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Website | atom.io |
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Website | jupyter.org |
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Based on our record, Jupyter seems to be a lot more popular than Hydrogen of nteract. While we know about 202 links to Jupyter, we've tracked only 3 mentions of Hydrogen of nteract. 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.
Hydrogen [https://atom.io/packages/hydrogen] running on Atom is the cleanest multi-lingual data science IDE in existence and has been my go-to for years. Haven't found a drop-in replacement elsewhere [vscode's language support is scattered: native python integration, different for R, Julia etc]. Anyone on HN have recs [besides vim slime or send-to-terminal options in other editors, which work but are clunky] ? - Source: Hacker News / almost 2 years ago
I can share two of my vim-user solutions with you. First one is a Atom based and is a bit more convinient if you have to do some plotting. I have vim-mode-plus plugin for vim-like navigation in the code, atom ex-mode plugin for ex commands within atom and a top of that I use Hydrogen for in place code execution. Source: almost 3 years ago
Of it still doesn't work you could try to install the package hydrogen which works with an iPython kerbel like the jupiter notebook (hydrogen package). Source: about 3 years ago
Jupyter Notebooks is very popular among data people specially Python users. So, I tried to find a way to run the Groovy kernel inside a Jupyter Notebook, and to my surprise, I found a way, BeakerX! - Source: dev.to / 24 days ago
Note. Nowadays, there are many flavors of notebooks (Jupyter, VSCode, Databricks, etc.), but they’re all built on top of IPython. Therefore, the Magics developed should be reusable across environments. - Source: dev.to / 25 days ago
They make it easy to launch multiple case-by-case data science projects and run your local code right from Jupyter Notebook. - Source: dev.to / about 2 months ago
Talking to some colleagues and friends lately gathering some ideas of a nice Machine Learning project to build, I’ve seen that there’s a gap of knowledge in terms of how do one exactly uses a Machine Learning model trained? Just imagine yourself building a model to solve some problem, you are probably using Jupyter Notebook to perform some data clean up, perform some normalization and further tests. Then you... - Source: dev.to / 3 months ago
This year I decided to commit to a set of tools on day 1 (Polars and Jupyter) and use them for the whole challenge. It seemed silly to do a whole new meandering walkthrough, so instead I'll highlight a few things that stuck out after finishing the challenge and sitting on it for a few days. Here we go! - Source: dev.to / 3 months ago
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