Jupyter VS Runcell
Compare Jupyter VS Runcell and see what are their differences
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runcell (Jupyter AI agent) is an AI copilot built for notebooks. It understands your current kernel state—variables, DataFrame schemas, imports, outputs—and proposes the next best cell to move your work forward. With one command, it can draft code, execute it safely, validate the output, and automatically refine on errors. You get production‑quality cells (with comments and docstrings) and a clear audit trail of changes.
Whether you’re exploring data, transforming pipelines, or teaching with notebooks, runcell eliminates “search–copy–paste–debug” loops. It nudges work toward best practices (tests, assertions, checkpoints), and documents decisions for future you—or your teammates. Bring your own LLM keys, keep data in your environment, and control exactly what leaves your notebook.
Core capabilities
Natural‑language to code
Drafts new cells or refactors existing ones from plain‑English prompts.
Incorporates notebook context (imports, variables, DataFrame dtypes, shapes) to produce runnable code.
Run, validate, iterate
Executes proposed cells, inspects outputs/exceptions, and auto‑fixes common issues (missing imports, dtype mismatches, off‑by‑one, plotting errors).
Context‑aware assistance
Explain cell: summarizes what a cell does and why.
Suggest next step: proposes analysis steps, visualizations, or checks based on your artifacts.
Inline docs: inserts comments, docstrings, and markdown rationale.
Environment & reproducibility
Detects missing packages and (optionally) generates a safe install cell; can export requirements.txt or environment.yml.
Data & visualization helpers
Quick EDA: profiling, missing‑value maps, summary stats.
Transformations: joins, groupby, window ops, feature engineering.
Visuals: histograms, pairplots, line/bar charts, residual plots, interactive charts (e.g., Plotly).
Works smoothly with pandas, numpy, polars, matplotlib/plotly, and visualization helpers like pygwalker.
Jupyter RuncellRuncell's answer:
Runcell is the AI Agent designed for Jupyter users. It is not a simple chatbot, but an agent that can take control of your jupyter and work for you.
Runcell's answer:
Comparing with Code agent, like cursor, claude code, which are designed for software enginner, those agent does not understand data, visualization, very well. And when they coding, they directly generate all code without considering how the data distribute, what is the result of previous cells execution which are important for data science.
Based on our record, Jupyter seems to be more popular. It has been mentiond 216 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 7 months ago
LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 8 months ago
Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 9 months ago
Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / about 1 year ago
One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / over 1 year ago
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
Cursor - The AI-first Code Editor. Build software faster in an editor designed for pair-programming with AI.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Claude Code - Transform hours of debugging into seconds with a single command. Experience coding at thought-speed with Claude's AI that understands your entire codebase—no more context switching, just breakthrough results.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
Augment Code - Enhances developer collaboration by providing codebase-aware chat, intuitive code suggestions, and advanced AI-driven explanations; accelerates coding tasks, assists in understanding unseen code structures, improving communication vastly within team…