Difference Between Jupyter Notebook and Python IDLE

Difference Between Jupyter Notebook and Python IDLE

5 mins read2.2K Views Comment
clickHere
Vikram
Vikram Singh
Assistant Manager - Content
Updated on Sep 7, 2023 17:42 IST

Jupyter Notebook is developed specifically for data science and scientific computing whereas Python IDLE is an integrated development environment that allows users to write. execute and debug Python code.
This article will briefly discuss the differences between Jupyter Notebook and Python IDLE.

2023_02_MicrosoftTeams-image-197.jpg

Jupyter Notebook and Python IDLE are used to write and execute Python code. Jupyter Notebook is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. On the other hand, Python IDLE is an integrated development environment (IDE) that provides a basic interface for writing and executing Python code.

Table of Content

Jupyter Notebook vs. Python IDLE

Feature Jupyter Notebook Python IDLE
Interface Web-based Desktop Application
Output Displayed in-line Separate Console Window
Multilingual Supports multiple languages Only supports Python
Collaboration Built-in support for sharing and collaborations No built-in collaboration feature
Code Organization Code organized into cells Code written in a single file
Debugging Limited support available Debugging support available
Visualization Excellent support Limited support
Learning Curve Steep learning curve Easy to learn
Use Cases Interactive data analysis, prototyping, and visualizations  Writing small scripts and exploring python syntax

Jupyter Notebook is better suited for data analysis, data visualization, and collaboration, while IDLE is better suited for small projects and exploring Python syntax.

Must Check: Top 5 Python IDE for Efficient Development

Must Check: Top 10 Python Built-in Functions

Jupyter Notebook

It was initially developed as a tool for data science and scientific computing. It has gained popularity among data scientists and researchers due to its interactive nature and the ability to combine code, text, and visualizations in a single document.

Features of Jupyter Notebook

Jupyter Notebook has several features, making it a popular tool for data scientists and researchers. These features include

  • Interactive user interface
  • Live code execution
  • Integration with several programming languages
  • Easy collaboration and sharing
  • Visualization of data and results
  • Support for markdown language
What is Programming What is Python
What is Data Science What is Machine Learning

Advantages and Disadvantages of Jupyter Notebook

Jupyter Notebook has several advantages and disadvantage over other IDEs, including:

  • Interactive and exploratory coding
  • Live data visualization and analysis
  • Reliance on a web browser
  • Resource-intensive and slow for large datasets
  • Limited debugging tools
  • Difficulties in version control
  • Security issues when running code from untrusted sources

Python IDLE

It is an integrated development environment with the Python programming language and has basic interface that allows users to write, execute, and debug Python code.

Features

It has several features that make it a popular choice for beginners and experienced Python developers. These features include:

  • Basic user interface
  • Debugging tools
  • Integration with the Python programming language
  • Syntax highlighting
  • Auto-completion
Programming Online Courses and Certification Python Online Courses and Certifications
Data Science Online Courses and Certifications Machine Learning Online Courses and Certifications

Advantages and Disadvantages

  • Lightweight and fast
  • Integrated debugging tools
  • Limited features and functionality
  • No support for collaborative work
  • Limited support for data visualization
  • No support for markdown language
  • No integration with other programming languages

Key Difference Between Jupyter Notebook and Python IDLE

  • Jupyter Notebook has an interactive user interface that allows users to combine code, text, and visualizations in a single document while IDLE has a basic interface that is suitable for beginners.
  • Jupyter Notebook allows users to execute code cells individually, while IDLE executes the entire script simultaneously.
  • Python IDLE has limited support for data visualization and relies on external libraries, while Jupyter Notebook has inbuilt support for data visualization and can display charts and graphs inline. 
  • Jupyter Notebook allows for easy collaboration and sharing of documents, while IDLE has no support for collaborative work.
  • Jupyter Notebook is commonly used in data science and scientific computing due to its interactive nature and ability to combine code and visualization in a single document while IDLE is less commonly used in data science and is more suited for general-purpose Python development.

Conclusion

In this article, we will discuss the key difference between Jupyter notebook and Python idle. Hope you will like the article.

Keep Learning!!

Keep Sharing!!

Related Reads

Working with Python Numbers
Understanding Tuples in Python
Understanding Tuples in Python
In this guide, you will learn all about Tuples in Python: creating tuples, accessing tuple items, looping through a tuple, and other tuple operations with the help of examples.
Type Conversion in Python: Types and Examples
Type Conversion in Python: Types and Examples
Type conversion in Python refers to the direct conversion of object of one data type to another data type. In this conversion, Python interpreter automatically performs conversion.
Getting started with Python Strings
Getting started with Python Strings
Python strings are immutable, i.e., you cannot modify the contents of the object. In this article, we will briefly discuss how to create, assign string to a variable, how to...read more
Python Sets Practice Programs For Beginners
Python Sets Practice Programs For Beginners
Python offers a rich collection of sequence object types, and sets are one of them.
Type Casting in Python
Type Casting in Python
Through this article, you will be able to learn about type conversion or type casting in Python. We will be discussing type casting into different data types.
Getting started with Python Strings
Getting started with Python Strings
Python strings are immutable, i.e., you cannot modify the contents of the object. In this article, we will briefly discuss how to create, assign string to a variable, how to...read more
Understanding Python Sets (With Examples)
Understanding Python Sets (With Examples)
In this article, you will discuss about Python Sets: creating sets, accessing, adding, and removing set items, looping through a set, and other set operations with the help of examples.
Introduction to Python Dictionary (With Examples)
Introduction to Python Dictionary (With Examples)
In this tutorial, you will learn all about Dictionary in Python: creating dictionaries, accessing dictionary items, looping through a dictionary, and other dictionary operations with the help of examples.
Find the Second Occurrence of a Substring in Python String
Find the Second Occurrence of a Substring in Python String
In this article, we will discuss different methods (find, split and regular expression module) to find the second occurrence of a substring in a given string.
Python Sets Practice Programs For Beginners
Python Sets Practice Programs For Beginners
Python offers a rich collection of sequence object types, and sets are one of them.
All About Python Lists Methods
All About Python Lists Methods
In this guide, you will learn all about lists in Python: accessing lists, changing list elements, looping through a list, and other list operations with examples.
Top 110+ Python Interview Questions and Answers
Top 110+ Python Interview Questions and Answers
Python is a widely-used general-purpose, object-oriented, high-level programming language. It is used to create web applications, and develop websites and GUI applications. The popularity of the language is due to...read more
Conditional Statements in Python – Python Tutorial
Conditional Statements in Python – Python Tutorial
A conditional statement as the name suggests itself, is used to handle conditions in your program. These statements guide the program while making decisions based on the conditions encountered by...read more
Methods to Check for Prime Numbers in Python
Methods to Check for Prime Numbers in Python
Prime numbers are the building blocks of mathematics, and they have fascinated mathematicians for centuries. Regarding Python programming, there are different methods to check whether the given number is a...read more

FAQs

What is the difference between Jupyter Notebook and Python IDLE?

Jupyter Notebook is an interactive web-based environment that allows users to create and share documents containing live code, visualizations, and narrative text. Python IDLE, on the other hand, is a basic integrated development environment (IDE) that comes bundled with Python for writing and executing Python code.

How do Jupyter Notebook and Python IDLE differ in terms of interface?

Jupyter Notebook has a web-based interface that is accessed through a web browser. It allows users to create and edit cells containing code, markdown, or other content. Each cell can be executed independently, enabling an interactive and exploratory coding experience. Python IDLE provides a graphical user interface (GUI) that resembles a traditional text editor. It offers a simple and straightforward interface for writing and running Python code.

Can Jupyter Notebook and Python IDLE execute the same Python code?

Yes, both Jupyter Notebook and Python IDLE can execute the same Python code. They use the same Python interpreter to run the code, so the behavior and results should be the same. However, the execution environment and workflow differ between the two. Jupyter Notebook allows code to be executed in separate cells, making it easy to test and modify code snippets individually. Python IDLE typically executes code as a whole file or a selected portion.

How do Jupyter Notebook and Python IDLE handle code execution and output?

Jupyter Notebook executes code in cells, allowing for selective execution and immediate output display. Each cell's output is displayed directly below it, making it convenient for data analysis, visualization, and step-by-step code execution. Python IDLE typically executes code as a whole file or a selected portion. The output is displayed in a separate interactive shell window or within the integrated shell, depending on the user's preference.

Which environment is better for data analysis and exploration: Jupyter Notebook or Python IDLE?

Jupyter Notebook is widely popular for data analysis and exploration tasks due to its ability to create interactive documents. Its support for markdown, inline visualizations, and step-by-step code execution makes it convenient for exploratory data analysis and iterative development. Python IDLE, while suitable for running Python code, may not provide the same level of interactivity and visualization capabilities as Jupyter Notebook. However, for simple data analysis tasks, Python IDLE can still be used effectively.

About the Author
author-image
Vikram Singh
Assistant Manager - Content

Vikram has a Postgraduate degree in Applied Mathematics, with a keen interest in Data Science and Machine Learning. He has experience of 2+ years in content creation in Mathematics, Statistics, Data Science, and Mac... Read Full Bio