{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/15"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/7"}],"enrichment":{"capability":"Distributes CPU-intensive processing of collections across multiple cores using Python's multiprocessing module, with a primary map() function for parallel execution.","skillfed_tags":["multiprocessing","parallel-computing","abandoned"],"use_cases":["Process multiple large gzip-compressed files in parallel, extracting and filtering lines across CPU cores.","Distribute CPU-intensive computations over a collection of data items without writing explicit multiprocessing boilerplate.","Parallelize operations like reading or transforming files when the work can be divided into independent tasks.","Leverage multiple cores for batch processing tasks in legacy codebases already using para."],"what_it_does":"Para is a lightweight wrapper around Python's multiprocessing module that simplifies distributing CPU-bound work across multiple processor cores on a single machine. It centers on a map() function that applies a function to each item in a collection, executing the work in parallel across available cores rather than sequentially. The library is designed for straightforward parallel processing tasks without the complexity of lower-level multiprocessing APIs.\n\nThe package has no external runtime dependencies and installs with minimal friction. However, it has been abandoned since 2020-01-17 and receives no maintenance. For projects already using it or for simple parallel-map use cases where the API fits, it remains functional; for new projects, you should evaluate whether modern alternatives better suit your Python version and concurrency needs.","worth_installing":"Yes, but with strong conditions. Para is worth installing only if you are maintaining existing code that already depends on it, or if you need a minimal, zero-dependency parallel-map utility and can accept that the package is unmaintained. For new projects, evaluate modern alternatives that receive active maintenance and may offer better compatibility with current Python versions. The permissive MIT license and low install friction are advantages, but abandonment is a significant drawback."},"id":"para","links":{"html":"https://skillfed.io/packages/para","md":"https://skillfed.io/packages/para.md","pypi":"https://pypi.org/project/para/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2020-01-17","license_spdx":null,"license_treatment":"permissive","name":"para","python_support":"unspecified","summary":"a set utilities that ake advantage of python's 'multiprocessing' module to distribute CPU-intensive tasks"},"popularity":{"monthly_downloads":247051,"position":8701,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.8"}