Jupyter Notebook is a web-based interactive computational environment. Supports Python, Julia, R etc and is largely for data analysis, data visualization and exploratory computing

JupyterLab is more of an IDE-like experience. It has a modular structure where you can open several notebooks at the same time. Offers more features and can be extended through JupyterLab Extensions.

Internally, a Jupyter notebook is store in JSON. Here is the basic structure:

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# This is a code cell\\n",
        "print('Hello, World!')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## This is a markdown cell\\n",
        "You can write text, add images, and format using Markdown."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}

• "cells": An array containing individual cells, where each cell can be of type "code," "markdown," or other types.
• "cell_type": Specifies the type of cell ("code" or "markdown").
• "execution_count": Represents the execution count of a code cell.
• "metadata": Stores metadata information for a cell.
• "outputs": Contains the output of a code cell.
• "source": Holds the actual content of the cell, either code or markdown text.
• "metadata": Additional metadata for the entire notebook, including kernel information and language details.
• "nbformat" and "nbformat_minor": Indicate the version of the notebook format.