TextDistance 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 Hamming hamming MLIPNS Mlipns mlipns Levenshtein Levenshtein levenshtein Damerau-Levenshtein DamerauLevenshtein damerau_levenshtein Jaro-Winkler JaroWinkler jaro_winkler , jaro Strcmp95 StrCmp95 strcmp95 Needleman-Wunsch NeedlemanWunsch needleman_wunsch Gotoh Gotoh gotoh Smith-Waterman SmithWaterman smith_waterman Token based Algorithm Class Functions Jaccard index Jaccard jaccard Sørensen?Dice coefficient Sorensen sorensen , sorensen_dice , dice Tversky index Tversky tversky Overlap coefficient Overlap overlap Tanimoto distance Tanimoto tanimoto Cosine similarity Cosine cosine Monge-Elkan MongeElkan monge_elkan Bag distance Bag bag Sequence based Algorithm Class Functions longest common subsequence similarity LCSSeq lcsseq longest common substring similarity LCSStr lcsstr Ratcliff-Obershelp similarity RatcliffObershelp ratcliff_obershelp Compression based Normalized compression distance with different compression algorithms. Classic compression algorithms: Algorithm Class Function Arithmetic coding ArithNCD arith_ncd RLE RLENCD rle_ncd BWT RLE BWTRLENCD bwtrle_ncd Normal compression algorithms: Algorithm Class Function Square Root SqrtNCD sqrt_ncd Entropy EntropyNCD entropy_ncd Work in progress algorithms that compare two strings as array of bits: Algorithm Class Function BZ2 BZ2NCD bz2_ncd LZMA LZMANCD lzma_ncd ZLib ZLIBNCD zlib_ncd See blog post for more details about NCD. Phonetic Algorithm Class Functions MRA MRA mra Editex 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: pip install textdistance With extra libraries for maximum speed: pip install " textdistance[extras] " With all libraries (required for benchmarking and testing ): pip install " textdistance[benchmark] " With algorithm specific extras: pip install " textdistance[Hamming] " Algorithms with available extras: DamerauLevenshtein , Hamming , Jaro , JaroWinkler , Levenshtein . Dev Via pip: pip install -e git+https://github.com/life4/textdistance.git#egg=textdistance Or clone repo and install with some extras: git clone https://github.com/life4/textdistance.git pip install -e " .[benchmark] " Usage All algorithms have 2 interfaces: Class with algorithm-specific params for customizing. Class instance with default params for quick and simple usage. All algorithms have some common methods: .distance(*sequences) -- calculate distance between sequences. .similarity(*sequences) -- calculate similarity for sequences. .maximum(*sequences) -- maximum possible value for distance and similarity. For any sequence: distance + similarity == maximum . .normalized_distance(*sequences) -- normalized distance between sequences. The return value is a float between 0 and 1, where 0 means equal, and 1 totally different. .normalized_similarity(*sequences) -- normalized similarity for sequences. The return value is a float between 0 and 1, where 0 means totally different, and 1 equal. Most common init arguments: qval -- q-value for split sequences into q-grams. Possible values: 1 (default) -- compare sequences by chars. 2 or more -- transform sequences to q-grams. None -- split sequences by words. as_set -- for token-based algorithms: True -- t and ttt is equal. False (default) -- t and ttt is different. Examples For example, Hamming distance : import textdistance textdistance . hamming ( 'test' , 'text' ) # 1 textdistance . hamming . distance ( 'test' , 'text' ) # 1 textdistance . hamming . similarity ( 'test' , 'text' ) # 3 textdistance . hamming . normalized_distance ( 'test' , 'text' ) # 0.25 textdistance . hamming . normalized_similarity ( 'test' , 'text' ) # 0.75 textdistance . Hamming ( qval = 2 ). distance ( 'test' , 'text' ) # 2 Any other algorithms have same interface. Articles A few articles with examples how to use textdistance in the real world: Guide to Fuzzy Matching with Python String similarity ? the basic know your algorithms guide! Normalized compression distance Extra libraries For main algorithms textdistance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install textdistance with extras for this feature. You can disable this by passing external=False argument on init: import textdistance hamming = textdistance . Hamming ( external = False ) hamming ( 'text' , 'testit' ) # 3 Supported libraries: Distance jellyfish py_stringmatching pylev Levenshtein pyxDamerauLevenshtein Algorithms: DamerauLevenshtein Hamming Jaro JaroWinkler Levenshtein Benchmarks Without extras installation: algorithm library time DamerauLevenshtein rapidfuzz 0.00312 DamerauLevenshtein jellyfish 0.00591 DamerauLevenshtein pyxdameraulevenshtein 0.03335 DamerauLevenshtein textdistance 0.83524 Hamming Levenshtein 0.00038 Hamming rapidfuzz 0.00044 Hamming jellyfish 0.00091 Hamming distance 0.00812 Hamming textdistance 0.03531 Jaro rapidfuzz 0.00092 Jaro jellyfish 0.00191 Jaro textdistance 0.07365 JaroWinkler rapidfuzz 0.00094 JaroWinkler jellyfish 0.00195 JaroWinkler textdistance 0.07501 Levenshtein rapidfuzz 0.00099 Levenshtein Levenshtein 0.00122 Levenshtein jellyfish 0.00254 Levenshtein pylev 0.15688 Levenshtein distance 0.28669 Levenshtein textdistance 0.53902 Total: 24 libs. Yeah, so slow. Use TextDistance on production only with extras. Textdistance use benchmark's results for algorithm's optimization and try to call fastest external lib first (if possible). You can run benchmark manually on your system: pip install textdistance[benchmark] python3 -m textdistance.benchmark TextDistance show benchmarks results table for your system and save libraries priorities into libraries.json file in TextDistance's folder. This file will be used by textdistance for calling fastest algorithm implementation. Default libraries.json already included in package. Running tests All you need is task . See Taskfile.yml for the list of available commands. For example, to run tests including third-party libraries usage, execute task pytest-external:run . Contributing PRs are welcome! Found a bug? Fix it! Want to add more algorithms? Sure! Just make it with the same interface as other algorithms in the lib and add some tests. Can make something faster? Great! Just avoid external dependencies and remember that everything should work not only with strings. Something else that do you think is good? Do it! Just make sure that CI passes and everything from the README is still applicable (interface, features, and so on). Have no time to code? Tell your friends and subscribers about textdistance . More users, more contributions, more amazing features. Thank you ??