20:"$Sreact.view_transition" 21:I[9754,["/_next/static/chunks/2t36f5p4pjzlb.js","/_next/static/chunks/1gyqeidjm5eoa.js","/_next/static/chunks/0_fjnxc4tt_l_.js","/_next/static/chunks/1ymt1shyhmu-4.js","/_next/static/chunks/3kv2zf0i7s2wc.js","/_next/static/chunks/1vs_3g_g75zmk.js","/_next/static/chunks/2g4dh802s8oz3.js","/_next/static/chunks/1bc3f0shukf1j.js","/_next/static/chunks/192kgs9v_t14j.js","/_next/static/chunks/0z6pw0v5c_lx5.js","/_next/static/chunks/08r60e6icmtkc.js","/_next/static/chunks/3trx6c0i7_x-3.js","/_next/static/chunks/40sro5bzp9bi_.js","/_next/static/chunks/1wgf3tk38y6cf.js"],"ProjectDetail"] 22:T41a8, Awesome Python · 索引

Python 工具索引 · curated by vinta

想知道「用 Python 做 X，该用哪个工具」？答案在一本索引里。

Awesome Python 是社区长期维护的精选目录。它不靠记忆整个生态，而是把 Python 生态里的框架、库与工具，按任务分门别类地摆成一张可翻阅的索引表——阅读时只需顺着需求，扫过一个个分类格子。

需求 Need

「我想用 Python 做数据分析，该用哪个工具？」

分类 · 分析

需求 Need

「我想快速搭一个 Web 服务？」

分类 · Web 框架

需求 Need

「我想做图像识别？」

分类 · 计算机视觉

02 分类格子

目录按任务划分成一个个有格的隔层

每个任务一个隔层，彼此用细细的规则线隔开。你要解决什么问题，就往对应的隔层里看——不必翻遍整个生态。

AI 分类 AI、深度学习、机器学习、自然语言处理与推荐系统，一个隔层覆盖智能方向。

Ml 分类把模型从研究带到工程落地所需的库与工具。

Wb 分类 Web 框架、Web API、服务器与 WebSocket 等构建网络服务的方向。

DB 分类数据库与数据接口，支撑存取与查询的一次次选择。

DV 分类 DevOps 与命令行工具，让自动化与部署更顺手。

Sc 分类安全类目，把近生态里的防护方案收拢在一格。

03 怎么读这本索引

阅读路径：先有需求，再找分类

一

写下需求

先把你想做的事讲清楚，比如「我要连数据库」或「我要搭 API」。

二

顺着分类扫过格子

目录把工具按任务归位，你只需扫对应的隔层，而非常看整张表。

三

在隔层里选工具

同类工具并排在格中，方便横向比较，再决定取舍。

04 目录规模

这本索引的广度

以下为资料库中可核实的仓库数据，以一条编辑索引行的方式呈现，而非营销计分牌。

314k+

收藏星标（Stars）

28k+

派生（Forks）

16 个

进行中的问题（Open issues）

以上数字取自仓库元数据，反映目录被社区使用与维护的概况。

05 维护脉络

由社区长期维护的出版物

如何维护

编辑精选，非全面收录——只收录经过筛选的工具。
分类由社区共同组织与维护，随生态持续更新。
部分条目可能未必即时更新，挑选时请自行核验。

所有能力均来自可核实的资料：仓库元数据与 README 中的官方索引站。

Awesome Python · 一本 Python 工具精选索引

23:T40dc, Awesome Python · The Index

A Curated Python Tools Index · by vinta

You want to do X in Python — which tool should you use? The answer lives in an index.

Awesome Python is a widely recognised, community-maintained catalogue. Instead of asking you to memorise the whole ecosystem, it sorts the best frameworks, libraries, and tools into ruled compartments keyed by the task at hand. You resolve a need not by browsing everything, but by scanning the right shelf.

Need

"I want to do data analysis in Python — which library?"

Category · Analysis

Need

"I want to stand up a web service quickly?"

Category · Web Frameworks

Need

"I want to do image classification?"

Category · Computer Vision

02 Category Compartments

A catalogue divided into ruled, shelved compartments by task

Each task owns a compartment, separated from its neighbours by a hairline rule. Name the problem, then look into that one compartment—no need to page through the whole ecosystem.

AI Category Artificial intelligence, deep learning, machine learning, natural language processing, and recommender systems share one compartment.

ML Category Libraries that carry models from research to production engineering.

Web Category Web frameworks, web APIs, servers, WebSocket, and template engines for building networked services.

DB Category Databases and data interfaces underpinning storage, retrieval, and queries.

Ops Category DevOps and command-line tooling that make automation and deployment feel lighter.

Sec Category Security, drawing near-ecosystem safeguards together into one compartment.

03 How to Read This Index

A lookup path: start from the need, arrive at the category

Name the need

Say what you want plainly — "I need a database," or "I want to build an API."

Scan the compartments

The catalogue files tools by task, so you only work through the relevant shelves rather than the whole list.

III

Choose within the shelf

Comparable tools sit side by side in one compartment, making a deliberate trade-off easier.

04 Collection Scale

The breadth of this index

The verified repository figures below are presented as an editorial index row, not a marketing scoreboard.

314k+

Stars

28k+

Forks

16 open

Open issues

Figures are drawn from the repository metadata and reflect how widely the catalogue is used and maintained.

05 Curation Lineage

A publication long maintained by its community

How it is maintained

Editorially curated, not exhaustive — only vetted tools are shelved.
Categories are organised and maintained by the community, evolving with the ecosystem.
Some entries may lag behind; verify before you build on one.

Every claim here is grounded in verifiable material — the repository metadata and the official index site named in the README.

Awesome Python · a curated index of Python tools

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