The Notebook is a 2004 American romantic drama film directed by Nick Cassavetes, from a screenplay by Jeremy Leven and Jan Sardi, and based on the 1996 novel of the same name by Nicholas Sparks. The film stars Ryan Gosling and Rachel McAdams as a young couple who fall in love in the 1940s. Their story is read from a notebook in the present day by an elderly man, telling the tale to a fellow nursing home resident.

Almost at the end of the journal in the notebook, Allie asks Noah what happened at the end of the story and Noah prompts her that she knows. She briefly recognizes him and remembers. Allie asks how long they have before she forgets again and Duke tells her possibly five minutes. They dance to their song, "I'll Be Seeing You", and she asks about their kids.

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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.

@HannesZiegler ,I have not knowingly modified jupyter_notebook_config.py file. Where is it typically located, and what should the c.NotebookApp.ip or c.NotebookApp.port configuration options be set to? Thx.

Hmmm, interesting. I have no jupyter_notebook_config.py in the location described, or anywhere I can find. Is this the problem, perhaps?

Adding to the evidence, another user in my group is experiencing the exact same issue upon upgrading to 3.0.2.

No, if you haven't had occasion to mess with the configs, then there won't be a config to mess with (it has to be intentionally created with the command jupyter notebook --generate-config).

Are your notebooks located locally, or on a server or network location with a UNC path?

I've tried everything I can think of to kill a running notebook that is stuck in a loop and nothing works. The Interrupt Kernel button is always grayed out, tried restarting the kernel through the command prompt (P Key) but this doesnt seem to do anything either. The only way I've figured out how to stop the process is to kill the entire application in task manager.

Anyway, I think as long as Jupyter users are educated well enough to develop the habit of recompiling the whole notebook from scratch in the end instead of just leaving the results from out-of-order execution as the final product, Jupyter is probably fine.

Anyway, Jupyter notebooks are not easy to copy and paste because they are not plain text. Well, they are, but they are essentially JSON and feel more like Word (essentially XML) instead of the plain text in the sense of .txt. By comparison, R Markdown documents are true plain text (in the sense of .txt), so you can easily Ctrl + C and Ctrl + V (or Command + C / V).

Joel seems to have helped Jupyter users debug many painful problems due to the hidden state and out-of-order execution issues. For some reason, I have never heard such problems from an R Markdown notebook user. Perhaps R users are just too shy to ask or they quietly give up.

For problems #3, #4 and #7, I have argued that notebooks are not suitable for software engineering, and you should consider using a true IDE. For R Markdown users, there is an obvious IDE for you to use. Problem #5 is also solved by the IDE.

Note that we (R Markdown developers) released R Markdown with the batch mode first, and introduced the notebook mode about four years later. That probably explains why the state problem seems to be rare in the R Markdown world.

Can notebooks be used to write a well-structured, thoughtful, and reproducible (data analysis) report? I believe they surely can. Nothing stops you from extracting the useful parts from the scratch pads to write a formal document.

Only the notebook mode can suffer from the out-of-order execution problem. For the other three ways, the first two have a tiny problem, which may or may not bother you: they require a manual operation each time you recompile the document (click a button, or type a function call).

My answer is, if your project is small enough, it is fine to write a few external R scripts and source() them in the notebook, otherwise you may consider using the IDE to write an R package, and include your notebooks as package vignettes. To quote Carl Boettiger:

I think a good .Rmd notebook acts like an #rstats vignette: complex code belongs in functions in a package, (with docs and tests if appropriate); the notebook part merely ensures code underlying the narrative & results are reproducible.

In this post, I first talked about the two cultures, namely the data analysis culture, in which people often use code, and the software engineering culture, in which people often create code. I believe notebooks are great for exploratory data analysis, but bad for software engineering.

Notebooks are a common tool in data science and machine learning for developing code and presenting results. In Databricks, notebooks are the primary tool for creating data science and machine learning workflows and collaborating with colleagues. Databricks notebooks provide real-time coauthoring in multiple languages, automatic versioning, and built-in data visualizations.

Click Import. The notebook is imported and opens automatically in the workspace. Changes you make to the notebook are saved automatically. For information about editing notebooks in the workspace, see Develop code in Databricks notebooks.

Welcome to the Jupyter Notebook documentation site. Jupyter Notebookis 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.

A computational notebookis a shareable document that combines computercode, plain language descriptions, data, rich visualizations like 3D models,charts, graphs and figures, and interactive controls. A notebook, along withan editor like Jupyter Notebook, provides a fast interactive environment forprototyping and explaining code, exploring and visualizing data, and sharingideas with others.

Jupyter Notebook is a sibling to other notebook authoring applications underthe Project Jupyter umbrella, like JupyterLaband Jupyter Desktop.Jupyter Notebook offers a lightweight, simplified experience compared to JupyterLab.

Running the workflow doesn't cause an error in the Python tool, but it doesn't work either. No data is output. I can't attach a sample workflow since the non-functional Python tool presumably still contains sensitive information in it. The specific use is to download data from the Qualtrics API as described in this thread. If I bring in a new Python tool into a new workflow, the same thing happens where the Jupyter notebook never loads.

If neither service starts after placing the jupyter-notebook.exe in Scripts folder and reopening Designer and clicking on a Python Tool, confirm that it is still present in the Scripts folder because there are times when an anti-virus will remove it.

Thank you! The specified folder does contain jupyter-notebook.exe still. I'm looking into the anti-virus route since I'll have to go through our IT team to figure that out. It seems strange it worked fine before the Alteryx update and isn't working now though.

The Jupyter Notebook App is a server-client application that allowsediting and running notebook documentsvia a web browser.The Jupyter Notebook App can be executed on a local desktoprequiring no internet access (as described in this document)or can be installed on a remote server and accessed through the internet.

When you open a Notebook document, the associated kernel is automatically launched.When the notebook is executed (either cell-by-cell or with menu Cell -> Run All),the kernel performs the computation and produces the results.Depending on the type of computations, the kernel may consume significantCPU and RAM. Note that the RAM is not released until the kernel is shut-down.

The Notebook Dashboard is the component whichis shown first when you launch Jupyter Notebook App.The Notebook Dashboard is mainly used to open notebook documents,and to manage the running kernels (visualize and shutdown).

Any R Markdown document can be used as a notebook, and all R Notebooks can be rendered to other R Markdown document types. A notebook can therefore be thought of as a special execution mode for R Markdown documents. The immediacy of notebook mode makes it a good choice while authoring the R Markdown document and iterating on code. When you are ready to publish the document, you can share the notebook directly, or render it to a publication format with the Knit button.

By default, RStudio enables inline output (Notebook mode) on all R Markdown documents, so you can interact with any R Markdown document as though it were a notebook. If you have a document with which you prefer to use the traditional console method of interaction, you can disable notebook mode by clicking the gear button in the editor toolbar, and choosing Chunk Output in Console (Figure 3.4).

The primary difference is that when executing chunks in an R Markdown document, all the code is sent to the console at once, but in a notebook, only one line at a time is sent. This allows execution to stop if a line raises an error.

Output: The most obvious difference is that most forms of output produced from a notebook chunk are shown in the chunk output rather than, for example, the RStudio Viewer or the Plots pane. Console output (including warnings and messages) appears both at the console and in the chunk output.

Working directory: The current working directory inside a notebook chunk is always the directory containing the notebook .Rmd file. This makes it easier to use relative paths inside notebook chunks, and also matches the behavior when knitting, making it easier to write code that works identically both interactively and in a standalone render. 0852c4b9a8

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