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Plotly VS Vega-Lite

Compare Plotly VS Vega-Lite and see what are their differences

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Plotly logo Plotly

Low-Code Data Apps

Vega-Lite logo Vega-Lite

High-level grammar of interactive graphics
  • Plotly Landing page
    Landing page //
    2023-07-31
  • Vega-Lite Landing page
    Landing page //
    2019-09-21

Plotly features and specs

  • Interactivity
    Plotly offers highly interactive plots that allow users to pan, zoom, and hover over data points for more information. This enhances the user experience and provides deeper insights.
  • High-quality visualizations
    It provides aesthetically pleasing and highly customizable charts, making it suitable for publication-quality visuals.
  • Versatility
    Plotly supports multiple chart types including line charts, scatter plots, bar charts, and 3D plots, making it suitable for a wide range of applications.
  • Python integration
    Plotly is well-integrated with Python and works seamlessly with other popular data science libraries like Pandas, NumPy, and Scikit-learn.
  • Web-based
    The plots can be easily embedded in web applications or dashboards, making it ideal for sharing insights over the internet.
  • Open-source
    Plotly offers an open-source version, which allows users to create and share visualizations without any cost.

Possible disadvantages of Plotly

  • Performance
    Rendering very large datasets can sometimes be slow, which may not be suitable for real-time data visualization requirements.
  • Learning curve
    Even though the library is well-documented, the extensive range of features can have a steep learning curve for beginners.
  • Cost for advanced features
    While the basic functionality is free, more advanced features, such as export to certain formats and additional customizable options, require a paid subscription.
  • Dependency management
    Plotly has a number of dependencies that need to be managed properly, which can sometimes complicate the setup process.
  • Complexity
    For simple visualizations, Plotly might be overkill and simpler libraries like Matplotlib or Seaborn could be more appropriate.

Vega-Lite features and specs

  • Declarative Language
    Vega-Lite uses a high-level JSON syntax that simplifies the process of creating complex visualizations by allowing users to specify the visualization in terms of what they want to see rather than how to draw it.
  • Expressive Power
    It supports a wide range of visualizations, including bar charts, line charts, scatter plots, and more complex layered and faceted visualizations, making it suitable for many types of data visualization needs.
  • Interactivity
    Vega-Lite allows for the easy creation of interactive visualizations using selections, thereby enhancing user engagement and insight discovery.
  • Compatibility with Vega
    Visualizations created in Vega-Lite can be automatically compiled to Vega, allowing access to the more extensive feature set of Vega when needed.
  • Responsive Design
    Vega-Lite visualizations are designed to be responsive, adapting well to different screen sizes and resolutions.
  • Ease of Integration
    Being based on a JSON syntax, Vega-Lite visualizations can easily be integrated with web applications, making it a popular choice for adding interactive charts to websites.

Possible disadvantages of Vega-Lite

  • Complexity Limitations
    While Vega-Lite is powerful, it has limitations compared to programming libraries like D3.js when creating highly customized or complex visualizations.
  • Learning Curve
    Even though it simplifies the process compared to lower-level libraries, there is still a learning curve associated with understanding its syntax and the structure of its JSON specification.
  • Performance Constraints
    For very large datasets, performance might become an issue because the library may need to handle more data than it’s optimized for, potentially slowing down rendering times.
  • Limited Customization
    While Vega-Lite offers a good degree of customization, there are limits to how much you can customize visualizations compared to raw Vega or other visualization libraries.
  • Dependence on JSON
    Some users might find the JSON format limiting in terms of readability and maintainability, especially for very complex visualizations.

Plotly videos

+ Add

Create Real-time Chart with Javascript | Plotly.js Tutorial

More videos:

  • Review - Introducing plotly.py 3.0
  • Review - Is Plotly The Better Matplotlib?
  • Tutorial - Plotly Tutorial 2021
  • Review - Data Visualization as The First and Last Mile of Data Science Plotly Express and Dash | SciPy 2021

Vega-Lite videos

+ Add

Vega-Lite: A Grammar of Interactive Graphics

Category Popularity

0-100% (relative to Plotly and Vega-Lite)

Plotly

Vega-Lite

Data Visualization
87 87%
Data Visualization
13% 13
Data Dashboard
82 82%
Data Dashboard
18% 18
Charting Libraries
92 92%
Charting Libraries
8% 8
Developer Tools
0 0%
Developer Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Plotly and Vega-Lite

Plotly Reviews

Best 8 Redash Alternatives in 2023 [In Depth Guide]
Plotly is specifically designed for companies who want to build and deploy analytic applications like dashboards using Python, Julia, or R without needing DevOps or Javascript developers.
Source: www.datapad.io
5 Best Python Libraries For Data Visualization in 2023
Plotly is a web-based data visualization toolkit that comes with unique functionalities such as dendrograms, 3D charts, and also contour plots, which is not very common in other libraries. It has a great API offering scatter plots, line charts, bar charts, error bars, box plots, and other visualizations. Plotly can even be accessed from a Python Notebook.
Source: www.technotification.com
Top 8 Python Libraries for Data Visualization
Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly (plotly.py) is built on top of the Plotly JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique...
Source: www.geeksforgeeks.org
5 top picks for JavaScript chart libraries
Plotly is a graphing library that’s available for various runtime environments, including the browser. It supports many kinds of charts and graphs that we can configure with a variety of options.
Source: blog.logrocket.com

Vega-Lite Reviews

We have no reviews of Vega-Lite yet.
Be the first one to post

Social recommendations and mentions

Plotly might be a bit more popular than Vega-Lite. We know about 33 links to it since March 2021 and only 24 links to Vega-Lite. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Plotly mentions (33)

  • Python for Data Visualization: Best Tools and Practices
    Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / about 1 month ago
  • Generative AI Powered QnA & Visualization Chatbot
    Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / 3 months ago
  • Build a Stock Dashboard in less than 40 lines of Python code!🤓
    In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / 11 months ago
  • Python equivalent to power bi/power query?
    For dashboards: - https://plotly.com/ is probably my favourite, but there are others like streamlit, voila and others... Source: over 1 year ago
View more

Vega-Lite mentions (24)

  • Vega – A declarative language for interactive visualization designs
    - In our case some features were missing (and are still missing) - exponential average - that is most commonly used to smooth ML training curves. [1] https://vega.github.io/vega-lite/ [2] https://dvc.org/doc/user-guide/experiment-management/visualizing-plots#visualizing-plots. - Source: Hacker News / 8 months ago
  • Show HN: I made first declaritive SVG,canvas framework
    We use the slightly simpler vega-lite from the same group. It typically gets us 98% of the way there quite quickly. Its from the same team, just a more simple wrapper around D3. https://vega.github.io/vega-lite/. - Source: Hacker News / 11 months ago
  • Ask HN: What's the best charting library for customer-facing dashboards?
    I like Vega-Lite: https://vega.github.io/vega-lite/ It’s built by folks from the same lab as D3, but designed as “a higher-level visual specification language on top of D3” [https://vega.github.io/vega/about/vega-and-d3/] My favorite way to prototype a dashboard is to use Streamlit to lay things out and serve it and then use Altair [https://altair-viz.github.io/] to generate the Vega-Lite plots in Python. Then if... - Source: Hacker News / about 1 year ago
  • Gnuplotlib: Non-Painful Plotting for NumPy
    I also have difficulties with Gnuplot and Matplotlib. I like Vega that allows me to create visualisations in a declarative way. If I really need something special I go with d3.js, which had a really steep learning curve but with ChatGPT it should have become easier for beginners. [1] https://vega.github.io/vega-lite/. - Source: Hacker News / over 1 year ago
  • Elixir Livebook is a secret weapon for documentation
    To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/). In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at... - Source: Hacker News / over 1 year ago
View more

What are some alternatives?

When comparing Plotly and Vega-Lite, you can also consider the following products

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

Observable - Interactive code examples/posts

Chart.js - Easy, object oriented client side graphs for designers and developers.

Vega Visualization Grammar - Visualization grammar for creating, saving, and sharing interactive visualization designs

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...

Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application

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