v0.1.0 · initial release

EazyDataFix

Open-source Python library for data quality assessment and automated data cleaning. Three functions, zero configuration, and a report you can trust.

Get Started GitHub

MIT License·Python 3.9+·Zero required dependencies beyond pandas

main.py

python

Colab GitHub

1import eazydatafix as edf23report = edf.assess("employees.csv")4report.summary()56result = edf.fix("employees.csv")7result.applied_fixes8result.to_csv("clean.csv")

Why EazyDataFix?

Data scientists spend a significant share of every project cleaning data before any analysis can begin. The work is repetitive — check for nulls, drop duplicates, coerce obvious dtypes, run a distribution sanity check — but it is different enough on every dataset that ad-hoc scripts pile up.

EazyDataFix collapses this loop into three explicit calls: edf.assess(), edf.fix() and edf.profile(). Each returns a structured, serialisable object so the entire cleaning pipeline stays auditable — even in notebooks.

Installation

Requires Python 3.9+. Available on PyPI.

terminal

bash

pip install eazydatafix

Quick Start

Assess a dataset, run the automatic fixer, and export the cleaned copy.

quickstart.py

python

Colab GitHub

import pandas as pd
import eazydatafix as edf

df = pd.read_csv("employees.csv")

report = edf.assess(df)
report.summary()

result = edf.fix(df)
cleaned_df = result.dataframe
result.to_csv("clean.csv")

Python 3.11— Expected output

>>> report = edf.assess(df)
>>> report.summary()
QualityReport(<DataFrame>)
  rows              1,204
  columns              12
  quality_score     94.0
  missing_values      38  (0.3%)
  duplicates           6  (0.5%)
 
>>> result = edf.fix(df)
>>> result.to_csv("clean.csv")
wrote clean.csv (1,198 rows × 12 cols)