Difference Between .ipynb and .py Files: Understanding Their Uses and IDE Support

Python is a versatile programming language used in various domains, from web development to data science and artificial intelligence. However, depending on the use case, developers and researchers use different file formats to store and execute Python code. Two common file formats are .py and .ipynb. Understanding their differences, advantages, and ideal use cases can help developers choose the right format for their projects.

What is a .py File?

A .py file is a standard Python script written in plain text. It contains Python code that can be executed sequentially in an Integrated Development Environment (IDE) or terminal.

Common Use Cases

• Running Python scripts for automation.

• Writing reusable modules and libraries.

• Developing web applications and software.

• Running batch processing tasks.

Advantages

• Lightweight: .py files are simple text files that can be easily edited and executed.

• Fast Execution: Since they do not contain additional metadata, they run efficiently.

• Compatible with Version Control: Easy to track changes using Git and other version control systems.

• Universally Supported: Works across all Python environments.

Disadvantages

• No Built-in Visualization: Unlike .ipynb, .py files do not support inline visual outputs like graphs.

• Lack of Interactivity: Requires manual execution of the entire script instead of executing sections of code.

What is an .ipynb File?

A .ipynb (IPython Notebook) file is a JSON-based format used primarily for Jupyter Notebook, an interactive computing environment. This format allows users to combine code, Markdown text, visualizations, and explanations in a single document.

Common Use Cases

• Data science and machine learning projects.

• Creating educational tutorials and documentation.

• Prototyping algorithms and experiments.

• Conducting exploratory data analysis (EDA).

Advantages

• Interactivity: Supports execution of code in separate cells, allowing for incremental testing.

• Rich Visualizations: Can display plots, graphs, tables, and other multimedia outputs inline.

• Supports Markdown and LaTeX: Allows for better documentation and explanation of code.

• Great for Collaboration: Facilitates knowledge sharing through tools like Google Colab and JupyterHub.

Disadvantages

• Heavier File Size: .ipynb files store metadata, outputs, and formatting, making them bulkier than .py files.

• Performance Overhead: Slower execution compared to running a plain .py script.

• Less Compatible with Version Control: The JSON structure makes it harder to track changes in Git repositories.

Key Differences Between .py and .ipynb

Feature	.py File	.ipynb File
File Format	Plain text	JSON-based notebook
Execution	Runs sequentially	Allows cell-wise execution
Interactivity	No inline outputs or visuals	Supports visualizations and Markdown
Use Case	Scripts, modules, automation	Data science, research, tutorials
Performance	Fast execution	Slower due to interactivity

Which One Should You Use?

The choice between .py and .ipynb depends on the project requirements:

• Use .py files if you are developing software, running automation scripts, or deploying Python applications.

• Use .ipynb files if you need interactive coding, visualizations, and incremental testing, especially in data science and research.

Popular IDEs and Editors for Each File Type

For .py Files

• VS Code: A lightweight and extensible editor with Python support.

• PyCharm: Feature-rich IDE for professional development.

• Sublime Text: Fast and customizable text editor.

• Atom: Open-source editor with Python extensions.

• IDLE: Default Python editor, ideal for beginners.

For .ipynb Files

• Jupyter Notebook: The primary environment for .ipynb files.

• JupyterLab: A more advanced version of Jupyter Notebook.

• Google Colab: Cloud-based Jupyter environment with GPU support.

• VS Code: Supports Jupyter notebooks via an extension.

• PyCharm Professional: Offers Jupyter notebook support.

Converting Between .py and .ipynb

Sometimes, it may be necessary to convert between these formats:

Convert .ipynb to .py

Use Jupyter’s nbconvert utility:

jupyter nbconvert --to script my_notebook.ipynb

Convert .py to .ipynb

Using nbformat and jupytext, you can convert a .py file to a Jupyter notebook.

import nbformat
from nbformat.v4 import new_notebook, new_code_cell

# Read Python script
with open("script.py") as f:
    code = f.read()

# Create a new notebook and add code
nb = new_notebook()
nb.cells.append(new_code_cell(code))

# Save as .ipynb
with open("script.ipynb", "w") as f:
    nbformat.write(nb, f)

Conclusion

Understanding the difference between .py and .ipynb files is crucial for choosing the right tool for your Python projects. While .py files are ideal for software development and automation, .ipynb files excel in interactive coding and data analysis. Choosing the right format ensures efficiency, collaboration, and better project management.