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Jupyter

Using IPython Jupyter Magic Commands to Improve the Notebook Experience

A post on creating a custom IPython Jupyter Magic commandLearn to apply some magic to your notebooks. Image by author using DALL-E-3. A version of this post originally appeared here.IntroductionJupyter Notebooks are commonplace in data science. They allow a mixture of “repeat, evaluation, loop” (REPL) code writing and documentation in a single place. They’re most commonly used for analysis and brainstorming purposes, but also, more contentiously, some prefer notebooks to scripts to run production code (but we won’t focus…

Tips and Tricks to Organize Jupyter Notebook Visualizations

Optimize your data science workflow by automating matplotlib output — with 1 line of code. Here’s how.Naming things is hard. After a long enough day, we’ve all ended up with the highly-descriptive likes of “graph7(1)_FINAL(2).png” and “output.pdf" Look familiar?We can do better — and quite easily, actually.When we use data-oriented “seaborn-esque” plotting mechanisms, the ingredients for a descriptive filename are all there. A typical call looks like this,sns.scatterplot(data=tips, x="total_bill",…

Enhancing Data Science Workflows: Mastering Version Control for Jupyter Notebooks

A hands-on guide to facilitate collaboration and reproducibility with Jupytext, nbstripout, and nbconvertContinue reading on Towards Data Science » A hands-on guide to facilitate collaboration and reproducibility with Jupytext, nbstripout, and nbconvertContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All…

Kubeflow Fundamentals Part 6: Working With Jupyter Lab Notebooks

Welcome to the sixth blog post in our “Kubeflow Fundamentals” series specifically designed for folks brand new to the Kubelfow project. The aim of the series is to walk you through a detailed introduction of Kubeflow, a deep-dive into the various components, add-ons, and how they all come together to deliver a complete MLOps platform. If you missed the previous installments in the “Kubeflow Fundamentals” series, you can find them here: In this post, we’ll focus on getting a little more familiar with Jupyter notebooks and…

Build a Web App with Jupyter and Mercury | by Alan Jones | May, 2023

TUTORIALMercury provides a simple way to transform Jupyter Notebooks into interactive web apps.Am I referring to code development or CO2 emissions? Photo by Etienne Girardet on UnsplashWhere would we be without Jupyter Notebooks? Surely they are one of the staples of the Data Science community.They are great for prototyping and/or incrementally building and demonstrating a data science application. But they are not so good at presentation.If you want to show your stakeholders the results of your work as a web application,…

Run Interactive Sessions With ChatGPT In Jupyter Notebook

Before we can start working on our conversational sessions with ChatGPT, some things need to be done first.OpenAI API key 🔑Since we want to use ChatGPT, we first need a valid OpenAI API key. The needed key can be created under this link and then by clicking on the+ Create new secret key button.OpenAI offers a free trial period before you charge money. The prices are super fair in my opinion, considering that hosting your own LLM is more expensive in many cases.Installing the OpenAI package 📦Once we have the key, we also…

Build Elegant Web Apps Right From Jupyter Notebook with Mercury | by Avi Chawla | Apr, 2023

Notebooks to beautiful web apps in no timePhoto by NASA on UnsplashEffective communication is pivotal in all data-driven projects. Data professionals often need to communicate their findings and insights to stakeholders, including business leaders, technical teams, and other data scientists.While traditional methods of communicating data insights, such as PowerPoint presentations and static reports are widely preferred, they are often time-consuming to create.What’s more, these services require one to leave the comfort of…

Deploy Machine Learning Models Right From Your Jupyter Notebook | by Avi Chawla | Mar, 2023

Deploy machine learning models in one line of codePhoto by Roman Synkevych 🇺🇦 on UnsplashAmidst this AI revolution, building intelligent systems at scale has been of great interest lately to countless organizations.While plenty of time and energy is being actively spent in training large machine learning models, taking these models to production and maintaining them is a task of its own.This, in some cases, may even require specialized teams.And while more and more organizations are resorting to artificial intelligence…

The Perfect Text Editor for Jupyter: A Complete Python IDE | by Dimitris Poulopoulos | Mar, 2023

From syntax highlighting to code completion, a complete Python IDE inside JupyterPhoto by Max Duzij on UnsplashThis article is part of a series. Check out the full series: Part I, Part II, Part III.Over the past few days, we’ve been building a complete Python IDE inside Jupyter. In this article, we will add the final touches and package everything in a Docker image to create a portable working environment for data scientists and Machine Learning engineers.Jupyter is not exactly an IDE. It’s not even an IPython UI, as many…

Jupyter Already Has a Perfect Text Editor: Building a Python IDE | by Dimitris Poulopoulos | Mar, 2023

Make Jupyter your all-in-one IDE and elevate your Python development experiencePhoto by Fotis Fotopoulos on UnsplashThis article is part of a series. Check out the full series: Part I, Part II.In previous parts of this series, we covered why many developers do not consider Jupyter as a fully integrated development environment and how the lack of a powerful text editor is one of the main reasons.Thus, we decided to look at JupyterLab as a platform that we could use to create our own Python IDE from scratch. Extending the…