Related Products
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About
The JavaScript InfoVis Toolkit provides tools for creating interactive data visualizations for the web. The best way to start is to take a look at the demos page. Each demo has a See the Example Code link that takes you to the code for that example. The actual library code is included in the HTML file by building the lib each time with only the needed requirements taken from the name of the visualization and the build.json file. The required library code is built by the build.py file. In order to create a new visualization you need to set up the server environment to include test JavaScript files for your new visualization and also you need to add the new visualization files into the Source folder.
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About
Preact provides the thinnest possible Virtual DOM abstraction on top of the DOM. It builds on stable platform features, registers real event handlers and plays nicely with other libraries. Most UI frameworks are large enough to be the majority of an app's JavaScript size. Preact is different: it's small enough that your code is the largest part of your application. That means less JavaScript to download, parse and execute - leaving more time for your code, so you can build an experience you define without fighting to keep a framework under control. Preact is fast, and not just because of its size. It's one of the fastest Virtual DOM libraries out there, thanks to a simple and predictable diff implementation. We automatically batch updates and tune Preact to the extreme when it comes to performance. We work closely with browser engineers to get the maximum performance possible out of Preact.
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About
Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Developers in need of a tool for creating interactive data visualizations for the web
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Audience
Developers searching for a JavaScript Libraries solution
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Audience
Engineers and data scientists requiring a solution to manage and improve their machine learning research
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationSenchaLabs
Founded: 2013
United States
philogb.github.io/jit/
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Company InformationPreact
preactjs.com
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Company Informationscikit-learn
United States
scikit-learn.org/stable/
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Alternatives |
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Categories |
Categories |
Categories |
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Integrations
AppSignal
Azure Static Web Apps
CodeSandbox
CodeSnack IDE
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
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Integrations
AppSignal
Azure Static Web Apps
CodeSandbox
CodeSnack IDE
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
|
Integrations
AppSignal
Azure Static Web Apps
CodeSandbox
CodeSnack IDE
DagsHub
Databricks Data Intelligence Platform
Flower
Guild AI
Intel Tiber AI Studio
Keepsake
|
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