editdistpy
Fast Levenshtein and Damerau optimal string alignment algorithms.
Decision gist · record as of 2026-08-14
Yes, if you need fast edit-distance computation in Python. The library is actively maintained, has no external dependencies, installs cleanly on modern Python versions, carries a permissive MIT license, and shows good performance on short and medium strings. Install it if fuzzy string matching or similarity measurement is core to your application; skip it if you only need exact string matching or have no string-comparison requirements.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Medium install friction due to compiled wheels; however, prebuilt binaries are available for Python 3.10–3.14 across Linux, macOS (Intel and ARM), Windows, and musl systems, so installation typically succeeds without compilation.
- Actively maintained with a recent release.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-07-12 (33 days) · last repo commit 2026-08-14 · 27 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 382,152 downloads/mo, #7,091 on PyPI
Alternatives
Verify before relying
from editdistpy import levenshtein
import sys
string_1 = "flintstone"
string_2 = "hanson"
max_distance = sys.maxsize
result = levenshtein.distance(string_1, string_2, max_distance)
print(result) # 6- Whether the package is actively maintained beyond the recent release date (last commit and maintenance status are current as of the fact sheet date).
- Performance characteristics on very long strings or in high-throughput scenarios compared to alternatives.
What it is and what it does
editdistpy is a compiled Python library that calculates edit distances—the minimum number of single-character edits needed to transform one string into another. It implements two algorithms: the classic Levenshtein distance (insertions, deletions, substitutions) and the Damerau-Levenshtein optimal string alignment distance (which also allows transpositions). The library is ported from a C# implementation and supports an optional `max_distance` parameter; when the distance would exceed this threshold, the function returns -1 instead of computing the full result, which can significantly speed up comparisons when you only care whether strings are "close enough" within a bound.
The package has no runtime dependencies and is distributed as precompiled wheels for modern Python versions (3.10–3.14) on common platforms. It is suitable for tasks like fuzzy string matching, spell-checking, duplicate detection, and record linkage where you need to measure how different two strings are. The library is actively maintained and carries an MIT license.
Use it for
- Spell-checking or autocorrect: find candidate corrections by computing edit distances from a misspelled word to a dictionary.
- Duplicate detection: identify similar product names, user entries, or records that may refer to the same entity.
- Fuzzy search: rank search results by string similarity when exact matches are unavailable.
- Data deduplication: merge or flag records with similar identifiers or names across datasets.
- Typo tolerance in user input: accept user queries that are within a small edit distance of known commands or entities.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need fast edit-distance computation in Python.
The library is actively maintained, has no external dependencies, installs cleanly on modern Python versions, carries a permissive MIT license, and shows good performance on short and medium strings. Install it if fuzzy string matching or similarity measurement is core to your application; skip it if you only need exact string matching or have no string-comparison requirements.
Install
editdistpy on PyPI
Before you install
Medium install friction due to compiled wheels; however, prebuilt binaries are available for Python 3.10–3.14 across Linux, macOS (Intel and ARM), Windows, and musl systems, so installation typically succeeds without compilation. Actively maintained with a recent release.
Requires Python 3.10 or later.
License in practice
MIT license permits commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
from editdistpy import levenshtein
import sys
string_1 = "flintstone"
string_2 = "hanson"
max_distance = sys.maxsize
result = levenshtein.distance(string_1, string_2, max_distance)
print(result) # 6Verify before relying
- Whether the package is actively maintained beyond the recent release date (last commit and maintenance status are current as of the fact sheet date).
- Performance characteristics on very long strings or in high-throughput scenarios compared to alternatives.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 33 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 382,152 / month, #7,091 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Rust |
Evidence: editdistpy-0.4.0-cp310-cp310-macosx_10_12_x86_64.whl; editdistpy-0.4.0-cp310-cp310-macosx_11_0_arm64.whl; editdistpy-0.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; editdistpy-0.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; editdistpy-0.4.0-cp310-cp310-musllinux_1_1_aarch64.whl; editdistpy-0.4.0-cp310-cp310-musllinux_1_1_x86_64.whl; editdistpy-0.4.0-cp310-cp310-win32.whl; editdistpy-0.4.0-cp310-cp310-win_amd64.whl; editdistpy-0.4.0-cp311-cp311-macosx_10_12_x86_64.whl; editdistpy-0.4.0-cp311-cp311-macosx_11_0_arm64.whl; editdistpy-0.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; editdistpy-0.4.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; editdistpy-0.4.0-cp311-cp311-musllinux_1_1_aarch64.whl; editdistpy-0.4.0-cp311-cp311-musllinux_1_1_x86_64.whl; editdistpy-0.4.0-cp311-cp311-win32.whl; editdistpy-0.4.0-cp311-cp311-win_amd64.whl; editdistpy-0.4.0-cp312-cp312-macosx_10_12_x86_64.whl; editdistpy-0.4.0-cp312-cp312-macosx_11_0_arm64.whl; editdistpy-0.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; editdistpy-0.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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See also editdistance · pyxDamerauLevenshtein · strsimpy · edlib · Levenshtein · kaldialign · polyleven · pylev · textdistance · symspellpy