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Learn MorePros of Jupyter
Pros of Replit
Pros of Jupyter
- In-line code execution using blocks18
- In-line graphing support10
- Can be themed7
- Multiple kernel support6
- Best web-browser IDE for Python3
- Export to python code3
- LaTex Support2
- HTML export capability1
- Multi-user with Kubernetes1
Pros of Replit
- Less Complicated6
- Continuous Deployment3
- Free base plan and Premium plan is cheap2
- Github integration2
- Supports a Reasonable amount of languages2
- Helpfull Community1
- Emmet support0
- Emmet support0
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Cons of Jupyter
Cons of Replit
Cons of Jupyter
Be the first to leave a con
Cons of Replit
- Limited Storage, CPU, Ram2
- Server cannot stay 24/72
- Very Limited Database API2
- Poor support2
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- 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 Replit?
It is a platform for creating and sharing software. You can write your code and host it all in the same place. It is also a place to learn how to code.
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What companies use Jupyter?
What companies use Replit?
What companies use Jupyter?
What companies use Replit?
See which teams inside your own company are using Jupyter or Replit.
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What tools integrate with Jupyter?
What tools integrate with Replit?
What tools integrate with Jupyter?
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What are some alternatives to Jupyter and Replit?
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.
Spyder
It is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.
Anaconda
A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.