EazyDataFix

Agentic EDA you can inspect, reproduce, and trust.

EazyDataFix is a deterministic-first Python framework that understands datasets, plans appropriate analyses, executes them reproducibly, and generates traceable reports without requiring an LLM.

It combines dataset understanding, semantic-role detection, deterministic planning, modular execution, traceable findings, and reproducible reporting. The same package also supports data-quality assessment, validation, cleaning, preparation, and exploratory data analysis.

EazyDataFix v0.4.0 adds reproducible notebook export and explicit human approval checkpoints to the deterministic Agentic EDA workflow.

Install with pip install eazydatafix · Documentation · PyPI

Quick start

Run the complete deterministic Agentic EDA workflow:

import eazydatafix as edf

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

report = edf.export_agentic_eda_report(
    workflow,
    dataset="employees.csv",
    output_dir="eda-report",
)

print(workflow.priority_findings)
print(workflow.follow_up_actions)
print(report.generated_files)

Export the same deterministic workflow as a ready-to-run Jupyter Notebook:

notebook = edf.export_agentic_eda_notebook(
    workflow,
    dataset="employees.csv",
    output_path="agentic-eda.ipynb",
)

print(notebook.generated_files)

Notebook generation uses the Python standard library and does not require Jupyter or nbformat. DataFrame inputs produce a deterministic JSON companion file so the notebook can reload the original analytical dataset.

Require explicit human approval between planning and execution when needed:

checkpoint = edf.prepare_agentic_eda_approval("employees.csv")

# Review checkpoint.eda_result and checkpoint.eda_plan before approving.
approved_checkpoint = edf.approve_agentic_eda_plan(
    checkpoint,
    approved_step_ids=None,
    reviewer="Suneel Kumar Kola",
    notes="Approved for execution",
)

workflow = edf.resume_agentic_eda(
    "employees.csv",
    approved_checkpoint,
)

approved_step_ids=None approves every step selected by the original deterministic plan. A supplied list approves only those originally selected steps, in planner order. Changed datasets fail fingerprint validation before execution. Dependency steps must be included explicitly in subset approvals; missing dependencies fail approval and are never added automatically.

This workflow:

1. Understands the dataset
2. Assigns semantic roles
3. Plans relevant analyses
4. Executes selected analyses
5. Generates traceable findings and actions
6. Exports reproducible reports and visualisations

Installation

pip install eazydatafix

For Parquet support:

pip install "eazydatafix[parquet]"

Requires Python 3.10 or later. Tested with Python 3.10–3.13.

Why EazyDataFix

Deterministic First

Metrics, findings, and recommendations come from reproducible calculations.

Traceable Decisions

Plans, actions, questions, and visualisations identify their source analysis step.

Safe by Default

Caller DataFrames are not mutated by the deterministic EDA workflow.

AI Optional

v0.4.0 does not require an LLM. Optional grounded narratives are planned for a future release.

Workflow

flowchart LR
    A[Dataset] --> B[Understand]
    B --> C[Assign Semantic Roles]
    C --> D[Plan Analyses]
    D --> E[Execute]
    E --> F[Generate Findings and Actions]
    F --> G[Export Reports and Visualisations]

Current capabilities

Data Quality

• Missing-value analysis
• Duplicate detection
• Completeness checks
• Validity checks
• Consistency checks
• Accuracy checks
• Timeliness checks
• Data-quality scoring

Deterministic EDA

• Numeric analysis
• Categorical analysis
• Boolean analysis
• Datetime analysis
• Correlation review
• IQR outlier analysis
• Skewness analysis
• Class-imbalance analysis

Agentic Workflow

• Semantic column-role detection
• Deterministic analysis planning
• Modular analysis execution
• Priority findings
• Traceable follow-up actions
• Visualisation recommendations
• Unresolved domain questions
• Partial-failure isolation
• Human approval checkpoints between planning and execution
• Dataset fingerprint validation before approved execution

Reporting

• Console
• HTML
• PDF
• Excel
• CSV
• JSON
• Markdown
• Deterministic PNG visualisations
• Ready-to-run Jupyter Notebook export

Input Support

• pandas DataFrames
• CSV
• Excel
• JSON
• Parquet with the optional dependency

Example output

A data-quality assessment can produce a concise console summary:

EASYDATAFIX DATA QUALITY REPORT

Score         : 90.37 / 100
Grade         : A
Completeness  : 96.97%
Uniqueness    : 100.00%
Validity      : 55.00%
Consistency   : 100.00%
Accuracy      : 100.00%
Timeliness    : 100.00%

An Agentic EDA report with HTML, JSON, and optional Markdown output can produce:

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

HTML and JSON are generated by default; Markdown is generated when requested. The exact charts depend on the dataset and the workflow's deterministic visualisation recommendations.

API overview

Public API	Purpose
edf.profile(...)	Describe dataset structure, columns, types, and memory use.
edf.assess(...)	Measure data quality and return validations and recommendations.
edf.assess_ai_readiness(...)	Evaluate suitability for AI-oriented data use.
edf.eda(...)	Generate deterministic exploratory statistics and semantic roles.
edf.plan_eda(...)	Select and explain relevant follow-up analyses.
edf.execute_eda(...)	Execute selected deterministic analysis steps.
edf.run_agentic_eda(...)	Run understanding, planning, execution, and follow-up decisions.
edf.prepare_agentic_eda_approval(...)	Prepare understanding and planning without executing analysis steps.
edf.approve_agentic_eda_plan(...)	Approve all or selected originally planned steps.
edf.reject_agentic_eda_plan(...)	Explicitly reject a pending analysis plan.
edf.resume_agentic_eda(...)	Resume an approved plan after dataset fingerprint validation.
edf.export_agentic_eda_report(...)	Export Agentic EDA reports and recommended visualisations.
edf.export_agentic_eda_notebook(...)	Export a reproducible, ready-to-run Jupyter Notebook.
edf.fix(...)	Apply the existing configurable dataset-cleaning pipeline.
edf.prepare(...)	Prepare types and columns for downstream analysis.
edf.analysis_ready(...)	Clean and prepare a dataset in one workflow.

Detailed API documentation is maintained on the documentation website.

Resources

• Project Website
• Getting Started
• Documentation
• API Reference
• Roadmap
• Changelog
• PyPI
• GitHub Repository
• GitHub Issues

Project status

• Current stable version: v0.4.0
• Development status: Beta
• Python support: 3.10–3.13
• Licence: MIT

The public API may continue evolving before v1.0.

Roadmap preview

• v0.3.0 — Deterministic Agentic EDA Foundation — Released
• v0.4.0 — Notebook Export and Human Approval — Released
• v0.5.0 — Optional Grounded AI Narratives — Planned
• v1.0.0 — Stable Production API — Goal

See the full roadmap for milestone details.

Contributing

Contributions, issue reports, and focused feature proposals are welcome. Contribution guidance is being prepared; use GitHub Issues to report bugs or discuss a change for now.

Licence

EazyDataFix is available under the MIT Licence.