{"categories":[{"label":"Build Tools","url":"https://skillfed.io/packages/category/software-development-build-tools/5"}],"enrichment":{"capability":"Iterative proportional fitting algorithm that adjusts multidimensional arrays or dataframes so their aggregates along specified dimensions match target marginals, with both fast numpy and slower pandas implementations.","skillfed_tags":["contingency-tables","statistical-adjustment"],"use_cases":["Adjust population survey data to match known demographic totals across multiple geographic and age categories.","Rescale economic input-output tables to match independently estimated row and column sums.","Balance contingency tables in social science research when marginal distributions are known but cell values are uncertain.","Calibrate multidimensional traffic or flow models to match observed aggregate counts on subsets of dimensions.","Prepare synthetic microdata by adjusting initial samples to match target distributions on multiple cross-tabulated variables."],"what_it_does":"ipfn implements the iterative proportional fitting algorithm, a mathematical technique used in economics, demography, and social sciences to adjust a multidimensional contingency table so that its marginal sums (aggregates along one or more dimensions) match known target values. The package automatically selects between a fast numpy implementation and a slower but more user-friendly pandas implementation based on input type.\n\nYou provide an original array or dataframe, a list of target marginals, and the dimensions along which each marginal should apply. The algorithm iteratively rescales the table until convergence. It exposes control over iteration limits, convergence tolerance, and verbosity, returning either the fitted result, a success flag, or detailed iteration diagnostics depending on the verbosity level.","worth_installing":"Yes, if you need iterative proportional fitting and can tolerate dormancy. The package is stable and low-friction to install, with no known vulnerabilities and a permissive MIT license. However, expect no active maintenance\u2014last release was 2021-12-30. Use it for well-understood IPF problems; do not expect bug fixes or updates if you encounter edge cases."},"id":"ipfn","links":{"html":"https://skillfed.io/packages/ipfn","md":"https://skillfed.io/packages/ipfn.md","pypi":"https://pypi.org/project/ipfn/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2021-12-30","license_spdx":null,"license_treatment":"permissive","name":"ipfn","python_support":"unspecified","summary":"Iterative Proportional Fitting with N dimensions, for python"},"popularity":{"monthly_downloads":102246,"position":12881,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.4.4"}