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:zap: A Fast, Extensible Progress Bar for Python and CLI
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Data Apps & Dashboards for Python. No JavaScript Required.
Interactive Data Visualization in the browser, from Python
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
List of Data Science Cheatsheets to rule the world
Machine Learning Toolkit for Kubernetes
JupyterLab computational environment.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Python training for business analysts and traders
Jupyter Interactive Notebook
Performance analysis of predictive (alpha) stock factors
DeepAnalyze is the first agentic LLM for autonomous data science. 🎈你的AI数据分析师,自动分析大量数据,一键生成专业分析报告!
An open source extension that connects AI agents to computational notebooks in JupyterLab.
Build data pipelines, the easy way 🛠️
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
AI Infra / AI Orchestration / AI Control Plane
Plotting library for IPython/Jupyter notebooks
The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
🛠 All-in-one web-based IDE specialized for machine learning and data science.
GeoAI: Artificial Intelligence for Geospatial Data
Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps. https://deepnote.com/
Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet
Python & JS/TS SDK for running AI-generated code/code interpreting in your AI app
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
A system for assigning and grading notebooks
CoCalc: Collaborative Calculation in the Cloud
🪐 🔧 Model Context Protocol (MCP) Server for Jupyter.
Productionise & schedule your Jupyter Notebooks as easily as you wrote them.