What is Jupyter Notebook and why do you need it

#programming #beginners #tutorial #datascience

If you want to code in Python or get into data science, Jupyter Notebook is for you. It has become the standard for most developers.

What is Jupyter Notebook

Jupyter Notebook is an environment where you can immediately see the result of your code execution. Unlike a traditional IDE, the code can be split into chunks and executed in any order. It’s like writing code on a napkin and telling it, "Run."

You can write a function, test it, load a file into memory, and even visualize a graph right after executing the code. It’s very convenient for testing specific steps or making quick changes.

What languages are supported

Jupyter is usually used for Python, but it supports other languages as well:

→ Ruby

→ Perl

→ R

→ bash scripts

To use these, you need magic commands, which also extend Python's capabilities. We will discuss these commands in more detail in another article.

Jupyter Notebook in the cloud

The easiest way to work with Jupyter is in the cloud, for example, through Google Colab. You just need to open a browser and start writing code. The advantage is that you don't need to install anything, but the downside is that performance may be slower, and some libraries you need might not be available.

Jupyter Notebook on your computer

If you need full control, install Jupyter on your computer:

pip3 install jupyter jupyter notebook

This will open a browser with a local development environment. An alternative is to install "Anaconda," which includes everything you need: Python, Jupyter, and many other useful tools.

How to work with Jupyter Notebook

Write code, run it, and instantly see the result. For example, paste any code snippet and immediately see the output as a graph — everything is clear and simple.

Where Jupyter Notebooks are used

→ Machine Learning and Neural Networks: Convenient for testing and visualizing results.

→ Step-by-Step Development: Code is stored in independent cells, which can be executed separately, making it easy to experiment and quickly verify hypotheses.

→ Learning Python: Write code and immediately see the result.

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