The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
Repository Index
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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 🐍
Data Apps & Dashboards for Python. No JavaScript Required.
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
Low-code framework for building custom LLMs, neural networks, and other AI models
[SIGMOD'27] Easy Data Preparation with latest LLMs-based Operators and Pipelines.
🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.
A system for quickly generating training data with weak supervision
pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
An intuitive library to add plotting functionality to scikit-learn objects.
CLI task management & automation tool
Python Streaming DataFrames for Kafka
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
Build, test, deploy, iterate - Dev and prod tool for data science pipelines
A Python library built on pandas and geopandas for linearly referenced data management, engineering, and analysis using the DataFrame accessor pattern (.lr).
Data acquisition for Raspberry Pi and Micropython