![]() ![]() Azure Notebooks are especially great for academic-style presentations, especially because you can use LaTeX formulae inside any text. While you will not be able to make fancy presentations in terms of design, but for many cases content and simplicity matter. ![]() You can mark cells as separate slides, or as continuations to previous slide - and then in presentation mode it will look like animation. One great feature that differentiates Azure Notebooks from all other similar services is the pre-installed RISE extension, which allows you to make presentations. You can write an article in the form of an Azure Notebook, which will automatically gather data from public sources, produce some live graphs, and compute resulting figures that will be used to drive reader to the conclusion. Writing a text with some arguments supported by data, for example, in data-driven journalism or computational journalism.Readers would not only be able to see how the code works, but they will be able to modify the code in place and further play with it For example, if you want to explain what affine transformation is, you can provide the explanation first (which can also include formulae, because Azure Notebooks support LaTeX), and then include some executable examples of applying affine transformation to a sample picture. Writing instructions or explaining some concepts that are related to algorithms.There are plenty of scenarious where it can be useful: Notebook is a great way to add thorough instructions to your code, or to add executable code to your text. Write Documented Code / Data-Driven Journalism Installing packages on F# notebooks is done through Paket manager, and is documented here.Ĥ. If you need to configure your Python environment in some way or install specific packages - you can also do it through config files, or including pip install commands into the notebook. clone it into their own copy of your project, and start playing and editing notebooks onlineīecause, unlike in Google Colab, you share on per-project basis, you can share several notebooks through one link, and you can also include data, README, and other useful information into the project.With the link other people will be able to: Each project has a unique link, and if you want to share it - just use this link (also making sure the project is marked as public). Notebooks are organized into Projects, which are similar to GitHub repositories, but without version control, and any project can be made either private, or public.Īzure Notebooks is a great way to share the code with other people. Azure Notebooks allow you to keep all your projects online. ![]() In those cases, you can just log into online Notebook environment and start coding right away in any of the supported languages: Python 2 and 3, R or F#.Ĭoming back to the point of visiting your friend's house - you probably want to be able to have access to your code immediately, without the need to carry the USB stick or download it from OneDrive. This requires some time and disk space, and while it will probably pay off if you are into serious development - spending time on environment setup is not something you want to do just to try a piece of code, or if you drop by to a party at your friend's house and want to show them your latest data analysis result. Whether you want to learn Python, or do some experimentation with F# - you typically need to install your development environment, be that Visual Studio or Anaconda. There are many advantages of using Azure Notebooks instead of local Jupyter installation, and I will try to cover them here. This is exactly what Azure Notebooks are - public Jupyter server hosted in the Azure cloud, which you can use from anywhere through the browser to write your code. However, using cloud resources as notebook dev environment sounds like even better alternative. In more demanding situations, your dev environment can be hosted on some high-performance compute server, and accessed through the web. The way most data scientists work is to install their own copy of Python dev environment (such as Anaconda or even better Miniconda) on their computer, start Jupyter server and then edit/run code on your own PC/Mac. For those of you who are not - Jupyter is a great system that allows you to combine markdown-based text and executable code in one web-based and web-editable document called notebook. If you are a data scientist or machine learning software engineer as I am, you probably write most of your code in Jupyter Notebooks. ![]()
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