Chinese translation review — read-through of all 26 lectures

This repository holds a machine-generated draft of the QuantEcon Python Programming lectures in Chinese. Individual lectures have been reviewed as they were synced, but the edition has never had an end-to-end read by a Chinese speaker. That is what this issue tracks.

There is no deadline. Work through it whenever there is time, at whatever pace suits. One lecture a month is genuinely useful. Tick lectures off below as they are done.

What you are looking at

Source	lecture-python-programming (English)
Translated by	Claude, per-lecture — the model and sync date for each file are recorded in .translate/state/<name>.md.yml
Terminology	a Chinese glossary held in the translation tooling, injected into every translation
Published	https://quantecon.github.io/lecture-python-programming.zh-cn
Status	draft — assume nothing has been verified by a human

The precedent that matters: when the Persian edition was reviewed, the native speaker found meaning-inverting mistranslations that read perfectly fluently. A machine translation that reads well can still say the opposite of what the English says. A careful reading is the only thing that catches that.

Where to read

Unlike the French edition, this one is published — so read the live pages, linked in the checklist below. They render figures, maths and executed code output as a reader actually sees them, which the raw .md files do not.

To read the English side by side, the same file lives at lectures/<name>.md in the English repo.

How to submit feedback: one PR per lecture

1. Create a branch, e.g. review/numpy
2. Edit lectures/<name>.md directly — fix the Chinese as you would write it
3. Open a PR titled e.g. Review: numpy.md, referencing this issue
4. Tick the lecture off in the checklist below

One PR per lecture, please — not one big PR. Smaller PRs are easier to discuss, and a disagreement about one lecture then does not hold up the other twenty-five.

Commenting rather than editing is completely fine too — a PR with notes, or a comment on this issue. Do not let the mechanics get in the way of the feedback.

What is most valuable

In rough order:

1. Meaning errors. The Chinese says something the English does not. These matter most and are the ones only a human reader finds.
2. Wrong technical terms. A term rendered in a way a Chinese economist or programmer would not recognise.
3. Terminology that should be consistent but is not — the same English term rendered differently across lectures.
4. Unnatural Chinese. Grammatically correct but not how anyone would write it. Academic register, please — these are teaching materials.
5. Anything in the code that looks wrong. Code should not have been translated at all, but comments inside it were. The Persian review caught a genuine code bug this way.

Please do not spend time on these

• Anything wrong in the English. That is a source issue — raise it upstream instead and it will flow back here. Fixing it only in the Chinese makes the two editions diverge.
• translation: blocks in the file header. Machine bookkeeping — please leave them alone.
• Punctuation spacing around —— and full-width marks. Convention in this edition is no space adjacent to ——; where the tooling has got that wrong it is a known class being fixed at the source rather than per-file.

Terminology is worth more than a file fix

If a term is wrong, it is probably wrong in several lectures. Rather than fixing each occurrence, say so and it can be pinned in the glossary — then every future translation gets it right automatically, including lectures nobody has reviewed yet.

What to check in each lecture

Condensed from the per-lecture checklists this issue replaces:

Area	What to look for
Prose	accuracy against the English, fluency, consistent key terms, nothing left untranslated
Maths	formulas render, notation matches the source, symbols agree with the surrounding text
Figures	figures render, axis labels and captions translated where appropriate
Code	cells run, comments translated accurately, prose variable names match the code
Links	cross-references resolve, external links live, bibliography intact
Structure	headings match the source, admonitions and directives render, no broken MyST
Edge cases	proper nouns, numbers and units, full-width punctuation（，。；：！？）, stray English

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Lectures

• about_py.md — 关于本系列讲座
• autodiff.md — 自动微分探险
• debugging.md — 调试与错误处理
• functions.md — 函数
• getting_started.md — 入门指南
• intro.md — 面向经济学与金融学的Python编程
• jax_intro.md — JAX
• matplotlib.md — Matplotlib
• names.md — 名称与命名空间
• need_for_speed.md — 科学计算中的 Python
• numba.md — Numba
• numpy.md — NumPy
• numpy_vs_numba_vs_jax.md — NumPy vs Numba vs JAX
• oop_intro.md — OOP I：对象与方法
• pandas.md — Pandas
• pandas_panel.md — Pandas 面板数据
• polars.md — Polars
• python_advanced_features.md — 更多语言特性
• python_by_example.md — 入门示例
• python_essentials.md — Python 基础要点
• python_oop.md — OOP II：构建类
• scipy.md — SciPy
• sympy.md — SymPy
• troubleshooting.md — 故障排除
• workspace.md — 编写较长的程序
• writing_good_code.md — 编写高质量代码

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status.md is excluded deliberately — it is an auto-generated {nb-exec-table} page with no translatable prose.

Inizia con una lezione cinese pubblicata, come lectures/numpy.md, e confronta la relativa pagina online con lectures/numpy.md nel repository inglese. Verifica prosa, terminologia, matematica, figure, codice, link e struttura rispetto alla checklist dell’issue; il lavoro è completato quando viene inviata una PR di revisione mirata o viene fornito un feedback chiaramente segnalato per quella lezione.