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Jupyter
Jupyter

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SageMath
SageMath

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+ 1
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Jupyter vs SageMath: What are the differences?

Developers describe Jupyter as "Multi-language interactive computing environments". The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. On the other hand, SageMath is detailed as "A free open-source mathematical software system". It is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more.

Jupyter and SageMath can be primarily classified as "Data Science Notebooks" tools.

Jupyter is an open source tool with 6.12K GitHub stars and 2.58K GitHub forks. Here's a link to Jupyter's open source repository on GitHub.

- No public GitHub repository available -

What is Jupyter?

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

What is SageMath?

It is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more.
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        What are some alternatives to Jupyter and SageMath?
        Apache Zeppelin
        A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.
        PyCharm
        PyCharm’s smart code editor provides first-class support for Python, JavaScript, CoffeeScript, TypeScript, CSS, popular template languages and more. Take advantage of language-aware code completion, error detection, and on-the-fly code fixes!
        IPython
        It provides a rich architecture for interactive computing with a powerful interactive shell, a kernel for Jupyter. It has a support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing.
        Franchise
        Chart with a single click. Compare queries side by side. Download your work and share it with anyone. If your data is in a CSV, JSON, or XLSX file, loading it is as simple as dropping the file into Franchise.
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        How developers use Jupyter and SageMath
        Avatar of Mark Melnykowycz
        Mark Melnykowycz uses JupyterJupyter

        When integrated with cloud platforms it's an easy way to code and test data science projects. The web-based nature makes it easy to transition between coding on your local machine or the cloud.

        Avatar of Eliana Abraham
        Eliana Abraham uses JupyterJupyter

        EECS 445 and MDST

        Used it for that sometimes mostly I tried to use it for something I was doing in MDST. I have failed to actually properly use it to it's full potential.

        Avatar of GadgetSteve
        GadgetSteve uses JupyterJupyter

        Code and results + Markdown all in a shareable web page.

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