{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/10"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/7"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"Manipulates complex, non-rectangular data structures (jagged arrays, nested records, heterogeneous types, sparse or masked data) using Numpy-like vectorized operations instead of Python loops.","skillfed_tags":["legacy-abandoned","columnar-data","scientific-computing"],"use_cases":["Process physics detector data or scientific measurements with variable-length event records and nested hierarchies efficiently.","Convert JSON or nested Python data structures into columnar arrays for vectorized numerical operations.","Load and manipulate Parquet or HDF5 files containing ragged or heterogeneous data without manual loop-based unpacking.","Work with sparse or masked datasets where not all records have the same structure or completeness.","Perform batch operations on lists of different lengths (e.g., variable-size batches in machine learning) using Numpy-like syntax."],"what_it_does":"Awkward Array is a pure Python and Numpy library that extends Numpy's vectorization capabilities to complex, non-rectangular data structures. While Numpy excels at operations on regular rectangular arrays, it struggles with variable-length lists, nested records, heterogeneous types, sparse data, or masked values\u2014forcing users back to slow Python loops. Awkward Array bridges this gap by allowing you to manipulate these complex structures with the same efficient, vectorized syntax you'd use in Numpy, accessing data as columnar structures with memory efficiency comparable to Numpy arrays.\n\nThe library supports loading data from JSON, Python objects, HDF5, Parquet, ROOT files, or Arrow buffers, and can handle deeply nested hierarchies, cross-references, lazy loading, and on-demand Python class instantiation. However, this package (awkward0) is the legacy 0.x branch and has been abandoned since early 2021; it is no longer maintained and may have compatibility issues with modern Python and dependency versions.","worth_installing":"No. This package is abandoned (last commit 2021-02-08, repository archived) and superseded by the active awkward package. Installing awkward0 locks you into unmaintained code with no bug fixes or compatibility updates for modern Python and numpy versions. Use the current awkward package instead unless you have a specific legacy dependency constraint."},"id":"awkward0","links":{"html":"https://skillfed.io/packages/awkward0","md":"https://skillfed.io/packages/awkward0.md","pypi":"https://pypi.org/project/awkward0/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2021-02-08","license_spdx":null,"license_treatment":"permissive","name":"awkward0","python_support":"unspecified","summary":"Manipulate arrays of complex data structures as easily as Numpy."},"popularity":{"monthly_downloads":282122,"position":8088,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.15.5"}