{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"Numbagg provides fast N-dimensional aggregation and moving-window functions compiled with Numba, including reductions (nansum, nanmean, nanstd), grouping operations, and forward/backward fill, optimized for multi-core parallelization.","skillfed_tags":["jit-compiled","parallel-computing","scientific-computing"],"use_cases":["Compute moving averages and standard deviations on time-series data with NaN handling faster than pandas.","Apply grouped aggregations (sum, mean, count) on multi-dimensional scientific data.","Calculate rolling correlations and covariances on financial or climate datasets.","Forward-fill or backward-fill missing values in large arrays with better performance than pandas.","Parallelize custom reductions across multiple cores on arrays too large for pure NumPy."],"what_it_does":"Numbagg is a library of fast aggregation and moving-window functions for N-dimensional NumPy arrays, written in Numba and exposed as generalized ufuncs. It includes standard reductions (nansum, nanmean, nanstd, nanvar), grouping operations (group_nansum, group_nanmean, etc.), moving-window statistics (move_mean, move_std, move_corr), and fill operations (ffill, bfill). The library is designed to outperform pandas and NumPy on multi-core systems through Numba's JIT compilation and parallelization, though single-threaded performance is comparable to NumPy and the first call to any function incurs JIT overhead.\n\nIt targets scientific and data-analysis workflows where performance on large arrays matters and flexible axis handling across many dimensions is needed. The main trade-off is that functions are JIT-compiled on first use, so initial calls are slower; subsequent calls are fast. Dependencies are minimal (numpy and numba), and the package supports Python 3.10 and later.","worth_installing":"Yes. Numbagg is worth installing if you work with large N-dimensional NumPy arrays and need fast aggregations or moving-window functions, especially on multi-core systems. Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The JIT compilation overhead on first call is a minor drawback but typical for Numba-based libraries and acceptable for most scientific workflows."},"id":"numbagg","links":{"html":"https://skillfed.io/packages/numbagg","md":"https://skillfed.io/packages/numbagg.md","pypi":"https://pypi.org/project/numbagg/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-12-15","license_spdx":null,"license_treatment":"unclear","name":"numbagg","python_support":"supports_current","summary":"Fast N-dimensional aggregation functions with Numba"},"popularity":{"monthly_downloads":974005,"position":4601,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.9.4"}