D3.js vs Matplotlib

Overview

D3.js

Stacks2.0K

Followers1.7K

Votes653

GitHub Stars111.7K

Forks22.9K

Matplotlib

Stacks1.6K

Followers336

Votes11

D3.js vs Matplotlib: What are the differences?

Introduction

D3.js (Data-Driven Documents) and Matplotlib are both popular data visualization libraries, but they have key differences in terms of their features, syntax, and use cases.

Syntax: D3.js is primarily a JavaScript library, while Matplotlib is a Python library. D3.js uses a declarative approach, where the visualization code is tightly integrated with HTML and CSS. On the other hand, Matplotlib has a procedural interface, where plots are created by calling functions and methods.
Interactivity: D3.js is known for its powerful interactivity capabilities. It provides extensive support for creating interactive and dynamic visualizations, such as interactive charts, zooming, panning, and brushing. Matplotlib, on the other hand, focuses more on static visualizations and lacks the same level of built-in interactivity as D3.js.
Data Binding: D3.js excels at data binding, allowing developers to easily connect data elements to visual elements. This enables efficient updates and transitions when the underlying data changes. Matplotlib, while capable of handling data, does not have the same level of flexibility and ease of data binding as D3.js.
Rendering: D3.js renders visualizations directly in the browser using SVG (Scalable Vector Graphics) or HTML5 Canvas. This allows for high-quality and scalable plots that can be easily integrated into web applications. In contrast, Matplotlib primarily renders visualizations as static images or SVG files, which are not as flexible for integration in dynamic web environments.
Community and Ecosystem: D3.js has a vibrant community and a rich ecosystem of plugins and extensions. It is widely used for creating interactive web-based visualizations and has extensive documentation and resources available. Matplotlib also has a large user base, but its ecosystem is more focused on static plotting in scientific and data analysis domains.
Learning Curve: D3.js has a steeper learning curve compared to Matplotlib. Its extensive API and functional programming style can be challenging for beginners. Matplotlib, on the other hand, has a simpler and more intuitive interface, making it easier to get started with basic visualization tasks.

In Summary, D3.js and Matplotlib have different strengths and use cases. D3.js is suited for interactive web-based visualizations with dynamic data, while Matplotlib is more suitable for static visualizations and scientific plotting in Python.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on D3.js, Matplotlib

Ayaskant

SSE-II at Akamai

Oct 25, 2019

Needs advice

I want to get suggestions on these 2 open source js libraries (D3.js & echarts) that help in creating charts or graphs on the UI. Which one will be better for bar graphs. Which is easy to learn and start with? Which provides better features and community support?

My requirements are 1 - Plot data in X-Y axis graph where x-axis will present time till seconds level and Y-Axis will present the data corresponding to that time.

2 - Zoom-in and zoom out feature.

56k views56k

Comments

Detailed Comparison

D3.js	Matplotlib
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.	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.
Declarative Approach for Individual Nodes Manipulation; Functions Factory; Web Standards; Built-in ELement Inspector to Debug; Uses SVG, Canvas, and HTML; Data-driven approach to DOM Manipulation; Voronoi Diagrams; Maps and topo.	-
Statistics
GitHub Stars 111.7K	GitHub Stars -
GitHub Forks 22.9K	GitHub Forks -
Stacks 2.0K	Stacks 1.6K
Followers 1.7K	Followers 336
Votes 653	Votes 11
Pros & Cons
Pros 195 Beautiful visualizations 103 Svg 92 Data-driven 81 Large set of examples 61 Data-driven documents Cons 11 Beginners cant understand at all 6 Complex syntax	Pros 11 The standard Swiss Army Knife of plotting Cons 5 Lots of code
Integrations
JavaScript React Native AngularJS React Bootstrap	No integrations available

What are some alternatives to D3.js, Matplotlib?

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.

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.

Chart.js

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

Recharts

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

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

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

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

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 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

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

Related Comparisons

D3.js vs Matplotlib: What are the differences?

Introduction

D3.js (Data-Driven Documents) and Matplotlib are both popular data visualization libraries, but they have key differences in terms of their features, syntax, and use cases.

Syntax: D3.js is primarily a JavaScript library, while Matplotlib is a Python library. D3.js uses a declarative approach, where the visualization code is tightly integrated with HTML and CSS. On the other hand, Matplotlib has a procedural interface, where plots are created by calling functions and methods.
Interactivity: D3.js is known for its powerful interactivity capabilities. It provides extensive support for creating interactive and dynamic visualizations, such as interactive charts, zooming, panning, and brushing. Matplotlib, on the other hand, focuses more on static visualizations and lacks the same level of built-in interactivity as D3.js.
Data Binding: D3.js excels at data binding, allowing developers to easily connect data elements to visual elements. This enables efficient updates and transitions when the underlying data changes. Matplotlib, while capable of handling data, does not have the same level of flexibility and ease of data binding as D3.js.
Rendering: D3.js renders visualizations directly in the browser using SVG (Scalable Vector Graphics) or HTML5 Canvas. This allows for high-quality and scalable plots that can be easily integrated into web applications. In contrast, Matplotlib primarily renders visualizations as static images or SVG files, which are not as flexible for integration in dynamic web environments.
Community and Ecosystem: D3.js has a vibrant community and a rich ecosystem of plugins and extensions. It is widely used for creating interactive web-based visualizations and has extensive documentation and resources available. Matplotlib also has a large user base, but its ecosystem is more focused on static plotting in scientific and data analysis domains.
Learning Curve: D3.js has a steeper learning curve compared to Matplotlib. Its extensive API and functional programming style can be challenging for beginners. Matplotlib, on the other hand, has a simpler and more intuitive interface, making it easier to get started with basic visualization tasks.

D3.js vs Matplotlib

Overview

D3.js vs Matplotlib: What are the differences?