{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"Formulaic converts tabular data into model matrices using Wilkinson formula syntax, supporting dense and sparse outputs across multiple dataframe libraries including pandas, Polars, and PyArrow.","skillfed_tags":["statistical-modeling","dataframe-transformation","formula-dsl"],"use_cases":["Prepare training data for linear regression or GLM models by converting formulas into design matrices with automatic categorical encoding.","Reuse categorical encodings learned on a training set when transforming new test or production data.","Work with Polars or PyArrow dataframes while maintaining formula-based data transformation workflows.","Generate sparse model matrices for high-dimensional datasets to reduce memory footprint.","Integrate formula-based preprocessing into statistical modeling pipelines used by statsmodels or custom estimators."],"what_it_does":"Formulaic is a Python implementation of Wilkinson formulas\u2014a domain-specific language for specifying statistical model matrices from tabular data. It takes a formula string like 'y ~ x + z' and a dataframe, then automatically handles categorical encoding, intercept inclusion, and matrix construction. The package outputs model matrices as pandas DataFrames, NumPy arrays, or SciPy sparse matrices, making it useful for feeding data into machine learning and statistical models.\n\nThe library supports input from pandas, Polars, PyArrow, and any dataframe library compatible with narwhals. It also allows encoding choices from one dataset to be reused on others, supports symbolic differentiation of formulas, and includes extensible plugins for custom input/output formats. Seven runtime dependencies (numpy, pandas, scipy, narwhals, interface-meta, typing-extensions, wrapt) keep the installation lightweight.","worth_installing":"Yes. Formulaic is actively maintained, MIT-licensed, has low install friction, and fills a clear gap as a high-performance successor to patsy for formula-based model matrix generation. It supports modern dataframe libraries (Polars, PyArrow) and is already adopted by established projects like Glum, Lifelines, and Linearmodels. No known vulnerabilities."},"id":"formulaic","links":{"html":"https://skillfed.io/packages/formulaic","md":"https://skillfed.io/packages/formulaic.md","pypi":"https://pypi.org/project/formulaic/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-02","license_spdx":"MIT","license_treatment":"permissive","name":"formulaic","python_support":"supports_current","summary":"An implementation of Wilkinson formulas."},"popularity":{"monthly_downloads":3889607,"position":2459,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.2.2"}