🛠️ EazyDataFix

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

EazyDataFix 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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🌐 Documentation

📚 Documentation Website

https://eazydatafix.com

📖 API Reference

https://eazydatafix.com/docs

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🚀 Quick Links

• 📦 PyPI — https://pypi.org/project/eazydatafix/
• 🌍 Documentation — https://eazydatafix.com
• 📖 API Reference — https://eazydatafix.com/docs
• 💻 GitHub — https://github.com/suneelprojects/eazydatafix

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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
• 🧭 Deterministic EDA Planning and Execution
• 🤖 Deterministic Agentic EDA Orchestration
• 📊 Reproducible Agentic EDA Reports and PNG Visualisations

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Installation

EazyDataFix supports Python 3.10–3.13.

pip install eazydatafix

For Parquet support:

pip install eazydatafix[parquet]

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Core APIs

import eazydatafix as edf

edf.profile(...)

edf.assess(...)

edf.assess_ai_readiness(...)

edf.eda(...)

edf.plan_eda(...)

edf.execute_eda(...)

edf.run_agentic_eda(...)

edf.export_agentic_eda_report(...)

edf.fix(...)

edf.prepare(...)

edf.analysis_ready(...)

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Quick Start

import eazydatafix 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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Deterministic EDA

Generate a structured exploratory data analysis result without using an LLM.

import eazydatafix as edf

eda_result = edf.eda("employees.csv")

print(eda_result.shape)
print(eda_result.semantic_roles)
print(eda_result.identifier_columns)
print(eda_result.datetime_columns)
print(eda_result.numeric_statistics)
print(eda_result.categorical_summaries)
print(eda_result.observations)
print(eda_result.recommendations)

edf.eda(...) accepts pandas DataFrames, CSV, Excel, JSON, and Parquet files through the existing EazyDataFix datasource system.

EDA deterministically classifies columns as numeric measures, categorical dimensions, identifiers, datetimes, or booleans. Identifier, datetime, and boolean columns are excluded from numeric statistics and correlations.

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Deterministic EDA Planner

Build a reproducible follow-up analysis plan from an existing EDAResult.

import eazydatafix as edf

eda_result = edf.eda("employees.csv")
plan = edf.plan_eda(eda_result)

for step in plan.selected_steps:
    print(step.name, step.priority, step.reason)

for step in plan.skipped_steps:
    print(step.name, step.reason)

print(plan.warnings)
print(plan.deterministic_summary)

The planner uses semantic roles and statistics from EDAResult to explain why each supported analysis is selected or skipped. It does not call an LLM.

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Deterministic EDA Executor

Execute selected plan steps through deterministic analysis handlers.

import eazydatafix as edf

execution = edf.execute_eda("employees.csv")

for step in execution.executed_steps:
    print(step.name, step.status, step.output)

print(execution.execution_order)
print(execution.warnings)
print(execution.deterministic_summary)

edf.execute_eda(...) automatically creates the EDAResult and EDAPlan when they are not supplied. Existing results and plans can be reused explicitly:

eda_result = edf.eda("employees.csv")
plan = edf.plan_eda(eda_result)
execution = edf.execute_eda(
    "employees.csv",
    result=eda_result,
    plan=plan,
)

Execution results can be converted to a JSON-ready dictionary with execution.to_dict(). Selected steps record success or failure; planned skips remain visible with skipped status.

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Deterministic Agentic EDA

Run dataset understanding, planning, execution, and traceable follow-up decision generation as one reproducible workflow.

import json

import eazydatafix as edf

config = edf.AgenticEDAConfig(
    correlation_threshold=0.85,
    outlier_iqr_multiplier=1.5,
    class_imbalance_threshold=0.80,
)
workflow = edf.run_agentic_eda("employees.csv", config=config)

print(workflow.overall_status)
print(workflow.priority_findings)
print(workflow.follow_up_actions)
print(workflow.recommended_visualisations)
print(workflow.unresolved_questions)

json_output = json.dumps(workflow.to_dict(), indent=2)

Every action, visualisation, question, and finding identifies its source execution step, target columns, priority, reason, and prerequisites. The orchestrator is deterministic, does not mutate DataFrames, and does not use an LLM. Visualisation recommendations and unresolved questions can be disabled, and recommendation counts can be bounded with AgenticEDAConfig.

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Agentic EDA Reports

Convert an existing AgenticEDAResult into reproducible HTML and JSON report artifacts. Markdown is available as an optional format.

import eazydatafix as edf

workflow = edf.run_agentic_eda("employees.csv")

report = edf.export_agentic_eda_report(
    workflow,
    dataset="employees.csv",  # Optional: enables honest raw-data charts.
    output_dir="eda-report",
    formats=["html", "json", "markdown"],
)

print(report.generated_files)
print(report.generated_visualisations)
print(report.skipped_visualisations)
print(report.status)

Without dataset, charts supported by structured execution outputs—such as missing values, categorical distributions, correlations, and datetime frequencies—are still generated. Histograms and box plots are explicitly recorded as skipped unless a matching dataset is supplied. The dataset is validated against the workflow and copied; the workflow and caller DataFrame are never mutated.

Example output:

eda-report/
├── agentic-eda-report.html
├── agentic-eda-report.json
├── agentic-eda-report.md
└── visualisations/
    ├── 01-missing-value-chart-phone-salary.png
    ├── 02-bar-chart-department.png
    └── 03-time-series-line-chart-joining-date.png

Report filenames, section order, chart filenames, JSON key ordering, and artifact tracking are deterministic. Existing known artifact files are overwritten predictably; unrelated files in the output directory are preserved.

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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

EazyDataFix 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

EazyDataFix accepts datasets in a variety of formats.

Both edf.assess(...) and edf.fix(...) work with:

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

Loading is handled by the modular eazydatafix.datasources package, allowing custom data source plugins.

import pandas as pd

from eazydatafix.datasources import (
    DataSource,
    default_registry,
)


class TSVDataSource(DataSource):

    name = "tsv"

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

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


default_registry.register(TSVDataSource())

# edf.assess(...) and edf.fix(...) now support TSV files.

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Why EazyDataFix?

EazyDataFix 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, EazyDataFix provides a consistent way to measure and improve data quality with minimal code.

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Roadmap

✅ Completed

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

🚀 Coming Soon

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

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Contributing

Contributions are always welcome.

Feel free to:

• ⭐ Star the repository
• 🐛 Report bugs
• 💡 Suggest features
• 🔧 Submit pull requests

GitHub Repository:

https://github.com/suneelprojects/eazydatafix

Documentation:

https://eazydatafix.com

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License

MIT License

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Made with ❤️ by Suneel Kumar Kola

🌐 https://eazydatafix.com