I'm new to Ipython Notebook. I can cut and paste from other apps into my notebooks, but how do I copy/paste code out of notebook into a different app?
I'm accessing a Linux VNC session via Chicken. I can cut/paste with wild abandon between OSX/Linux using both command X/C/V and/or middle mouse button. I can also copy code into IPython notebook. I'm stopped dead in my tracks trying to get code out of IpyNotebook.
Using Notebook's 'Edit/Copy Cell' doesn't work, neither does 'Ctrl-m c'.
I'm running IPython 0.13.1
You have to select and copy code using normal Ctrl-C Ctrl-V. 'Edit/Copy Cell' is a specific action in javascript that does a little more and that browser security policy prevent us to bind with clipboard.
Related
I welcome instructions to do this either with Jupyter Notebook or Jupyter Lab.
My intent for learning purposes, is to download and open various notebooks from GitHub, and open new notebooks to replicate the original notebook from GitHub.
I could simply switch back-and-forth from one notebook to the other to achieve my goal. But, it would be a lot easier if I could have both notebooks opened vertically in two separate windows. Is there an easy way to do some sort of split-screen viewing?
You can do this out of the box in JupyterLab by opening both notebooks and then dragging one of the tabs to the right side of the screen. You can’t do this in the legacy jupyter notebook viewer - it needs to be jupyter lab.
See the jupyterlab docs on the interface for more info.
I have got downloaded a file that got downloaded in a format .ipynb extension but its not in a readable format. Can anyone help me to figure out how to make it in a readable format? Attaching a screenshot of the file when I tried opening it in notepad.
how to open it in readable format
One of the easiest ways to just view a notebook file that is also 100% secure in case what you are being sent is sensitive: nbpreview.
When you go there it asks you to choose a local file. The file isn't uploaded anywhere. It remains in your browser's local cache so it is useful for sensitive stuff that cannot be public.
Similarly, you can upload it to the notebook{sharing}space which is billed as "the fastest way to share your notebooks". It would provide you with a link to view the notebook you have and can be private if you limit sharing the link.
If the notebook can be posted to Github (repository or gist) or online, you can point nbviewer at it and have it rendered nicely. In fact, although it is technically 'static', nbviewer can render some interactive Plotly plots and widget controls that enable playing back animations comprised of frames. This rendering form is also very nice for sharing with non-programmers as the GitHub cruft is not surrounding the content.
You can use Jupyter running in your browser and backed by a free Jupyter community-run service to view the notebook file as an active notebook, on what is equivalent to a temporary remote machine.
Go to Try Jupyter and select either 'JupyterLab' or 'Jupyter Notebook' from the offerings presented. I'd suggest JupyterLab as the steps outline below are made easier as you have the file navigation pane on the left.
After your session spins up in your browser, if you chose JupyterLab, drag your file from your local machine into the file navigation pane on the left side. It will get a gray dashed line around it when you have dragged it to the right place. Drop it in and let it upload. Now double click on it to open it.
If you don't want to drag-and-drop or you chose Jupyter notebooks (classic notebook interface) make a text file and paste in the content you showed. (It's json format as that is the underlying .ipynb format presently.) Save that file with an .ipynb extension. You should then be able to open it the Jupyter Dashboard. (Note the following in the rest of this section was written before the 'Try Jupyter' offerings were switched to using the exerpimental JupyterLite and so your mileage may vary. If you drag-and-drop into JupyterLite, it actually is in your machine; however, it is in a virtual system in your browser that your local file system cannot access directly. To get what this section was specifically written for now, go here and click on the launch binder badge to trigger a session on a true remote machine served by MyBinder.) You used to click on the logo in the upper left to get to the dashboard but it will now take you to JupyterLab and you can double click to open your notebook file. If you really need the classic dashboard, change the end of the URL to change /lab to /tree.
Note because the environment backing your notebook hasn't been set up to handle everything, you'll be out of luck for now trying to run it. You'd have to add your pokemon.csv and install anything else besides pandas. There are ways to use the MyBinder system to handle that as well; however, probably best learned about later this stage.
The MyBinder session is temporary and unique to you. It will close after 10 minutes of inactivity and no longer be accessible. You can always open another session later and redo the steps. Or learn about other options eventually.
Related:
Nbpreview and a lot of the related tools mentioned here are also covered in Sharing R code in Jupyter notebooks.
(These following suggestions mostly assume the notebook is already online, usually at Github. You can still use a local notebook by uploading it to the session as I suggest above. They do though provide additional ways to learn about using MyBinder to serve active notebooks in your browser.)
How to save code file on GitHub and run on Jupyter notebook?
Run a Jupyter notebook directly online (without downloading it locally)
You need Jupyter Notebook in your machine. That is one option. Otherwise, you can upload the file into GitHub and open the .ipynb file from there.
I have installed Jupiter notebook using Anaconda. I run it as using jupyter notebook --no-browser in command prompt in windows 10. I want to run it as the background so that I don't accidentally close the command prompt. The problem here is, many users of systems accidentally close the command prompt, which affects other users.
Open Anaconda navigator.
Launch the jupyter notebook from it. It will run as a background process. Even if you close the Anaconda navigator, it works. So when you close the anaconda navigator, it asks do you want to kill the background process, make sure you unselect the checkbox. Check below screenshot.
This method worked well.
You can use START /b, but that will terminate the program when command window is closed. You will need a VBScript to accomplish this. This post show a simple script to accomplish this: https://superuser.com/questions/62525/run-a-batch-file-in-a-completely-hidden-way
I have a Jupyter Notebook that plots some data and lets the user interact with it via a slider.
What would be the easiest way to make a web app with a similar functionality? (reusing as much of the code...)
I believe the easiest way is to use voilà.
After installing you just have to run:
voila <path-to-notebook> <options>
And you will have a server running your notebook as a web app, with all the input code omitted.
AppMode is "A Jupyter extension that turns notebooks into web applications".
From the README:
Appmode consist of a server-side and a notebook extension for Jupyter.
Together these two extensions provide the following features:
One can view any notebook in appmode by clicking on the Appmode button in the toolbar. Alternatively one can change the url from
baseurl/notebooks/foo.ipynb to baseurl/apps/foo.ipynb. This also
allows for direct links into appmode.
When a notebook is opened in appmode, all code cells are automatically executed. In order to present a clean UI, all code cells
are hidden and the markdown cells are read-only.
A notebook can be opened multiple times in appmode without interference. This is achieved by creating temporary copies of the
notebook for each active appmode view. Each appmode view has its
dedicated ipython kernel. When an appmode page is closed the kernel is
shutdown and the temporary copy gets removed.
To allow for passing information between notebooks via url parameters, the current url is injected into the variable
jupyter_notebook_url.
To be complete - there exists also https://www.streamlit.io/ .
I still dont understand the exact difference between voila and streamlit.
At the moment I just struggle with the possibility to re-run everything with new parameters... I have bad luck with voila still.
Edit: I see that streamlit requires a raw python, not .ipynb, this fact would mean that this answer is completely wrong, I will search a bit more on streamlit before further action/comment.
Edit2: Voila looks great. However, I found few things that uncover the underlying complexity and thus a troubles that may arise.
callbacks. Widgets work great in jupyter, but since it is not possible to re-run one cell, sometimes the logic must be modified to work in Voila.
interactive java objects need a special treatment, e.g. matplotlib has a cheap solution, but there was nothing for e.g. jsroot
links. It is easy to create (a file and) a download link in jupyter, Voila can also serve a file, but it needs another extra treatment.
After all, I pose myself a question - is it better to learn many tricks and modifications to jupyter or to use some other system? I am going to see if streamlit can give em some answer.
The Jupyter Dashboards Bundlers extension from the Jupyter Incubator is one way to do it while retaining interactivity.
EDIT: While pip installing this package will also install the cms package dependency, like dashboard_bundlers, cms needs to be explicitly enabled/quick-setup as a notebook extension for the dashboard tools to work.
#raphaelts has the right idea and should be the accepted answer. As of Dec 2019, Voila is the most appropriate method to deploy Jupyter notebooks to production as a stand alone webapp. Think internal datascience teams sharing their analytics workload with internal C-Suite teams using SPA stlye Notebooks with all the code hidden and custom GUI/interactions thrown in. Recently discussed on HN
https://news.ycombinator.com/item?id=20160634 and the official announcement from the Jupyter maintainer https://blog.jupyter.org/and-voil%C3%A0-f6a2c08a4a93enter link description here
As mentioned above, voilà is a very powerful tool which hides the input cells from your notebooks and therefore provides a clean interface. In order to deploy your notebook with voilà you need to follow the specific steps of your organization. But if you want to quickly run it on your machine, simply install it with pip install voila. Then you can enter start from the command-line: voila my_notebook.ipynb or use the "Voila" button which should have appeared in your Jupyter notebook.
Note, however, that using voilà is only one part of the story. You also need to build the required interactivity, ie. to set up how to respond to input changes. There are quite a few frameworks for this.
The simplest one is to use the interact function or the observe method from the Ipywidgets library. This is very direct, but things can easily get out of control as you start having more and more widgets and complexity.
There are complete frameworks, some of them mentioned above. E.g. streamlit, dash and holoviz. These are very powerful and suited for larger projects.
But if you want to keep it simple, I also recommend to check out autocalc. It is a very easy-to-use library, which lets you define the dependencies between your widgets/variables and let all the recalculation be triggered automatically. A tutorial can be found here.
Disclaimer: I am the author of the autocalc package.
The easiest way is to use the Mercury framework. You can reuse all your code. To convert the notebook to web app you will need to add the YAML header in the first cell of the notebook (very similar to R Markdown). The widgets are generated based on YAML. The end-user can tweak widgets values and click the Run button to execute the notebook from the top to the bottom. You can easily hide the notebook's code (if you want) by setting the show-code: False in the YAML. The example notebook and corresponding web app are below.
Example of the notebook with YAML header
Example web app generated from notebook with Mercury
I am using the LATEST (2.2.0) ipython notebook, when I create a notebook with a loop to write many lines (about 20000 lines), then it run forever I guess since I always see the running icon at the top right. Even if I restart the computer and reopen the notebook again, it will into a running mode automatically, then I almost unable to do anything in this page. I have to copy the code and new another page to fix it.
How can I fix such hang issue during open a too large notebook? I have tried the kernel "interrupt" and "restart" menu and it seems no any effect at all.
IPython notebook is not intended to do such tasks with too much calculation or too many output data because actually such things is for standalone program rather than a notebook.
To fix such issues, you need to create a standalone application (script) to do it from console, then paste the meaningful result into IPython notebook.