# jupyter Published articles for jupyter. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why I Stopped Using nbdev DevFeed: [Why I Stopped Using nbdev]() Original publisher: [Read original article]() Author: Hamel Husain Published: 2026-01-18T08:00:00Z Content type: opinion Language: en Sources: [Hamel Husain]() Topics: [Artificial Intelligence](), [ai-coding](), [jupyter](), [Python](), [Development]() Tags: [ai](), [ai-coding](), [developer](), [jupyter](), [jupyter-notebooks](), [nbdev](), [python](), [software](), [tools]() ### AI overview The author explains why they stopped using nbdev, a literate-programming development environment built around Jupyter notebooks. They argue that AI coding tools work less effectively with nbdev's notebook-to-library workflow, while AI can now help understand and maintain documentation separately from code. The author concludes that disciplined development and documentation depend more on developer effort than on the tooling environment. ### Source excerpt Programmers love to proclaim they've found the best tool. Paul Graham called Lisp his "secret weapon." DHH described Ruby as "a magical glove that just fit my brain perfectly." Pieter Levels ships million-dollar products with vanilla PHP and jQuery. These declarations aren't about the languages themselves. They're about developers finding tools that fit how they think. When the environment clicks, you move fast. I had that experience with nbdev, a development environment for literate programming that I helped build and maintain1. I created hundreds of projects with it and was one of its biggest proponents. Today, I no longer use it. AI coding tools changed the trade-offs. Fighting the AI The beauty of nbdev is its workflow. You write code, documentation and tests in one source of truth: Jupyter notebooks. Afterwards, these notebooks are transpiled into a Python library and documentation website. This workflow is idiosyncratic. AI coding tools, trained on vast amounts of conventional source code, get confused. They struggle to differentiate between editing the notebook and editing the final source code. It feels like fighting the AI instead of working with it. I write software to solve problems, not to write code. I want to work in an environment where AI has the highest chance of success. With nbdev, I was swimming upstream. Some argue that AI tools encourage lazy thinking: that without guardrails, developers skip the hard work of breaking problems into steps. But thinking step-by-step is a human skill. Notebooks don't force you to write clean code. AI tools don't force you to think carefully. Discipline comes from the developer, not the environment. Tools Don't Matter As Much As I Thought A central promise of literate programming is better documentation. By keeping code and docs in one place, you reduce the chance they become stale. Strangely, many nbdev projects lacked sufficient documentation for my taste. Sometimes, this helped me learn a codebase by contributin ## Jupyter Agents: training LLMs to reason with notebooks DevFeed: [Jupyter Agents: training LLMs to reason with notebooks]() Original publisher: [Read original article]() Author: Baptiste Colle; Hanna Yukhymenko; Leandro von Werra Published: 2025-09-10T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Jupyter Notebook](), [Data analysis](), [Data Science](), [LLMs](), [Fine-tuning](), [Open Source Models & Datasets](), [Benchmark](), [Human-AI evaluation](), [smolagents](), [qwen](), [ide]() Tags: [agents](), [analysis](), [benchmark](), [cursor](), [data](), [data-science](), [datasets](), [evaluation](), [fine-tuning](), [jupyter](), [llm](), [llms](), [qwen](), [research](), [smolagents]() ### AI overview The article presents Jupyter Agent, a system that executes code inside Jupyter notebooks to support data analysis and data science workflows. It describes a pipeline for generating training data, fine-tuning smaller models, and evaluating them on the DABStep benchmark, with examples involving Qwen models and smolagents. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Jupyter Notebooks on Heroku with Persistent Storage DevFeed: [Jupyter Notebooks on Heroku with Persistent Storage]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2025-04-02T06:54:00Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [jupyter](), [Disk image](), [PostgreSQL](), [Security](), [Retrieval Augmented Generation (RAG)]() Tags: [ai](), [cloud](), [cloud-infrastructure](), [data-analytics](), [developer-tools](), [heroku](), [jupyter](), [jupyter-notebooks](), [managed-inference-and-agents](), [news](), [open-source](), [pgvector](), [postgresql](), [python](), [rag](), [security](), [storage]() ### AI overview Heroku introduces Heroku-Jupyter, an open-source solution for running Jupyter Notebooks with persistent PostgreSQL storage, deployment support, password protection, and customizable environments. The article also describes using Heroku-Jupyter with pgvector and Heroku Managed Inference and Agents for retrieval-augmented generation applications. ### Source excerpt We're excited to introduce Heroku-Jupyter, an open-source, production-ready solution for running Jupyter Notebooks on Heroku with persistent storage, seamless deployment, and built-in security. Whether you're a data scientist, educator, or developer, you can now spin up a cloud-based Jupyter environment in minutes. Why Jupyter on Heroku? Jupyter Notebooks provide an interactive computing environment ideal for [...] The post Jupyter Notebooks on Heroku with Persistent Storage appeared first on Heroku. ## 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! ## AI GPU Clusters, From Your Laptop, With Livebook DevFeed: [AI GPU Clusters, From Your Laptop, With Livebook]() Original publisher: [Read original article]() Published: 2024-09-24T00:00:00Z Content type: article Language: en Sources: [The Fly Blog]() Topics: [Elixir](), [fly.io](), [jupyter](), [Cloud](), [Machine learning](), [Serverless](), [GPU](), [PyTorch]() Tags: [access-control](), [ai](), [ai-and-ml](), [cdn](), [close-to-users](), [deploy-app-servers](), [docker](), [elixir](), [fly](), [fly-io](), [gpu](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [infrastructure](), [jupyter](), [networking](), [postgresql-clusters](), [serverless](), [servers]() ### AI overview This recap introduces Livebook, FLAME, and the Nx stack as Elixir components for interactive notebooks, elastic code execution, and Elixir-native AI and machine learning. It describes moving computation between a laptop and remote Fly.io infrastructure, including GPU-backed workloads, and discusses private network access to other applications such as databases. ### Source excerpt Livebook, FLAME, and the Nx stack: three Elixir components that are easy to describe, more powerful than they look, and intricately threaded into the Elixir ecosystem. A few weeks ago, Chris McCord (👋) and Chris Grainger showed them off at ElixirConf 2024. We thought the talk was worth a recap. Let's begin by introducing our cast of characters. Livebook is usually described as Elixir's answer to Jupyter Notebooks. And that's a good way to think about it. But Livebook takes full advantage of the Elixir platform, which makes it sneakily powerful. By linking up directly with Elixir app clusters, Livebook can switch easily between driving compute locally or on remote servers, and makes it easy to bring in any kind of data into reproducible workflows. FLAME is the Elixir's answer to serverless computing. By having the library manage a pool of executors for you, FLAME lets you treat your entire application as if it was elastic and scale-to-zero. You configure FLAME with some basic information about where to run code and how many instances it's allowed to run with, and then mark off any arbitrary section of code with Flame.call. The framework takes care of the rest. It's the upside of serverless without committing yourself to blowing your app apart into tiny, intricately connected pieces. The Nx stack is how you do Elixir-native AI and ML. Nx gives you an Elixir-native notion of tensor computations with GPU backends. Axon builds a common interface for ML models on top of it. Bumblebee makes those models available to any Elixir app that wants to download them, from just a couple lines of code. Here is quick video showing how to transfer a local tensor to a remote GPU, using Livebook, FLAME, and Nx: Let's dive into the keynote. Poking a hole in your infrastructure Any Livebook, including the one running on your laptop, can start a runtime running on a Fly Machine, in Fly.io's public cloud. That Elixir machine will (by default) live in your default Fly.io organization, giving ## Bringing Interactive Computing to Zed: Introducing REPL Support DevFeed: [Bringing Interactive Computing to Zed: Introducing REPL Support]() Original publisher: [Read original article]() Author: Kyle Kelley Published: 2024-07-24T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [Development](), [jupyter](), [kernels](), [Python](), [Deno](), [R](), [Markdown]() Tags: [analysis](), [code](), [development](), [jupyter](), [jupyter-notebooks](), [kernel](), [kernels](), [language](), [markdown](), [python](), [release](), [typescript]() ### AI overview Zed introduces a built-in REPL that lets users run code line by line, block by block, or from a selection, visualize data, and iterate within the editor. The feature integrates REPL-style interaction with Jupyter-like output and supports installed kernels for languages including Python, TypeScript through Deno, and R. ### Source excerpt Run code, visualize data, and iterate right in your editor with Zed's new REPL support. ## How to Export Datadog Metrics for Exploration in Jupyter Notebooks DevFeed: [How to Export Datadog Metrics for Exploration in Jupyter Notebooks]() Original publisher: [Read original article]() Author: Kunal Kohli Published: 2022-11-29T11:53:22Z Content type: tutorial Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [telemetry](), [dashboards]() Tags: [analysis](), [api](), [cpu](), [dashboards](), [guide](), [how-to](), [jupyter](), [metrics](), [python](), [rest-api](), [time-series]() ### AI overview A step-by-step guide to exporting Datadog metrics with its REST API and analyzing them locally in Python and Jupyter Notebooks. ### Source excerpt A step-by-step guide for extracting Datadog metrics in Python for in-depth analysis in Jupyter Notebooks. ## Datalore Enterprise 2021.3: ячейки SQL, подключение Jupyter-ноутбуков к БД, интерактивные отчеты и многое другое DevFeed: [Datalore Enterprise 2021.3: ячейки SQL, подключение Jupyter-ноутбуков к БД, интерактивные отчеты и многое другое]() Original publisher: [Read original article]() Author: alenaguzharina (JetBrains) Published: 2021-12-07T14:10:12Z Content type: release Language: ru Sources: [JetBrains RU]() Topics: [Data Science](), [jupyter](), [SQL](), [Databases](), [SQLAlchemy](), [R](), [Scala](), [Amazon Redshift](), [Azure SQL](), [MariaDB](), [MySQL](), [Oracle Database]() Tags: [azure-sql](), [data](), [data-science](), [database](), [datalore](), [jupyter](), [jupyterhub](), [mysql](), [oracle](), [plus-a5524f17cbdd](), [postgresql](), [python](), [scala](), [sql](), [tag-4d3456108bed](), [tag-b0c5f1e2d8ce](), [tag-baf5012a40ff]() ### AI overview This release article presents Datalore Enterprise 2021.3, a data science platform for collaborative Jupyter notebooks. The update adds database connections and native SQL cells, with query results transferred to pandas DataFrames, support for R and Scala notebooks, reactive features, chart builders, and interactive report publishing. ### Source excerpt Привет, Хабр! Datalore Enterprise -- это data science платформа для совместной работы с Jupyter-ноутбуками. Ее можно установить в частное облако или на приватный сервер компании. Новая версия 2021.3 позволяет специалистам по анализу и обработке данных работать с базами данных и SQL-кодом внутри Jupyter-ноутбуков, а также легко делиться результатами работы с коллегами. Также мы интегрировали поддержку ноутбуков R и Scala, добавили новый реактивный режим, реализовали конструкторы графиков и множество других функций. Читайте дальше, чтобы узнать о новых возможностях Datalore Enterprise 2021.3! Читать далее ## \[Перевод\] 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 ## A Science Experiment: part 2 DevFeed: [A Science Experiment: part 2]() Original publisher: [Read original article]() Published: 2021-08-26T12:50:28Z Content type: article Language: en Sources: [Bert Hubert's writings]() Topics: [Database](), [dataset](), [data](), [Jupyter Notebook](), [Software](), [jupyter]() Tags: [blog](), [data](), [database](), [dataset](), [dna](), [experiment](), [jupyter](), [jupyter-notebook](), [science](), [software]() ### AI overview This second installment describes SkewDB, an open database of GC/TA and other skews across 25,000 bacterial chromosomes, along with open-source software and a Jupyter notebook for exploring it. The author reports tentative findings about Firmicute skew prediction and unusual bacterial chromosome patterns, while emphasizing that the results require scientific review. ### Source excerpt And we're back! As noted in part 1, there is the possibility I've discovered something interesting in biology. Lacking an academic peer group, I'm using a series of blog posts to gather feedback and to keep myself honest. Because chances continue to be high that as an outsider I am deluding myself. The full backstory can be found in part 1 without which this installment will not make much sense. ## 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 ## Continuous Deployment for AWS Glue DevFeed: [Continuous Deployment for AWS Glue]() Original publisher: [Read original article]() Author: Mohammed Published: 2020-10-15T15:51:25Z Content type: tutorial Language: en Sources: [Brave New Geek]() Topics: [AWS Glue](), [Continuous Deployment (CD)](), [GitHub Actions](), [Jupyter Notebook](), [Amazon S3]() Tags: [analytics](), [analytics-pipeline](), [aws](), [aws-glue](), [ci-cd](), [continuous-delivery](), [continuous-deployment](), [etl](), [github](), [github-actions](), [jupyter](), [jupyter-notebook](), [s3](), [serverless]() ### AI overview A tutorial for automating continuous deployment of AWS Glue ETL jobs. It uses GitHub Actions to generate a Python script from a Jupyter notebook, copy it to Amazon S3, and update the Glue job to use the new script. ### Source excerpt AWS Glue is a managed service for building ETL (Extract-Transform-Load) jobs. It's a useful tool for implementing analytics pipelines in AWS without having to manage server infrastructure. Jobs are implemented using Apache Spark and, with the help of Development Endpoints, can be built using Jupyter notebooks. This makes it reasonably easy to write ETL processes in an interactive, iterative fashion. Once finished, the Jupyter notebook is converted into a Python script, uploaded to S3, and then run as a Glue job. ## From YAGNI to YDNIY DevFeed: [From YAGNI to YDNIY]() Original publisher: [Read original article]() Author: Itamar Turner-Trauring Published: 2020-09-18T00:00:00Z Content type: tutorial Language: en Sources: [Itamar Turner-Trauring]() Topics: [Python](), [Conda](), [jupyter](), [Linux](), [macOS](), [pip](), [data](), [C++]() Tags: [c-plus-plus](), [conda](), [jupyter](), [jupyter-notebooks](), [linux](), [macos](), [python](), [releases]() ### AI overview The article introduces YDNIY, or "You Don't Need It Yet," as a complement to YAGNI. It argues that features that are necessary but not release blockers can be delayed when a product remains useful without them. The author illustrates this with the staged development of the Python memory profiler Fil across Linux, macOS, Jupyter, Conda, and multiple memory-allocation APIs. ### Source excerpt How do you ship a product on schedule? One useful approach is applying the You Ain't Gonna Need It principle, or YAGNI for short: leave out all the things that seem nice-to-have, but you have no proof you actually need. But beyond the things you don't need, there are still plenty of features you pretty clearly do need... but are not blockers on releasing your product. So beyond YAGNI, there's also YDNIY: You Don't Need It Yet. Let's see an example of this principle in practice, visualize the principle as a flowchart, and then compare it to another popular acronymed concept, the Minimum Viable Product. A real world example: shipping a new memory profiler In March 2020 I shipped the initial release of a new memory profiler for Python, Fil. Here's how it changed over time in terms of features, from May to August 2020: 0.3.0, initial release: Installable via pip packaging tool, runs only on Linux, only profiles complete program runs. 0.3.3: Support for an additional memory allocation API. 0.4.0: Support for out-of-memory situations. 0.5.0: macOS support. 0.6.0: Support for mmap() allocation API. 0.7.0: Support for C++ memory allocation API. 0.9.0: Much faster and lower overhead in some use cases, added support for yet another memory allocation API. 0.10.0: Support for running inside Jupyter notebooks, and native support for installing via the Conda packaging tool. All of the features I added in later releases were clearly necessary from the start; YAGNI did not apply. Lots of people use macOS, the target audience of data scientists and scientists often use Conda and Jupyter, all those memory allocation APIs are used in the real world, and so on. But even a tool that only runs complete programs on Linux, and only tracks the most popular memory allocation APIs, is still useful to some people. If I had waited until all those features were implemented to ship an initial release, all the people who used the profiler during the first four months of its existence would have had ## Data Integrity Protection in Presto DevFeed: [Data Integrity Protection in Presto]() Original publisher: [Read original article]() Author: Piotr Findeisen, Starburst Data Published: 2020-06-25T00:00:00Z Content type: article Language: en Sources: [Trino Blog]() Topics: [data](), [Kubernetes](), [Amazon Elastic Kubernetes Service](), [releases](), [Amazon EC2](), [AWS CloudFormation]() Tags: [aws](), [benchmarks](), [cloud](), [cloudformation](), [data](), [enterprise](), [java](), [jupyter](), [kubernetes](), [pdf](), [performance](), [release](), [releases]() ### AI overview The article investigates a serious data-integrity issue discovered during Presto performance benchmarks for the Starburst Enterprise Presto 332-e release. It describes block encoding, an unexpected decoded length, and the environment changes considered while diagnosing the problem, including Java 11 and AWS EKS-based Kubernetes clusters. ### Source excerpt It all started on an Thursday afternoon in March, when Karol Sobczak was grilling Presto with heavy rounds of benchmarks, as we were ramping up to Starburst Enterprise Presto 332-e release. Karol discovered what seemed to be a serious regression, and turned out to be even more serious Cloud environment issue. ## When Jupyter Notebooks Won't Import Libraries DevFeed: [When Jupyter Notebooks Won't Import Libraries]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2020-01-03T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Jupyter Notebook](), [Python](), [Data Science]() Tags: [data-science](), [execution](), [flow](), [installation](), [jupyter](), [jupyter-notebooks](), [kernel](), [python]() ### AI overview An account of difficulties using Jupyter Notebooks with Python virtual environments. It discusses external data, dependency conflicts, attempts to change the Jupyter kernel, and the decision to convert the notebook into a Python script that can run in a virtual environment. ### Source excerpt I don't claim to be an expert when it comes to Python. At best I'm an apprentice striving to be a journey man. One of the interesting tools in the Python / Data Science ecosystem is the Jupyter Notebook which gives a cell based representation of code, visualizations, documentation and execution flow and allows you to package things up for distribution i.e. hand your work, in a complete fashion, from say Data Scientist 1 ("Rebekah") to Data Scientist 2 ("Dawn"). This is a laudable goal and one that I theoretically agree with. Note: Jupyter Notebooks do NOT include data so that's still external to the notebook, something that can easily bite you (as it is currently biting me). I know a lot of pythonistas simply install libraries to their local machine and just have a collection of tools that the throw at problems. This, however, is a terrible practice due to code deprecation, version conflicts, etc. I say this with authority because I've been through this in the Ruby world before we all regularly started using Ruby virtual environments / Ruby version managers like RBENV / RVM which manage dependencies on a per project basis. Knowing this, my first practice with Python, is to always create a virtual environment, generally using Virtual ENV (VENV). So, when I set out today to use a Jupyter Notebook, my first approach was to make this work with a Jupyter Notebook. And, alas, I haven't been so lucky as to make this work cleanly with a Python virtual environment like VENV. Here are some of the things I tried: Installing jupyter into the virtual env and running from bin/jupyter notebook Following Angelo Basille's Directions Following Janakiev's Directions Changing the Kernel If you're curious about how to use Python Virtual Environments, I wrote a solid tutorial back in September that I used to get a full installation of Tensor Flow up and running. I've referred back to this over and over, each time I needed a Python Virtual Environment, so I know it works. I'm sure there i ## Using Python and OCaml in the same Jupyter notebook DevFeed: [Using Python and OCaml in the same Jupyter notebook]() Original publisher: [Read original article]() Author: Laurent Mazare Published: 2019-12-16T00:00:00Z Content type: tutorial Language: en Sources: [Jane Street]() Topics: [OCaml](), [Python](), [Jupyter Notebook](), [jupyter](), [Programming](), [NumPy]() Tags: [jupyter](), [jupyter-notebook](), [libraries](), [modules](), [ocaml](), [programming](), [programming-language](), [python]() ### AI overview A technical tutorial on running Python and OCaml code in the same Jupyter notebook. It explains how Jane Street uses pyml, OCaml bindings to the Python C API, plus a ppx extension and library to create bindings, exchange values, and reuse OCaml systems from Python workflows. ### Source excerpt The cover image is based on Jupiter family by NASA/JPL. ## Commas in big numbers everywhere: An OpenType adventure DevFeed: [Commas in big numbers everywhere: An OpenType adventure]() Original publisher: [Read original article]() Author: Tristan Hume Published: 2019-10-14T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Font](), [data](), [jupyter](), [Terminal](), [browser]() Tags: [browser](), [documentation](), [efficiency](), [feature](), [fonts](), [jupyter](), [jupyter-notebooks](), [numbers](), [terminal]() ### AI overview This article describes Numderline, a font that uses OpenType shaping features to insert commas into numbers and emphasize digit grouping. It explains the basics of font shaping, including glyph substitutions and positioning, and discusses how reverse substitution enables grouping digits from the end of a number. ### Source excerpt My job involves a lot of staring at large numbers, mostly latencies in nanoseconds, and picking out magnitudes like microseconds. I noticed myself constantly counting digits in my text editor, in my terminal, and in Jupyter notebooks in my browser.