🛠️ EasyDataFix

A modern Python library for data quality assessment, validation, and automated data cleaning.

EasyDataFix helps data analysts, data scientists, machine learning engineers, and ETL developers quickly identify data quality issues and generate professional reports with just a few lines of code.

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✨ Features

• 📊 Data Quality Assessment
• ✅ Missing Value Detection
• 🔍 Duplicate Detection
• ✔️ Data Validation
• 🧹 Data Consistency Checks
• 🎯 Data Accuracy Checks
• ⏱️ Timeliness Checks
• 💡 Intelligent Recommendations
• 📄 Console Report
• 🌐 HTML Report
• 📑 PDF Report
• 📈 Excel Report
• 📋 CSV Report
• 📦 JSON Report
• 📝 Markdown Report

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Installation

pip install easydatafix

For Parquet support:

pip install easydatafix[parquet]

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Quick Start

import easydatafix as edf

report = edf.assess("employees.csv")

report.summary()

report.to_html()

report.to_pdf()

report.to_excel()

report.to_json()

report.to_csv()

report.to_markdown()

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Example Console Output

======================================================================
                       🛠️ EASY DATA FIX REPORT
======================================================================

Overall Score : 90.37

Grade         : A

Completeness  : 96.97%

Uniqueness    : 100.00%

Validity      : 55.00%

Consistency   : 100.00%

Accuracy      : 100.00%

Timeliness    : 100.00%

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Supported Quality Dimensions

Dimension	Status
Completeness	✅
Uniqueness	✅
Validity	✅
Consistency	✅
Accuracy	✅
Timeliness	✅

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Report Formats

EasyDataFix can generate reports in multiple formats.

report.summary()

report.to_html()

report.to_pdf()

report.to_excel()

report.to_json()

report.to_csv()

report.to_markdown()

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Supported Data Sources

EasyDataFix accepts datasets in a variety of formats. edf.assess(...) and edf.fix(...) both work with:

• Pandas DataFrame
• CSV files
• Excel files (.xlsx / .xls)
• JSON files
• Parquet files

Loading is handled by the modular easydatafix.datasources package, which lets you plug in your own formats:

import pandas as pd

from easydatafix.datasources import (
    DataSource,
    DatasetLoader,
    default_registry,
)


class TSVDataSource(DataSource):

    name = "tsv"

    def can_load(self, source) -> bool:
        from pathlib import Path
        return isinstance(source, Path) and source.suffix.lower() == ".tsv"

    def load(self, source) -> pd.DataFrame:
        return pd.read_csv(source, sep="\t")


default_registry.register(TSVDataSource())

# Now edf.assess(...) and edf.fix(...) accept .tsv files too.

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Why EasyDataFix?

EasyDataFix is designed to make data quality assessment simple and accessible.

Whether you're validating datasets before machine learning, preparing ETL pipelines, or cleaning business reports, EasyDataFix provides a consistent way to measure and improve data quality.

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Roadmap

Version 0.1

• Assessment Engine
• Validation Engine
• Recommendation Engine
• Reporting Engine
• Auto Fix Foundation

Version 0.2

• Data Profiling
• CLI Support
• Streamlit Dashboard
• SQL Support
• Apache Spark Support
• Interactive Charts
• AI Recommendations

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Contributing

Contributions are welcome.

Feel free to fork the repository, open issues, or submit pull requests.

Repository:

https://github.com/suneelprojects/easydatafix

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License

MIT License

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Made with ❤️ by Suneel Kumar Kola