I want to be able to show the result of a python computation and have some explanation of it in Markdown. This seems like a fairly simple operation, but I can't figure out how to do it.
Is there any way to do this without installing any extensions to Jupyter?
In the toolbar (see image here http://jupyter-notebook.readthedocs.io/en/latest/_images/jupyter-notebook-default.png), you can set the cell as Markdown in the drop down menu for explanatory text.
Would it suffice if you yourself didn't have to personally handle the installations to Jupyter?
I know OP says specifically, without installing an extension, but what is described is addressed with the Python-Markdown extension, see here. And this question comes up at the top of the list when one Googles "jupyter mix markdown and python print cell".
You can easily use Python-Markdown in an active notebook launched via the MyBinder system from here; the repo for that is here if you want to fork it and further adapt it by adding your own notebooks.
Related
How can I add Indentation lines like what is shown above in Jupyter Lab/Notebook?
I have tried a lot and went into the settings (not much as I am a beginner and I don't know most of the stuff) but in Jupyter I can only get code numbers from the View button but not these beautiful lines which help a lot when doing the indentation.
The default editor used by Jupyter Notebook and JupyterLab (CodeMirror) does not support indentation markers as for today; it is possible to extend it with an add-on but it seems that there isn't one yet (see Indentation Markers with CodeMirror). I imagine that you would need to create a custom overlay mode to make it work, and it might be in scope for jupyterlab-lsp extension if you would like to suggest this via issue on GitHub.
JupyterLab also allows to use a different editor via extension (and there was a proof-of-concept for that) but I don't think that anyone volunteered to maintain any other editor integration recently.
In the near future JupyterLab will likely migrate CodeMirror 6, and depending on availability of such feature in it it will either have a support for it or not. Or maybe someone will contribute a support for it to an extension. It might turn out to be easier to implement in CM6.
I am new to Jupyter Notebook, what is the key difference between the Jupyter Notebook and JupyterLab, suggest me to choose the best one, which should be used in future.
Jupyter Notebook is a web-based interactive computational environment for creating Jupyter notebook documents. It supports several languages like Python (IPython), Julia, R etc. and is largely used for data analysis, data visualization and further interactive, exploratory computing.
JupyterLab is the next-generation user interface including notebooks. It has a modular structure, where you can open several notebooks or files (e.g. HTML, Text, Markdowns etc) as tabs in the same window. It offers more of an IDE-like experience.
For a beginner I would suggest starting with Jupyter Notebook as it just consists of a filebrowser and an (notebook) editor view. It might be easier to use.
If you want more features, switch to JupyterLab. JupyterLab offers much more features and an enhanced interface, which can be extended through extensions:
JupyterLab Extensions (GitHub)
1 - To answer your question directly:
The single most important difference between the two is that you should start using JupyterLab straight away, and that you should not worry about Jupyter Notebook at all. Because:
JupyterLab will eventually replace the classic Jupyter Notebook.
Throughout this transition, the same notebook document format will be
supported by both the classic Notebook and JupyterLab
As of version 3.0, JupyterLab also comes with a visual debugger that lets you interactively set breakpoints, step into functions, and inspect variables.
2 - To contradict the numerous claims in the comments that plotly does not run well with JLab:
JupyterLab is an absolutely fantastic tool both to build plotly figures, and fire up complete Dash Apps both inline, as a tab, and externally in a browser.
3 - And you would probably also like to know this:
Other posts have suggested that Jupyter Notebook (JN) could potentially be easier to use than JupyterLab (JL) for beginners. But I would have to disagree.
A great advantage with JL, and arguably one of the most important differences between JL and JN, is that you can more easily run a single line and even highlighted text. I prefer using a keyboard shortcut for this, and assigning shortcuts is pretty straight-forward.
And the fact that you can execute code in a Python console makes JL much more fun to work with. Other answers have already mentioned this, but JL can in some ways be considered a tool to run Notebooks and more. So the way I use JupyterLab is by having it set up with an .ipynb file, a file browser and a python console like this:
And now you have these tools at your disposal:
View Files, running kernels, Commands, Notebook Tools, Open Tabs or Extension manager
Run cells using, among other options, Ctrl+Enter
Run single expression, line or highlighted text using menu options or keyboard shortcuts
Run code directly in a console using Shift+Enter
Inspect variables, dataframes or plots quickly and easily in a console without cluttering your notebook output.
At this time (mid 2019), with JupyterLab 1.0 release, as a user, I think we should adopt JupyterLab for daily use. And from the JupyterLab official documentation:
The current release of JupyterLab is suitable for general daily use.
and
JupyterLab will eventually replace the classic Jupyter Notebook. Throughout this transition, the same notebook document format will be supported by both the classic Notebook and JupyterLab.
Note that JupyterLab has a extensible modular architecture. So in the old days, there is just one Jupyter Notebook, and now with JupyterLab (and in the future), Notebook is just one of the core applications in JupyterLab (along with others like code Console, command-line Terminal, and a Text Editor).
(I am using JupyterLab with Julia)
First thing is that Jupyter lab from my previous use offers more 'themes' which is great on the eyes, and also fontsize changes independent of the browser, so that makes it closer to that of an IDE. There are some specifics I like such as changing the 'code font size' and leaving the interface font size to be the same.
Major features that are great is
the drag and drop of cells so that you can easily rearrange the code
collapsing cells with a single mouse click and a small mark to remind of their placement
What is paramount though is the ability to have split views of the tabs and the terminal. If you use Emacs, then you probably enjoyed having multiple buffers with horizontal and vertical arrangements with one of them running a shell (terminal), and with jupyterlab this can be done, and the arrangement is made with drags and drops which in Emacs is typically done with sets of commands.
(I do not believe that there is a learning curve added to those that have not used the 'notebook' original version first. You can dive straight into this IDE experience)
This answer shows the python perspective. Jupyter supports various languages besides python.
Both Jupyter Notebook and Jupyterlab are browser compatible interactive python (i.e. python ".ipynb" files) environments, where you can divide the various portions of the code into various individually executable cells for the sake of better readability. Both of these are popular in Data Science/Scientific Computing domain.
I'd suggest you to go with Jupyterlab for the advantages over Jupyter notebooks:
In Jupyterlab, you can create ".py" files, ".ipynb" files, open terminal etc. Jupyter Notebook allows ".ipynb" files while providing you the choice to choose "python 2" or "python 3".
Jupyterlab can open multiple ".ipynb" files inside a single browser tab. Whereas, Jupyter Notebook will create new tab to open new ".ipynb" files every time. Hovering between various tabs of browser is tedious, thus Jupyterlab is more helpful here.
I'd recommend using PIP to install Jupyterlab.
If you can't open a ".ipynb" file using Jupyterlab on Windows system, here are the steps:
Go to the file --> Right click --> Open With --> Choose another app --> More Apps --> Look for another apps on this PC --> Click.
This will open a file explorer window. Now go inside your Python installation folder. You should see Scripts folder. Go inside it.
Once you find jupyter-lab.exe, select that and now it will open the .ipynb files by default on your PC.
If you are looking for features that notebooks in JupyterLab have that traditional Jupyter Notebooks do not, check out the JupyterLab notebooks documentation. There is a simple video showing how to use each of the features in the documentation link.
JupyterLab notebooks have the following features and more:
Drag and drop cells to rearrange your notebook
Drag cells between notebooks to quickly copy content (since you can have more than one open at a time)
Create multiple synchronized views of a single notebook
Themes and customizations: Dark theme and increase code font size
I'm new to python and I've been using PyCharm (4.5 Community Edition) as my IDE on a Mac OS X v11 system. I've installed the markdown plug-in and enabled the package in pycharm (Actions>Plugins>Markdown is checked, program restart complete).
When I create a new markdown file (test.md or test.markdown) I can see the preview tab. But I don't see anything in the preview tab. Also, I can't "run / execute" this file. So seeing a preview would be nice. Ideally, I would like to be able to execute python chunks with markdown comments in-between and see the output rendered in a local browser if this at all is possible.
This was really easy in the Rstudio IDE and a super powerful way to communicate findings.
Any suggestions? Help much appreciated.
Uninstall Markdown Support (by Jetbrains) (has no side preview)
Install Markdown Navigator 2.0 (by Vladimir Schneider) (has side-preview)
https://github.com/vsch/idea-multimarkdown
This should give you a per-compiled side view of you .md file.
I'm on the same boat. Making transition from RStudio to PyCharm. You can install markdown plugins directly within the PyCharm settings. Within Pycharm I searched and downloaded "Markdown support". Once you're editing the Markdown file, you should have a "view" to see the markdown in raw or rendered format and even some formatting buttons like in a normal Text Editor.
I think you need to follow these tag instructions to have the HTML render correctly.
You should also take a look at IntelliLang for language injections, which I think are equivalent to R "chunks".
The default setting for Markdown files - with a fresh copy of Pycharm - is preview mode only:
Just change it to side-by-side mode:
No requirement to install additional plug-ins.
https://www.jetbrains.com/help/idea/markdown.html#preview
Currently PyCharm has two plugin options for .md format: PlantUML and Mermaid.
According to the official docs, in order to enable md support:
Go to File -> Settings -> Languages & Frameworks -> Markdown.
(in recent versions: Pycharm -> Preferences -> Languages & Frameworks -> Markdown)
Install either of these plugins.
Close and Reopen PyCharm (it did not work instantly for me)
In order to see a file preview move a mouse to the upper right corner and select this option.
I've found that a much better way to achieve what I was trying to accomplish when I asked this question is to utilize jupyter notebook (jupyter.org/).
I am working on a scientific python project performing a bunch of computations generating a lot of data.
The problem comes when reports have to be generated from these data, with images embedded (mostly computed with matplotlib). I'd like to use a python module or tool to be able to describe the reports and "build" HTML pages for these reports (or any format supported by a browser).
I was thinking about generating an ipython notebook but I was unable to find if there is a way to do so (except creating the json but I'm doubtful about this approach).
The other way is using Sphinx a bit like the matplotlib but I am not sure how I could really fine-tune the layouts of my various pages.
The last option is to use jinja2 templates (or django-templates or any template engine working) and embed matplotlib code inside.
I know it's vague but was unable to find any kind of reference.
nbconvert has been merged into IPython itself, so please do not use the standalone version anymore. It is now fully template base so you can change things from just tweeking the css, fully re-wrote your templates, or just overwrite the current part of templates you want.
Notebook format is a pure json file, is takes ~20 lines to write a program that loop through it and re-run the codecell. That plus command line argument it is not hard to write a notebook, make it a 'template' notebook and run it on multiple dataset without opening a browser.
Some resources :
programatically run nbconvert, and run a notebook headless (first link)
I think you want to work in ipython notebook and then use nbconvert
Currently, this is it's own utility, that already works (albeit with some installation hurdles, but working) but it is currently being implemented directly into the ipython notebook machinery, which I believe should be released in autumn, or so.
The goal is (and Fernando Perez has demonstrated that this works), that a notebook becomes a fully documented, image containing pdf-document after the conversion.
Using the inline-modus of ipython notebook,
ipython notebook --pylab inline
you can execute your matplotlib-scripts in a browser interactively (thus generating your plots). Then go to
File -> Print View (in the notebook-menu, NOT the browser menu)
and save the generated html-File (via the browser menu). This will include all the plots you generated before as well as the python code. Of course, you cannot modify these html-Files anymore without the notebook-server in the background.
Is this what you mean?
I just found this old question and want to add PWeave to the list, which is perfectly suited to generate reports from python code / jupyter notebooks. I use it to share my work with colleagues that aren't invested with programming alot.
It also integrates into Spyder, THE scientific IDE for python, using the spyder-reports module.
Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers.
We don’t allow questions seeking recommendations for books, tools, software libraries, and more. You can edit the question so it can be answered with facts and citations.
Closed 5 years ago.
Improve this question
In RStudio, you can run parts of code in the code editing window, and the results appear in the console.
You can also do cool stuff like selecting whether you want everything up to the cursor to run, or everything after the cursor, or just the part that you selected, and so on. And there are hot keys for all that stuff.
It's like a step above the interactive shell in Python -- there you can use readline to go back to previous individual lines, but it doesn't have any "concept" of what a function is, a section of code, etc.
Is there a tool like that for Python? Or, do you have some sort of similar workaround that you use, say, in vim?
IPython Notebooks are awesome. Here's another, newer browser-based tool I've recently discovered: Rodeo. My impression is that it seems to better support an RStudio-like workflow.
spyder or install python(x,y). it is great.
If you are new to Python, you can install the free Anaconda distribution (http://continuum.io/downloads.html), which will install Spyder for you, as well as Python 2.7 and IPython. Spyder is very similar to RStudio.
Jupyter Notebook (previously known as IPython notebook) is a really cool project for interactive data manipulation in Python (and other languages, including R). It basically allows you to interactively code and document what you're doing in one interface and later on save it as a:
notebook (.ipynb)
script (a .py file including only the source code)
static html (and therefore pdf as well)
You can even share your notebooks online with others using the nbviewer service, where people publish whole books. Furthermore, GitHub renders your .ipynb files. You can publish your Jupyter Notebooks as reproducible research articles on Authorea. For collaborative editing by multiple users, check out Google Colab built on top of Jupyter.
The default Jupyter Notebook version starts a web application locally (or you deploy it to a server) and you use it from your browser. As Ryan also mentioned in his answer, Rodeo is an interface more similar to RStudio built on top of the Jupyter kernel.
JupyterLab is a newer take on the UI allowing for more flexibility in how you edit your notebooks, control interactive widgets and even run commands in terminal emulators.
There's also a Qt console for IPython, a similar project with inline plots, which is a desktop application.
Jupyter is a normal Python package and can be installed using pip install jupyter. To get all the scientific libraries running on your computer, however, it might be easier to try the official Jupyter Docker containers. For example, assuming your notebooks are in ~/code/jupyter, you can run the container as:
docker run -it --rm -p 8888:8888 -v ~/code/jupyter:/home/jovyan/work jupyter/datascience-notebook
Check out Rodeo from Yhat if you're looking for something like RStudio for Python.
Rodeo has:
text editor (uses Atom under the hood)
Vim / Emacs mode
an IPython console
autocomplete
docstrings
ability to see plots, dataframes, variables
You might want to look into JupyterLab (the next generation of Jupyter Notbooks): https://github.com/jupyter/jupyterlab.
JupyterLab aims to create a more desktop-like experience on the Web.
Update:
As of March 2018 JupyterLab is in beta. "The beta releases are suitable for general usage. For JupyterLab extension developers, the extension APIs will continue to evolve until the 1.0 release. Eventually, JupyterLab will replace the classic Jupyter Notebook after JupyterLab reaches 1.0."
To run Jupyter Lab as a Desktop Application, see christopherroach.com/articles/jupyterlab-desktop-app (Thanks to PatrickT).
Here's a quick preview:
You can arrange a notebook next to a graphical console atop a terminal that is monitoring the system, while keeping the file manager on the left:
For more details see: https://blog.jupyter.org/2016/07/14/jupyter-lab-alpha/ and here: http://www.techatbloomberg.com/blog/inside-the-collaboration-that-built-the-open-source-jupyterlab-project/.
Pycharm is a really decent IDE. From what I have seen so far it is the most similar to Rstudio. Another nice piece is that it allows you to install new Python libraries in a fashion similar to Rstudio (which otherwise can be a nightmare). There is now a free 'community' edition.
I think it is worth while to mention that RStudio v1.1.359 Preview is released. It has terminal feature that can be used for Python.
Download is available here
Documentation is available here
spyder is you need!
https://code.google.com/p/spyderlib/
Spyder (previously known as Pydee) is a powerful interactive development environment for the Python language with advanced editing, interactive testing, debugging and introspection features
For a nicer interactive shell for Python, have a look at DreamPie. It's not really an IDE though (as RStudio seems to be?)
Wing IDE, and probably also other Python IDEs like PyCharm and PyDev have features like this. In Wing you can either select and execute code in the integrated Python Shell or if you're debugging something you can interact with the paused debug program in a shell (called the Debug Probe). There is also special support for matplotlib, in case you're using that, so that you can work with plots interactively.