Compare Bokeh vs. marimo

Bokeh simplifies the creation of standard visualizations while also accommodating unique or specialized scenarios. It allows users to publish plots, dashboards, and applications seamlessly on web pages or within Jupyter notebooks. The Python ecosystem boasts a remarkable collection of robust analytical libraries such as NumPy, Scipy, Pandas, Dask, Scikit-Learn, and OpenCV. With its extensive selection of widgets, plotting tools, and user interface events that can initiate genuine Python callbacks, the Bokeh server serves as a vital link, enabling the integration of these libraries into dynamic, interactive visualizations accessible via the browser. Additionally, Microscopium, a project supported by researchers at Monash University, empowers scientists to uncover new functions of genes or drugs through the exploration of extensive image datasets facilitated by Bokeh’s interactive capabilities. Another useful tool, Panel, which is developed by Anaconda, enhances data presentation by leveraging the Bokeh server. It streamlines the creation of custom interactive web applications and dashboards by linking user-defined widgets to a variety of elements, including plots, images, tables, and textual information, thus broadening the scope of data interaction possibilities. This combination of tools fosters a rich environment for data analysis and visualization, making it easier for researchers and developers to share their insights.

Introducing an innovative reactive notebook designed for Python, which allows you to conduct repeatable experiments, run scripts seamlessly, launch applications, and manage versions using git. 🚀 Comprehensive: it serves as a substitute for jupyter, streamlit, jupytext, ipywidgets, papermill, and additional tools. ⚡️ Dynamic: when you execute a cell, marimo automatically runs all related cells or flags them as outdated. 🖐️ Engaging: easily connect sliders, tables, and plots to your Python code without the need for callbacks. 🔬 Reliable: ensures no hidden states, guarantees deterministic execution, and includes built-in package management for consistency. 🏃 Functional: capable of being executed as a Python script, allowing for customization via CLI arguments. 🛜 Accessible: can be transformed into an interactive web application or presentation, and functions in the browser using WASM. 🛢️ Tailored for data: efficiently query dataframes and databases using SQL, plus filter and search through dataframes effortlessly. 🐍 git-compatible: stores notebooks as .py files, making version control straightforward. ⌨️ A contemporary editor: features include GitHub Copilot, AI helpers, vim keybindings, a variable explorer, and an array of other enhancements to streamline your workflow. With these capabilities, this notebook elevates the way you work with Python, promoting a more efficient and collaborative coding environment.