JupyterLab Now Available

Project Jupyter announces the general availability of JupyterLab:

JupyterLab is an interactive development environment for working with notebooks, code and data. Most importantly, JupyterLab has full support for Jupyter notebooks. Additionally, JupyterLab enables you to use text editors, terminals, data file viewers, and other custom components side by side with notebooks in a tabbed work area.

JupyterLab provides a high level of integration between notebooks, documents, and activities:

  • Drag-and-drop to reorder notebook cells and copy them between notebooks.

  • Run code blocks interactively from text files (.py, .R, .md, .tex, etc.).

  • Link a code console to a notebook kernel to explore code interactively without cluttering up the notebook with temporary scratch work.

  • Edit popular file formats with live preview, such as Markdown, JSON, CSV, Vega, VegaLite, and more.

I like this, as I’m a big fan of notebooks but sometimes you just want to write some diagnostic queries and an IDE is way better for that. H/T Giovanni Lanzani

Related Posts

Creating Big Data Clusters with Azure Data Studio

Niels Berglund takes us through the creation of a Big Data Cluster by using Azure Data Studio to generate a notebook: I wrote a blog post back in November 2018, about how to install and deploy SQL Server 2019 Big Data Cluster on Azure Kubernetes Service. Back then SQL Server 2019 Big Data Cluster was in private preview, (CTP 2.1 I […]

Read More

Python and R Data Reshaping

John Mount takes us through a couple of data shaping packages: The advantages of data_algebra and cdata are: – The user specifies their desired transform declaratively by example and in data. What one does is: work an example, and then write down what you want (we have a tutorial on this here).– The transform systems can print what a transform is going to […]

Read More


February 2018
« Jan Mar »