프로젝트 주피터

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Pythagoras0 (토론 | 기여)님의 2020년 12월 21일 (월) 01:57 판 (→‎노트: 새 문단)
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노트

  • Take a look at my posts on Setting up Anaconda and Jupyter Notebook, and Setting up EC2 for Jupyter Notebook on AWS.[1]
  • If you have an existing Jupyter notebook on your Google Drive, simply click on File/Open drive notebook to open it.[1]
  • Jupyter Notebook was created to make it easier to show one’s programming work, and to let others join in.[2]
  • Jupyter Notebook lets you author visualizations, but also share them and allow interactive changes to the shared code and data set.[2]
  • With a Jupyter Notebook, you can view code, execute it, and display the results directly in your web browser.[2]
  • Because Jupyter Notebook is built on web technology, it can display all the types of multimedia supported in a web page.[2]
  • (This is just an example of what Jupyter Notebook can do, don’t feel like you need to understand it just yet![3]
  • If you can’t get Jupyter Notebook to work on your computer at all, there’s a hosted service called Wakari that you can use for free.[3]
  • You interact with Jupyter Notebook using your web browser.[3]
  • On Linux or OS X, you can start Jupyter Notebook from the command line.[3]
  • If the Jupyter Notebook doesn't open automatically in your browser, copy and paste the URL provided by the command line into your browser.[4]
  • When running in Jupyter Notebook, the Q# code is compiled, and the cell outputs the name of any operations that it finds.[4]
  • The operations that should be completed in order to have Jupyter Notebook ready to use on local server.[5]
  • The bulk of this tutorial discusses executing python code in Jupyter notebooks.[6]
  • You can also use Jupyter notebooks to execute R code.[6]
  • There are many ways to share a static Jupyter notebook with others, such as posting it on GitHub or sharing an nbviewer link.[7]
  • But what if you want to share a fully interactive Jupyter notebook that doesn't require any installation?[7]
  • In this post, I'm going to review six services you can use to easily run your Jupyter notebook in the cloud.[7]
  • Users don't have to create an account, and they'll feel right at home if they already know how to use the Jupyter Notebook.[7]
  • The command starts Jupyter Notebook and opens it in your default web browser as shown below.[8]
  • It also allows Jupyter Notebook to support multiple languages.[9]
  • If you’d rather watch a video instead of read an article, please watch the following instructions on how to use a Jupyter Notebook.[9]
  • To launch a Jupyter notebook, open your terminal and navigate to the directory where you would like to save your notebook.[9]
  • Lastly, in 2014, Project Jupyter started as a spin-off project from IPython.[10]
  • Don't miss this gallery of interesting IPython notebooks or this KDnuggets article.[10]
  • Your Kubeflow deployment includes support for spawning and managing Jupyter notebooks.[11]
  • In this tutorial, you use a locally hosted Jupyter notebook.[12]
  • Note: Semicolons are not normally used in Python, but in Jupyter notebooks, they can be used to suppress the output of the function.[12]
  • The IPython Notebook is now known as the Jupyter Notebook.[13]
  • For more details on the Jupyter Notebook, please see the Jupyter website.[13]
  • Note: By default, the Visual Studio Code Python extension will open a Jupyter Notebook (.ipynb) in the Notebook Editor.[14]
  • It's possible for malicious source code to be contained in a Jupyter Notebook.[14]
  • You can export a Jupyter Notebook as a Python file (.py), a PDF, or an HTML file.[14]
  • Using the add cell icons at the top and bottom of the Jupyter Notebook, will add a code cell at the top and bottom respectively.[14]
  • In 2015, Jupyter notebook was released as a part of The Big Split™ of the IPython codebase.[15]
  • You need some configuration before starting Jupyter notebook remotely.[15]
  • Google’s Colaboratory project, for instance, provides a Google-themed front-end to the Jupyter notebook.[16]
  • One is JupyterHub, a service that allows institutions to provide Jupyter notebooks to large pools of users.[16]
  • The purpose of Jupyter notebooks is to provide a more accessible interface for code used in digitally-supported research or pedagogy.[17]
  • Jupyter Notebook (not to be confused with the Jupyter notebook files themselves, which have an .ipynb extension), and the newer Jupyter Lab.[17]
  • If you are using the command line to launch Jupyter Notebook, pay attention to the directory you are in when you launch it.[17]
  • The Jupyter Notebook file browser interface is the main way to open a Jupyter notebook (.ipynb) file.[17]
  • The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects.[18]
  • (In fact, this article was written as a Jupyter Notebook![18]
  • Your first Jupyter Notebook will open in new tab — each notebook uses its own tab because you can open multiple notebooks simultaneously.[18]
  • Provided as a free service as part of Project Jupyter, it is available at nbviewer.jupyter.org.[18]
  • Spun off from IPython in 2014 by Fernando Pérez, Project Jupyter supports execution environments in several dozen languages.[19]
  • Jupyter Notebook (formerly IPython Notebooks) is a web-based interactive computational environment for creating Jupyter notebook documents.[19]
  • Jupyter Notebook can connect to many kernels to allow programming in different languages.[19]
  • To start the Jupyter Notebook, click on Launch, which will take you to the Jupyter homepage.[20]
  • You can rename the Jupyter Notebook by clicking on Untitled.[20]
  • A Jupyter Notebook can be downloaded in multiple formats.[20]
  • Jupyter Notebook is an open source web application that allows you to create interactive Python documents.[21]
  • One way to code in Python is to use a Jupyter notebook.[22]
  • If you are viewing this section as a Jupyter notebook, the text you are now reading is in a text cell.[22]

소스

  1. ↑ 1.0 1.1 Running Jupyter Notebook on Colab
  2. ↑ 2.0 2.1 2.2 2.3 What is Jupyter Notebook? Data analysis made easier
  3. ↑ 3.0 3.1 3.2 3.3 OpenTechSchool – Introducing Jupyter Notebook
  4. ↑ 4.0 4.1 Develop with Q# Jupyter Notebooks - Microsoft Quantum
  5. ↑ Download, Install and Execute Jupyter Notebook Environment
  6. ↑ 6.0 6.1 Jupyter Notebook Tutorial
  7. ↑ 7.0 7.1 7.2 7.3 Six easy ways to run your Jupyter Notebook in the cloud
  8. ↑ Using the Jupyter Notebook environment
  9. ↑ 9.0 9.1 9.2 How To Use Jupyter Notebooks
  10. ↑ 10.0 10.1 (Tutorial) Jupyter Notebook: The Definitive Guide
  11. ↑ Jupyter Notebooks
  12. ↑ 12.0 12.1 Visualizing BigQuery data in a Jupyter notebook
  13. ↑ 13.0 13.1 The Jupyter Notebook — IPython
  14. ↑ 14.0 14.1 14.2 14.3 Working with Jupyter Notebooks in Visual Studio Code
  15. ↑ 15.0 15.1 jupyter/notebook: Jupyter Interactive Notebook
  16. ↑ 16.0 16.1 Why Jupyter is data scientists’ computational notebook of choice
  17. ↑ 17.0 17.1 17.2 17.3 Introduction to Jupyter Notebooks
  18. ↑ 18.0 18.1 18.2 18.3 How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial
  19. ↑ 19.0 19.1 19.2 Project Jupyter
  20. ↑ 20.0 20.1 20.2 Getting Started With Jupyter Network
  21. ↑ Jupyter Notebook
  22. ↑ 22.0 22.1 Python and Jupyter Notebooks

노트

위키데이터

말뭉치

  1. To get started with IPython in the Jupyter Notebook, see our official example collection.[1]
  2. The Project Jupyter team has partnered with O’Reilly Media for this event; for more details, including submitting a talk, see the JupyterCon website.[1]
  3. Mining the Social Web is an open source data science project and book that features nearly 130 examples with IPython Notebook and a Vagrant-powered virtual machine environment.[1]
  4. Project Jupyter (including IPython) is transforming interactive development and data exploration across multiple industries.[2]
  5. “We kicked off this project in June with Project Jupyter and NumFOCUS Foundation as well as IBL Education, responsible for the Open EDX deployment at the conference.[3]
  6. To conclude: this partnership with Project Jupyter is here to stay.[3]
  7. The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects.[4]
  8. (In fact, this article was written as a Jupyter Notebook![4]
  9. With Jupyter Notebook open in your browser, you may have noticed that the URL for the dashboard is something like http://localhost:8888/tree .[4]
  10. Your first Jupyter Notebook will open in new tab — each notebook uses its own tab because you can open multiple notebooks simultaneously.[4]
  11. They aren’t the only forum for such conversations — IPython, the interactive Python interpreter on which Jupyter’s predecessor, IPython Notebook, was built, is another.[5]
  12. Google’s Colaboratory project, for instance, provides a Google-themed front-end to the Jupyter notebook.[5]
  13. Whereas the standard Jupyter notebook assigns each notebook its own kernel, JupyterLab creates a computing environment that allows these components to be shared.[5]
  14. Project Jupyter started as a spin-off from IPython project in 2014.[6]
  15. As a server-client application, the Jupyter Notebook App allows you to edit and run your notebooks via a web browser.[7]
  16. Lastly, in 2014, Project Jupyter started as a spin-off project from IPython.[7]
  17. With jupyterhub, you can spawn, manage, and proxy multiple instances of the single-user Jupyter notebook server.[7]
  18. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text.[8]
  19. Jupyter Notebook (not to be confused with the Jupyter notebook files themselves, which have an .ipynb extension), and the newer Jupyter Lab.[9]
  20. Jupyter Notebook is widely-used and well-documented, and provides a simple file browser along with the environment for creating, editing, and running the notebooks.[9]
  21. While Jupyter Lab is meant to eventually replace Jupyter Notebook, there is no indication that Jupyter Notebook will stop being supported anytime soon.[9]
  22. Because of its comparative simplicity and ease of use for beginners, this tutorial uses Jupyter Notebook as the software for running notebook files.[9]
  23. After downloading your project files, you can use them with other Jupyter Notebook solutions.[10]
  24. This tutorial uses the sample project, Markowitz Notebook–an investment portfolio analysis that demonstrates a Jupyter Notebook running both R language and Python.[11]
  25. Project Jupyter is an open-source project that exists to develop software, open standards, and services for interactive and reproducible computing.[12]
  26. In this talk I will give an overview of Project Jupyter and its open-source software and open standards for interactive and exploratory computing.[12]
  27. In this tutorial, you use a locally hosted Jupyter notebook.[13]
  28. jupyter notebook Jupyter should now be running and open in a browser window.[13]
  29. In this episode of DataFramed, Hugo speaks with Brian Granger, co-founder and co-lead of Project Jupyter, physicist and co-creator of the Altair package for statistical visualization in Python.[14]
  30. They’ll speak about data science, interactive computing, open source software and Project Jupyter.[14]
  31. With over 2.5 million public Jupyter notebooks on github alone, Project Jupyter is a force to be reckoned with.[14]
  32. Jupyter Notebook (open source code), which began as the iPython Notebook project, is a development environment for writing and executing Python code.[15]
  33. Project Jupyter is the top-level project name for all of the subprojects under development, which includes Jupyter Notebook.[15]
  34. IPython Notebook was the original project that proved that there was great demand among data scientists and programmers for an interactive, repeatable development environment.[15]
  35. Jupyter Notebook's powerful analysis and visualization environment can be intimidating even for experienced developers that are new to the tool.[15]
  36. The Jupyter Notebook is a popular tool for learning and performing data science in Python (and other languages used in data science).[16]
  37. This video tutorial will teach you about Project Jupyter and the Jupyter ecosystem and get you up and running in the Jupyter Notebook environment.[16]
  38. Jupyter Notebook is built off of IPython, an interactive way of running Python code in the terminal using the REPL model (Read-Eval-Print-Loop).[17]
  39. The IPython Kernel runs the computations and communicates with the Jupyter Notebook front-end interface.[17]
  40. It also allows Jupyter Notebook to support multiple languages.[17]
  41. If you’d rather watch a video instead of read an article, please watch the following instructions on how to use a Jupyter Notebook.[17]
  42. Project Jupyter is an open source project that develops the Notebook and other components that relate to it.[18]
  43. A Jupyter Notebook rendered as a webpage.[18]
  44. The Jupyter Notebook hints at what the academic journals of tomorrow will look like and paints a promising picture.[18]
  45. If you'd like to experiment with a Jupyter Notebook, please visit our demo page.[18]
  46. There are many ways to share a static Jupyter notebook with others, such as posting it on GitHub or sharing an nbviewer link.[19]
  47. But what if you want to share a fully interactive Jupyter notebook that doesn't require any installation?[19]
  48. In this post, I'm going to review six services you can use to easily run your Jupyter notebook in the cloud.[19]
  49. Binder is a service provided by the Binder Project, which is a member of the Project Jupyter open source ecosystem.[19]
  50. Spun off from IPython in 2014 by Fernando Pérez, Project Jupyter supports execution environments in several dozen languages.[20]
  51. Jupyter Notebook (formerly IPython Notebooks) is a web-based interactive computational environment for creating Jupyter notebook documents.[20]
  52. Jupyter Notebook can connect to many kernels to allow programming in different languages.[20]
  53. The Jupyter Notebook has become a popular user interface for cloud computing, and major cloud providers have adopted the Jupyter Notebook or derivative tools as a frontend interface for cloud users.[20]
  54. More specifically, the Jupyter Notebook is an open-source web application that allows you to create and share documents which contain live code, equations, visualizations and narrative text.[21]
  55. The Jupyter Notebook should be reminiscent of an R Markdown document.[21]
  56. " Spun-off from IPython in 2014 by Fernando Pérez, Project Jupyter supports execution environments in several dozen languages.[22]
  57. To start Jupyter from the command line, type jupyter notebook .[23]
  58. Part 2 provides an in-depth data exploration using Jupyter Notebook, using the code built in Part 1.[23]

소스

  1. ↑ 1.0 1.1 1.2 Jupyter and the future of IPython — IPython
  2. ↑ Project Jupyter
  3. ↑ 3.0 3.1 Our partnership with Project Jupyter: the value of an open-source data science community
  4. ↑ 4.0 4.1 4.2 4.3 How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial
  5. ↑ 5.0 5.1 5.2 Why Jupyter is data scientists’ computational notebook of choice
  6. ↑ Project Jupyter
  7. ↑ 7.0 7.1 7.2 (Tutorial) Jupyter Notebook: The Definitive Guide
  8. ↑ Install Project Jupyter for Linux using the Snap Store
  9. ↑ 9.0 9.1 9.2 9.3 Introduction to Jupyter Notebooks
  10. ↑ Export a Jupyter Notebook project from the Azure Notebooks Preview
  11. ↑ Deploying a Jupyter Notebook project — Anaconda Platform 5.0.2.1 documentation
  12. ↑ 12.0 12.1 Project Jupyter: From Computational Notebooks to Large Scale Data Science with Sensitive Data with Brian Granger
  13. ↑ 13.0 13.1 Visualizing BigQuery data in a Jupyter notebook
  14. ↑ 14.0 14.1 14.2 #44 Project Jupyter and Interactive Computing (with Brian Granger) by DataFramed
  15. ↑ 15.0 15.1 15.2 15.3 Jupyter Notebook
  16. ↑ 16.0 16.1 Using Jupyter Notebooks for Data Science Analysis in Python LiveLessons
  17. ↑ 17.0 17.1 17.2 17.3 How To Use Jupyter Notebooks
  18. ↑ 18.0 18.1 18.2 18.3 How will the children of the future learn about science?
  19. ↑ 19.0 19.1 19.2 19.3 Six easy ways to run your Jupyter Notebook in the cloud
  20. ↑ 20.0 20.1 20.2 20.3 Project Jupyter
  21. ↑ 21.0 21.1 Getting started with Jupyter and JupyterHub · Teach Data Science
  22. ↑ Blissfully SaaS Directory
  23. ↑ 23.0 23.1 Explore your project with Jupyter Notebooks and deploy it to the Python Package index
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