VS code is calling??
Picture this: a normal day at work, me happily doing my work in the Jupyter Notebook. Life was sweet, analyzing data, creating charts and shift+enter. Then, out of nowhere, my collegue from the next cubicle throws a hurricane of words, passionately declaring that VS Code is the ultimate coding IDE. Now, I’m no tech guru, but being yelled at about code editors is not your average Tuesday.
Anyway I decided to give VS Code a shot and realized VS code wasn’t just hype; it was genuinely friendlier. So I decided to make this as my next content for medium and here I go…
One of the big advantages of VS Code is that it’s really good at handling big projects. It helps you write and organize your code neatly, and it even has a tool that helps find and fix mistakes in your code. Plus, it plays nicely with Git, and helps you work with others.
It’s got this amazing search tool that finds files and symbols super fast, so you don’t get lost in your code mess. And guess what? There’s a cool trick called multiple cursors that lets you change things in lots of places at once.
Another advantage of VS Code is its customization options. Users can customize the appearance of the editor by selecting from a range of color themes and font styles. They can also customize the layout and arrangement of the editor by docking and undocking windows, or by using the built-in terminal. This allows users to tailor the environment to their specific needs and preferences.
Now, let’s talk about Jupyter Notebook. It’s like an open-source online playground for coding that you can interact with. In the beginning, it was mainly for Python, but now it plays nice with lots of other languages like R, Julia, and Haskell. Basically you can run every line of your code and view the outputs
A key benefit of Jupyter Notebook lies in its dynamic and hands-on nature. Users can actively engage with their code, exploring and experimenting within a flexible and responsive setting. This quality is particularly advantageous for workflows in data science and machine learning, where the swift testing of various concepts and observing immediate outcomes is crucial. Jupyter Notebook also facilitates the seamless integration of code, visual elements, and text within a unified document, simplifying the process of documenting and distributing one’s work. It’s also got this cool inline plotting support, letting users see their visualizations right in the notebook, making it super handy. It also has support for rich media, such as HTML, LaTeX, and images, which allows users to include a wide range of content in their notebooks. You have the option to share your Jupyter Notebook and colaborate with others as well.
The conclusion is if you work on data analysis projects where you need to view the outputs every now and then, use jupyter notebook. At the time of plugging in your project where you organize the code and package it , use VS Code.
That’s that. Arigato!!
