textdistance 4.6.3

Compute distance between the two texts.

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:

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(*sequences) -- calculate distance between sequences.
2. .similarity(*sequences) -- calculate similarity for sequences.
3. .maximum(*sequences) -- maximum possible value for distance and similarity. For any sequence: distance + similarity == maximum.
4. .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.
5. .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:

1. 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.
2. 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:

1. Distance
2. jellyfish
3. py_stringmatching
4. pylev
5. Levenshtein
6. pyxDamerauLevenshtein

Algorithms:

1. DamerauLevenshtein
2. Hamming
3. Jaro
4. JaroWinkler
5. 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 :heart: