Lark VS python-patterns

Compare Lark vs python-patterns and see what are their differences.

Lark

Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity. (by lark-parser)
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Lark python-patterns
35 31
4,497 39,439
1.6% -
7.5 0.0
19 days ago 26 days ago
Python Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Lark

Posts with mentions or reviews of Lark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-13.

python-patterns

Posts with mentions or reviews of python-patterns. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-11-27.

What are some alternatives?

When comparing Lark and python-patterns you can also consider the following projects:

pyparsing - Python library for creating PEG parsers [Moved to: https://github.com/pyparsing/pyparsing]

PyPattyrn - A simple library for implementing common design patterns.

PLY - Python Lex-Yacc

TheAlgorithms - All Algorithms implemented in Python

pydantic - Data validation using Python type hints

sortedcontainers - Python Sorted Container Types: Sorted List, Sorted Dict, and Sorted Set

sqlparse - A non-validating SQL parser module for Python

algorithms

Atoma - Atom, RSS and JSON feed parser for Python 3

more-itertools - More routines for operating on iterables, beyond itertools

Construct - Construct: Declarative data structures for python that allow symmetric parsing and building

python-ds - No non-sense and no BS repo for how data structure code should be in Python - simple and elegant.