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scikit-learn: machine learning in Python
⭐AI-driven public opinion & trend monitor with multi-platform aggregation, RSS, and smart alerts.🎯 告别信息过载，你的 AI 舆情监控助手与热点筛选工具！聚合多平台热点 + RSS 订阅，支持关键词精准筛选。AI 智能筛选新闻 + AI 翻译 + AI 分析简报直推手机，也支持接入 MCP 架构，赋能 AI 自然语言对话分析、情感洞察与趋势预测等。支持 Docker ，数据本地/云端自持。集成微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 等渠道智能推送。
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Streamlit — A faster way to build and share data apps.
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
🕸️ Web apps in pure Python 🐍
Open-source data movement for ELT pipelines and AI agents — from APIs, databases & files to warehouses, lakes, and AI applications. Both self-hosted and Cloud.
1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
Statsmodels: statistical modeling and econometrics in Python
Open Machine Learning Course
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
Automatically visualize your pandas dataframe via a single print! 📊 💡
A Grammar of Graphics for Python
Missing data visualization module for Python.
Visualize and compare datasets, target values and associations, with one line of code.
The Python ensemble sampling toolkit for affine-invariant MCMC
What's in your data? Extract schema, statistics and entities from datasets
NFStream: a Flexible Network Data Analysis Framework.
Visualize large time series data with plotly.py
High-assurance AI data visualization IDE & analytics workbench. Natural language to charts with local ground-truth table verification.
The power of Chart.js with Python
A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.