within a cell in order to interact with the operating system’s terminal or use the terminal view added as addon [5].
– Opening and exploring files is clunky as one needs to load the file first and choose an appropriate way to display it programmatically.
This requires more effort than opening e.
g.
a jpg file with a double click within an IDE.
– Testing and modularity are difficult to handle within Jupyter notebooks.
– Seemingless integration with a version control system is missing, although there is interesting progress with add ons like nbdime making diffing and merging of notebooks easier [7].
– Absence of a convenient visual debugging plus profiling functionality, despite really promising developments like the PixieDebugger [10].
I want to highlight that this is not an exhaustive list of Pros and Cons.
A statement listed under in the Cons section does not mean that the mentioned functionality is not achievable at all.
It is also added in case its not intuitively available in Jupyter notebook.
Let’s look into the details with the currently available version of JupyterLab (0.
35.
6) and see what is gonna be covered when moving from Jupyter notebook to JupyterLab.
Python and Jupyter notebook files sharing a single kernelJupyterLab lets you develop complex python code as well as Jupyter notebooks and making it easy to connect them to the same kernel.
I see this as a key feature for tackling the Cons.
In the following animation you see how to connect multiple Python files and notebooks in JupyterLab.
Creation of two Python files and one Jupyter notebook in JupyterLab.
Consecutively, you see the selection of one common kernel for each of the files.
At the end you can observe that all three files have access to the same kernel as they are using the the variables a and b interactively.
Now look at the bellow animation as it shows the simplicity of loading data into a dataframe, developing models separately while testing and visualizing them with the power of Jupyter notebooks in a seamless manner.
All of this is possible in addition to having one common variable inspector and file explorer.
You can see here a simple manual function approximation task.
Exploration of the csv file and loading it into a dataframe in a kernel which is shared among the open files.
The dataframe is visible in the variable inspector.
First the given x and y vectors are plotted in blue.
Afterwards, the function approximator plotted in orange is iteratively improved by manually adjusting the function fun in the file model.
py.
The approximator covers fully the given data input at the end.
Therefore, only an orange line is visible anymore.
Effectively this decouples extraction, modeling and visualization without having to write and read files to share the data frames.
This is a massive time saver for your daily work, as it reduces the risk of mistakes in the file loads, and because it's much faster to setup your EDA along with trials in the early stages of projects.
Furthermore, it helps to reduce the number of code lines in case you add as many asserts into your data pipeline as me.
In case you need a terminal really quick within the same context of your project, then you can just simply open the launchpad and create a new Terminal view.
This is particular useful if want to check the resources needed by your model or algorithm, as shown in the following animation.
JupyterLab- Ian Rose (UC Berkeley), Chris Colbert (Project Jupyter) at 14:30 shows how to open a terminal within JupyterLab [9].
Opening a data file is also pretty neat with JupyterLab.
It is rendered in a nicely e.
g.
in tabular form for csv files and utilizes lazy loading, hence making it fast plus it supports enormous file sizes.
The next animation shows opening the IRIS data set from a csv file.
JupyterLab- Ian Rose (UC Berkeley), Chris Colbert (Project Jupyter) at 19:15 shows the IRIS data set in a csv file being opened with a simple click [9].
You can also open image files with just a click, which comes pretty handy when working on computer visions tasks.
In the following animation you see how Jupyterlab renders an image of the hubble telescope in a separate of the last used panel.
JupyterLab- Ian Rose (UC Berkeley), Chris Colbert (Project Jupyter) at 17:58, shows an image being rendered in by clicking on it in the built in file explorer [9].
Furthermore, you can navigate and utilize Git with JupyterLab’s Git extension as shown below.
Parul Pandey’s gif showing the navigation in the Git extension provided in [6].
There is no visual debugging and profiling functionality available in JupyterLab at the time writing this article.
It is currently planned for a future release [11].
Hence, development will start earliest after version 1.
0 has been released.
Despite this plans, there is work being done to enable PixieDebugger for notebooks in Jupyterlab [12].
ConclusionJupyterLab adds a complete IDE around Jupyter notebooks making it surely a strong evolution of the Jupyter notebooks.
It integrates so well into the data scientists’ daily work that it can be also seen as the Next Gen tool.
The ease of decoupling data extraction, transformation, modeling visualization and testing is already really powerful.
With this in mind I hope seeing the 1.
0 release popping soon.
In case you got excited about the JupyterLab project and want to try it yourself, just follow the instructions in Parul Pandey ‘s article:Jupyter Lab: Evolution of the Jupyter NotebookAll good things (must) come to an end to make way for something better.
towardsdatascience.
com[1] Project Jupyter, JupyterLab Overview (2018), https://jupyterlab.
readthedocs.
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Tache, JupyterLab: The evolution of the Jupyter web interface (2017), https://www.
oreilly.
com/ideas/jupyterlab-the-evolution-of-the-jupyter-web-interface[3] J.
Wexler, Facets: An Open Source Visualization Tool for Machine Learning Training Data (2017), https://ai.
googleblog.
com/2017/07/facets-open-source-visualization-tool.
html[4] 5agado, Interactive Visualizations In Jupyter Notebook (2017), Interactive Visualizations In Jupyter Notebook[5] I.
Rose and G.
Nestor, JupyterLab: The Evolution of the Jupyter Notebook (2018), https://www.
youtube.
com/watch?v=NSiPeoDpwuI&feature=youtu.
be&t=254[6] P.
Pandey, Jupyter Lab: Evolution of the Jupyter Notebook (2019) https://towardsdatascience.
com/jupyter-lab-evolution-of-the-jupyter-notebook-5297cacde6b[7] Project Jupyter, Jupyter Notebook Diff and Merge tools (2019), https://github.
com/jupyter/nbdime[8] Jupyter Contrib Team, Variable Inspector (2019), https://jupyter-contrib-nbextensions.
readthedocs.
io/en/latest/nbextensions/varInspector/README.
html[9] I.
Rose and C.
Colbert, JupyterLab Next-generation user-interface for Project Jupyter (2018), https://www.
youtube.
com/watch?v=t5q19rz_FNw&feature=youtu.
be[10] D.
Taieb, The Visual Python Debugger for Jupyter Notebooks You’ve Always Wanted (2018), https://medium.
com/codait/the-visual-python-debugger-for-jupyter-notebooks-youve-always-wanted-761713babc62[11] Project Jupyter, JupyterLab (2019), https://github.
com/jupyter/roadmap/blob/master/jupyterlab.
md[12] Project Jupyter, JupyterLab (2017), https://github.
com/jupyterlab/jupyterlab/issues/3049[13] M.
Bartels, NASA Releases Treasure Trove Of Incredible New Images Of Jupiter From Its Juno Mission (2017), https://www.
newsweek.
com/nasa-releases-treasure-trove-incredible-new-images-jupiter-its-juno-mission-705210This article aims to provide reasoning as to why JupyterLab might be the IDE of choice for a Python data scientist, by combining the author’s practical experience with a profound literature research.
It should not act as installation guide, nor as listing and comparing of features.
.