---
id: editdistpy
version: "0.4.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# editdistpy — Fast Levenshtein and Damerau optimal string alignment algorithms.

License: permissive · Maintenance: active · Downloads: 382.2K/mo

## 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 above — verify before relying.

Computes Levenshtein and Damerau-Levenshtein edit distances between strings with optional early cutoff to return -1 when distance exceeds a specified threshold.

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

pip install editdistpy
uv add editdistpy
poetry add editdistpy

## Installing editdistpy

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.

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)  # 6

Requires Python 3.10 or later.

Verify 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_current
- Install friction: medium
- Maintenance: active
- Downloads: 382.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

levenshtein distance, edit distance algorithm, string similarity, damerau levenshtein, string alignment distance, fuzzy string matching, optimal string alignment, string-algorithms, fuzzy-matching

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