{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Provides efficient matrix classes for tabular data that mix dense, sparse, and categorical columns, optimized for statistical algorithms.","skillfed_tags":["matrix-computation","statistical-computing","sparse-data"],"use_cases":["Estimating generalized linear models where sandwich products appear in Hessian computation","Weighted least squares with mixed-type predictors where sandwich products are central to normal equations","L1-penalized coordinate descent on subsets of columns requiring fast matrix-vector products on active sets","Standardizing predictors to mean zero and unit variance while preserving sparsity structure","Building statistical algorithms on tabular data with many categorical features encoded as one-hot indicators"],"what_it_does":"Tabmat provides specialized matrix classes\u2014DenseMatrix, SparseMatrix, CategoricalMatrix, SplitMatrix, and StandardizedMatrix\u2014designed to efficiently represent and compute on tabular data that combines dense columns, sparse columns, and one-hot-encoded categorical features. It targets statistical and econometric workflows where operations like sandwich products, matrix-vector products, and standardization are frequent bottlenecks. The library depends on formulaic, narwhals, numpy, and scipy, and aims to be a drop-in replacement for numpy.ndarray and scipy.sparse.csc_matrix where possible.\n\nThe core design trades API breadth for speed and memory efficiency. Each matrix type supports a unified set of operations (matrix-vector products, sandwich products, getcol) with additional methods on individual subclasses. CategoricalMatrix exploits the structure of one-hot encoding to avoid storing redundant data. SplitMatrix combines dense, sparse, and categorical parts in a single object to accelerate multiplications across heterogeneous data.","worth_installing":"Yes, if you are building statistical or econometric algorithms on mixed-type tabular data and need performance beyond generic numpy/scipy. The library is actively maintained, has no known vulnerabilities, and provides a unified API across heterogeneous matrix types. License treatment is unclear, so verify licensing before use in proprietary code."},"id":"tabmat","links":{"html":"https://skillfed.io/packages/tabmat","md":"https://skillfed.io/packages/tabmat.md","pypi":"https://pypi.org/project/tabmat/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-04","license_spdx":null,"license_treatment":"unclear","name":"tabmat","python_support":"supports_current","summary":"Efficient matrix representations for working with tabular data."},"popularity":{"monthly_downloads":325279,"position":7587,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"4.2.1"}