{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Percentify provides one-call exploratory statistics and data-quality diagnostics for pandas and Polars DataFrames, ranking issues worst-first with suggested fixes.","skillfed_tags":["data-quality","exploratory-analysis","pandas-polars"],"use_cases":["Run `profiler()` before modeling to catch data issues and get a 0-100 health score for a CI data-quality gate.","Use `missing()` to quickly see which columns have gaps and how much data is lost per column.","Call `vif()` to detect multicollinearity and identify which features to drop before regression.","Apply `outliers()` to find the percentage of outliers in each column and decide on treatment.","Use `correlate()` to find feature pairs that move together and test whether the relationship is statistically significant.","Call `imbalance()` on a target column to measure class skew and decide on resampling or weighting strategies."],"what_it_does":"Percentify is a data diagnostics library that wraps common exploratory and quality-check operations into single-call functions for pandas and Polars DataFrames. Its flagship function, `profiler()`, scans a DataFrame for data issues\u2014missing values, outliers, collinearity, class imbalance, skew\u2014ranks them by severity, and suggests fixes. The library also provides functions for variance analysis (cv, pca_variance, pca_loadings), statistical testing (permutation_test, bootstrap_ci, effect_size), correlation detection, and formatting utilities.\n\nThe package is designed around a specific philosophy: each function returns the single most common answer in one call, with clear output sorted worst-first, and points users to underlying libraries (pandas, scipy, statsmodels, scikit-learn) when deeper customization is needed. It treats pandas and Polars as first-class backends\u2014pass either type and get the same type back without flags or manual conversion. Runtime dependencies are numpy, pandas, and scipy.","worth_installing":"Yes. Percentify fills a real gap: it automates the 80% of exploratory checks you run on every dataset into one or two function calls, with low install friction, active maintenance, permissive licensing, and genuine dual support for pandas and Polars. No known vulnerabilities. Best for teams doing frequent data intake and quality validation."},"id":"percentify","links":{"html":"https://skillfed.io/packages/percentify","md":"https://skillfed.io/packages/percentify.md","pypi":"https://pypi.org/project/percentify/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-13","license_spdx":null,"license_treatment":"permissive","name":"percentify","python_support":"supports_current","summary":"Data Exploratory stats and Quality diagnostics for pandas and Polars DataFrames. One easy call each."},"popularity":{"monthly_downloads":190981,"position":9896,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.2"}