PyParsing – A Python Parsing Module
Introduction
The pyparsing module is an alternative approach to creating and executing simple grammars, vs. the traditional lex/yacc approach, or the use of regular expressions. The pyparsing module provides a library of classes that client code uses to construct the grammar directly in Python code.
[Since first writing this description of pyparsing in late 2003, this technique for developing parsers has become more widespread, under the name Parsing Expression Grammars - PEGs. See more information on PEGs here .]
Here is a program to parse "Hello, World!" (or any greeting of the form "salutation, addressee!"):
from pyparsing import Word, alphas
greet = Word(alphas) + "," + Word(alphas) + "!"
hello = "Hello, World!"
print(hello, "->", greet.parse_string(hello))The program outputs the following:
Hello, World! -> ['Hello', ',', 'World', '!']
The Python representation of the grammar is quite readable, owing to the self-explanatory class names, and the use of ‘+’, ‘|’ and ‘^’ operator definitions.
The parsed results returned from parse_string() is a collection of type ParseResults, which can be accessed as a nested list, a dictionary, or an object with named attributes.
The pyparsing module handles some of the problems that are typically vexing when writing text parsers:
extra or missing whitespace (the above program will also handle "Hello,World!", "Hello , World !", etc.)
quoted strings
embedded comments
The examples directory includes a simple SQL parser, simple CORBA IDL parser, a config file parser, a chemical formula parser, and a four- function algebraic notation parser, among many others.
Documentation
There are many examples in the online docstrings of the classes and methods in pyparsing. You can find them compiled into online docs. Additional documentation resources and project info are listed in the online GitHub wiki. An entire directory of examples can be found here.
AI Instructions
There are also instructions for AI agents to use when helping you to create your parser. They can be pulled from the GitHub project repository, at pyparsing/ai/best_practices.md. You can also tell the AI to access them programmatically after installing pyparsing, either from the CLI with python -m pyparsing.ai.show_best_practices or within python with import pyparsing; pyparsing.show_best_practices().
License
MIT License. See header of the pyparsing __init__.py file.
History
See CHANGES file.
Performance benchmarks
For usage instructions and details on the performance benchmark suite, see tests/README.md in this repository.
1 maintainer
Metadata
1 maintainer
Release files for pyparsing 3.3.0
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