# pandas-dev/pandas SLOC report

> As of 2026-09-16 (commit f60d9048ca59), pandas-dev/pandas contains 349,365 total lines: 295,098 code, 20,584 comments, 33,683 blank, across 460 files in 11 languages (top: Python 79.5%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

pandas-dev/pandas has 295,098 source lines of code out of 349,365 total lines across 460 files, counted from the f60d9048ca5983075b493bc5d827b83e11735af0 ref at commit f60d9048ca59 by the OctoCounts tokei engine on 2026-09-16. The report is cached by commit, tokei version, and analysis options, so recounting the same revision returns exactly these numbers.

OctoCounts produced this report by resolving pandas-dev/pandas to commit f60d9048ca59, downloading the repository source archive, and counting every source file with tokei, the open-source line counter written in Rust. The table below breaks the count down by programming language into files, total lines, code lines, comment lines, and blank lines, so the figures can be compared across languages and projects. Results are cached by commit, tokei version, and analysis options, so counting the same revision again reproduces exactly these numbers.

## Repository size insights

This is a large codebase by counted code lines. Code represents 84.5% of all lines, comments represent 5.9%, and the repository averages 642 code lines per file. Python accounts for 79.5% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 338 | 273,249 | 234,614 | 14,117 | 24,518 |
| Cython | 65 | 55,661 | 45,045 | 3,793 | 6,823 |
| C | 11 | 8,395 | 6,496 | 955 | 944 |
| Autoconf | 8 | 3,872 | 3,047 | 239 | 586 |
| C++ | 5 | 2,771 | 2,327 | 281 | 163 |
| C Header | 13 | 3,124 | 1,900 | 812 | 412 |
| TOML | 1 | 721 | 546 | 141 | 34 |
| Meson | 8 | 565 | 479 | 38 | 48 |
| C++ Header | 1 | 490 | 386 | 20 | 84 |
| Pan | 8 | 273 | 258 | 2 | 13 |
| Markdown | 2 | 244 | 0 | 186 | 58 |

Top language (Python 79.5%). Generated at 2026-09-16T13:15:57.278594846+00:00. Canonical report: https://octocounts.com/github/pandas-dev/pandas

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/pandas-dev/pandas/commit/f60d9048ca5983075b493bc5d827b83e11735af0?analysis=%7B%22ignoredDirs%22%3A%5B%5D%2C%22ignoredLanguages%22%3A%5B%5D%2C%22profile%22%3A%22default%22%2C%22includeDocs%22%3Afalse%2C%22includeTests%22%3Afalse%2C%22includeGenerated%22%3Afalse%7D).

## Report FAQ

### How many lines of code does pandas-dev/pandas have?

pandas-dev/pandas has 349,365 total lines, including 295,098 code lines, 20,584 comment lines, and 33,683 blank lines.

### How was the pandas-dev/pandas line count measured?

OctoCounts resolved the public GitHub repository to commit f60d9048ca59, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for pandas-dev/pandas?

This OctoCounts report was generated from f60d9048ca5983075b493bc5d827b83e11735af0 at commit f60d9048ca59 on 2026-09-16T13:15:57.278594846+00:00.

## Similar repository reports

- [isce-framework/isce2](https://octocounts.com/github/isce-framework/isce2) — Python, 296,129 code lines
- [jundot/omlx](https://octocounts.com/github/jundot/omlx) — Python, 293,587 code lines
- [CunrenLiang/isce2](https://octocounts.com/github/CunrenLiang/isce2) — Python, 296,722 code lines
- [alexgreensh/token-optimizer](https://octocounts.com/github/alexgreensh/token-optimizer) — Python, 292,919 code lines
- [saleor/saleor](https://octocounts.com/github/saleor/saleor) — Python, 286,919 code lines
- [Imbad0202/academic-research-skills](https://octocounts.com/github/Imbad0202/academic-research-skills) — Python, 285,318 code lines

## Related OctoCounts pages

- [Recently analyzed repositories](https://octocounts.com/recent)
- [Popular SLOC reports](https://octocounts.com/popular)
- [Trending GitHub repositories](https://octocounts.com/trending)
- [Hall of Monoliths](https://octocounts.com/hall-of-monoliths)
- [TensorFlow vs PyTorch](https://octocounts.com/compare/tensorflow-vs-pytorch)
- [Django vs Ruby on Rails](https://octocounts.com/compare/django-vs-rails)
- [Laravel vs Django](https://octocounts.com/compare/laravel-vs-django)
- [GitHub SLOC counter guide](https://octocounts.com/docs/github-sloc-counter)
- [Counting methodology](https://octocounts.com/docs/methodology)
- [SLOC and code metrics glossary](https://octocounts.com/docs/glossary)
- [Original research: how filtering changes SLOC counts](https://octocounts.com/research)
- [OctoCounts API docs](https://octocounts.com/docs/api)