Plotly can be used with Jupyter Notebooks, but they provide different integration options. Matplotlib can render plots directly in Jupyter Notebooks using the %matplotlib inline magic command. Plotly, on the other hand, provides a JupyterLab extension called "plotly-extension" that allows users to create and interact with Plotly plots within JupyterLab.

In summary, Matplotlib is a simple and flexible library for creating basic visualizations, while Plotly is a more powerful and interactive library that is well-suited for creating complex and dynamic plots for web-based applications and collaboration.

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Detailed Comparison

Plotly.js
Plotly.js
Matplotlib
Matplotlib

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.

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.

Feature parity with MATLAB/matplotlib graphing; Online chart editor; Fully interactive (hover, zoom, pan); SVG and WebGL backends; Publication-quality image export
-
Statistics
GitHub Stars
17.9K
GitHub Stars
-
GitHub Forks
1.9K
GitHub Forks
-
Stacks
399
Stacks
1.6K
Followers
694
Followers
336
Votes
69
Votes
11
Pros & Cons
Pros
  • 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
Cons
  • 18
    Terrible document
Pros
  • 11
    The standard Swiss Army Knife of plotting
Cons
  • 5
    Lots of code
Integrations
Python
Python
React
React
MATLAB
MATLAB
Jupyter
Jupyter
Julia
Julia
No integrations available

What are some alternatives to Plotly.js, Matplotlib?

D3.js

D3.js

It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

Highcharts

Highcharts

Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

Chart.js

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ECharts

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

ZingChart

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

amCharts

amCharts

amCharts is an advanced charting library that will suit any data visualization need. Our charting solution include Column, Bar, Line, Area, Step, Step without risers, Smoothed line, Candlestick, OHLC, Pie/Donut, Radar/ Polar, XY/Scatter/Bubble, Bullet, Funnel/Pyramid charts as well as Gauges.

CanvasJS

CanvasJS

Lightweight, Beautiful & Responsive Charts that make your dashboards fly even with millions of data points! Self-Hosted, Secure & Scalable charts that render across devices.

AnyChart

AnyChart

AnyChart is a flexible JavaScript (HTML5) based solution that allows you to create interactive and great looking charts. It is a cross-browser and cross-platform charting solution intended for everybody who deals with creation of dashboard, reporting, analytics, statistical, financial or any other data visualization solutions.

ApexCharts

ApexCharts

A modern JavaScript charting library to build interactive charts and visualizations with simple API.

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