I recently started using Google Colab and absolutely love the autocomplete UI. I usually code in Jupyter Notebook and hence am used to an autocomplete which only returns the single methods and takes a second or two to load. Google Colab on the other hand is instant, returns the method and also tells you the expected arguments and a description of what a method does. I love it, it reminds me of my old days in eclipse.
Therefore I wanted to ask if there is a module/plugin for Jupyter Notebook to have this UI. Otherwise, is there a different IDE like Jupyter (with these code snippets) with the advanced autocomplete functions? Possibly open source. Thanks.
You can try to use the Hinterland, it's an extension that enable code autocompletion menu for every keypress in a code cell, instead of only calling it with tab.
You can lean how to enable an extension in here, and read more about Hinterland in here.
I hope it helped you
I read and heard a lot about Jupyter notebooks recently. I gave them a try and found it terribly obstructing to basically have to use an editor with the functionality of Windows' Notepad. Besides that I feel like I didn't get the fundamental point of Jupyter notebooks:
Can I not achieve everything that Jupyter does by editing plain .py files in any editor that is linked to a Python/IPython console? Specifically, I can edit Python code and run parts of it using the standard Spyder setup
or even with a properly setup Vim or Emacs.
The big difference being of course that any of these three setups gives me incredibly much more power to do all the other things that facilitate coding, like fast editing commands, code completion, debugging, refactoring, ...
You can save results and graphs of your runs like a report.
And it is better readable.
It is very good to share your results with others.
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
Is it possible to plug IPython notebook into existing Python project and to be able to reuse some of the existing code w/o copy-pasting it into a notebook?
I am looking for a way to use IPython Notebooks as a part of a large Python project to quickly test hypothesis and to analyze data on the spot.
P.S. It would also be nice to be able to import Python files into a Notebook. Is it possible?
I see this is an old question, but I want to answer it if someone still looks it up.
P.S. It would also be nice to be able to import Python files into a Notebook. Is it possible?
You can import any python script (filexy.py) from the same folder as your notebook by simply stating import filexy.
Relating to that, I'd suggest that you define functions for your most reused code bits and gather them in a library (filexy.py) that you import in your notebook. Use the notebook as a short, clean, "working-desk" and your filexy.py library as the "toolbox".
That way you can also solve:
I am looking for a way to use IPython Notebooks as a part of a large Python project to quickly test hypothesis and to analyze data on the spot.
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.