SQL Interface within JupyterLab

Learn how to use and modify SQL tables within Jupyter labs.

This article was originally published on Datacamp.

Jupyter Notebooks are an essential part of any Data Science workflow, so much so that many of the organizations like Netflix find them indispensable. The browser-based computing environment, coupled with reproducible document format, has made them the de-facto choice of millions of data scientists and researchers around the globe. Jupyter boasts of a great international community coming from almost every country on earth. A lot of kernels have been developed by the community themselves.

JupyterLab showing its work area with notebooks, text files, terminals, and notebook outputs all capable of interacting with each other.

Jupyter Lab is the next-generation user interface for Project Jupyter offering all the familiar building blocks of the classic Jupyter Notebook like the notebook, terminal, text editor, file browser, rich outputs, etc. However, unlike the classic notebooks, all these features are provided in a flexible and powerful user interface. The basic idea of the Jupyter Lab is to bring all the building blocks that are in the classic notebook, plus some new stuff, under one roof

Let’s first get Jupyter Lab installed and running on our systems

Installation

JupyterLab can be installed using conda, pip or pipenv.

#conda
conda install -c conda-forge jupyterlab
#pip
pip install jupyterlab
#pipenv
pipenv install jupyterlab
pipenv shell

Have a look at the official installation documentation for more details.

Starting JupyterLab

You can start the Jupyter by simply typing the following at the console:

jupyter lab

JupyterLab will open automatically in the browser with an interface resembling the one below. This means everything is in place, and you are good to go.