$npx skillfedfor your agent

probableparsing

Common methods for propbable parsers

SkipPyPI Scientific/EngineeringReleased Mar 20165.3M downloads / mopermissive licensePure Python

Decision gist · record as of 2026-08-14

pure-Python wheel — probableparsing-0.0.1-py2.py3-none-any.whl
v0.0.1 · released 2016-03-28

No, unless you are maintaining existing code that already uses it. The package is abandoned (last commit 2023-04-28, no updates since 2016), has minimal community adoption (6 repository stars), and offers no documentation or active support. For new projects, seek actively maintained parsing or information extraction libraries instead.AI-flagged interpretation of the facts on this page — verify before relying

Before you install

  • Package targets Python 2.7 and has not been updated since 2016; compatibility with modern Python versions is uncertain.
  • Installation is frictionless with no runtime dependencies, but the package is abandoned—last updated in 2016 with no commits since 2023-04-28.
  • Use only if maintaining legacy code that already depends on it.

License · maintenance · safety

permissive license (permissive) — MIT license is permissive and places no restrictions on use, modification, or distribution.

last release 2016-03-28 (3791 days) · last repo commit 2023-04-28 · 6 stars

0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,294,658 downloads/mo, #2,122 on PyPI

Verify before relying

pip install probableparsing==0.0.1
import probableparsing
  • What specific parsing problems the 'probable parsing' approach is designed to solve
  • Whether this package remains compatible with modern Python versions
  • What 'probable parsers' means and how they differ from standard parsing approaches
  • Current usability and whether any breaking changes exist for Python 3
Same gist for agents: .md · .json

What it is and what it does

probableparsing is a utility library providing common methods for building probable parsers—a parsing technique used in information extraction and data cleaning workflows. The package has no runtime dependencies, making it lightweight to install, but it has been abandoned since its initial 2016 release with no active maintenance or updates.

The library is classified as Alpha-stage software and was designed for developers and researchers working on scientific information analysis tasks. Given its age and lack of maintenance, it is best suited for legacy systems already using it rather than as a foundation for new projects. The permissive MIT license allows free use and modification.

Use it for

  • Maintaining or extending legacy information extraction systems that already depend on this library
  • Learning about probable parsing techniques in academic or research contexts
  • Building custom parsers for semi-structured data in established codebases

Worth the install?

AI-flagged interpretation of the facts on this page. Verify before relying on it.

Skip

No, unless you are maintaining existing code that already uses it.

The package is abandoned (last commit 2023-04-28, no updates since 2016), has minimal community adoption (6 repository stars), and offers no documentation or active support. For new projects, seek actively maintained parsing or information extraction libraries instead.

Install

probableparsing on PyPI

Before you install

Installation is frictionless with no runtime dependencies, but the package is abandoned—last updated in 2016 with no commits since 2023-04-28. Use only if maintaining legacy code that already depends on it.

Package targets Python 2.7 and has not been updated since 2016; compatibility with modern Python versions is uncertain.

License in practice

MIT license is permissive and places no restrictions on use, modification, or distribution.

Quickstart

pip install probableparsing==0.0.1
import probableparsing

Verify before relying

  • What specific parsing problems the 'probable parsing' approach is designed to solve
  • Whether this package remains compatible with modern Python versions
  • What 'probable parsers' means and how they differ from standard parsing approaches
  • Current usability and whether any breaking changes exist for Python 3

Package facts

Licensepermissive license permissive
Python supportNot specified
Install frictionLow. Pure-Python wheel
Runtime dependenciesNone
MaintenanceAbandoned 3,791 days since the last release
Last repo commit
First released
Downloads5,294,658 / month, #2,122 on PyPI 30-day window, as of 2026-08-14
Known vulnerabilitiesNone known OSV.dev, checked 2026-08-14
Classifiers
Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: Python :: 2.7Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software Development :: Libraries :: Python Modules

Evidence: probableparsing-0.0.1-py2.py3-none-any.whl

Tags

Capabilities
probable parsing utilitiesparser helper methodsinformation extraction librarydata parsing toolkitparsing common functionsprobable parser frameworktext parsing utilities
Topics
abandonedlegacyinformation-extraction

Let your AI agent find packages like this

Example. Real query, live index.

You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.

wish › “probable parsing utilities”

Give your agent the search over MCP, or paste the wish link into any chat.

More Scientific/Engineering packages

numpy Worth it
PyPI · Software Development · released Aug 2026

NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.

BSD-3-Clause AND 0BSD AND MIT AND Zlib AND CC0-1.0compiled wheel · 3.12+
1.1Bdownloads / mo
pandas Worth it
PyPI · Scientific/Engineering · released Jul 2026

pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.

BSD-3-Clausecompiled wheel · 3.11+
769.1Mdownloads / mo
scipy Worth it
PyPI · Libraries · released Jun 2026

scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.

BSD-3-Clausecompiled wheel · 3.12+
449.0Mdownloads / mo
scikit-learn Worth it
PyPI · Software Development · released Jun 2026

scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.

Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.

BSD-3-Clausecompiled wheel · 3.11+
235.5Mdownloads / mo
dill Worth it
PyPI · Software Development · released Jan 2026

dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.

BSD-3-Clausepure Python · 3.9+
208.1Mdownloads / mo
multiprocess Worth it
PyPI · Software Development · released Jan 2026

Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.

Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.

BSD-3-Clausepure Python · 3.9+
202.7Mdownloads / mo

See also stop-words · textacy · commoncode · goose3 · pygmars · probablepeople · cli-helpers · pyparsing · breadability · pockets