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# TextDistance  [](https://travis-ci.org/orsinium/textdistance) [](https://pypi.python.org/pypi/textdistance) [](https://pypi.python.org/pypi/textdistance) [](https://github.com/orsinium/textdistance) [](LICENSE) **TextDistance** -- python library for comparing distance between two or more sequences by many algorithms. Features: * 30+ algorithms * Pure python implementation * Simple usage * More than two sequences comparing * Some algorithms have more than one implementation in one class. * Optional numpy usage for maximum speed. ## Algorithms ### Edit based | Algorithm | Class | Functions | |-------------------------------------------------------------------------------------------|----------------------|------------------------| | [Hamming](https://en.wikipedia.org/wiki/Hamming_distance) | `Hamming` | `hamming` | | [MLIPNS](http://www.sial.iias.spb.su/files/386-386-1-PB.pdf) | `Mlipns` | `mlipns` | | [Levenshtein](https://en.wikipedia.org/wiki/Levenshtein_distance) | `Levenshtein` | `levenshtein` | | [Damerau-Levenshtein](https://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance) | `DamerauLevenshtein` | `damerau_levenshtein` | | [Jaro-Winkler](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) | `JaroWinkler` | `jaro_winkler`, `jaro` | | [Strcmp95](http://cpansearch.perl.org/src/SCW/Text-JaroWinkler-0.1/strcmp95.c) | `StrCmp95` | `strcmp95` | | [Needleman-Wunsch](https://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm) | `NeedlemanWunsch` | `needleman_wunsch` | | [Gotoh](https://www.cs.umd.edu/class/spring2003/cmsc838t/papers/gotoh1982.pdf) | `Gotoh` | `gotoh` | | [Smith-Waterman](https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm) | `SmithWaterman` | `smith_waterman` | ### Token based | Algorithm | Class | Functions | |-------------------------------------------------------------------------------------------|----------------------|---------------| | [Jaccard index](https://en.wikipedia.org/wiki/Jaccard_index) | `Jaccard` | `jaccard` | | [Sørensen–Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) | `Sorensen` | `sorensen`, `sorensen_dice`, `dice` | | [Tversky index](https://en.wikipedia.org/wiki/Tversky_index) | `Tversky` | `tversky` | | [Overlap coefficient](https://en.wikipedia.org/wiki/Overlap_coefficient) | `Overlap` | `overlap` | | [Tanimoto distance](https://en.wikipedia.org/wiki/Jaccard_index#Tanimoto_similarity_and_distance) | `Tanimoto` | `tanimoto` | | [Cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) | `Cosine` | `cosine` | | [Monge-Elkan](https://www.academia.edu/200314/Generalized_Monge-Elkan_Method_for_Approximate_Text_String_Comparison) | `MongeElkan` | `monge_elkan` | | [Bag distance](https://github.com/Yomguithereal/talisman/blob/master/src/metrics/distance/bag.js) | `Bag` | `bag` | ### Sequence based | Algorithm | Class | Functions | |-----------|-------|-----------| | [longest common subsequence similarity](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem) | `LCSSeq` | `lcsseq` | | [longest common substring similarity](https://docs.python.org/2/library/difflib.html#difflib.SequenceMatcher) | `LCSStr` | `lcsstr` | | [Ratcliff-Obershelp similarity](http://collaboration.cmc.ec.gc.ca/science/rpn/biblio/ddj/Website/articles/DDJ/1988/8807/8807c/8807c.htm) | `RatcliffObershelp` | `ratcliff_obershelp` | ### Compression based [Normalized compression distance](https://en.wikipedia.org/wiki/Normalized_compression_distance#Normalized_compression_distance) with different compression algorithms. Classic compression algorithms: | Algorithm | Class | Function | |----------------------------------------------------------------------------|-------------|--------------| | [Arithmetic coding](https://en.wikipedia.org/wiki/Arithmetic_coding) | `ArithNCD` | `arith_ncd` | | [RLE](https://en.wikipedia.org/wiki/Run-length_encoding) | `RLENCD` | `rle_ncd` | | [BWT RLE](https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform) | `BWTRLENCD` | `bwtrle_ncd` | Normal compression algorithms: | Algorithm | Class | Function | |----------------------------------------------------------------------------|--------------|---------------| | Square Root | `SqrtNCD` | `sqrt_ncd` | | [Entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) | `EntropyNCD` | `entropy_ncd` | Work in progress algorithms that compare two strings as array of bits: | Algorithm | Class | Function | |--------------------------------------------|-----------|------------| | [BZ2](https://en.wikipedia.org/wiki/Bzip2) | `BZ2NCD` | `bz2_ncd` | | [LZMA](https://en.wikipedia.org/wiki/LZMA) | `LZMANCD` | `lzma_ncd` | | [ZLib](https://en.wikipedia.org/wiki/Zlib) | `ZLIBNCD` | `zlib_ncd` | See [blog post](https://articles.life4web.ru/eng/ncd/) for more details about NCD. ### Phonetic | Algorithm | Class | Functions | |------------------------------------------------------------------------------|----------|-----------| | [MRA](https://en.wikipedia.org/wiki/Match_rating_approach) | `MRA` | `mra` | | [Editex](https://anhaidgroup.github.io/py_stringmatching/v0.3.x/Editex.html) | `Editex` | `editex` | ### Simple | Algorithm | Class | Functions | |---------------------|------------|------------| | Prefix similarity | `Prefix` | `prefix` | | Postfix similarity | `Postfix` | `postfix` | | Length distance | `Length` | `length` | | Identity similarity | `Identity` | `identity` | | Matrix similarity | `Matrix` | `matrix` | ## Installation ### Stable Only pure python implementation: ```bash pip install textdistance ``` With extra libraries for maximum speed: ```bash pip install textdistance[extras] ``` With all libraries (required for [benchmarking](#benchmarks) and [testing](#test)): ```bash pip install textdistance[benchmark] ``` With algorithm specific extras: ```bash pip install textdistance[Hamming] ``` Algorithms with available extras: `DamerauLevenshtein`, `Hamming`, `Jaro`, `JaroWinkler`, `Levenshtein`. ### Dev Via pip: ```bash pip install -e git+https://github.com/orsinium/textdistance.git#egg=textdistance ``` Or clone repo and install with some extras: ```bash git clone https://github.com/orsinium/textdistance.git pip install -e .[benchmark] ``` ## Usage All algorithms have 2 interfaces: 1. Class with algorithm-specific params for customizing. 2. Class instance with default params for quick and simple usage. All algorithms have some common methods: 1. `.distance(*s