Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It is a popular tool among data scientists, researchers, and educators for interactive computing and data analysis. The name “Jupyter” is derived from the three core programming languages it originally supported: Julia, Python, and R.

What is Jupyter Notebook?

Three fundamental programming languages—Julia, Python, and R—that it initially supported are where Jupyter Notebook gets its name. But now since it supports more than 40 programming languages, it is a flexible option for a range of computational jobs. Because the notebook interface is web-based, users may use their web browsers to interact with it.

Components of Jupyter Notebook

The Jupyter Notebook is made up of the three components listed below. –

1. The notebook web application

It is an interactive web application that allows you to write and run code.

Users of the notebook online application can:

• Automatic syntax highlighting and indentation are available when editing code in the browser.
• Activate the browser’s code.
• Check out the computations’ output in media formats like HTML, LaTex, PNG, PDF, etc.
• Create and use widgets in JavaScript.
• Contains mathematical formulas presented in Markdown cells

2. Kernels

The independent processes launched by the notebook web application are known as kernels, and they are used to execute user code in the specified language and return results to the notebook web application.

The following languages are available for the Jupyter Notebook kernel:

• Python
• R
• Julia
• Ruby
• Scala
• node.js

3. Notebook documents

All content viewable in the notebook online application, including calculation inputs and outputs, text, mathematical equations, graphs, and photos, is represented in the notebook document.

Types of cells in Jupyter Notebook

1. Code Cell: A code cell’s contents are interpreted as statements in the current kernel’s programming language. Python is supported in code cells because Jupyter notebook’s kernel is by default written in that language. The output of the statement is shown below the code when it is executed. The output can be shown as text, an image, a matplotlib plot, or a set of HTML tables.
2. Markdown Cell: Markdown cells give the notebook documentation and enhance its aesthetic appeal. This cell has all formatting options, including the ability to bold and italicize text, add headers, display sorted or unordered lists, bulleted lists, hyperlinks, tabular contents, and images, among others.
3. Raw NBConvert Cell: There is a location where you can write code directly in Raw NBConvert Cell. The notebook kernel does not evaluate these cells..
4. Heading Cell: The header cell is not supported by the Jupyter Notebook. The panel displayed in the screenshot below will pop open when you choose the heading from the drop-down menu.

Key features of Jupyter Notebook

• Several programming languages are supported.
• Integration of Markdown-formatted text.
• Rich outputs, such as tables and charts, are displayed.
• flexibility in terms of language switching (kernels).
• Opportunities for sharing and teamwork during export.
• Adaptability and extensibility via add-ons.
• Integration of interactive widgets with data science libraries.
• Quick feedback and live code execution.
• Widely employed in scientific research and education.

Getting Started with Jupyter Notebook

The easiest way to install jupyter notebook is through the terminal:

Step 1: Python’s latest version for this method(https://www.python.org/downloads/).

Step 2 : Updating pip using cmd.

python -m pip install --upgrade pip

Upgrading pip

Step 3: Install the jupyter notebook using the command pip install jupyter notebook in the terminal.(refer to the image)

pip install jupyter notebook 

Step 4: Use the command jupyter notebook in terminal to run the notebook.

jupyter notebook

After you type the command, this home page should open up in your default browser.

Jupyter Home Page

Applications of Jupyter Notebook

1. Data science workflows: Organizing and recording the steps involved in data analysis.
2. Making slide displays and reports for presentations.
3. Data analysis involves investigating and displaying data.
4. The creation and evaluation of machine learning models.
5. NLP: Text analysis and natural language processing.

Notebook Extensions

Extensions for Jupyter Notebook are add-ons or modules that improve the environment’s functionality. Jupyter Notebook is further enhanced and customizable by these extensions, which offer more capabilities and settings. The Jupyter JavaScript API and the page’s DOM are both accessible to extensions.

Although Jupyter Notebooks come with lots of built-in abilities, extensions let you add more. Actually, Jupyter supports four different kinds of extensions:

• Kernel
• IPython kernel
• Notebook
• Notebook server

You can download jupyter_contrib_nbextensions, one of the most well-liked extension sets, from GitHub. This is actually a set of pip-installed extensions made available by the Jupyter community.

Keyboards Shortcuts

Working with code and markdown cells in Jupyter Notebook requires the use of keyboard shortcuts to increase productivity. Here are several significant Jupyter Notebook keyboard shortcuts:

• Ctrl+Enter: Enter the current cell while holding down the control key.
• Y: Switch to the Code cell type.
• M: Markdown cell type change.
• D, D: Delete the current cell by pressing D twice.
• R: Switch to Raw cell type.
• Shift + S: Checkpoint and save the notebook.
• Z: Undelete a deleted cell.
• A: Add a new cell above the existing one.
• H: Display the keyboard shortcuts list.
• B: Add a new cell below the existing one.

Advantages

1. Supports interactive experimentation and step-by-step code execution for data exploration.
2. Multilingual: Supports a variety of programming languages.
3. Rich Documentation: Enables the creation of code-, text-, and visualization-filled notebooks.
4. Data Visualization: Works nicely with libraries for data visualization.
5. Support from the community: Gains from a vibrant community and a large ecosystem.

Disadvantages

1. Learning curve: Beginners may find it difficult.
2. Version control: Complicated when controlling notebook versions.
3. Resource Consuming: May use a lot of system resources.
4. Not Good for Big Projects: Not good for big software development projects.
5. Dependency Management: Managing dependencies takes more work.