If you are learning data science, you must have seen data scientists working with Jupyter notebooks. So what is a Jupyter notebook and why is it used for data science? In this article, I will take you through everything you need to know about a Jupyter notebook.
What is a Jupyter Notebook?
Jupyter notebook is an open-source web application that allows you to create and share Data Science reports containing live code, algorithms, visualizations, and explanations of your code and reports. You can also use an IDE or a data science code editor, but you don’t have any tools to present your work in the form of reports and explanations. This is where Jupyter notebooks are better than a code editor or an IDE.
Now, some tasks can be best done on an IDE or a code editor. For example, it’s best to develop an end-to-end application for your machine learning model in an IDE or a code editor. But to visualize and analyze the data you want to train a machine learning model on, it is best to use a Jupyter notebook.
Suppose you are working on a task of analyzing the world happiness report using the python programming language. In this task, you have to analyze a lot of data using so many visualizations. If you are using a code editor for this task, any visualizations you created before will appear first. Whereas in Jupyter notebooks we do cell-by-cell coding, so whenever we code for a new visualization on a new cell, it will show only the latest figure.
Summary
So every time you analyze a large dataset, you need to create a lot of visualizations, so it is better to use a Jupyter notebook. Jupyter notebooks are also preferred if you want to present your work in the form of a report. But if you want to build an app, it is better to use an IDE or a code editor. Hope you enjoyed this article on what a Jupyter notebook is and why it is used for data science. Please feel free to ask your valuable questions in the comments section below.
