{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Provides numba-optimized window operations (rolling, expanding, seasonal) on numpy arrays as a faster alternative to pandas rolling and expanding methods, plus online classes for incremental updates.","skillfed_tags":["performance-optimization","time-series","numba-jit"],"use_cases":["Accelerate batch computation of rolling means, max, min, or std on large numeric arrays where pandas is too slow.","Implement streaming window statistics by using online classes (RollingMean, ExpandingMax, etc.) to update results as new samples arrive.","Compute seasonal rolling or expanding operations (e.g., 7-day rolling statistics with a seasonal period) on time-series data.","Replace pandas rolling/expanding calls in performance-critical loops where the overhead of pandas indexing is unacceptable."],"what_it_does":"window-ops wraps numba-compiled functions to compute rolling, expanding, and seasonal window statistics on numpy arrays. It targets the same operations as pandas rolling and expanding methods but trades pandas' flexibility for raw speed on numeric arrays. The library includes both batch transformations (returning an array of the same length as the input) and online classes that maintain state and emit one result per update, useful for streaming or incremental scenarios.\n\nThe package depends on numba and numpy; numba's JIT compilation happens at first call. It supports Python 3.6 through 3.10 and is permissively licensed under Apache 2.0. Maintenance is dormant\u2014the last release was in March 2024 and the repository shows no recent activity\u2014so it is stable for existing use cases but will not receive new features or active bug fixes.","worth_installing":"Yes, if you have a specific performance bottleneck with rolling or expanding operations on numpy arrays and can tolerate dormant maintenance. The library is stable, permissively licensed, and offers substantial speedups (4x to 78x in the published benchmarks) for its narrow use case. Not recommended if you need active development, broad window function coverage, or integration with pandas DataFrames."},"id":"window-ops","links":{"html":"https://skillfed.io/packages/window-ops","md":"https://skillfed.io/packages/window-ops.md","pypi":"https://pypi.org/project/window-ops/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-03-04","license_spdx":null,"license_treatment":"permissive","name":"window-ops","python_support":"supports_current","summary":"Implementations of window operations such as rolling and expanding."},"popularity":{"monthly_downloads":215813,"position":9393,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.15"}