So when I saw my "Advantages of Using R Notebooks Instead of Jupyter Notebooks" post was trending on HN this morning, I remembered Neuron is a strong idea to work outside both technologies, easily complemented by the multitude of VS Code extensions/theming. : 559161-6437, Wolfram Language (Mathematica) vs. Python for data science projects - go to homepage, Prototype Development of Data-Driven Apps, http://www.wolfram.com/language/fast-introduction-for-programmers/en/, “An Elementary Introduction to the Wolfram Language”, pattern matching is powerful and prominent, for example in function declaration, symbolic, you can pass everything into a function (has a lot of advantages but also makes it harder to debug), most of the time there is only one obvious way to do things, for example plotting, Dynamic and Manipulate functions for more interactivity, instant API (although only in the Wolfram Cloud or your own Wolfram Enterprise Cloud), hard to find a job / hard to recruit people who know Wolfram, lots of possibilities to deploy a trained model, a lot of online courses, podcasts and other resources, use of google-colab or Kaggle for learning ML without a local GPU, pandas is easier to use than the “Dataset” in Mathematica. In Matlab you create a new cell by starting a line with two comment characters (%%), in the Jupyter extension you use #%%. Important: You must run Mathematica ("math") before you open Jupyter Notebook. this is a pretty interesting idea. Source: Fernando Perez started IPython, very much inspired by Mathematica (he was switching from Mathematica to Python). The Wolfram/Mathematica community is comparably small and therefore it is harder to find relevant information, although the Mathematica community](https://mathematica.stackexchange.com) on stackexchange is really helpful. https://github.com/Microsoft/PTVS/wiki/Using-IPython-with-PT... note quite the cell by cell, markdown experience, but you get inline resizable graphs, !shell commands, etc. Better than nothing for sure, but not enough if you need or want to work with notebooks. Use free-form input to get instant answers to questions, create and customize graphs, and turn static examples into dynamic models. That's a cool idea. more integration is on its way. I only get glitches in Mathematica when I am hitting extremely huge curated data from Wolfram and trying to do something at the same time. At the moment I like to open notebooks in a tab in the browser built into Visual Studio, with the editor open in a tab next to it. Other issues you have though, probably are present. Wolfram|Alpha Notebook Edition combines the best of both Wolfram|Alpha and Mathematica into a single, unified tool perfect for teaching and learning. You may need to download version 2.0 now from the Chrome Web Store. MKL) for performance reasons. You can trust that many of these issues will get worked out in the long run. Thanks for sharing. I created a dummy context manager with a unique name and used an importhook to do an AST rewrite to the body of the context manager. An a simple task such as adding a new column can be tricky: So I would say pandas wins because it is just as powerful and easier to use. For complex code you nest functions inside functions which leads to a lot of , but fortunately Mathematica formats it on the fly in a meaningful way. The double square brackets are comparable to the brackets in Python. This was summer student project. You can just put in some basic phrases in Mathematica, and you are connected to the data, albeit curated by Wolfram, but still simple and immediate. Now I will use Mathematica power in production, a whole new level. Even PTVS gained cell support recently. The documentation in Mathematica is really good, but Python has a much bigger community and it is very likely that you find an answer for exactly your problem. I've used it. In the following example I will use the built-in data to plot the population of the 5 closest cities near our company location in Älvdalen, Sweden. Also datasets can be slower to work with, especially for big and nested datasets an Association is much faster. I would like support for actual notebooks as well, for when I want to mix text, code, and figures but without having to work in a browser. It is very common that popular add-ons are not maintained for months. What do you mean by "Same with VScode"? I've been looking at the PR and the implementation is really nice, I'm excited to see what it turns into :). I really like the cell format of Jupyter (inspired by Matlab I presume), I don't really want to select the code before I run it. The lastest github repo for this extension has the code we're working on. I've also used it for the past months (latest version at MELPA), and haven't had any problems. The popular notebook format was invented by Stephen Wolfram and still to-date the notebook on Mathematica is more powerful compared to Jupyter notebooks. In that case you should check out Yhat's Rodeo IDE. Open source is the complete antithesis to "extinguishing software". If you are on a personal connection, like at home, you can run an anti-virus scan on your device to make sure it is not infected with malware. What are the plans for more integration? Nevertheless Python code is easier to read. It seems like this might change in the next release of Mathematica. Both are well worth the time. Then you will edit Jupyter configuration file (use sudo or chmod -R 777 /home/anaconda3): Go to GCP VPC and create a static IP for your instance, SSH into it. 8. https://docs.google.com/presentation/d/1n2RlMdmv1p25Xy5thJUh... His YouTube live coding on building a neural net from scratch really shows the power of using VSCode to do AI work and complements his assertions.
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