IPython vs Jupyter: What are the differences?

IPython and Jupyter are closely related interactive computing tools used in data science and scientific computing environments. Let's explore the key differences between them.

1. IPython vs Jupyter Notebooks: IPython is an interactive command-line terminal for Python that provides enhanced features compared to the default Python shell. It includes features like tab completion, object introspection, and can execute code written in different programming languages. On the other hand, Jupyter Notebook is a web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. It supports multiple programming languages, including Python, R, and Julia. While IPython is a component of Jupyter Notebooks, Jupyter provides a more comprehensive interactive computing environment with additional features and support for multiple programming languages.

2. Kernel-based architecture: IPython uses a single kernel for executing code written in different languages. However, Jupyter Notebooks utilize a kernel-based architecture that allows users to choose different kernels for executing code. This means that you can have notebooks with code written in Python, R, and other languages, all within the same Jupyter environment. Each kernel runs in a separate process and communicates with the Jupyter Notebook web application through a set of protocols. This architecture provides flexibility and enables users to work with different programming languages seamlessly.

3. Notebook interface vs command-line interface: The primary interface of IPython is a command-line terminal, where users interact with the tool by entering commands and executing code. In contrast, Jupyter Notebooks provide a web-based interface with a notebook-like structure. Users can create notebooks, write code in cells, and execute them individually or all at once. The notebook interface allows for a more visual and interactive experience, as users can mix code with explanatory text, equations, and visualizations in a single document.

4. Larger community and ecosystem: While IPython has a dedicated community, Jupyter Notebooks have a larger and more diverse user base. Jupyter Notebooks are widely adopted in academia, data science, and industry, with numerous resources, tutorials, and extensions available online. The larger community and ecosystem around Jupyter Notebooks make it easier to find support, discover new tools, and collaborate with other users.

5. Extension ecosystem: Jupyter Notebooks have a rich extension ecosystem that allows users to enhance the functionality of the tool. Users can install various extensions to add features like table of contents, code formatting, spell checking, and more. These extensions can be customized and configured to match individual needs, providing a highly personalized and efficient workflow.

6. Collaboration and sharing: While IPython does not have native support for collaboration and sharing, Jupyter Notebooks are designed with collaboration in mind. Jupyter Notebooks can be easily shared with others by exporting them in different formats like HTML, PDF, or Markdown. They can also be hosted on platforms like GitHub or Jupyter Notebook Viewer, allowing for seamless collaboration and sharing of interactive documents.

In summary, IPython is an interactive computing environment primarily used for Python programming, providing enhanced features like interactive shells and rich media display capabilities. Jupyter, on the other hand, is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text, supporting multiple programming languages beyond just Python.