This interface can be achieved in two possible ways: 1. Therefore it is a great idea to have a seamless interface between SQL databases and Jupyter Notebook/Lab so that accessing and manipulating data becomes easier and more efficient. Most of the times, the data that we work with is stored in files called databases, and an essential task of a Data Scientist is to be able to access data from databases and then analyze it. Just double click or drag a file on to this area to start working. It comprises of the notebooks, documents, consoles, terminals, etc. The main work area is the place where the actual activity takes place.

This means everything is in place, and you are good to go.

JupyterLab will open automatically open in the browser with an interface resembling the one below. You can start Jupyter by simply typing the following in the console: jupyter lab Have a look at the official installation documentation for more details. JupyterLab can be installed using conda, pip, or pipenv. Let’s first get Jupyter Lab installed and running on our systems.

JupyterLab showing its work area with notebooks, text files, terminals, and notebook outputs all capable of interacting with each other. 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. However, unlike the classic notebooks, all these features are provided in a flexible and powerful user interface. 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. The community themselves has developed a lot of kernels. Jupyter boasts of a great international community coming from almost every country on earth. 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 Notebooks are an essential part of any Data Science workflow, so much so that many of the organizations like Netflix find them indispensable.

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