# rhiever/datacleaner

A Python tool that automatically cleans data sets and readies them for analysis.

Repository: https://github.com/rhiever/datacleaner
Canonical: https://ross.abutalabs.com/products/datacleaner
Language: Python
License: MIT
License Family: permissive
Topics: python, data-science, machine-learning, automation
Last push: 2019-05-22T13:53:35+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3840, "days_push": 2660, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1079, forks 205 (observed 2026-08-28T04:03:30.032467+00:00)

## What it is
A Python package and command-line tool that automatically cleans pandas DataFrames, handling missing values and encoding categorical variables for machine learning analysis. It works on data already loadable into pandas and is built on pandas and scikit-learn preprocessing.

## Use cases
- automatically clean a csv dataset before analysis
- impute missing values with mode or median per column
- encode categorical string variables as numbers
- drop rows with missing values from a dataframe
- preprocess data for machine learning pipelines
- clean datasets from the command line

## When to choose
- your data is already in a pandas-compatible format and needs quick cleaning
- you want simple automated imputation and categorical encoding
- you prefer a lightweight CLI or one-line Python call

## When to avoid
- you need to parse unstructured or messy raw text into structured data
- you need advanced feature engineering or custom cleaning logic
- you need actively maintained tooling with modern Python support

## Facets
- artifact type: library
- maturity: maintenance
- function: etl, data-science, machine-learning, cli
- domain: data-science, machine-learning
- platform: python, cli, cross-platform
- tags: data-cleaning, pandas, preprocessing, missing-values, categorical-encoding, data-engineering

## Member repositories
- rhiever/datacleaner (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:30.032467+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:53:06.201506+00:00, confidence not recorded.
  - readme: https://github.com/rhiever/datacleaner (fetched 2026-08-28T04:03:30.032467+00:00, sha 8328157efd28)
  - registry_pypi: https://pypi.org/pypi/datacleaner/json (fetched 2026-08-29T12:54:19.721099+00:00, sha beea244db4c9)
- Data as of 2026-08-30T08:39:29.467469+00:00.