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 v0.5.0 adds optional evidence-cited AI narratives to completed deterministic Agentic EDA workflows. Deterministic analysis remains the authoritative source of metrics and continues to work without an LLM.

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

The deterministic workflow does not require an LLM. The v0.5.0 release adds optional grounded narratives through a provider adapter; existing workflows continue to run without an API key or AI dependency.

Optional grounded AI narrative

Create a business-facing narrative only after deterministic analysis is complete. The provider receives an immutable, compact evidence brief, not the raw dataset. Every generated statement must cite one or more evidence IDs from that brief. EazyDataFix rejects malformed or unknown citations, invented numbers, unsupported causal language, and claims without sufficient lexical support in their cited evidence. The narrative is bound to the exact workflow by a SHA-256 fingerprint, so it cannot be exported with a different or modified workflow.

These deterministic checks reduce unsupported output but cannot prove the semantic truth of AI-written text. Review the narrative before using it for a decision. HTML and Markdown reports include an evidence-reference section for that review.

import eazydatafix as edf
from eazydatafix.narratives import OpenAINarrativeProvider

workflow = edf.run_agentic_eda("employees.csv")
provider = OpenAINarrativeProvider(model="your-openai-model")

narrative = edf.generate_agentic_eda_narrative(workflow, provider)

report = edf.export_agentic_eda_report(
    workflow,
    output_dir="eda-report",
    formats=["html", "json", "markdown"],
    narrative=narrative,
)

Install the adapter only when needed:

pip install "eazydatafix[openai]"

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.generate_agentic_eda_narrative(...)	Generate a cited optional AI narrative from deterministic workflow evidence.
edf.fix(...)	Apply controlled, configurable cleaning with optional dry-run audit records.
edf.run(...)	Run profile → assess → fix → EDA as one deterministic workflow.
edf.prepare_with_report(...)	Prepare data with deterministic change and readiness details.
edf.infer_schema(...) / edf.validate_contract(...)	Infer and enforce pipeline data contracts.
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: v0.5.0
• Development release candidate: v1.0.0
• 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 — Released
• v0.6.0 — Controlled, Auditable Cleaning — Planned
• v0.7.0 — Data Preparation and Feature Readiness — Planned
• v0.8.0 — Data Validation and Contracts — Planned
• v0.9.0 — Production Workflow — Planned
• v1.0.0 — Stable Production API — Release candidate

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.