Writing Books with Jupyter

write a book with jupyter notebook

I’m a huge fan of the Jupyter project, and love to see all the progress being made with JupyterHub and especially JupyterLab .

Recently learned of another Jupyter project, Jupyter Book :

Jupyter Book is an open source project for building beautiful, publication-quality books and documents from computational material.

It’s still early in development, but this could scratch a real itch that I have: pushing computational notebooks beyond working documents and into prose, especially long-form writing, reveals some sticking points . Jupyter Book looks to address exactly these problems with features including:

Write publication-quality content in markdown You can write in either Jupyter markdown, or an extended flavor of markdown with publishing features. This includes support for rich syntax such as citations and cross-references, math and equations, and figures. Insert notebook outputs into your content Generate outputs as you build your documentation, and insert them in-line with your content across pages. Add interactivity to your book You can toggle cell visibility, include interactive outputs from Jupyter, and connect with online services like Binder. Generate a variety of outputs This includes single- and multi-page websites, as well as PDF outputs. Build books with a simple command-line interface You can quickly generate your books with one command, like so: jupyter-book build mybook/

I often wish I had these features when preparing notebooks for my courses , especially the citations and cross-references. It was a pain getting my Whirlwind Tour of Python fork going, that’s for sure.

And the pages are gorgeous :

Check out that dynamic table of contents on the right. And that’s there by default , not some third-party extension.

Jim Bagrow

Associate Professor of Mathematics & Statistics

My research interests include complex networks, computational social science, and data science.

This is a guide for creating your own book using Jupyter Notebooks and Jekyll. Book content is written in markdown and Jupyter Notebooks, and jupyter-book converts these into a book fit for hosting on the web.

Install the command-line interface

First off, make sure you have the CLI installed so that you can work with Jupyter Book. The Jupyter-Book CLI allows you to create, build, upgrade, and otherwise control your Jupyter Book. You can install it via pip with the following command:

The book building process

Building a Jupyter Book broadly consists of three steps:

  • Create your book template . Jupyter Book expects a particular collection of files and folders that work with the Static Site Generator Jekyll. The anatomy of a Jupyter Book section covers the general structure of Jupyter Books, and the create your book template guide shows how to create your own book structure.
  • Convert each page of your book into HTML . We first create each page of your book into HTML. This converts your .ipynb , .md , etc files into HTML that can be understood by a website. It also uses your book's metadata to insert tags and other layout elements into each page's HTML. See the building each page's HTML section for more information.
  • Build your book's HTML from these pages . Once we have HTML for each page, we can stitch them together for a book. At the end of this step, you should have standalone HTML that can be hosted online. See the build your book's html section for more information.
  • Host your book's HTML online . Once your book's HTML is built, you can host it online as a public website. This guide covers a few ways to do this .

To begin, check out the next section. You can follow this guide linearly, or use it as a reference later on.

write a book with jupyter notebook

Announcing the new Jupyter Book

Chris Holdgraf

Chris Holdgraf

Jupyter Blog

Note: this announcement is cross-posted between the Jupyter Blog and the Executable Book Project updates blog

Jupyter Book is an open source project for building beautiful, publication-quality books, websites, and documents from source material that contains computational content. With this post, we’re happy to announce that Jupyter Book has been re-written from the ground up, making it easier to install, faster to use, and able to create more complex publishing content in your books. It is now supported by the Executable Book Project , an open community that builds open source tools for interactive and executable documents in the Jupyter ecosystem and beyond.

This post is a short overview of the new features in Jupyter Book, and gives some background on the future of the project.

You can also browse the new Jupyter Book documentation , or check out the Jupyter Book GitHub repository .

❗❗ Jupyter Book is still in beta, so things are always changing. We’d love your feedback, ideas, and PRs for how to make the project better!❗❗

What does the new Jupyter Book do?

The new version of Jupyter Book will feel very similar. However, it has a lot of new features due to the new Jupyter Book stack underneath (more on that later).

The new Jupyter Book has the following main features (with links to the relevant documentation for each):

✅ Write publication-quality content in markdown You can write in either Jupyter markdown, or an extended flavor of markdown with publishing features . This includes support for rich syntax such as citations and cross-references , math and equations , and figures .

✅ Write content in Jupyter Notebooks This allows you to include your code and outputs in your book. You can also write notebooks entirely in markdown to execute when you build your book.

✅ Execute and cache your book’s content For .ipynb and markdown notebooks, execute code and insert the latest outputs into your book. In addition, cache and re-use outputs to be used later.

✅ Insert notebook outputs into your content Generate outputs as you build your documentation, and insert them in-line with your content across pages.

✅ Add interactivity to your book You can toggle cell visibility , include interactive outputs from Jupyter, and connect with online services like Binder.

✅ Generate a variety of outputs This includes single- and multi-page websites, as well as PDF outputs .

✅ Build books with a simple command-line interface You can quickly generate your books with one command, like so: jupyter-book build mybook/

These are just a few of the major changes that we’ve made. For a more complete idea of what you can do, check out the Jupyter Book documentation

Major changes

In the next few sections we’ll talk about some major changes that you may notice in Jupyter Book.

An enhanced flavor of markdown

The biggest enhancement to Jupyter Book is support for the MyST Markdown language . MyST stands for “Markedly Structured Text”, and is a flavor of markdown that implements all of the features of the Sphinx documentation engine , allowing you to write scientific publications in markdown. It draws inspiration from RMarkdown and the reStructuredText ecosystem of tools. Anything you can do in Sphinx, you can do with MyST as well.

MyST Markdown is a superset of Jupyter Markdown (AKA, CommonMark), meaning that any default markdown in a Jupyter Notebook is valid in Jupyter Book . If you’d like extra features in markdown such as citations, figures, references, etc, then you may include extra MyST Markdown syntax in your content.

For example, here’s how you can include a citation in the new Jupyter Book:

And here’s how you can include a figure:

A smarter build system

While the old version of Jupyter Book used a combination of Python and Jekyll to build your book’s HTML, the new Jupyter Book uses Python all the way through. This means that building the HTML for your book is as simple as:

In addition, the new build system leverages Jupyter Cache to execute notebook content only if the code is updated , and to insert the outputs from the cache at build time. This saves you time by avoiding the need to re-execute code that hasn’t been changed.

More book output types

By leveraging Sphinx, Jupyter Book will be able to support more complex outputs than just an HTML website. For example, we are currently prototyping PDF Outputs , both via HTML as well as via LaTeX. This gives Jupyter Book more flexibility to generate the right book for your use case.

You can also run Jupyter Book on individual pages . This means that you can write single-page content (like a scientific article) entirely in Markdown.

A new stack

The biggest change under-the-hood is that Jupyter Book now uses the Sphinx documentation engine instead of Jekyll for building books. By leveraging the Sphinx ecosystem, Jupyter Book can more effectively build on top of community tools, and can contribute components back to the broader community.

Instead of being a single repository, the old Jupyter Book repository has now been separated into several modular tools. Each of these tools can be used on their own in your Sphinx documentation , and they can be coordinated together via Jupyter Book:

  • The MyST markdown parser for Sphinx allows you to write fully-featured Sphinx documentation in Markdown.
  • MyST-NB is an .ipynb parser for Sphinx that allows you to use MyST Markdown in your notebooks. It also provides tools for execution, cacheing, and variable insertion of Jupyter Notebooks in Sphinx.
  • The Sphinx Book Theme is a beautiful book-like theme for Sphinx, build on top of the PyData Sphinx Theme .
  • Jupyter Cache allows you to execute a collection of notebooks and store their outputs in a hashed database. This lets you cache your notebook’s output without including it in the .ipynb file itself.
  • Sphinx-Thebe converts your “static” HTML page into an interactive page with code cells that are run remotely by a Binder kernel.
  • Finally, Jupyter Book also supports a growing collection of Sphinx extensions , such as sphinx-copybutton , sphinx-togglebutton , sphinx-comments , and sphinx-panels .

We’ll write a more developer-focused post in the future to describe each of these components in more detail.

Jupyter Book and its related projects will continue to be developed as a part of the Executable Book Project , a community that builds open source tools for high-quality scientific publications from computational content in the Jupyter ecosystem and beyond.

If you’d like to learn more about Jupyter Book or get started building your own books, then check out the new Jupyter Book documentation . Jupyter Book is still in beta and is constantly being improved, so your feedback and contributions are always welcome .

If there are particular features you’d like to see, open an issue or give a 👍 to a pre-existing issue, and it will be bumped up on the feature request leaderboard .

Get involved

If you’d like to contribute to any of the projects listed in this post, you are highly encouraged to do so! The Executable Book Project (and thus Jupyter Book) is run as an open project that welcomes contributions from others .

We are excited about all the new features and improvements in the new Jupyter Book, and look forward to seeing the new books that the community creates with this stack!

Chris Holdgraf

Written by Chris Holdgraf

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How to build a jupyter book from scratch

pabloinsente/jupyter-book-tutorial

Folders and files, repository files navigation, how to create an on-line jupyter book from scratch.

I recently used the jupyter-book package to build an on-line book version of one of my projects . This package simplifies the creation of websites hosting interactive Jupyter Notebooks , by providing a series of scripts that will transform your Notebooks into Jekyll pages. Jekyll is a minimalistic static website and blog generator. Websites built with Jekyll can be freely and easily hosted with GitHub pages .

Instructions about how to build and deploy jupyter-book can be found in the package documentation , which is an example of a jupyter-book in itself. Although the documentation is extensive and detailed, I had significant trouble building and deploying my notebooks in jupyter-book format. This is completely understandable, since jupyter-book is a project in active development, with lots of moving pieces, so sometimes the documentation may be unclear for some users (like me).

My purpose is to provide a step-by-step guide to build a minimalistic version of jupyter-book. I'll explain everything as plain as possible, without very minimal assumptions about background knowledge. I only assume you know what the terminal is, and that you can type stuff in it to make things happen in your computer. I think jupyter-book is an amazing project, and that providing a guide to build and deploy a book may significantly help to increment its adoption, saving time and trouble to potential users.

The situation

You have a bunch of Jupyter Notebooks that you want to share with people. These may be the result of your analysis, a tutorial, workshop, or some other thing. Now, you have a couple of options:

  • Run locally : put everything on GitHub, and ask people to get the repo, set up their computers with all the requirements, and run everything locally.
  • Run in binder : put everything in GitHub, create a link to https://mybinder.org/ with the right configuration, so that people can run the notebooks in the cloud.
  • Run on a website : build a website with links to cloud computing instances.

Option 1 one is easier for you, but harder for the user. Option 2 , is a bit harder for you, but easy for the user. Option 3 is the hardest for you, but easier for the user . Let's say you're willing to do more work to create the best possible experience for the user. This is when using jupyter-books is your best option. It minimizes the time and effort required to built the website and provides an outstanding experience to the user.

What you need to begin

At the minimum, you need these things:

  • A python 3.6 or 3.7 installation https://www.python.org/downloads/
  • A git installation https://git-scm.com/book/en/v2/Getting-Started-Installing-Git
  • A UNIX-based system (Mac OS or any Linux distribution). If you are a Windows user, consider to use the Linux Subsystem for Windows , Gitbash , or Cywin
  • A couple of jupyter-notebooks ( .ipynb extension) and/or markdown documents ( .md extension)

That's it. Technically, if you look at the jupyter-book documentation , you'll see that there other things that would be nice to have beforehand, like a configuration file (i.e., _config.yml ) and a table of contents file (i.e., toc.yml ). However, those files are not strictly necessary to begin your project, so we will restrict ourselves to the bare minimum: some content you want to put on-line .

Creating the book, step-by-step

Now, we will create the book using the command line, step-by-step.

Step 1: create directory for the book

Open the terminal and navigate to your Desktop by:

Now, let's create a directory for the project

Copy and paste your Jupyter Notebooks to the my-book-files/notebooks/ folder

Step 2: create and activate a virtual environment

It is always a good idea to create a virtual environment to isolate your dependencies for each project. This is not strictly required, yet highly recommended. Make sure you are in the Desktop directory in your terminal and move to the project directory:

Once there, run this in the terminal to create the virtual environment:

Once the virtual environment is activated, packages will be installed in that environment, without interfering with your python system installation.

HEADS UP : if you close the terminal or deactivate venv , make sure to re-activate the virtual environment with source venv/bin/activate before typing any code.

Step 3: install jupyter-book package

The jupyter-book package can be pip-installed like this:

Now, if you run:

You should see something like this (version may change) printed to the terminal:

Step 4: create a jupyter-book template

The whole point of jupyter-book is to provide a template that will make creating your book-site fast and easy. A template for your book can be created by running:

If correctly created, the (last part of the) output should look similar to this:

Step 5: remove the files in the my-book/contents/ directory

The contents/ folder in the my-book/ folder is the one containing the files and structure that will be used by the jupyter-book package to build your site. As of now, it contains a few demo files. Let's remove those files to then replace them with your content.

Assuming that you are in my-book-files/ directory, type this to remove the demo files:

Now, type this to look at the directory contents:

And the output should look similar to:

This indicates that my-book/content/ contains the feature/ directory, an images/ directory, and a LICENSE.md file. I keep the license file because it is a standard practice to have an open-source license for every project. If that doesn't work for you, just change the contents of the license as you need.

Step 6: add your contents to the my-book/contents/ directory

Again, assuming you are in the my-book-files/ directory in the terminal and that your content is in the my-book-files/notebooks/ . Type this to incorporate your files into the book:

This will recursively copy the contents of your notebook/ directory into the my-book/content/features/ directory.

Note about images : if you have images linked to your notebooks, this is what you need to do:

Make sure that the images are linked to your notebook by a relative path linked to the images/ folder. The file structure should look like this:

Now, if you have images, type this to pass the images into the book images folder:

And you are done.

Note about an intro or about file :

If you have some sort of introduction to your notebooks, let's say, an intro.md file, you just need to repeat the process of copying the file to the my-book/content/ directory:

Assuming that you have a notebooks/ directory, an images/ directory, and an about.md file, your my-book/content/ directory should look like this now:

Step 7: create a table of contents file matching the contents of your book

Now that you have your contents in place, you need a table of contents to match that. The table of contents is the my-book/_data in the toc.yml file. You have two options for this: to modify the existing toc.yml file to match the contents or to create a new one from scratch. The existing one has too much info to absorb, so I'll create a new one to explain each component. Feel free to use your favorite text editor to edit the file.

From the jupyter-book documentation, we know that each toc.yml has the following schema:

Next, let's populate the file with a few elements. Remember that the path to the files are relative to the contents/ directory.

  • each .md and .ipynb file has to have some content in it. Also remember to add a top header (with # My title), since it will be used as the title to the section on the website.
  • Remember to omit the file extension ( .md and .ipynb ) when passing the URL

Now, let's remove the old toc.yml file and paste the new one:

Step 8: add a requirements.txt file

The chances are that you are using some python package for your analysis. If so, you'll need to create a requirements.txt file containing the package names and versions for your project:

Then, populate your requirements.txt with your dependencies. For instance, in my case they look like this:

Note that if you just want the latest version of the package to be used, you can omit the ==version-number part (as in the vega_datasets ), and just pass the package name. This is not recommended because future releases of the package may break your code functionality. The best is just to figure out the exact package versions you are using. One way is to type pip list (assuming you are using pip ) or conda list (if you are using conda ) in the terminal, in the directory and environment where you ran your code.

Now, let's remove the old requirements.txt file and paste the new one:

Step 9: create a GitHub account and a repository for the project

If you have a GitHub account already, you may be tempted to use your existing account to deploy your project. This may not be a good idea because that may interfere with your ability to use your GitHub account to deploy something more personal in the future, like a resume page or a blog. Also, if you are using GitHub pages already, deploying the site without messing up with your existing website may be tricky. Therefore, it is highly recommended to create a GitHub account for the project . You'll need an email account not associated with an existing GitHub account. With that, you can go to https://github.com/ and follow the Sign Up instructions.

Once you are ready with your new account, create a new empty repository for the project. That is, no README , no LICENSE , no .gitignore . Just empty. Normally, GitHub should prompt you to create a new repo when you verify your email account. If you created one, you can use that one for the next steps. Otherwise, follow steps 1 through 6 in the instructions here https://help.github.com/en/github/getting-started-with-github/create-a-repo

You can name your repo as you like, but let's assume that you name it my-book-site/ . Now we need to connect the online repo with your machine. This time, make sure you are in your local my-book/ directory, and follow these instructions:

The terminal should print back a message like this

Next, stage the files to be committed

Now you need to commit the files that will be pushed to GitHub. Depending on your git configuration, the terminal may prompt you to enter your user.email and user.name . Use your newly created GitHub email and username for this, omitting the --global option (if you set up this globally, it may generate problems for you later on).

Next, connect your new GitHub repo with your local directory. You can get the URL of your repo in the repo home-page bellow " Quick setup — if you’ve done this kind of thing before "

Finally, you need to push your local files to the GitHub repo. Notice that git should prompt you to enter your username and password. Use your newly created ones for the project:

Step 10: create a _config.yml configuration file

The last file we need to add is the _config.yml file. Again, one option is just to edit the existing _config.yml file in the my-book/ directory. I'll fill in this step-by-step, again, just for the sake of explaining in detail what are you doing here:

This may look long and complicated, but it is not. You just need to copy-paste everything to your _config.yml and change a couple of lines that I'll flag for you with capitals as #@@@ CHANGE THIS - number @@@# . To make sure you don't miss any, I numbered the changes from 1 to 9.

Once you are done with the 9 changes, replace the old _config.yml with the new one:

Step 11: build the html files to deploy with jupyter-book

Everything is in place now. The last step before deploying your site is to build the html files like this:

If successful, the terminal should print something like:

Step 12: push the site contents to GitHub and deploy as GitHub pages

You can test your site locally by navigating into your book and running make serve . You need Ruby and a bunch of tools (gems) installed to do that, so I won't get into that now. Let's push and deploy on GitHub pages.

First, add, commit, and push the changes to GitHub:

Now, go to the repo Setting as shown in the picture below

write a book with jupyter notebook

Then, enable GitHub pages in your master branch as shown below

write a book with jupyter notebook

If successful, you should see this message:

write a book with jupyter notebook

After a couple of minutes (usually 1 or 2 minutes), you should see this message (refresh the page too see the update):

write a book with jupyter notebook

Finally if you click the link, and everything went well, your site should be ready to use! for instance, the page that I created for this tutorial can be found here , and it looks like this:

write a book with jupyter notebook

Bonus: updating your site

You may need to update your site from time to time. There are two situations: when you modify files maintaining the same structure, and when you add new elements, like another markdown file or Jupyter Notebook.

In the first scenario, you need to:

  • add the changes to the website local repo
  • rebuild the site running jupyter-book build my-book/ again
  • push the changes to GitHub.

The site should be rebuilt online by GitHub pages with the changes

In the second scenario, you need to:

  • add the new files to the website local repo
  • modify the toc.yml to match your new file structure ( url )

That's it. I hope this tutorial was helpful. For more details and info please check the oficial jupyter-book documentation .

This is my professional website: https://pablocaceres.org/

This is my Twitter account: https://twitter.com/CodeBug88

This is my GitHub account: https://github.com/pabloinsente

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Jupyter Notebook

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. It is a popular tool among data scientists, researchers, and educators for interactive computing and data analysis . The name “Jupyter” is derived from the three core programming languages it originally supported: Julia, Python , and R.

What is Jupyter Notebook?

Three fundamental programming languages—Julia, Python, and R—that it initially supported are where Jupyter Notebook gets its name. But now since it supports more than 40 programming languages, it is a flexible option for a range of computational jobs. Because the notebook interface is web-based, users may use their web browsers to interact with it.

Components of Jupyter Notebook

The Jupyter Notebook is made up of the three components listed below. –

1. The notebook web application

It is an interactive web application that allows you to write and run code.

Users of the notebook online application can:

  • Automatic syntax highlighting and indentation are available when editing code in the browser.
  • Activate the browser’s code.
  • Check out the computations’ output in media formats like HTML , LaTex, PNG, PDF, etc.
  • Create and use widgets in JavaScript.
  • Contains mathematical formulas presented in Markdown cells

The independent processes launched by the notebook web application are known as kernels, and they are used to execute user code in the specified language and return results to the notebook web application.

The following languages are available for the Jupyter Notebook kernel:

3. Notebook documents

All content viewable in the notebook online application, including calculation inputs and outputs, text, mathematical equations, graphs, and photos, is represented in the notebook document.

Types of cells in Jupyter Notebook

  • Code Cell : A code cell’s contents are interpreted as statements in the current kernel’s programming language. Python is supported in code cells because Jupyter notebook’s kernel is by default written in that language. The output of the statement is shown below the code when it is executed. The output can be shown as text, an image, a matplotlib plot, or a set of HTML tables.
  • Markdown Cell : Markdown cells give the notebook documentation and enhance its aesthetic appeal. This cell has all formatting options, including the ability to bold and italicize text, add headers, display sorted or unordered lists, bulleted lists, hyperlinks, tabular contents, and images, among others.
  • Raw NBConvert Cell : There is a location where you can write code directly in Raw NBConvert Cell. The notebook kernel does not evaluate these cells..
  • Heading Cell : The header cell is not supported by the Jupyter Notebook. The panel displayed in the screenshot below will pop open when you choose the heading from the drop-down menu.

Key features of Jupyter Notebook

  • Several programming languages are supported.
  • Integration of Markdown-formatted text.
  • Rich outputs, such as tables and charts, are displayed.
  • flexibility in terms of language switching (kernels).
  • Opportunities for sharing and teamwork during export.
  • Adaptability and extensibility via add-ons.
  • Integration of interactive widgets with data science libraries.
  • Quick feedback and live code execution.
  • Widely employed in scientific research and education.

Getting Started with Jupyter Notebook

The easiest way to install jupyter notebook is through the terminal:

Step 1: Python’s latest version for this method( https://www.python.org/downloads/ ).

Step 2 : Updating pip using cmd.

Screenshot-2023-09-13-205527

Upgrading pip

Step 3: Install the jupyter notebook using the command pip install jupyter notebook in the terminal.(refer to the image)

Step 4: Use the command jupyter notebook in terminal to run the notebook.

After you type the command, this home page should open up in your default browser.

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Jupyter Home Page

Applications of Jupyter Notebook

  • Data science workflows: Organizing and recording the steps involved in data analysis.
  • Making slide displays and reports for presentations.
  • Data analysis involves investigating and displaying data.
  • The creation and evaluation of machine learning models.
  • NLP: Text analysis and natural language processing.

Notebook Extensions

Extensions for Jupyter Notebook are add-ons or modules that improve the environment’s functionality. Jupyter Notebook is further enhanced and customizable by these extensions, which offer more capabilities and settings. The Jupyter JavaScript API and the page’s DOM are both accessible to extensions.

Although Jupyter Notebooks come with lots of built-in abilities, extensions let you add more. Actually, Jupyter supports four different kinds of extensions:

  • IPython kernel
  • Notebook server

You can download jupyter_contrib_nbextensions, one of the most well-liked extension sets, from GitHub. This is actually a set of pip-installed extensions made available by the Jupyter community.

Keyboards Shortcuts

Working with code and markdown cells in Jupyter Notebook requires the use of keyboard shortcuts to increase productivity. Here are several significant Jupyter Notebook keyboard shortcuts:

  • Ctrl+Enter: Enter the current cell while holding down the control key.
  • Y: Switch to the Code cell type.
  • M: Markdown cell type change.
  • D, D: Delete the current cell by pressing D twice.
  • R: Switch to Raw cell type.
  • Shift + S : Checkpoint and save the notebook.
  • Z: Undelete a deleted cell.
  • A : Add a new cell above the existing one.
  • H : Display the keyboard shortcuts list.
  • B : Add a new cell below the existing one.
  • Supports interactive experimentation and step-by-step code execution for data exploration.
  • Multilingual: Supports a variety of programming languages.
  • Rich Documentation: Enables the creation of code-, text-, and visualization-filled notebooks.
  • Data Visualization: Works nicely with libraries for data visualization.
  • Support from the community: Gains from a vibrant community and a large ecosystem.

Disadvantages

  • Learning curve: Beginners may find it difficult.
  • Version control: Complicated when controlling notebook versions.
  • Resource Consuming: May use a lot of system resources.
  • Not Good for Big Projects: Not good for big software development projects.
  • Dependency Management: Managing dependencies takes more work.

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How to use Jupyter Notebook on Windows, Linux, and macOS

Quick links, what is jupyter notebook, how to install jupyter notebook: pip vs anaconda, launching jupyter notebook for the first time, getting familiar with the jupyter notebook interface, how to write and execute code in jupyter notebook.

If you want to get into development, there’s never been a better time to do so. You can use ChatGPT to learn how to code , start software development on affordable Chromebooks , and browse through thousands of easily accessible tutorials and courses. Python is one of the best programming languages to start with, as it’s relatively easy to learn yet powerful in fields like data science, artificial intelligence, and scientific computing.

However, to make the most of Python, you need to be familiar with the concept of Jupyter Notebook, which is a user-friendly, open-source web application that lets you write, execute, and share Python code interactively. In this guide, we’ll teach you how to install and create your first notebook.

Think of Jupyter Notebook as an interactive web application for creating and sharing documents with live code, equations, visualization, and narrative text. Its user-friendly interface makes it highly accessible, and its robust features make it all the more valuable for seasoned developers. People often use it for the development and presentation of data science projects. A “notebook” is similar to a document that can execute your code, display the output, and show explanations to make the whole process easier to understand.

Jupyter Notebook is not constrained to a specific language or system configuration. It supports over 40 programming languages, including R, Julia, and Scala. You can use it on the best laptops for software development or even on a cheaper Chromebook. On top of all that, Jupyter Notebooks can be easily converted to other formats, such as PDFs or HTML. This makes them a strong tool for presentations.

We've already discussed how to install Jupyter Notebook in a previous guide. There are two primary methods: by using PIP or with Anaconda. PIP is a package manager for Python, and it simplifies the process of installing, upgrading, and managing dependencies or libraries. It’s the default package management system that people use for installing and managing software packages written in Python. If you’re comfortable with Python and its ecosystem and only need Jupyter Notebook without additional packages, installing via PIP is a good choice.

You could also use Anaconda. It’s a free and open-source distribution of the Python and R programming languages. It simplifies package management even further by giving you a sleek graphical user interface. You can use it to launch, update, and manage packages without touching the command line.

If you plan on diving into data science, Anaconda comes bundled with most of the libraries and tools that you need. It all depends on your workflow and what you plan to learn in the future, so we’ll be covering both methods in this guide.

Now that you've successfully installed Jupyter Notebook, it's time to put it to good use. We recommend grabbing a comfortable chair , as you'll spend a lot of time getting familiar with Python and Jupyter in the near future.

Now, before you dive into the dozens of tutorials and videos online, you must understand the lay of the land. We can't teach you everything that there is to know about Jupyter Notebook in this section, but we'll break down some of the major components.

First, let's launch Jupyter Notebook for the first time.

  • If you installed Jupyter Notebook via PIP, open the Command Prompt app in Windows (or the Terminal on macOS or Linux) and type jupyter notebook. Hit Enter on your keyboard to launch the app.
  • If you installed Jupyter via Anaconda, open the Anaconda Navigator, find Jupyter Notebook, and click on Launch. Close
  • Once you're in the notebook dashboard, find and click on the New button. It's usually located at the top right.
  • From the dropdown menu, select Python 3 to create a new notebook.
  • A new tab will open in your browser with your new notebook. You should see a toolbar at the top and an empty cell below it. This is where you can write your code.

You should now have a blank notebook open in front of you. Now, let's get familiar with cells, command mode, edit mode, markdown, keyboard shortcuts, and everything else you need to know to get accustomed with Jupyter Notebook.

With a new notebook in front of you, you're now ready to start experimenting with everything that Jupyter Notebook has to offer. Let's start by exploring the toolbar.

The Toolbar

When you get more used to Jupyter, you'll be using keyboard shortcuts more than the toolbar. However, it still helps to know the function of each available button.

  • Starting from the left, you can click on the floppy disk icon to save your progress. Alternatively, you can also press the Ctrl + S ( Cmd + S on Mac) keys to save your progress manually. Jupyter Notebook does have an autosave feature, but you should frequently save manually so you don't lose any progress.
  • Clicking on the plus (+) icon will allow you to add a new cell below the existing ones.
  • The next button features a scissor icon, and clicking on this will cut or delete a selected cell.
  • The next two buttons are for copying and pasting selected cells. You can also just use the traditional Ctrl + C and Ctrl + V keyboard shortcuts for this.
  • Next to the copy/paste icons, you'll find two arrow icons that allow you to move cells up or down depending on how you want to arrange them.
  • The next couple of icons allow you to run, stop, restart, or rerun all the commands or code in the notebook. These actions are indicated by play , stop , restart , and fast-forward icons.
  • Finally, there's a drop-down menu that allows you to change the cell type for a selected cell. You can set this to Code , Markdown , or Raw NBConvert. More on all that later.

That's all there is to it for the toolbar. At the very end, there's also a command pallette that displays all the commands and their respective keyboard shortcuts that you can use within the notebook. We recommend going through this when you can, as keyboard shortcuts can save you a lot of time.

What are cells in Jupyter Notebook?

Think of cells as the main body of your notebook. Within each cell, you can write text using the Markdown markup language, or write and execute Python code. Cells can be interacted with in two modes — Command mode and Edit mode.

Command mode allows you to add or delete cells, change cell type, navigate between different cells, and rearrange their position in the notebook. When you're in command mode, a cell will be highlighted with a blue outline. You can also press A on your keyboard to add a new cell above an existing one, press B to add one below it, or press X to delete a highlighted cell.

Edit mode allows you to add, remove, or replace the content within a cell. When in edit mode, the cell that you're working with will be highlighted with a green outline.

You can switch between both modes with ease. To enter command mode, press the Esc key or click outside the cell. Similarly, to enter edit mode, press the Enter key or click inside the cell you want to edit.

Markdown mode and writing text

While programming, it's good practice to leave comments around your code to make it more readable. Markdown is a simple markup language that allows you to write or format text within Jupyter Notebook, and it's a bit more sophisticated compared to comments in traditional code editors like Visual Studio.

If you want to write text within a cell, you can change the cell type from the toolbar. While in command mode, you can also use keyboard shortcuts to speed things up. Select a code cell while in command mode, and simply press M on your keyboard to make it a Markdown cell.

In markdown cells, you can write plain text, but there are quite a few formatting options as well.

  • To add a heading, add a # sign before the text to turn it into a header. If you add two # signs before a text, it will turn into a second-level header. Similarly, adding three will turn into a third-level header.
  • Add two ** signs before and after a text to make it bold.
  • To make the text italic, add a _ sign before and after it.
  • Add a ` sign before and after a text to make it an in-line code block. This is also called formatted code in Jupyter.
  • For lists, start consecutive lines with the - sign to make bulleted lists. To create numbered lists, start lines with numbers followed by a period.

Here's what all the options should look like while you're writing them as text within the cells:

While you're still in editing mode, press the Shift + Enter keys on your keyboard to run the cell. Here's what the output should look like:

Up to this point, we've discussed cells, markdown mode, and the toolbar. However, let's not forget about one of the most important aspects of Jupyter Notebook: writing and executing code. Follow the steps below to write your first piece of code within Jupyter Notebook.

  • Open a new notebook, and click within the empty cell to enter Edit mode.
  • To test things out, you can enter any simple piece of code into this cell. Printing something like "Hello World" is always a safe bet. Here's the command for printing "Hello World" in Python:

print("Hello, World!")

  • To run your code, press Shift + Enter on your keyboard. You should see the words "Hello, World!" as the output displayed below our cell.
  • To save the notebook, click the Save icon in the toolbar to save your work. Alternatively, you can also press the Ctrl + S keys on your keyboard to save your progress.

Congrats! You just wrote your first bit of code in Python while using Jupyter Notebook. It's not exactly complex, but it's something to get you started. After saving your notebook, you can close the browser tab to exit Jupyter Notebook.

Making the most of Jupyter Notebook

By now, you should be a bit familiar with the Jupyter Notebook interface and all the features it has to offer. Try creating basic programs like simple arithmetic operations or experimenting with variable assignments to get you started. While you're at it, get familiar with the Markdown features in Jupyter Notebook for making your code more readable. Finally, make a habit of regularly saving your notebook to avoid losing any important work. Happy coding!

How to use Jupyter Notebook on Windows, Linux, and macOS

Introduction to Python including Installation, Jupyter Notebooks and Visual Studio Code

By: Rajendra Gupta   |   Updated: 2024-02-28   |   Comments   |   Related: More > Python

What is Python programming? Is it a popular programming language? How does a programmer you install Python on Windows along with integrated development environments?

Python is a high-level or general-purpose programming language widely adopted for web development, Data Science, Machine Learning (ML) and Artificial Intelligence (AI). Python is an easy-to-understand, scalable, open-source language that allows developers to write a program with a small amount of code. It is also highly productive since it has modules and libraries for specific implementations.

Highlights of Python include:

  • Easy to learn
  • Useful in web applications
  • Used in Machine Learning
  • Used in GUI
  • Built-in mathematical functions

According to the IEEE Spectrum report , Python is the number one programming language in the general "Spectrum" ranking for 2023.

Top Programming Languages 2023

In this data science series, you can learn Python concepts, fundamentals and terminology with a deep dive into the advanced topics as we move through the software development tips.

Installing Python

Python is available on Windows, Linux/Unix, macOS, and docker containers. This tip explores the installation on the Windows operating system.

Step 1 Choose the Python Version

Python 3.12.2 is the latest version. You can download it or any active Python release from the Python website .

Download Python

Unless you require a specific release, click Download Python 3.12.2 , which downloads the python-3.12.2-amd64.exe.

Step 2: Launch Install Wizard

Launch the setup and put ticks in the checkboxes highlighted below. It installs the PIP package manager, IDLE (Python editor), and additional documents with the file associations by default.

Install Python 3.12.2

We can install Python directly with default options or click on Customize installation to choose the features and location.

In the Optional Features, make sure you check all features. Click Next .

Optional features

On the Advanced Options window, choose the following options:

  • Install Python 3.12 for all users
  • Associate files with Python
  • Add Python to environment variables

You can also customize the install location. If we install Python for all users, its default location is C:\Program Files\Python.

Advanced options

Click Install . It will quickly finish the Python 3.12.2 installation, displaying the image below.

Successful setup

Step 3: Verify Installation

Use Command Prompt.

To verify the installation, launch the command prompt and run the following command:

The command should return the installed Python version (below).

Command prompt - version

If you get the error that says Python is not a recognized program, add the Python path into the environment variable.

Command prompt

To add a Python directory to the PATH environment variable, type Edit the System environment variable in the start menu.

Edit system environment variables

In the Advanced tab, locate and click on Environment Variables.

System properties

Double-click on the PATH and add the Python directory as C:\Program Files\Python312

Edit environment variables

Click OK and save the environment variable changes.

Relaunch the command prompt and run it to check the Python version; it should be successful.

Launch the Python Interpreter IDLE.

Another way to check the Python installation is to launch the Python interpreter IDLE. To do so, type IDLE in the start menu.

IDLE Shell

It launches the Python interpreter, which shows the Python version and a few keywords, such as "help", "copyright", or "license()" for more information.

Launch IDLE

You can try running the sample code to print a string "Hello, world!" in the IDLE, as shown below.

Sample code

Python Integrated Development Environment (IDE)

Python IDLE or Command-line interpreter is unsuitable for large projects or programs. You can use integrated development environments such as Visual Studio Code, PyCharm, Atom, and Jupyter Notebook. You can also use the Anaconda Python distribution, a bundled package with Python and Jupyter Notebook, and install commonly used packages and package managers.

Let's explore two popular tools: Jupyter Notebooks and Visual Studio Code (VS Code). You can use any IDE you like.

Jupyter Notebook Installation

The Jupyter Notebook is a flexible and user-friendly tool for Python. Browse the Jupyter website and click on the Jupyter Notebook -> Install the Notebook.

Jupyter Notebook Classic Notebook Interface

It gives instructions for installing the JupyterLab and notebook.

JupyterLab

However, before installing the Jupyter Notebook, we need pip (package management system) to install the packages in Python. If you do not have pip installed, it gives the following error. However, we already installed it using the custom installation.

Command prompt

To verify the pip package, run the command pip --version , which will give the output below.

Run pip

Run the command below to install Jupyterlab:

Command to install JupyterLab

Install the Jupyter Notebook with the below command.

Install Jupyter Notebook

Launch the Jupyter Notebook with the command below.

It opens the Jupyter URL. Click on New-> Notebook to launch a new Jupyter Notebook, as shown below.

Jupyter new notebook

You can write the code in the highlighted line. For example, let's write a sample Python code to greet the user.

As shown below, we have executed the Python script in this Jupyter notebook.

Executed Python script in Jupyter notebook

Visual Studio Code Python IDE

VS Code is another popular integrated development editor (IDE) that supports multiple languages like SQL, PowerShell, and Python. It is Microsoft's IDE for Windows, Linux, and macOS, and it features intelligent code completion, code refactoring, source control, and debugging.

Download it from the VS Code website and follow the installation wizard to complete its installation.

Visual Studio Code

You need to install the Python Extension to use Python in VS Code. Click Extensions from the left-hand vertical menu bar and search for Python.

Click Install in the Python installation.

Install

After the package installation, it shows the option to disable, uninstall, or switch to the pre-release version.

Pre-release version

Relaunch VS Code. Using the Get Started with Python Development option, you can walk through Python development with VS Code.

Visual Studio Code

Otherwise, click New File from the Start links on the left side of the interface. It asks you to select the language for the new file.

Select language

Click Select a language and choose Python.

Language - Python

As shown below, write and execute the script in the VS Code Python IDE.

Write/execute script

  • Stay tuned for more Python tutorials in the upcoming software engineering tips.
  • Recursion Programming Techniques using Python
  • Create a Python Django Website with a SQL Server Database
  • Create Your First REST API using Python, Flask and HTML Code
  • Getting Started with Python Pandas and Dataset Profiling
  • Python Basic Built-in Data Types
  • Introduction to Creating Interactive Data Visualizations with Python matplotlib in VS Code
  • Creating a Python Graphical User Interface Application with Tkinter

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Guru99

Jupyter Vodič za prijenosno računalo: Kako instalirati i koristiti Jupyter?

Daniel Johnson

Što je Jupyter Bilježnica?

Jupyter Bilježnica je web aplikacija otvorenog koda za pisanje i dijeljenje živih kodova, jednadžbi, vizualizacija s elementima bogatog teksta. Omogućuje prikladan način za pisanje odlomaka, jednadžbi, naslova, poveznica i slika za pokretanje analize podataka. Također je koristan za dijeljenje interaktivnih algoritama s publikom u svrhu podučavanja ili demonstracije.

Uvod u Jupyter Aplikacija za prijenosno računalo

Korištenje električnih romobila ističe Jupyter Notebook App je sučelje na kojem možete pisati svoje skripte i kodove putem web preglednika. Aplikacija se može koristiti lokalno, što znači da vam nije potreban pristup internetu ili udaljeni poslužitelj.

Introduction to Jupyter Notebook App

Svako računanje se vrši putem kernela. Nova jezgra se stvara svaki put kada pokrenete a Jupyter Notebook.

Kako koristiti Jupyter Bilježnica

U nastavku ćete naučiti kako koristiti Jupyter Bilježnica. Napisat ćete jednostavan redak koda kako biste se upoznali s okolinom Jupyter.

Korak 1) U radni direktorij dodajete mapu koja će sadržavati sve bilježnice koje ćete izraditi tijekom tutorijala TensorFlow .

Otvorite terminal i pišite

Objašnjenje koda

  • mkdir jupyter_tf: Stvorite nazive mapa jupyter_tf
  • jupyter bilježnica: Otvori Jupyter Web-aplikacije

Use Jupyter Notebook

Korak 2) Možete vidjeti novu mapu unutar okruženja. Kliknite na mapu jupyter_tf.

Use Jupyter Notebook

Korak 3) Unutar ove mape izradit ćete svoju prvu bilježnicu. Kliknite na gumb Novo i Python 3 .

Use Jupyter Notebook

Korak 4) Vi ste unutar Jupyter okoliš. Do sada se vaša bilježnica zove Untitled.ipynb. Ovo je zadano ime koje je dao Jupyter. Preimenujmo ga klikom na file i preimenovati

Use Jupyter Notebook

Možete ga preimenovati Introduction_jupyter

Use Jupyter Notebook

U AWS-u Jupyter Bilježnica, pišete kodove, bilješke ili tekst unutar ćelija.

Use Jupyter Notebook

Unutar ćelije možete napisati jedan red koda.

Use Jupyter Notebook

ili više linija. Jupyter čita kod jedan redak za drugim.

Use Jupyter Notebook

Na primjer, ako napišete following kod unutar ćelije.

Use Jupyter Notebook

To će proizvesti ovaj izlaz.

Use Jupyter Notebook

Korak 5) Spremni ste za pisanje prve linije koda. Možete primijetiti da ćelija ima dvije boje. Zelena boja znači da ste u način uređivanja .

Use Jupyter Notebook

Međutim, plava boja označava da ste unutra način izvršavanja .

Use Jupyter Notebook

Vaš prvi redak koda bit će ispis Guru99!. Unutar ćelije možete pisati

Postoje dva načina za pokretanje koda Jupyter:

  • Klikni i pokreni
  • Tipkovnica Prečaci

Za pokretanje koda možete kliknuti na Ćelija a zatim Pokrenite ćelije i odaberite ispod

Use Jupyter Notebook

Možete vidjeti da je kod ispisan ispod ćelije i da se nova ćelija pojavila odmah nakon izlaza.

Use Jupyter Notebook

Brži način za pokretanje koda je korištenje Tipkovnica Prečaci . Za pristup tipkovnim prečacima idite na Pomoć i Tipkovnica Prečaci

Use Jupyter Notebook

Ispod popisa prečaca za a MacOS tipkovnica. Možete urediti prečace u uređivaču.

Use Jupyter Notebook

Following su prečaci za Windows

Use Jupyter Notebook

Napiši ovaj redak

i pokušajte koristiti tipkovničke prečace za pokretanje koda. Koristite alt+enter. izvršit će ćeliju i umetnuti novu praznu ćeliju ispod, kao što ste to učinili prije.

Use Jupyter Notebook

Korak 6) Vrijeme je da zatvorite Bilježnicu. Ići file i kliknite na Zatvori i zaustavi

Use Jupyter Notebook

bilješke : Jupyter automatski sprema bilježnicu s kontrolnom točkom. Ako imate following poruka:

Use Jupyter Notebook

To znači Jupyter nije spremio datoteku od zadnje kontrolne točke. Možete ručno spremiti bilježnicu

Use Jupyter Notebook

Bit ćete preusmjereni na glavnu ploču. Možete vidjeti da je vaša bilježnica spremljena prije minutu. Možete se sigurno odjaviti.

Use Jupyter Notebook

Instalirati Jupyter Prijenosno računalo s AWS-om

Ispod je korak po korak proces instalacije i pokretanja Jupyter Bilježnica na AWS-u:

Ako nemate račun na AWS-u, napravite besplatni račun ovdje .

Nastavit ćemo kako slijedi

  • 1. dio: Postavite par ključeva
  • 2. dio: Postavite sigurnosnu grupu

Dio 3: Pokretanje instance

Dio 4: Instalirajte Docker

Dio 5: instalacija jupyter, dio 6: bliska veza, 1. dio: postavite par ključeva.

Korak 1) Idi na Usluge i naći EC2

Install Jupyter Notebook with AWS

Korak 2) U ploči i kliknite na Parovi ključeva

Install Jupyter Notebook with AWS

Korak 3) Pritisnite Stvori par ključeva

Install Jupyter Notebook with AWS

  • Možete ga nazvati Docker ključ
  • Kliknite Stvori

Install Jupyter Notebook with AWS

Naziv datoteke Docker_key.pem preuzimanja.

Install Jupyter Notebook with AWS

Korak 4) Kopirajte ga i zalijepite u ključ mape. Uskoro će nam trebati.

Samo za Mac OS korisnike

Ovaj korak odnosi se samo na korisnike Mac OS-a. Za Windows ili korisnici Linuxa, prijeđite na DIO 2

Morate postaviti radni direktorij koji će sadržavati ključ datoteke

Prije svega, stvorite mapu pod nazivom ključ. Za nas se nalazi unutar glavne mape Docker. Zatim postavite ovu putanju kao radni direktorij

Install Jupyter Notebook with AWS

2. DIO: Postavite sigurnosnu grupu

Korak 1) Morate konfigurirati sigurnosnu grupu. Možete mu pristupiti pomoću ploče

Set up a Security Group

Korak 2) Kliknite na Create Security Group

Set up a Security Group

Korak 3) Na sljedećem ekranu

  • Unesite naziv sigurnosne grupe “jupyter_docker” i Opis sigurnosne grupe za Docker
  • Morate dodati 4 pravila na vrh
  • ssh: raspon portova 22, izvor bilo gdje
  • http: raspon priključaka 80, izvor bilo gdje
  • https: raspon portova 443, izvor bilo gdje
  • Prilagođeni TCP: raspon portova 8888, izvor bilo gdje

Set up a Security Group

Korak 4) Novostvorena sigurnosna grupa bit će navedena

Set up a Security Group

Napokon ste spremni za izradu instance

Launch Instance

Korak 1) Kliknite na Pokreni instancu

Launch Instance

Zadani poslužitelj je dovoljan za vaše potrebe. Možeš odabrati Amazon Linux AMI. Trenutna instanca je 2018.03.0.

AMI je kratica za Amazon Slika stroja. Sadrži informacije potrebne za uspješno pokretanje instance koja se izvodi na virtualnom poslužitelju pohranjenom u oblaku.

Launch Instance

Imajte na umu da AWS ima poslužitelj posvećen dubokom učenju kao što su:

  • AMI dubokog učenja (Ubuntu)
  • AMI dubokog učenja
  • Deep Learning Base AMI (Ubuntu)

Svi oni dolaze s najnovijim binarnim datotekama okvira dubokog učenja unaprijed instaliranim u zasebnim virtualnim okruženjima:

  • TensorFlow,

Potpuno konfiguriran s NVidia CUDA, cuDNN i NCCL kao i Intel MKL-DNN

Korak 2) Odaberite t2.mikro . To je besplatni poslužitelj razine. AWS nudi besplatno ovaj virtualni stroj opremljen s 1 vCPU-om i 1 GB memorije. Ovaj poslužitelj pruža dobar kompromis između performansi računanja, memorije i mreže. Pogodan je za male i srednje baze podataka

Launch Instance

Korak 3) Zadržite zadane postavke na sljedećem zaslonu i kliknite Dalje: Dodaj pohranu

Launch Instance

Korak 4) Povećajte pohranu na 10 GB i kliknite Dalje

Launch Instance

Korak 5) Zadržite zadane postavke i kliknite Dalje: Konfiguriraj sigurnosnu grupu

Launch Instance

Korak 6) Odaberite sigurnosnu grupu koju ste prije stvorili, a to je jupyter_lučki radnik

Launch Instance

Korak 7) Pregledajte svoje postavke i kliknite gumb za pokretanje

Launch Instance

Korak 8 ) Zadnji korak je povezivanje para ključeva s instancom.

Launch Instance

Korak 8) Instanca će se pokrenuti

Launch Instance

Korak 9) Ispod je sažetak instanci koje se trenutno koriste. Zabilježite javni IP

Launch Instance

Korak 9) Pritisnite Connect

Launch Instance

Pronaći ćete detalje povezivanja

Launch Instance

Pokrenite svoju instancu (korisnici Mac OS-a)

Najprije provjerite pokazuje li vaš radni direktorij unutar terminala na mapu s dockerom datoteke para ključeva

pokrenite kod

Otvorite vezu s ovim kodom.

Postoje dva koda. u nekim slučajevima, prvi kod izbjegava Jupyter da otvorite bilježnicu.

U tom slučaju upotrijebite drugi za prisilno spajanje Jupyter Bilježnica na EC2.

Prvi put od vas se traži da prihvatite vezu

Launch Instance (Mac OS Users)

Pokrenite svoju instancu (Windows korisnici)

Korak 1) Posjetite ovo web mjesto za preuzimanje PuTTY i PuTTYgen kit

Trebate je preuzeti

  • PuTTY: pokrenite instancu
  • PuTTYgen: pretvorite pem datoteku u ppk

Launch Instance (Windows Users)

Sada kada su oba softvera instalirana, trebate pretvoriti .pem datoteku u .ppk. PuTTY može čitati samo .ppk. Pem datoteka sadrži jedinstveni ključ koji je izradio AWS.

Korak 2) Otvorite PuTTYgen i kliknite na Učitaj. Pregledajte mapu u kojoj se nalazi .pem datoteka.

Launch Instance (Windows Users)

Korak 3) Nakon što učitate datoteku, trebali biste dobiti obavijest koja vas obavještava da je ključ uspješno uvezen. Kliknite OK

Launch Instance (Windows Users)

Korak 4) Zatim kliknite Spremi privatni ključ. Bit ćete upitani želite li spremiti ovaj ključ bez zaporke. Kliknite na Da.

Launch Instance (Windows Users)

Korak 5) Sačuvajte ključ

Launch Instance (Windows Users)

Korak 6) Idite na AWS i kopirajte javni DNS

Launch Instance (Windows Users)

Otvorite PuTTY i zalijepite javni DNS u naziv hosta

Launch Instance (Windows Users)

  • Na lijevoj ploči otvorite SSH i otvorite Auth
  • Pregledajte privatni ključ. Trebali biste odabrati .ppk
  • Kliknite Otvori.

Launch Instance (Windows Users)

Kada se ovaj korak završi, otvorit će se novi prozor. Kliknite Da ako vidite ovaj skočni prozor

Launch Instance (Windows Users)

Morate se prijaviti kao: ec2-user

Launch Instance (Windows Users)

Povezani ste s Amazon Linux AMI.

Launch Instance (Windows Users)

Dok ste povezani s poslužiteljem putem Putty/Terminala, možete instalirati Lučki radnik spremnik.

Izvršite following kodovi

Ponovno pokrenite vezu

Windows korisnici koriste SSH kao što je gore navedeno

Korak 1) stvoriti Jupyter s,

unaprijed izgrađena slika.

  • docker run: Pokreni sliku
  • v: priložiti svezak
  • ~/work:/home/jovyan/work: Svezak
  • 8888:8888: luka
  • jupyter/datascience-notebook: Slika

Za ostale slike prije izrade, idite ovdje

Dopustiti očuvanje Jupyter AWS prijenosno računalo

Korak 2) Instaliraj stablo da vidiš,

naš radni imenik sljedeći

Install Docker

Korak 3) Provjerite spremnik i njegov naziv

Koristite naredbu

  • Dobijte ime i otvorite zapisnik Jupyter. U ovom Jupyter vodič, naziv spremnika je vigilant_easley. Koristite naredbu docker logs vigilant_easley
  • Nabavite URL

Install Docker

Korak 4) U URL-u,

Zamijenite (90a3c09282d6 ili 127.0.0.1) s javnim DNS-om vaše instance

http://(90a3c09282d6 or 127.0.0.1):8888/?token=f460f1e79ab74c382b19f90fe3fd55f9f99c5222365eceed

Install Docker

Korak 5) Novi URL postaje,

http:// ec2-174-129-135-16.compute-1.amazonaws.com: 8888/?token=f460f1e79ab74c382b19f90fe3fd55f9f99c5222365eceed

Korak 6) Kopirajte i zalijepite URL u svoj preglednik.

Jupyter Otvara

Install Docker

Korak 7) Možete napisati novu bilježnicu,

u vašoj radnoj mapi

Install Docker

Zatvorite vezu u terminalu

Vratite se na AWS i zaustavite poslužitelj.

Close Connection

Rješavanje problema

Ako ikada docker ne radi, pokušajte obnoviti sliku pomoću

  • Jupyter bilježnica je web aplikacija na kojoj možete pokrenuti svoj Python i R kodovi . Lako je dijeliti i isporučiti bogato Analiza podataka s Jupyter.
  • Lansirati jupyter, napišite u terminalu: jupyter bilježnica
  • Svoju bilježnicu možete spremiti gdje god želite
  • Ćelija sadrži vaš Python kod. Kernel će čitati kod jedan po jedan.
  • Možete koristiti prečac za pokretanje ćelije. Standardno: Ctrl+Enter
  • Gaussova jezgra u strojnom učenju: Python kernel metode
  • Vodič za PyTorch: regresija, primjer klasifikacije slike
  • Vodič za učenje prijenosa PyTorcha s primjerima
  • Pandas read_csv() s primjerom
  • Tensorflow vodič PDF za početnike (preuzmite odmah)
  • TensorFlow protiv Kerasa: Ključna razlika između njih
  • 11 NAJBOLJIH TensorFlow knjiga (ažuriranje 2024.)
  • Pandas Cheat Sheet za Data Science u Pythonu

a post with jupyter notebook

July 04, 2023

2023   ·   formatting   jupyter     ·   sample-posts  

To include a jupyter notebook in a post, you can use the following code:

Let’s break it down: this is possible thanks to Jekyll Jupyter Notebook plugin that allows you to embed jupyter notebooks in your posts. It basically calls jupyter nbconvert --to html to convert the notebook to an html page and then includes it in the post. Since Kramdown is the default Markdown renderer for Jekyll, we need to surround the call to the plugin with the ::nomarkdown tag so that it stops processing this part with Kramdown and outputs the content as-is.

The plugin takes as input the path to the notebook, but it assumes the file exists. If you want to check if the file exists before calling the plugin, you can use the file_exists filter. This avoids getting a 404 error from the plugin and ending up displaying the main page inside of it instead. If the file does not exist, you can output a message to the user. The code displayed above outputs the following:

Note that the jupyter notebook supports both light and dark themes.

IMAGES

  1. Getting started with Jupyter Notebook for Python

    write a book with jupyter notebook

  2. How to Use Jupyter Notebook (Basics for Beginners + Best Practices)

    write a book with jupyter notebook

  3. Cómo usar Jupyter Notebook: una guía definitiva

    write a book with jupyter notebook

  4. Jupyter Notebook: How to Install and Use • Python Land Tutorial

    write a book with jupyter notebook

  5. Getting started with Jupyter Notebooks

    write a book with jupyter notebook

  6. How to Write and Run Code in Jupyter Notebook

    write a book with jupyter notebook

VIDEO

  1. How to write program in Jupyter Notebook

  2. Storytelling in Markdown || Write Different Headings in Jupyter notebook💯💯🧑‍💻

  3. PYTHON| HOW TO CHANGE TEXT COLOR IN JUPYTER NOTRBOO| KHOW TO WRITE HEADING| #pythonforbeginners

  4. Jupyter Notebooks

  5. Jupyter Book overview

  6. How to write the best Jupyter notebook?

COMMENTS

  1. Create your first book

    However, there is a known incompatibility for notebook execution when using Python 3.8. See Working on Windows. See the sections of this tutorial to the left to get started! Overview. Create a template book. Build your book. Create your own content file. Publish your book online. Build a small example project.

  2. Writing Books with Jupyter

    Jupyter Book is an open source project for building beautiful, publication-quality books and documents from computational material. It's still early in development, but this could scratch a real itch that I have: pushing computational notebooks beyond working documents and into prose, especially long-form writing, reveals some sticking points.

  3. Built with Jupyter Book

    Share your built books via web services and hosted websites. Create interactive and web-native components and services. This documentation is organized into a few major sections. Tutorials are step-by-step introductory guides to Jupyter Book. Topic Guides cover specific areas in more depth, and are organized as discrete "how-to" sections.

  4. Create a template book

    Finally, you can combine Jupyter Notebook and text formats with Jupyter Book. This allows you to write the structure of a Jupyter Notebook entirely with text. This requires the use of a special YAML metadata blog that tells Jupyter Book (via a tool called Jupytext) to create a notebook for the page and execute its content.

  5. How to Use Jupyter Notebook: A Beginner's Tutorial

    How to Use Magics in Jupyter. A good first step is to open a Jupyter Notebook, type %lsmagic into a cell, and run the cell. This will output a list of the available line magics and cell magics, and it will also tell you whether "automagic" is turned on. Line magics operate on a single line of a code cell.

  6. Writing (and publishing) a Book Written in Jupyter Notebooks

    The Jupyter Notebook can combine narrative, code, and graphics—the ideal combination for teaching anything programming related. That's why Andreas Müller cho...

  7. Jupyter Book 101: Beautiful, publication-quality documents from

    A short introduction to Jupyter Book, an open source tool for building beautiful, publication-quality books using Jupyter Notebooks and computational content...

  8. Jupyter Notebook: An Introduction

    The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text. Jupyter Notebook is maintained by the people at Project Jupyter. Jupyter Notebooks are a spin-off project from the IPython project, which used to have an IPython Notebook project itself.

  9. The Jupyter Book Guide

    This is a guide for creating your own book using Jupyter Notebooks and Jekyll. Book content is written in markdown and Jupyter Notebooks, and jupyter-book converts these into a book fit for hosting on the web. Install the command-line interface. First off, make sure you have the CLI installed so that you can work with Jupyter Book. The Jupyter ...

  10. Create Beautiful Reports and Books with Jupyter Book!

    Step 3: Running Jupyter Book. (We want to be in the folder cloned above with all the content when we run the following line) jupyter-book create book/. Okay, the setup is all done! What you'll ...

  11. How to create your first book using Jupyter Book

    This video will show you steps to generate your first book using Jupyter-book. This is the first tutorial of the series.

  12. Announcing the new Jupyter Book

    Write content in Jupyter Notebooks This allows you to include your code and outputs in your book. You can also write notebooks entirely in markdown to execute when you ... (AKA, CommonMark), meaning that any default markdown in a Jupyter Notebook is valid in Jupyter Book. If you'd like extra features in markdown such as citations, figures ...

  13. Writing With Jupyter Book

    With Jupyter Books you then write a series of Jupyter notebooks, each typically representing a chapter or sub-chapter. These are then converted, or "built," into one or more output formats, such ...

  14. How to Use Jupyter Notebooks: The Ultimate Guide

    Jupyter notebooks have two different modes of interaction: command mode and edit mode. In command mode, you can navigate between cells, add and delete cells, and change the cell type. In edit mode, you can edit the contents of a cell. In order to enter command mode, you can either press Escape or click outside a cell.

  15. GitHub

    How to create an on-line Jupyter Book from scratch. I recently used the jupyter-book package to build an on-line book version of one of my projects.This package simplifies the creation of websites hosting interactive Jupyter Notebooks, by providing a series of scripts that will transform your Notebooks into Jekyll pages. Jekyll is a minimalistic static website and blog generator.

  16. Working with Jupyter Notebooks in Visual Studio Code

    Save your Jupyter Notebook. You can save your Jupyter Notebook using the keyboard shortcut Ctrl+S or File > Save. Export your Jupyter Notebook. You can export a Jupyter Notebook as a Python file (.py), a PDF, or an HTML file. To export, select the Export action on the main toolbar. You'll then be presented with a dropdown of file format options.

  17. Jupyter Notebook

    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. It is a popular tool among data scientists, researchers, and educators for interactive computing and data analysis. The name "Jupyter" is derived from the three core ...

  18. Project Jupyter

    Try Jupyter. Use our tools without installing anything. Project Jupyter builds tools, standards, and services for many different use cases. This page has links to interactive demos that allow you to try some our tools for free online, thanks to mybinder.org, a free public service provided by the Jupyter community.

  19. Introduction to Jupyter Notebooks for Developers

    A Jupyter Notebook is "a web-based application used to create and share interactive notebook documents, which can contain live code, text, data visualizations, videos and other computational outputs." That is kind of useful, but before I dive in let me show another example:

  20. How to use Jupyter Notebook on Windows, Linux, and macOS

    Once you're in the notebook dashboard, find and click on the New button. It's usually located at the top right. From the dropdown menu, select Python 3 to create a new notebook.; A new tab will ...

  21. Build a small example project

    Mini-book source files#. The source files can be found on GitHub in the docs directory.These files are written in MyST Markdown, an extension of the Jupyter Notebook Markdown, that allows for additional scientific markup.They could alternatively have been written directly as Jupyter notebooks.

  22. Using IPython Jupyter Magic Commands to Improve the Notebook Experience

    IPython Jupyter Magic commands (e.g. lines in notebook cells starting with % or %%) can decorate a notebook cell, or line, to modify its behavior. Many are available by default, including %timeit to measure the execution time of the cell and %bash to execute shell commands, and others are provided by extensions such as %sql to write SQL queries ...

  23. Randall J. LeVeque

    DescriptionI will describe an on-going project to write a book exploring mathematical and computational aspects of wave propagation problems.AbstractI will d...

  24. Python Programming Introduction

    Browse the Jupyter website and click on the Jupyter Notebook -> Install the Notebook. It gives instructions for installing the JupyterLab and notebook. However, before installing the Jupyter Notebook, we need pip (package management system) to install the packages in Python.

  25. Jupyter Notebook Tutorial: How to Install & use Jupyter?

    A Jupyter notebook is a web application that allows the user to write codes and rich text elements. Inside the Notebooks, you can write paragraph, equations, title, add links ... In the session below, you will learn how to use Jupyter Notebook. You will write a simple line of code to get familiar with the environment of Jupyter.

  26. Build your book

    Aside: Source vs build files#. At this point, you have created a combination of Jupyter notebooks, markdown files, and configuration files, including _toc.yml and _config.yml.These files are your source files. The source files comprise all of the content and configuration that Jupyter Book needs to build your book.. In addition, you have created a collection of outputs in the _build folder.

  27. a post with jupyter notebook

    It basically calls jupyter nbconvert --to html to convert the notebook to an html page and then includes it in the post. Since Kramdown is the default Markdown renderer for Jekyll, we need to surround the call to the plugin with the ::nomarkdown tag so that it stops processing this part with Kramdown and outputs the content as-is.

  28. Write narrative content

    Write narrative content #. Write narrative content. #. In this section we'll cover the many ways that you can write content for your book. You can write book content in a variety of markup languages and file formats, and create special kinds of content for your book with MyST Markdown. See the sections below to get started. MyST Markdown ...

  29. How to remove duplicate entries for Python env interpreter in VS Code

    To learn more, see our tips on writing great answers. Sign up or log in. Sign up using Google ... jupyter-notebook; development-environment; or ask your own question. ... Book about sentient tanks, a fake war, fighting on the heart of a gas giant scoured by a neutron star, medical AI running rampant ...