# EvanLi/Github-Ranking: a daily generated star and fork leaderboard for GitHub

> EvanLi/Github-Ranking is a Python project that regenerates Markdown leaderboards of GitHub repositories by stars and forks, plus per-language Top 100 lists, every day. It is a reading artifact more than a library, and its value depends entirely on how it is run and refreshed.

**EvanLi/Github-Ranking** — :star:Github Ranking:star: Github stars and forks ranking list. Github Top100 stars list of different languages. Automatically update daily. | Github仓库排名，每日自动更新

- Repository: https://github.com/EvanLi/Github-Ranking
- Website: https://evanli.github.io/Github-Ranking
- Stars: 12,255 · Forks: 704
- Language: Python
- License: MIT
- Published: 2026-09-21 · Updated: 2026-09-21 · Language: en
- Canonical page: https://hysenlabs.com/projects/evanli-github-ranking

## What the ranking list actually contains

The README describes the project in one line: "A list of the most github stars and forks repositories." That is the whole product. The repository publishes Markdown tables of GitHub repositories ordered by star count and by fork count, and it repeats the exercise per programming language. The README's table of contents lists Most Stars, Most Forks, then ActionScript, C, C#, C++, Clojure, CoffeeScript, CSS, Dart, DM, Elixir, Go, Groovy, Haskell, HTML, Java, JavaScript, Julia, Kotlin, Lua, MATLAB, Objective-C, Perl, PHP, PowerShell, Python, R, Ruby, Rust, Scala, Shell, Swift, TeX, TypeScript and Vim script. Each language section is a top 10 inside the README, with a link to a fuller file under Top100/, for example Top100/Top-100-stars.md and Top100/ActionScript.md.

The audience is narrow but real. Someone writing a talk about open source adoption, a maintainer curious where a language community clusters, or a data journalist who needs a citable table can read the Markdown directly. The tables carry seven columns: Ranking, Project Name, Stars, Forks, Language, Open Issues, Description and Last Commit, so a reader can see not just popularity but whether a repository is still being committed to. Note the shape of the data before trusting it as a popularity signal: the fork leaderboard is topped by datasharing, Spoon-Knife and ProgrammingAssignment2, which are classroom and demo repositories. Fork counts measure how many people copied something for an exercise, not how many depend on it.

## How the daily regeneration works

The repository layout is the clearest description of the mechanism. At the top level there are Data/, Top100/, source/, requirements.txt, auto_run.sh, Gemfile and _config.yml. The README begins with a line reading "Last Automatic Update Time: 2026-09-21T04:07:42Z", which tells you the tables are produced by a scheduled job rather than edited by hand. The dependencies are two: pandas==0.24.1 and requests==2.27.1. requests is the HTTP client that talks to GitHub, pandas is what assembles and sorts the rows before they are written out as Markdown. The output lands in README.md and in the Top100/ directory, and Data/ holds the intermediate material the script works from.

The _config.yml and Gemfile pair point at a Jekyll site, and the homepage is hosted at evanli.github.io/Github-Ranking, so the same Markdown can be rendered as a browsable page. That is the full data flow: fetch, sort, write Markdown, publish. There is no database, no queue and no incremental update logic described in the README. Each run appears to rebuild the tables from the API responses it collects, which is why the timestamp at the top is the only state the reader gets.

Two consequences follow. First, the ranking is a point-in-time snapshot, not a time series: the README shows the current numbers and the last update time, and nothing in the repository listing suggests a historical archive of past rankings. Second, the numbers are whatever the GitHub API returned at that moment, so a repository's position can move for reasons unrelated to real growth.

## Installing it and generating your own tables

There is no published package and no release artifact. The README does not give a pip install line, and the repository has no releases. The way in is to clone the repository and install the two pinned dependencies. If you are on a modern Python, expect friction: pandas 0.24.1 is a 2019-era pin, and the README does not document a supported Python version.

```bash
git clone https://github.com/EvanLi/Github-Ranking.git
cd Github-Ranking
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
```

After that, requirements.txt resolves to exactly two packages:

```
pandas==0.24.1
requests==2.27.1
```

The repository ships auto_run.sh at the top level, which is the entry point the scheduled job uses. The README does not document its flags, its environment variables, or how it authenticates to GitHub, so read the script before running it:

```bash
cat auto_run.sh
```

The first real use is to run that script and then look at the timestamp line near the top of README.md. If the date changes, the generation path works. If it does not, the failure will be in the API call or in the pandas version, and the script's own output is where you will see it. What you should not expect is a command-line tool with options: the README presents the project as a list plus the automation that produces it, not as a general-purpose CLI.

## Where the ranking breaks down as a signal

Star counts are the project's primary sort key, and stars are a weak proxy for usefulness. The README's own top 10 makes the point. The list is dominated by curated link collections and roadmaps: build-your-own-x, awesome, public-apis, freeCodeCamp, free-programming-books, system-design-primer, developer-roadmap, coding-interview-university and awesome-python. Those are valuable, but they are not representative of the software people actually run, and a reader who treats the leaderboard as a map of important projects will get a distorted picture. A small library that a hundred thousand teams depend on will never appear next to a reading list.

The second failure mode is staleness at the row level. The table includes a Last Commit column, and it earns its place: coding-interview-university shows a last commit in 2025, and several fork-leaderboard entries show commits from 2023 or 2024. A repository can hold a high rank for years after it stops moving. The ranking is a stock measure, not a flow measure.

The third is configuration drift. requirements.txt pins pandas==0.24.1 and requests==2.27.1 with no upper bounds and no lockfile. Anyone who wants to reproduce the published tables has to install those exact versions, and the README does not say which Python they were built against. If you need a stable, reproducible pipeline rather than a daily Markdown refresh, this is the wrong tool.

## Alternatives and how they differ

The obvious alternative is the GitHub REST and GraphQL API itself. If you need rankings for a specific set of repositories, or you need them filtered by topic, license or creation date, calling the API directly gives you exactly the query you want and returns JSON you can store. EvanLi/Github-Ranking gives you none of that flexibility: the sort keys are fixed at stars and forks, the language buckets are fixed at the list in the table of contents, and the output is Markdown rather than structured data. The trade is convenience for control. A daily regenerated file you can read in a browser is genuinely easier than writing and hosting your own collector.

A second alternative is to build the collector yourself on top of requests and pandas, which is essentially what this project is. The repository is small enough that reading source/ and auto_run.sh tells you the whole design, and forking it to change the sort key or add a language is a reasonable move. That is a different decision from adopting it as a dependency: you are using it as a reference implementation.

A third option, for trend questions specifically, is a dataset or dashboard that records snapshots over time. This project publishes the current state and the last update timestamp; the README does not describe a historical archive. If your question is "which repositories gained the most stars this quarter," the repository as documented cannot answer it.

## Maintenance, licence and what an upgrade costs

The repository is not archived, and the last push was on 2026-09-21, the same day as the last automatic update timestamp in the README, so the generation job and the repository are in step. There are no releases, so there is no versioned upgrade path to follow. Upgrading means pulling the branch and re-reading the script, because the only contract is the file layout.

The real upgrade cost sits in requirements.txt. pandas==0.24.1 and requests==2.27.1 are hard pins. Moving to a newer pandas is not a drop-in change for a project written against that API, and the README does not document what breaks. If you run this yourself, budget for either keeping an old interpreter around or reading source/ and updating the pandas calls. If you only consume the published Markdown, this cost is not yours; it belongs to whoever runs the job.

The licence is MIT, which permits use, modification and redistribution provided the copyright notice and permission notice are preserved. That is permissive enough for most internal and commercial uses. The repository also carries a sponsorship link in the README; that is a funding request, not a licence term. Nothing here is legal advice, and if you plan to redistribute the tables at scale, read the LICENSE file rather than this paragraph. One more thing worth checking before you republish the data: the tables contain repository descriptions copied from GitHub, and the MIT licence on this project does not relicense that upstream text.

## Conclusion

Adopt it if you want a checked-in, diffable snapshot of GitHub star and fork rankings you can read without hitting the API yourself, or if you want to run the generation script on your own schedule. Do not adopt it as a live metrics API, a trend detector, or a source of historical time series: the README shows only the current tables and the timestamp of the last automatic update. Before relying on it, open requirements.txt and confirm the pinned pandas==0.24.1 and requests==2.27.1 install cleanly on your Python version, then check the timestamp line at the top of README.md to see when the tables were last regenerated.

## FAQ

### What are the top 10 repos on GitHub according to EvanLi/Github-Ranking?

The README's Most Stars table lists build-your-own-x first, followed by awesome, public-apis, freeCodeCamp, free-programming-books, openclaw, system-design-primer, developer-roadmap, coding-interview-university and awesome-python. The ranking is regenerated automatically, so the order reflects the star counts at the timestamp printed at the top of the file.

### Does EvanLi/Github-Ranking produce a ranking by country or by user?

No. The README documents rankings by stars and by forks overall, plus per-language top 10 and Top 100 lists. There is no country ranking and no per-user ranking in the tables or the table of contents.

### How often is the EvanLi/Github-Ranking list updated?

The README states that it is automatically updated daily and prints a Last Automatic Update Time near the top, for example 2026-09-21T04:07:42Z. Each run appears to rebuild the tables from GitHub API responses rather than appending to a history.

### What dependencies does EvanLi/Github-Ranking need to run?

requirements.txt pins pandas==0.24.1 and requests==2.27.1. The repository also contains auto_run.sh, which is the script the scheduled job uses; the README does not document its flags or how it authenticates to GitHub, so read it before running it.

### Is EvanLi/Github-Ranking a live API for star counts?

No. It publishes Markdown tables and a Jekyll site at evanli.github.io/Github-Ranking. If you need structured, queryable star data you would call the GitHub API yourself or fork the generation script in source/.

## Sources

- [EvanLi/Github-Ranking on GitHub](https://github.com/EvanLi/Github-Ranking)
- [Issues](https://github.com/EvanLi/Github-Ranking/issues)
- [License: MIT](https://github.com/EvanLi/Github-Ranking/blob/master/LICENSE)
- [Project website](https://evanli.github.io/Github-Ranking)
- [README](https://github.com/EvanLi/Github-Ranking/blob/master/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/evanli-github-ranking