# jupyterlab Published articles for jupyterlab. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Here and Now: Reusing Code at Feedzai with JupyterLab Snippets DevFeed: [Here and Now: Reusing Code at Feedzai with JupyterLab Snippets]() Original publisher: [Read original article]() Author: João Palmeiro Published: 2025-03-17T13:11:26Z Content type: article Language: en Sources: [Feedzai]() Topics: [jupyterlab](), [Data Science](), [Code](), [Data analysis]() Tags: [code](), [data-analysis](), [data-science](), [debugging](), [jupyter](), [jupyterlab](), [python](), [repositories](), [snippet]() ### AI overview Feedzai developed JupyterLab Snippets to help data scientists reuse code from the many notebooks created for daily work. The article describes research using notebook repository statistics and user interviews, and reports that duplicating notebooks, maintaining reference collections of snippets, and sharing notebooks or snippets are common practices. ### Source excerpt Data scientists use different Jupyter notebooks every day -- ranging from disposable ones for quick tasks to those shareable with clients. Over time, more and more notebooks accumulate, making it increasingly difficult to reuse them in whole or in part. To mitigate this problem and make the most relevant pieces of code quickly accessible to every data scientist, we developed JupyterLab Snippets at Feedzai -- our take on leveraging code snippets directly on JupyterLab. JupyterLab is a computational notebook platform that enables us to carry on data science work (and beyond) via notebooks. These notebooks, where code, text, and images come together, allow us to complete all kinds of tasks, keeping each input close to each output. At Feedzai, data scientists have access to different JupyterLab environments packed with custom notebooks and Python packages. Here they go from analyzing data to training models, from preparing reports to debugging the system -- a "lab of all trades", we could say. Given the importance of JupyterLab and notebooks in the daily work of our data scientists, we started a research project that culminated in JupyterLab Snippets. First, we collected statistics from internal notebook repositories across teams and conducted user interviews with junior and senior data scientists. We needed to know more about how data scientists actually use JupyterLab and notebooks, and what their ideas are for a better platform. A sneak peek of JupyterLab Snippets. From the very start, every data scientist can import snippets immediately or start their collection by creating one from scratch. After compiling the insights, several aspects became clear: JupyterLab and notebooks are heavily used for all sorts of data science and ad-hoc tasks (typically one per notebook). There is no single type of task (e.g., exploratory data analysis) that is more commonly addressed with notebooks than others. While file structures may look similar at first glance (each environment has at ## Using Bosque in JupyterLab DevFeed: [Using Bosque in JupyterLab]() Original publisher: [Read original article]() Author: brakmic Published: 2025-01-17T16:17:39Z Content type: tutorial Language: en Sources: [Harris Brakmic - Coding]() Topics: [Bosque](), [jupyterlab](), [Programming language](), [Extension](), [kernels](), [Python](), [TypeScript](), [Programming]() Tags: [article](), [bosque](), [coding](), [extensions](), [jupyter](), [jupyterlab](), [kernel](), [kernels](), [python](), [syntax-highlighting](), [typescript]() ### AI overview The article discusses Bosque, a Microsoft Research programming language centered on regularized programming, immutable state, and intent-focused coding. It also describes the author's experience building JupyterLab extensions and kernels, including Python-based kernels and TypeScript syntax highlighting with Lezer parsers. ### Source excerpt Discover the innovative world of Bosque, a programming language developed by Microsoft Research that champions the regularized programming paradigm. In this post, I explore the key advantages of Bosque, such as immutable state and intent-focused coding, and share my journey integrating it into JupyterLab. Learn how I developed custom kernels in Python and crafted syntax highlighting extensions using TypeScript and Lezer parsers. Whether you're intrigued by Bosque's approach or looking to enhance your JupyterLab environment, this article provides valuable insights and practical guidance to help you get started. Dive in to see how Bosque can transform your programming experience and streamline your development workflow! ## \[Перевод\] Программа раннего доступа к JetBrains DataSpell открыта для всех DevFeed: [\[Перевод\] Программа раннего доступа к JetBrains DataSpell открыта для всех]() Original publisher: [Read original article]() Author: andreycheptsov (JetBrains) Published: 2021-09-08T11:31:43Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [jetbrains](), [ide](), [Data Science](), [jupyter](), [Python]() Tags: [data-science](), [eap](), [ide](), [jetbrains](), [jupyter](), [jupyterhub](), [jupyterlab](), [machine-learning](), [python]() ### AI overview JetBrains opens the DataSpell early access program to everyone. The article describes improvements to Jupyter notebook workflows, remote notebooks, and the interactive Python console, including better result handling and automatic import fixes. ### Source excerpt С тех пор, как мы анонсировали JetBrains DataSpell в марте, наша новая IDE для Data Science была доступна для ограниченного числа пользователей, которые помогали нам с тестированием IDE и обратной связью. Сегодня мы запускаем программу раннего доступа для всех желающих. Всем, кто хотел участвовать в программе, придут приглашения. Вы можете скачать свежую EAP-сборку на сайте JetBrains DataSpell (регистрация не требуется). Читать далее ## Анонсируем версию Datalore Enterprise для команд Data Science DevFeed: [Анонсируем версию Datalore Enterprise для команд Data Science]() Original publisher: [Read original article]() Author: alenaguzharina (JetBrains) Published: 2021-07-06T15:00:01Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [Data Science](), [jupyter](), [Python](), [Amazon S3]() Tags: [aws](), [aws-s3](), [data-science](), [datalore](), [jetbrains](), [jupyter](), [jupyter-notebooks](), [jupyterhub](), [jupyterlab](), [python](), [tag-055aee430837](), [tag-752bfc3f8269](), [tag-baf5012a40ff]() ### AI overview JetBrains announces Datalore Enterprise, a version of Datalore designed for Data Science teams. It adds team-oriented capabilities to Jupyter notebooks, including PyCharm code assistance, persistent data storage, authentication, configurable environments, compute-resource management, versioning, real-time collaboration, and AWS S3 integration. ### Source excerpt Привет, Хабр! Jupyter-ноутбуки хоть и вызывают споры, однако все равно являются самым часто используемым инструментом для решения задач Data Science. Ноутбуки просты в личном использовании. Но если вы когда-либо пробовали организовать командную работу с ними, то наверняка сталкивались со сложностями. Именно поэтому мы в JetBrains решили выпустить специальную версию Datalore Enterprise для команд Data Science! Читать далее