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mmh3\n\n[![Documentation Status](https://readthedocs.org/projects/mmh3/badge/?version=stable)](https://mmh3.readthedocs.io/en/stable/)\n[![GitHub Super-Linter](https://github.com/hajimes/mmh3/actions/workflows/superlinter.yml/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster)\n[![Build](https://github.com/hajimes/mmh3/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master)\n[![PyPi Version](https://img.shields.io/pypi/v/mmh3.svg?style=flat-square\u0026logo=pypi\u0026logoColor=white)](https://pypi.org/project/mmh3/)\n[![Python Versions](https://img.shields.io/pypi/pyversions/mmh3.svg)](https://pypi.org/project/mmh3/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/license/mit/)\n[![Total Downloads](https://static.pepy.tech/badge/mmh3)](https://pepy.tech/projects/mmh3?versions=*%2C5.*%2C4.*%2C3.*%2C2.*)\n[![Recent Downloads](https://static.pepy.tech/badge/mmh3/month)](https://pepy.tech/projects/mmh3?versions=*%2C5.*%2C4.*%2C3.*%2C2.*)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.06124/status.svg)](https://doi.org/10.21105/joss.06124)\n\n`mmh3` is a Python extension for\n[MurmurHash (MurmurHash3)](https://en.wikipedia.org/wiki/MurmurHash), a set of\nfast and robust non-cryptographic hash functions invented by Austin Appleby.\n\nBy combining `mmh3` with probabilistic techniques like\n[Bloom filter](https://en.wikipedia.org/wiki/Bloom_filter),\n[MinHash](https://en.wikipedia.org/wiki/MinHash), and\n[feature hashing](https://en.wikipedia.org/wiki/Feature_hashing), you can\ndevelop high-performance systems in fields such as data mining, machine\nlearning, and natural language processing.\n\nAnother popular use of `mmh3` is to\n[calculate favicon hashes](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a),\nwhich are utilized by [Shodan](https://www.shodan.io), the world's first IoT\nsearch engine.\n\nThis page provides a quick start guide. For more comprehensive information,\nplease refer to the [documentation](https://mmh3.readthedocs.io/en/stable/).\n\n## Installation\n\n```shell\npip install mmh3\n```\n\n## Usage\n\n### Basic usage\n\n```pycon\n\u003e\u003e\u003e import mmh3\n\u003e\u003e\u003e mmh3.hash(b\"foo\") # returns a 32-bit signed int\n-156908512\n\u003e\u003e\u003e mmh3.hash(\"foo\") # accepts str (UTF-8 encoded)\n-156908512\n\u003e\u003e\u003e mmh3.hash(b\"foo\", 42) # uses 42 as the seed\n-1322301282\n\u003e\u003e\u003e mmh3.hash(b\"foo\", 0, False) # returns a 32-bit unsigned int\n4138058784\n```\n\n`mmh3.mmh3_x64_128_digest()`, introduced in version 5.0.0, efficienlty hashes\nbuffer objects that implement the buffer protocol\n([PEP 688](https://peps.python.org/pep-0688/)) without internal memory copying.\nThe function returns a `bytes` object of 16 bytes (128 bits). It is\nparticularly suited for hashing large memory views, such as\n`bytearray`, `memoryview`, and `numpy.ndarray`, and performs faster than\nthe 32-bit variants like `hash()` on 64-bit machines.\n\n```pycon\n\u003e\u003e\u003e mmh3.mmh3_x64_128_digest(numpy.random.rand(100))\nb'\\x8c\\xee\\xc6z\\xa9\\xfeR\\xe8o\\x9a\\x9b\\x17u\\xbe\\xdc\\xee'\n```\n\nVarious alternatives are available, offering different return types (e.g.,\nsigned integers, tuples of unsigned integers) and optimized for different\narchitectures. For a comprehensive list of functions, refer to the\n[API Reference](https://mmh3.readthedocs.io/en/stable/api.html).\n\n### `hashlib`-style hashers\n\n`mmh3` implements hasher objects with interfaces similar to those in `hashlib`\nfrom the standard library, although they are still experimental. See\n[Hasher Classes](https://mmh3.readthedocs.io/en/stable/api.html#hasher-classes)\nin the API Reference for more information.\n\n## Changelog\n\nSee [Changelog (latest version)](https://mmh3.readthedocs.io/en/latest/changelog.html)\nfor the complete changelog.\n\n### [5.2.0] - 2025-07-29\n\n#### Added\n\n- Add support for Python 3.14, including 3.14t (no-GIL) wheels. However, thread\n  safety for the no-GIL variant is not fully tested yet. Please report any\n  issues you encounter ([#134](https://github.com/hajimes/mmh3/pull/134),\n  [#136](https://github.com/hajimes/mmh3/pull/136)).\n- Add support for Android (Python 3.13 only) and iOS (Python 3.13 and 3.14) wheels,\n  enabled by the major version update of\n  [cibuildwheel](https://github.com/pypa/cibuildwheel)\n  ([#135](https://github.com/hajimes/mmh3/pull/135)).\n\n### [5.1.0] - 2025-01-25\n\n#### Added\n\n- Improve the performance of `hash128()`, `hash64()`, and `hash_bytes()` by\n  using\n  [METH_FASTCALL](https://docs.python.org/3/c-api/structures.html#c.METH_FASTCALL),\n  reducing the overhead of function calls\n  ([#116](https://github.com/hajimes/mmh3/pull/116)).\n- Add the software paper for this library\n  ([doi:10.21105/joss.06124](https://doi.org/10.21105/joss.06124)), following\n  its publication in the\n  [_Journal of Open Source Software_](https://joss.theoj.org)\n  ([#118](https://github.com/hajimes/mmh3/pull/118)).\n\n#### Removed\n\n- Drop support for Python 3.8, as it has reached the end of life on 2024-10-07\n  ([#117](https://github.com/hajimes/mmh3/pull/117)).\n\n### [5.0.1] - 2024-09-22\n\n#### Fixed\n\n- Fix the issue that the package cannot be built from the source distribution\n  ([#90](https://github.com/hajimes/mmh3/issues/90)).\n\n## License\n\n[MIT](https://github.com/hajimes/mmh3/blob/master/LICENSE), unless otherwise\nnoted within a file.\n\n## Frequently Asked Questions\n\n### Different results from other MurmurHash3-based libraries\n\nBy default, `mmh3` returns **signed** values for the 32-bit and 64-bit versions\nand **unsigned** values for `hash128` due to historical reasons. To get the\ndesired result, use the `signed` keyword argument.\n\nStarting from version 4.0.0, **`mmh3` is endian-neutral**, meaning that its\nhash functions return the same values on big-endian platforms as they do on\nlittle-endian ones. In contrast, the original C++ library by Appleby is\nendian-sensitive. If you need results that comply with the original library on\nbig-endian systems, please use version 3.\\*.\n\nFor compatibility with [Google Guava (Java)](https://github.com/google/guava),\nsee\n\u003chttps://stackoverflow.com/questions/29932956/murmur3-hash-different-result-between-python-and-java-implementation\u003e.\n\nFor compatibility with\n[murmur3 (Go)](https://pkg.go.dev/github.com/spaolacci/murmur3), see\n\u003chttps://github.com/hajimes/mmh3/issues/46\u003e.\n\n### Handling errors with negative seeds\n\nFrom the version 5.0.0, `mmh3` functions accept only **unsigned** 32-bit integer\nseeds to enable faster type-checking and conversion. However, this change may\ncause issues if you need to calculate hash values using negative seeds within\nthe range of signed 32-bit integers. For instance,\n[Telegram-iOS](https://github.com/TelegramMessenger/Telegram-iOS) uses\n`-137723950` as a hard-coded seed (bitwise equivalent to `4157243346`). To\nhandle such cases, you can convert a signed 32-bit integer to its unsigned\nequivalent by applying a bitwise AND operation with `0xffffffff`. Here's an\nexample:\n\n```pycon\n\u003e\u003e\u003e mmh3.hash(b\"quux\", 4294967295)\n258499980\n\u003e\u003e\u003e d = -1\n\u003e\u003e\u003e mmh3.hash(b\"quux\", d \u0026 0xffffffff)\n258499980\n```\n\nAlternatively, if the seed is hard-coded (as in the Telegram-iOS case), you can\nprecompute the unsigned value for simplicity.\n\n## Contributing Guidelines\n\nSee [Contributing](https://mmh3.readthedocs.io/en/stable/CONTRIBUTING.html).\n\n## Authors\n\nMurmurHash3 was originally developed by Austin Appleby and distributed under\npublic domain\n[https://github.com/aappleby/smhasher](https://github.com/aappleby/smhasher).\n\nPorted and modified for Python by Hajime Senuma.\n\n## External Tutorials\n\n### High-performance computing\n\nThe following textbooks and tutorials are great resources for learning how to\nuse `mmh3` (and other hash algorithms in general) for high-performance computing.\n\n- Chapter 11: _Using Less Ram_ in Micha Gorelick and Ian Ozsvald. 2014. _High\n  Performance Python: Practical Performant Programming for Humans_. O'Reilly\n  Media. [ISBN: 978-1-4493-6159-4](https://www.amazon.com/dp/1449361595).\n  - 2nd edition of the above (2020).\n    [ISBN: 978-1492055020](https://www.amazon.com/dp/1492055026).\n- Max Burstein. February 2, 2013.\n  _[Creating a Simple Bloom Filter](http://www.maxburstein.com/blog/creating-a-simple-bloom-filter/)_.\n- Duke University. April 14, 2016.\n  _[Efficient storage of data in memory](http://people.duke.edu/~ccc14/sta-663-2016/20B_Big_Data_Structures.html)_.\n- Bugra Akyildiz. August 24, 2016.\n  _[A Gentle Introduction to Bloom Filter](https://www.kdnuggets.com/2016/08/gentle-introduction-bloom-filter.html)_.\n  KDnuggets.\n\n### Internet of things\n\n[Shodan](https://www.shodan.io), the world's first\n[IoT](https://en.wikipedia.org/wiki/Internet_of_things) search engine, uses\nMurmurHash3 hash values for [favicons](https://en.wikipedia.org/wiki/Favicon)\n(icons associated with web pages). [ZoomEye](https://www.zoomeye.org) follows\nShodan's convention.\n[Calculating these values with mmh3](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a)\nis useful for OSINT and cybersecurity activities.\n\n- Jan Kopriva. April 19, 2021.\n  _[Hunting phishing websites with favicon hashes](https://isc.sans.edu/diary/Hunting+phishing+websites+with+favicon+hashes/27326)_.\n  SANS Internet Storm Center.\n- Nikhil Panwar. May 2, 2022.\n  _[Using Favicons to Discover Phishing \u0026 Brand Impersonation Websites](https://bolster.ai/blog/how-to-use-favicons-to-find-phishing-websites)_.\n  Bolster.\n- Faradaysec. July 25, 2022.\n  _[Understanding Spring4Shell: How used is it?](https://faradaysec.com/understanding-spring4shell/)_.\n  Faraday Security.\n- Debjeet. August 2, 2022.\n  _[How To Find Assets Using Favicon Hashes](https://payatu.com/blog/favicon-hash/)_.\n  Payatu.\n\n## How to Cite This Library\n\nIf you use this library in your research, it would be appreciated if you could\ncite the following paper published in the\n[_Journal of Open Source Software_](https://joss.theoj.org):\n\nHajime Senuma. 2025.\n[mmh3: A Python extension for MurmurHash3](https://doi.org/10.21105/joss.06124).\n_Journal of Open Source Software_, 10(105):6124.\n\nIn BibTeX format:\n\n```tex\n@article{senumaMmh3PythonExtension2025,\n  title = {{mmh3}: A {Python} extension for {MurmurHash3}},\n  author = {Senuma, Hajime},\n  year = {2025},\n  month = jan,\n  journal = {Journal of Open Source Software},\n  volume = {10},\n  number = {105},\n  pages = {6124},\n  issn = {2475-9066},\n  doi = {10.21105/joss.06124},\n  copyright = {http://creativecommons.org/licenses/by/4.0/}\n}\n```\n\n## Related Libraries\n\n- \u003chttps://github.com/wc-duck/pymmh3\u003e: mmh3 in pure python (Fredrik Kihlander\n  and Swapnil Gusani)\n- \u003chttps://github.com/escherba/python-cityhash\u003e: Python bindings for CityHash\n  (Eugene Scherba)\n- \u003chttps://github.com/veelion/python-farmhash\u003e: Python bindings for FarmHash\n  (Veelion Chong)\n- \u003chttps://github.com/escherba/python-metrohash\u003e: Python bindings for MetroHash\n  (Eugene Scherba)\n- \u003chttps://github.com/ifduyue/python-xxhash\u003e: Python bindings for xxHash (Yue\n  Du)\n\n[5.2.0]: https://github.com/hajimes/mmh3/compare/v5.1.0...v5.2.0\n[5.1.0]: https://github.com/hajimes/mmh3/compare/v5.0.1...v5.1.0\n[5.0.1]: https://github.com/hajimes/mmh3/compare/v5.0.0...v5.0.1\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhajimes%2Fmmh3","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhajimes%2Fmmh3","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhajimes%2Fmmh3/lists"}