Compare JetBrains DataSpell vs. JupyterLab vs. Spyder

Easily switch between command and editor modes using just one keystroke while navigating through cells with arrow keys. Take advantage of all standard Jupyter shortcuts for a smoother experience. Experience fully interactive outputs positioned directly beneath the cell for enhanced visibility. When working within code cells, benefit from intelligent code suggestions, real-time error detection, quick-fix options, streamlined navigation, and many additional features. You can operate with local Jupyter notebooks or effortlessly connect to remote Jupyter, JupyterHub, or JupyterLab servers directly within the IDE. Execute Python scripts or any expressions interactively in a Python Console, observing outputs and variable states as they happen. Split your Python scripts into code cells using the #%% separator, allowing you to execute them one at a time like in a Jupyter notebook. Additionally, explore DataFrames and visual representations in situ through interactive controls, all while enjoying support for a wide range of popular Python scientific libraries, including Plotly, Bokeh, Altair, ipywidgets, and many others, for a comprehensive data analysis experience. This integration allows for a more efficient workflow and enhances productivity while coding.

Project Jupyter is dedicated to the creation of open-source tools, standards, and services that facilitate interactive computing in numerous programming languages. At the heart of this initiative is JupyterLab, a web-based interactive development environment designed for Jupyter notebooks, coding, and data manipulation. JupyterLab offers remarkable flexibility, allowing users to customize and organize the interface to cater to various workflows in fields such as data science, scientific research, and machine learning. Its extensibility and modular nature enable developers to create plugins that introduce new features and seamlessly integrate with existing components. The Jupyter Notebook serves as an open-source web application enabling users to produce and share documents that incorporate live code, mathematical equations, visualizations, and descriptive text. Common applications of Jupyter include data cleaning and transformation, numerical simulations, statistical analysis, data visualization, and machine learning, among others. Supporting over 40 programming languages—including popular ones like Python, R, Julia, and Scala—Jupyter continues to be a valuable resource for researchers and developers alike, fostering collaborative and innovative approaches to computing challenges.

Spyder’s multi-language editor comes equipped with a robust set of tools designed to enhance the editing experience, making it both user-friendly and effective. Among its standout features are syntax highlighting powered by pygments, real-time code and style evaluation through pyflakes and pycodestyle, and advanced completion capabilities, calltips, and navigation tools facilitated by rope and jedi. Users can also take advantage of a function and class browser, as well as options for horizontal and vertical window splitting, among other functionalities. Additionally, the integrated IPython console enables the execution of commands and interaction with data directly within IPython interpreters, providing a seamless workflow. The variable explorer further enriches this experience by allowing users to explore and manage the objects created by their code, displaying the namespace contents of the currently active IPython session. This tool not only shows global objects, variables, and class instances but also provides GUI-based editing options for adding, removing, or modifying values, thus supporting a highly interactive coding environment. Ultimately, Spyder combines these diverse features to create an effective platform for developers looking to streamline their coding processes.