How To Download Jupyter Notebooks

JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.

Welcome to the Project Jupyter documentation site. Jupyter is a large umbrellaproject that covers many different software offerings and tools, including thepopular Jupyter Notebookand JupyterLab web-basednotebook authoring and editing applications. The Jupyter project and itssubprojects all center around providing tools (and standards)for interactive computing with computational notebooks.

It was spun off from IPython in 2014 by Fernando Prez and Brian Granger. Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Project Jupyter has developed and supported the interactive computing products Jupyter Notebook, JupyterHub, and JupyterLab.

In 2015, about 200,000 Jupyter notebooks were available on GitHub. By 2018, about 2.5 million were available.[6] In January 2021, nearly 10 million were available, including notebooks about the first observation of gravitational waves[7] and about the 2019 discovery of a supermassive black hole.[8]

Visual Studio Code supports local development of Jupyter notebooks. As of July 2022, the Jupyter extension for VS Code has been downloaded over 40 million times, making it the second-most popular extension in the VS Code Marketplace.[13]

In August 2023, Jupyter AI, a Jupyter extension, was released. This extension incorporates generative artificial intelligence into Jupyter notebooks, enabling users to explain and generate code, rectify errors, summarize content, inquire about their local files, and generate complete notebooks based on natural language prompts. [21]

While JSON is the most common format, it is possible to forgo some features (like storing images and metadata), and save notebooks as markdown documents using extensions like JupyText.[24] Jupytext is often used in conjunction with version control to make diffing and merging of notebook simpler.

When getting started with Jupyter Notebooks, you'll want to make sure that you are working in a trusted workspace. Harmful code can be embedded in notebooks and the Workspace Trust feature allows you to indicate which folders and their contents should allow or restrict automatic code execution.

Under the hood, Jupyter Notebooks are JSON files. The segments in a JSON file are rendered as cells that are comprised of three components: input, output, and metadata. Comparing changes made in a notebook using lined-based diffing is difficult and hard to parse. The rich diffing editor for notebooks allows you to easily see changes for each component of a cell.

Welcome to the ________________ documentation site. 1________________is a simplified notebook authoring application, and is a part of ProjectJupyter, a large umbrella projectcentered around the goal of providing tools (and standards)for interactive computing with computational notebooks.

You can use Google or search for Jupyter Notebook extensions. There are actually quite a few out there. One of the most popular extension sets is called 2____________________________, which you can get from GitHub. This is actually a collection of extensions that is provided by the Jupyter community and installed with pip.

The jupyter-vim plugin does not provide a lot of keybindings for the jupyter-workflow (run and jump to next cell, delete cell, jump to next/previous cell) but these should be not to difficult to come up with.

the use case is taking notes on programming topics where you want the notes to be linked. you could keep notes on the abstractions(the general or mathematical notion of what something is) separate from the implementations, except within the implementation notes(in a jupyter notebook) would have a runnable example.

+1 for that, having the ability to simply open natively a jupyter notebook (even without running it), and having the ability to link the markdown content would be revolutionary for computer science note taking

Jupyter Notebooks are available to all users at notebooks.msi.umn.edu. This interactive computing environment requires only a web browser, and enables data analysis and visualization on our HPC resources in a shareable, reproducible notebook format. Notebooks currently supports both the JupyterLab and Classic Notebook interfaces, computation with the Python (version 2 and 3) and R languages, and can also interoperate with user-installed Jupyter environments. MSI Python tutorial materials provide a useful overview.

I am the only one actually creating notebooks but I would like to make some notebooks visible to other team members in a read-only way. Ideally I could just share a URL with them that they would bookmark for when they want to see the notebook with refreshed data.

With JupyterHub you can create a multi-user Hub which spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. Due to its flexibility and customization options, JupyterHub can be used to serve notebooks to a class of students, a corporate data science group, or a scientific research group.

I am used to formatting my code using black and isort with normal python scripts. I have been slowly using jupyter notebooks as a development tool. I am curious to know how can I format using black in jupyter notebooks?

I'm having issues writing back dates into a dataset from a pandas dataframe in Juptyer notebooks. I have a datetime column , which I've converted from a string using pandas.to_datetime. However, when I use domojuypiter.write_dataframe, they are converted back into string in the Domo dataset. My dataframe has both numbers and text fields, which are passed without issues.

I've been creating a custom Jupyter notebook to generate an output which emulates the type of plotting which can be done with the Trend Analysis functionality within SystemLink. This is because we have an interest in creating dashboard tiles which are dynamic versions of some those trend analysis plots. Since there's no way to export notebooks of those plots like there are for reports, we tried to go the Jupyter route. On a general basis, we want to trend across a particular tests for all serial numbers of a unit under test.

With that being said, I've been able to successfully create a pandas data frame using a combination of the results API and steps API to query the results, and steps for those results respectively. Then I correlate the specific serial number to the particular step. I am able to generate a plot output which is more or less what I'd like to have as my dashboard tile using matplotlib, however when I try to either glue that matplotlib plot to my scrapbook output, or utilize the same SystemLink specific output highlighted here I get no output on my dashboard tile. I've also tried going off of some examples in Git and other Jupyter notebooks that I've been able to successfully link to tiles, but don't see where I'm going wrong with this one.

Note that this is actually a moderately common workflow in Python notebooks; converting notebook code into library code and using something like autoreload; of course, the same is not possible in Haskell, as far as I know.

My institute has a goal of making data more accessible for the customers. The idea is that we should use Jupyter notebooks to present browsable data and provide interactive plots of relevant measurements. I agree that this is a good idea, but I have close to zero experience with notebooks or Python. I see this as an opportunity to learn something useful.

The other problem I have is getting the Groovy kernel available to Jupyter. I installed BeakerX using pip but Jupyter still shows no other kernel than Python 3 available, so all the other notebooks in the tutorial do not work at all, as expected.

I threw away my existing Python and related installations and have reinstalled everything with miniforge today. It works really well ! It is easy to install stuff, and it is much easier to get an overview of what is installed and in what version. The dependency handling is excellent and the error messages are also far more informative. Now I have nearly everything in the tutorial notebooks working (using the scijava environment), except for the Python-based workbook 1-6 which depends on jnius. The updated notebook @imagejan linked to works fine though, also when using the scijava environment per the tutorial installation instructions. So, as far as I can see, the tutorial notebook 1-6 can be replaced with the new version; they seem to be identical in functionality.

The Jupyter Community Workshop in Washington, DC (November 2018) began thatprocess, with a book sprint aimed at producing the first version of thishandbook. The collaboratively written book consolidates explanations andexamples covering key topics, including: what is Jupyter, how to try Jupyter,sharing notebooks with students, locally installing Jupyter, cloud offerings,finding example notebooks, writing lessons in Jupyter, making collections for acourse, exporting to other formats with nbconvert, writing textbooks withJupyter, using Binder and JupyterHub, making assignments and auto-grading,making online courses, teaching with Jupyter in the classroom, active learningand flipped learning pedagogies with Jupyter, and guiding learners to createtheir own content in Jupyter. This open handbook will grow to encompass all youneed to know about Jupyter in teaching and learning.

.NET Interactiveprovides data scientists and developers a way to explore data, experiment with code, and try new ideaseffortlessly using .NET Core. Use .NET Interactive to build .NET Jupyter notebooks or custom interactive coding experiences. 5376163bf9

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