# Jupyter Notebook Jupyter Notebook is a simplified notebook authoring application for interactive computing, enabling users to prototype and explain code, explore and visualize data, and share ideas. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## How Jane Street Uses Expect Tests for Fast, Interactive Test Development DevFeed: [How Jane Street Uses Expect Tests for Fast, Interactive Test Development]() Original publisher: [Read original article]() Author: James Somers Published: 2023-01-09T00:00:00Z Content type: article Language: en Sources: [Jane Street]() Topics: [Testing](), [Jupyter Notebook](), [Emacs](), [Programming]() Tags: [emacs](), [jest](), [jupyter-notebook](), [programming](), [testing](), [tests]() ### AI overview The article explains Jane Street's expect tests, a testing workflow that fills blank expected-output blocks with program results, shows diffs, and lets developers accept those changes directly in Emacs. It presents the approach as a fast way to explore examples and iteratively improve code. ### Source excerpt At Jane Street we use a pattern/library called "expect tests" that makes test-writing feel like a REPL session, or like exploratory programming in a Jupyter notebook--with feedback cycles so fast and joyful that it feels almost tactile. Having used them for some time now this is the only way I'd ever want to write tests. ## UK Bank Holidays with Pandas on Jupyter DevFeed: [UK Bank Holidays with Pandas on Jupyter]() Original publisher: [Read original article]() Author: Simone Spaccarotella Published: 2022-09-21T17:22:24Z Content type: tutorial Language: en Sources: [BBC]() Topics: [pandas](), [Jupyter Notebook](), [JSON](), [Python](), [Data Science](), [data]() Tags: [coding](), [data-science](), [json](), [jupyter-notebook](), [kaggle](), [pandas](), [python]() ### AI overview A hands-on tutorial about using Pandas in a Jupyter Notebook to retrieve UK bank holiday data in JSON format and prepare it for analysis in a DataFrame. The article also describes the author's self-study practice in Python, data science, and machine learning. ### Source excerpt How I learned to manipulate JSON data with Pandas on a Jupyter Notebook and deconstruct it to a DataFrame ready for queries.Image by author created from Jupiter photo by NASA and Pandas photo by Pascal Müller on UnsplashA bit of context first I started a self-study path to learn the theoretical fundamentals of Data Science and Machine Learning. I have also been playing with Python ever since, with coding exercises during the "Friday 10% time" here at the BBC and on my own time when I have some to spare. I created this playground repository to refresh my Pythonic knowledge that was buried deep inside the funny head of mine. This repo is a mix of material and exercises to keep my Python coding skills sharp. GitHub - simonespa/python-playground: A playground repo for Python I also created a specific playground focused on Data Science and Machine learning. In this playground I practice with libraries such as NumPy, Pandas, Scikit-Learn, TensorFlow (et. all) with a Jupyter Notebook. It also contains notes and summaries of what I learn in forms of README or notebooks. GitHub - simonespa/machine-learning-playground: A playground to practice machine learning, data modelling and visualisation Nothing fancy or ground-breaking, just a couple of playground repos for me to practice and learn at my own pace. The 10% time at the BBC This happens every other Friday and it is about learning. Engineers use this time to learn new technologies and/or languages, discover new approaches and techniques. We can organise the day as we want. It could be a self-paced learning session (reading, watching tutorials, etc.), a day of study for an apprenticeship or a university degree etc., an actual hands-on prototyping session to work on a long-running personal project, or a group collaboration with other like-minded people. There is no limitation to how we plan our day, as long as we keep in mind the end goal. In simple terms, satisfy our own curiosity and creativity by strengthening our technic ## 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. ## A Five-Step Guide for Conducting Exploratory Data Analysis DevFeed: [A Five-Step Guide for Conducting Exploratory Data Analysis]() Original publisher: [Read original article]() Author: Cody Mazza-Anthony Published: 2021-04-28T19:43:52Z Content type: tutorial Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Data analysis](), [Data Science](), [datasets](), [dashboards](), [Jupyter Notebook](), [Shopify]() Tags: [analysis](), [context](), [data-analysis](), [data-science](), [dataset](), [datasets](), [features](), [framework](), [guide](), [jupyter-notebook](), [shopify](), [synthetic](), [tips]() ### AI overview A five-step guide to exploratory data analysis (EDA), covering how to understand a dataset, identify patterns such as correlations, missing data, and outliers, and use visualizations and hypotheses to support business and data product decisions. It illustrates the process with a synthetic dataset and discusses practical considerations such as dataset size, computational limits, and working in a Jupyter notebook. ### Source excerpt Have you ever been handed a dataset and then been asked to describe it? When I was first starting out in data science, this question confused me. My first thought was "What do you mean?" followed by "Can you be more specific?" The reality is that exploratory data analysis (EDA) is a critical tool in every data scientist's kit, and the results are invaluable for answering important business questions. ## 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. ## 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. ## Advent of Code 2016 DevFeed: [Advent of Code 2016]() Original publisher: [Read original article]() Published: 2016-12-24T00:00:00Z Content type: tutorial Language: en Sources: [Peter Norvig]() Topics: [Advent of Code](), [Code Challenge](), [Jupyter Notebook](), [Code]() Tags: [advent-of-code](), [code](), [jupyter-notebook](), [puzzle](), [solutions](), [test]() ### AI overview Peter Norvig documents his solutions to the Advent of Code puzzles released from December 1 through December 25, 2016. The notebook includes preparation, utility functions, tests, and selected explanations of his solving approach. ### Source excerpt Solutions to the puzzle site.