#COMBINING JUPYTERLAB AND LIGHTTABLE HOW TO#

In the following animation you see how to connect multiple Python files and notebooks in JupyterLab.Įxploration of the csv file and loading it into a dataframe in a kernel which is shared among the open files. I see this as a key feature for tackling the Cons.

#COMBINING JUPYTERLAB AND LIGHTTABLE CODE#

JupyterLab lets you develop complex python code as well as Jupyter notebooks and making it easy to connect them to the same kernel. Python and Jupyter notebook files sharing a single kernel 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. It is also listed under Cons in case its not intuitively available in Jupyter notebook. A statement listed under in the Cons section does indicate that the mentioned functionality is not achievable at all. I want to highlight that this is not an exhaustive list of Pros and Cons.

#COMBINING JUPYTERLAB AND LIGHTTABLE PLUS#

Absence of a convenient visual debugging plus profiling functionality, despite really promising developments like the PixieDebugger. 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. Testing and modularity are difficult to handle within Jupyter notebooks. a jpg file with a double click within an IDE. This requires more effort than opening e.g. Opening and exploring files is clunky as one needs to load the file first and choose an appropriate way to display it programmatically. One needs to prefix terminal commands with a exclamation mark ! within a cell in order to interact with the operating system’s terminal or use the terminal view added as addon. Thus, reading and writing files becomes clumsy. Jupyter notebooks do not provide a convenient file explorer view when developing code. I want to highlight though that there is a extremely helpful community-contributed unofficial extension using the notebook’s metadata. The absence of a built-in variable inspector is one of the first things experienced standard IDE users are missing in Jupyter notebooks. + Jupyter notebooks are a pretty neat tool for data storytelling and presentation as its possible to show a documentation along with a code’s output. + One can add a pretty and concise documentation of a piece of code by changing a cell from Code to Markdown. by just simply using %matplotlib notebook or ipywidgets. + The interaction with plots is very convenient e.g. tools like Google Facets have been developed for being used in Jupyter notebooks. + Jupyter notebooks are particularly strong when it comes to visualization functionalities. These factors are not surprising looking into some Pros and Cons based on my experience with Jupyter notebooks.

Users need the ability to easily collaborate.

Users want to combine and remix different Jupyter building blocks.

Jupyter notebooks’ “evolution of the Jupyter web interface” to JupyterLab is based on a user experience survey conducted in 2015 highlighting following three success factors : Wouldn’t it be nice if there would be a tool taking the best of each and consequently combines both worlds? JupyterLab is working towards that goal by enabling users to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner. Despite Python notebooks’ popularity, a classic Python IDE or text editor becomes more convenient the more coding needs to be done. It certainly helps to lower the barrier to start with programming and helps in education, because an input is presented together with its processed output instantly in a browser, which many users are very familiar with. Python notebooks got a lot of attention in the recent years as a tool showing code and results in an interactive and nicely layouted manner.

NASA / SWRI / MSSS / GERALD EICHSTÄDT / SEÁN DORAN © CC NC SA Why JupyterLab? A processed image of Jupiter from Juno’s ninth close flyby provided at.

Combining jupyterlab and lighttable