# TommyZihao/zihaopython

follow me and learn python easily

Repository: https://github.com/TommyZihao/zihaopython
Canonical: https://ross.abutalabs.com/products/zihaopython
Language: Jupyter Notebook
License: GPL-3.0
License Family: copyleft
Last push: 2021-05-10T03:03:12+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2835, "days_push": 1941, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1155, forks 743 (observed 2026-08-28T04:03:47.941264+00:00)

## What it is
A collection of Jupyter Notebook courseware, videos, and source code for learning Python in a fun, beginner-friendly way, created by Tongji Zihao. It covers Python basics, data visualization, AI, Raspberry Pi hardware, and blockchain for non-CS beginners.

## Use cases
- learn python from scratch
- beginner python tutorials with notebooks
- learn data visualization in python
- intro to ai for non-programmers
- python course with video lessons
- hands-on python practice projects

## When to choose
- you are a complete beginner to programming
- you prefer video + notebook style learning
- you want practical, fun examples instead of dry syntax

## When to avoid
- you need an actively maintained production library
- you want advanced or comprehensive python coverage
- you need English-language materials

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-visualization, machine-learning
- domain: education, tutorials, programming-languages, data-science
- platform: python, cross-platform
- tags: python-course, beginner-friendly, jupyter-notebooks, bilibili-videos, chinese-language, education

## Member repositories
- TommyZihao/zihaopython (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:47.941264+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:32:11.466515+00:00, confidence not recorded.
  - readme: https://github.com/TommyZihao/zihaopython (fetched 2026-08-28T04:03:47.941264+00:00, sha 8181dcf2610a)
- Data as of 2026-08-30T08:39:29.467469+00:00.