percentify
Data Exploratory stats and Quality diagnostics for pandas and Polars DataFrames. One easy call each.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and pandas 2.0+.
- Low install friction with a pure-Python wheel and three well-established runtime dependencies (numpy, pandas, scipy).
- Active maintenance with a release 32 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive; you can use this in commercial and proprietary projects with minimal restrictions, provided you include a copy of the license.
last release 2026-07-13 (32 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 190,981 downloads/mo, #9,896 on PyPI
Alternatives
Verify before relying
pip install percentify
import pandas as pd
from percentify import profiler
df = pd.DataFrame({"salary": [50000, None, 60000], "age": [25, 30, None]})
report = profiler(df)
print(report.to_frame()) # ranked findings with fixes
print(report.health) # 0-100 data-health score- Whether the package's Polars support is truly feature-parity with pandas across all functions.
- Performance characteristics on large datasets (memory usage, runtime scaling).
- Availability and completeness of the documentation at data-centt.github.io/percentify.
What it is and 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—missing values, outliers, collinearity, class imbalance, skew—ranks 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.
The 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—pass either type and get the same type back without flags or manual conversion. Runtime dependencies are numpy, pandas, and scipy.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
percentify on PyPI
Before you install
Low install friction with a pure-Python wheel and three well-established runtime dependencies (numpy, pandas, scipy). Active maintenance with a release 32 days ago.
Requires Python 3.10 or later and pandas 2.0+.
License in practice
Apache-2.0 is permissive; you can use this in commercial and proprietary projects with minimal restrictions, provided you include a copy of the license.
Quickstart
pip install percentify
import pandas as pd
from percentify import profiler
df = pd.DataFrame({"salary": [50000, None, 60000], "age": [25, 30, None]})
report = profiler(df)
print(report.to_frame()) # ranked findings with fixes
print(report.health) # 0-100 data-health scoreVerify before relying
- Whether the package's Polars support is truly feature-parity with pandas across all functions.
- Performance characteristics on large datasets (memory usage, runtime scaling).
- Availability and completeness of the documentation at data-centt.github.io/percentify.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpypandasscipy |
| Maintenance | Actively maintained 32 days since the last release |
| First released | |
| Downloads | 190,981 / month, #9,896 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: percentify-1.0.2-py3-none-any.whl
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