Introduction to Jupyter Notebook

The Jupyter Notebook is an open source web application that can be used to create and share documents that contain live text, code, equations and visualizations. It is maintained by the people at Project Jupyter.

Jupyter name comes from the core supported programming languages that it supports: Julia, Python, and R.

It allows users to write code in "cells," which are blocks of code that can be executed individually or together. This makes it easy to test and debug code, as well as to organize and present complex analyses. Additionally, the notebook allows users to include markdown text and LaTeX equations, making it easy to document their work and share their results with others.

It has the ability to support multiple programming languages. This includes popular languages like Python, R, and Julia, as well as many others. This makes it an incredibly versatile tool for data scientists, who can use it to work with data in whichever language they are most comfortable with.

Another advantage of Jupyter Notebook is its ability to create interactive visualizations. The notebook supports a wide range of visualization libraries, including Matplotlib, Seaborn, and Plotly, which allow users to create interactive graphs, charts, and maps. This makes it easy to explore and visualize data in a way that is both powerful and intuitive.

Jupyter Notebook is also a great tool for collaboration. Users can share their notebooks with others, either by sharing a file or by using a web-based service like GitHub or Binder. This allows others to view and execute the code, making it easy to collaborate on projects and share findings.

Jupyter Notebook is a great tool for teaching and learning. Its ease of use and versatility make it a valuable tool for educators who want to teach programming or data science. Additionally, there are many online resources and tutorials available that can help users learn how to use Jupyter Notebook and get the most out of its features.

Jupyter Notebook components

Jupyter Notebooks can include several kinds of ingredients, each organized into discrete blocks:

• Text and HTML. Plain text, or text annotated in the Markdown syntax to generate HTML, can be inserted into the document at any point. CSS styling can also be included inline or added to the template used to generate the notebook.
• Code and output. The code in Jupyter Notebook notebooks is typically Python code, although you may add support in your Jupyter environment for other languages such as R or Julia. The results of executed code appear immediately after the code blocks, and the code blocks can be executed and re-executed in any order you like, as often as you like. 
• Visualizations. Graphics and charts can be generated from code, by way of modules like Matplotlib, Plotly, or Bokeh. Like output, these visualizations appear inline next to the code that generates them. However, code can also be configured to write them out to external files if needed.
• Multimedia. Because Jupyter Notebook is built on web technology, it can display all the types of multimedia supported in a web page. You can include them in a notebook as HTML elements, or you can generate them programmatically by way of the IPython.display module.
• Data. Data can be provided in a separate file alongside the .ipynb file that constitutes a Jupyter Notebook notebook, or it can be imported programmatically—for instance, by including code in the notebook to download the data from a public Internet repository or to access it via a database connection.

Jupyter Notebook use cases

The most common use cases for Jupyter Notebook are data science, mathematics, and other research projects that involve visualizations of data or formulas. Apart from those, though, there are plenty of other use cases:

• Sharing a visualization, with or without interactivity. Visualization can be shared with or without interactivity, It allows the targeted audience to dive in and play around. 
• Documenting a process with code. Many programmers who blog about their programming experiences write up their posts in a Jupyter notebook. Others can download their notebook and recreate the exercise.
• Live documentation for a library or module. Most documentation for Python modules is static; a Jupyter notebook can be used as an interactive sandbox for learning how a module works. Any Python module that runs well in a notebook interface (essentially, anything that writes to stdout as part of its behavior) is a good candidate for this.
• Sharing code and data generally. All you need to do to share a Jupyter notebook and its associated data files (mostly CSV format) is pack it up into an archive.

Getting Up and Running With Jupyter Notebook

The Jupyter Notebook is not included with Python, so if you want to try it out, you will need to install Jupyter.
If you already have Python installed, then you can use a handy tool that comes with Python called pip to install Jupyter Notebook like this:

$ pip install jupyter

The next most popular distribution of Python is Anaconda. Anaconda has its own installer tool called conda that you could use for installing a third-party package. However, Anaconda comes with many scientific libraries preinstalled, including the Jupyter Notebook, so you don’t actually need to do anything other than install Anaconda itself.

Starting the Jupyter Notebook Server

Now that you have Jupyter installed, let’s learn how to use it. To get started, all you need to do is open up your terminal application and go to a folder of your choice. Then just go to that location in your terminal and run the following command:

$ jupyter notebook

This will start up Jupyter and your default browser should start (or open a new tab) to the following URL: http://localhost:8888/tree

Your browser should now look something like this:

Note that right now you are not actually running a Notebook, but instead, you are just running the Notebook server.

In the next article, we will create our first Jupyter notebook

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