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
Related
(my environment is vs code using pylance)
For example, arrays returned from train_test_split() or LinearRegression.predict() are not recognized as an array and do not offer any autocomplete suggestions. In google colab and spyder after typing my_returned_array., I get a long list of available array functions, but in VS code with pylance I get nothing.
Is there some additional configuration I need to do or is there some other extension I need to use?
Google Colab and Spyder maintain the state of all variables as parts of the program are run. VS Code does a similar thing with the Jupyter Notebooks extension while running in debug mode, allowing me to explore the states of variables, but pylance doesn't seem to pick up state information from the Jupyter notebook. While this would be a wonderful feature, it is not a necessary behavior.
I'm trying to migrate my entire workflow from eclipse and jupyter notebook all over to VS Code. I installed the python extension, which should come with Intellisense, but it only worse partly. I get suggestions after typing a period, but don't get any information on parameters nor documentation when hovering over with my mouse. Thank you so much for your help and have a wonderful new year!
P.S If anyone has any experience with using anaconda environments with VS Code, that would be greatly appreciated as well as I running into some problems with it recognizing the libraries.
Also you can see here that when I manually activate Intellisense, it doesn't recognize that it's in a method.
Sorry for the long string of edits, but I discovered that when typing print in a regular python file, it works, but not in a jupyter notebook file. Also, it still doesn't work for numpy. Thanks for the help everyone.
You could use the shortcut key "Ctrl+Space" to open the suggested options:
In addition, it is recommended that you use the extension "Pylance", which works better with the extension "Python".
Update:
Currently in VSCode, the "IntelliSense" document content is provided by the Python language service, which is mainly for Python files (".py" files call this function), while in Jupyter, the "IntelliSense" used by the ".ipynb" file comes from the extension "Jupyter". You could refer to the content of this link to use VS code insiders, and its notebook editor has better intellisense.
In VS code insiders:
After importing Pandas, when creating a pandas dataframe, Intellisense doesn't show the available attributes/methods of the created object.(Image 2, where I try to use the .head() function).
It detects the module pd(pandas) methods without any problem (see Image 1).
I don't have this problem when running a Jupyter Notebook or Jupyter Lab on the browser.
I'm using:
Windows 7
Python 3.8.3 in a Conda environment.
VSCODE 1.46.1
Python extension 2020.6.90262
Microsoft Language Server
Visual Studio Intellicode 1.2.8
IMAGE 1: It uses intellisense to detect the module methods/attributes
IMAGE 2: Intellisense doesn't show the pandas object available attributes/methods
The detection isn't working because IntelliSense has a hard time with pandas (and pandas.read_csv() especially). It works in Jupyter because it's accessing the live data while IntelliSense has to infer everything from the source code statically.
I would advise trying out Pylance as it's the new language server from Microsoft and we have tried to support pandas appropriately. If Pylance doesn't work then
try different values for your python.languageServer setting and see which one gives you the best result.
Go to your VS Code explorer and open that folder you are currently working in. This should solve the problem. Or go to file-> Open Folder. You can also open your current working folder by hotkey ctrl + o .
Close but no cigar. In 2021, language servers still often break. I think VS code is a good idea but sometimes they just break things. I use Intellij for work and it is heavier but better in that regard. I'm sure they will get it right eventually but sadly i don't think they are taking it as serious as they should since data scientists are a big part of their costumers and if you create a pandas object you might be working with its methods for a while rather than direct methods off the modules! So it REALLY helps if we can access lets say pandas.DataFrame.groupby for example rather than just things directly after pandas alone. I keep using VS code as I like keeping my browser up and really enjoy the advantages of having an unified place to keep my python, R and notebook code :) We just need to be patient!
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 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.