The Difference Between Jupyter and Python

Even though these two names are relatively similar, they have entirely distinct meanings.

There is always some confusion when Jupyter is mentioned because Python is world-famous, and Jupyter always somehow stays behind the famous line. 

In this article, you’ll find out what Python and Jupyter are and the difference between them.

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What is Python?

The Difference Between Jupyter and Python

Python is a well-liked programming language. In 1991, Guido van Rossum produced it, and it became available worldwide.

It’s employed for server-side web development, software development, mathematics, and system programming.

If you are wondering how versatile Python is, keep reading!

What Can You Do With Python?

For example, you may develop web applications using Python.

Besides that, workflows may be made with Python and other technologies, database systems are connectable with Python, files may also be read and changed by it, and extensive data management and advanced mathematical operations can be done – all with Python.

Apart from that above, Python may also be used to produce software ready for production or quick prototyping.

Overall, Python is an often used, versatile program!

Why Choose Python?

There are several reasons for that. 

  1. Python is compatible with many platforms, such as Windows, Mac, and more. 
  2. Python’s syntax is simple and is similar to that of English.
  3. Python’s syntax is somewhat different from other programming languages. That is because it enables programmers to construct applications with fewer lines of code.
  4. The interpreter mechanism used by Python enables the instantaneous execution of written code. As a consequence, prototyping could go smoothly.
  5. Python can be used in a functional, object-oriented, or procedural manner.

What is Jupyter?

So, why should we worry about Jupyter?

After all, unlike terms like data science, AI, or Web 2.0, Jupyter has never gained traction. 

Jupyter, in contrast to such large abstractions, is highly tangible. In addition, it is a piece of software that performs particular functions and is an open-source project.

Jupyter Notebooks are redefining how engineers and data scientists collaborate without generating fanfare. If cooperation is necessary for all essential work, then the most crucial tools we have are those that facilitate collaboration and increase the effectiveness of teamwork.

Jupyter is essentially a tool for collaboration, to put it simply. 

It is designed to be used for authoring and distributing text and code that is part of a web page. 

The code is executed on a server, and the results are converted to HTML and added to the page you are authoring. 

This server may be located anywhere, on your laptop, behind a firewall, or on a public network. Your thoughts, code, and the outcomes of running the code are all on your page.

Jupyter’s Architectural Style

Understanding Jupyter’s internals isn’t crucial, but knowing what it allows you to construct is essential. It is more than simply a tool; it is also a platform and an ecosystem that others can use to create their tools on top of.

Jupyter comprises three components, which you will discover if you keep reading!

#1 The Front of the Notepad

You may edit and use notebooks on the front end. Like any other online application, the front end is a JavaScript program sent to your browser. 

The front end is in charge of keeping track of your notebook in your local file system, transferring it to the Jupyter server, and storing it.

#2 The Jupyter Server

The Jupyter server might be a multi-user server or a relatively straightforward program that runs on your laptop. 

The most popular multi-user server for Jupyter is JupyterHub, which is part of the Jupyter project.

#3 The Kernel Protocol

The kernel protocol enables the server to delegate the duty of running code to a kernel tailored to a particular language. 

Python 2 and Python 3 kernels are included with Jupyter; however, there are other kernels for many additional languages.

What is the Difference Between Jupyter and Python?

You must comprehend IPython to comprehend the differences.

It is a command shell designed for Python that offers interactive computing. 

IPython used to offer interactive shells and a notebook interface that was browser-based and supported code, text, video, etc. Now, do you recall Jupyter Notebook from above?

Project Jupyter is a spin-off of this IPython project. 

The notebook interface and other language-independent components of IPython were relocated under the Jupyter namespace, while IPython continues to exist as a Python shell and kernel for Jupyter.

Jupyter Notebooks rose to prominence as a result. It offers kernels or execution environments for several languages. 

Although more than 100 Jupyter Kernels are available, IPython is the default kernel shipped with Jupyter Notebook.

A file is what a Python file looks like. It is simple text and has your code in it.

A.ipynb file (a python notebook) that contains the notebook code, execution results, and other internal settings in a specified format, is what Jupyter Notebook uses most frequently.

There you go, the explanation of how the two are related and what the difference is between the two of them. 

Fun fact: The name Jupyter refers to the programming languages it supports, which include Python, R, and Julia.

So, to conclude, and to add more to the difference:

  • Python is a general-purpose, interpreted programming language. Keep in mind that it is a programming language.
  • An open-source tool Jupyter Notebook enables you to create and share documents with real-time code, equations, visuals, and text.
  • Python only supports one programming language, but the Jupyter notebook allows you to write code in over 100 other computer languages.
  • Python cannot be used to execute C++, Java, R, or Matlab. However, you may use a Jupyter notebook to manage any programming language.


All in all, Python and Jupyter are very different, but they complement each other. 

Each has its own direction, a path. You cannot do the same things with each, but it will be helpful if you utilize both in your programming workflow.

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