Aniwa

See your data clearly.

Aniwa is an open-source universal dataset profiling and intelligence tool for developers, analysts, data engineers, researchers, and modern data teams.

Aniwa helps users quickly understand datasets through:

• schema profiling
• data quality analysis
• statistical summaries
• intelligent insights
• rich terminal reports
• shareable reports
• configurable profiling workflows

Whether you're working with CSV files, Excel spreadsheets, JSON datasets, or Parquet files, Aniwa provides a fast and elegant way to inspect, understand, and trust your data.

Full documentation available here: https://reginalderzoah.github.io/Aniwa/

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Current Version

v0.1.1

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Why Aniwa?

Modern data workflows constantly involve:

unknown datasets

Before trusting a dataset, teams need to answer questions like:

• What columns exist?
• What data types are present?
• Are there missing values?
• Are there duplicates?
• Are there suspicious patterns?
• Which columns may contain IDs or sensitive information?
• Is the dataset healthy?

Aniwa makes answering those questions:

fast, intelligent, and developer-friendly

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Features

Universal Dataset Support

Aniwa currently supports:

• CSV
• Excel (.xlsx/.xls)
• JSON
• Parquet

Future releases are planned to support:

• PostgreSQL
• MySQL
• DuckDB
• BigQuery
• Snowflake

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Core Profiling

Aniwa currently provides:

Dataset Summary

• row counts
• column counts
• dataset size analysis

Schema Profiling

• type inference
• schema overview
• mixed type detection

Data Quality Analysis

• null analysis
• duplicate detection
• uniqueness analysis
• sparse column detection

Statistical Profiling

• minimum values
• maximum values
• mean
• median
• standard deviation

Intelligent Insights

• possible ID detection
• high-cardinality warnings
• sparse column warnings
• suspicious quality patterns

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Reporting

Aniwa currently supports:

• Rich terminal reports
• JSON reports
• HTML reports

Upcoming releases are planned to include:

• Markdown reports
• Excel reports
• PDF reports
• charts
• report templates

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Quick Installation

Install Aniwa from PyPI:

pip install aniwa

Verify installation:

aniwa --help

Upgrade Aniwa:

pip install --upgrade aniwa

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Quick Start

Profile a dataset:

aniwa profile customers.csv

Generate a JSON report:

aniwa profile customers.csv --report json --output profile.json

Generate an HTML report:

aniwa profile customers.csv --report html --output profile.html

Run lightweight profiling:

aniwa profile customers.csv --mode fast

Run full profiling:

aniwa profile customers.csv --mode deep

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Configuration Files

Aniwa supports configuration-driven workflows.

Supported config formats:

• YAML
• TOML
• JSON

Aniwa automatically searches for:

aniwa.yaml
aniwa.yml
aniwa.toml
aniwa.json

Example:

mode: deep

report:
  format: html
  output_dir: reports/

sections:
  include:
    - summary
    - schema
    - statistics
    - insights

Use a custom config file:

aniwa profile customers.csv --config config.yaml

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Report Sections

Aniwa supports configurable report sections.

Current sections include:

• summary
• schema
• quality
• statistics
• insights

Example:

aniwa profile customers.csv --include summary,statistics

Exclude sections:

aniwa profile customers.csv --exclude statistics

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Example Console Output

┌──────────────────────────────┐
│      Aniwa Dataset Profile   │
├──────────────────────────────┤
│ Rows: 5                      │
│ Columns: 5                   │
│ Duplicate Rows: 1            │
└──────────────────────────────┘

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Documentation

Aniwa now includes a full documentation system.

Full documentation available here: https://reginalderzoah.github.io/Aniwa/

Documentation includes:

• getting started guides
• architecture documentation
• developer guides
• release notes
• roadmap
• philosophy

View documentation locally with MkDocs:

mkdocs serve

Build documentation:

mkdocs build

Documentation structure:

docs/
├── index.md
├── roadmap.md
├── philosophy.md
├── getting-started/
├── developer-guide/
└── release-notes/

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Installation for Development

Clone the repository:

git clone https://github.com/ReginaldErzoah/Aniwa.git
cd Aniwa

Create a virtual environment:

python -m venv .venv

Activate the environment:

Windows

source .venv/Scripts/activate

macOS/Linux

source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Install Aniwa locally:

pip install -e .

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Architecture

Aniwa currently follows a modular layered architecture:

CLI
→ Configuration
→ Readers
→ Profiling Engine
→ Models
→ Reports

This architecture prioritizes:

• modularity
• maintainability
• scalability
• contributor friendliness

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Project Structure

Aniwa/
│
├── aniwa/
│   ├── cli.py
│   ├── config/
│   ├── core/
│   ├── io/
│   ├── models/
│   ├── reports/
│   ├── templates/
│   └── utils/
│
├── docs/
├── tests/
├── examples/
│
├── README.md
├── CONTRIBUTING.md
├── SPRINT.md
├── mkdocs.yml
├── pyproject.toml
└── requirements.txt

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Roadmap

v0.1.x - Foundation

• universal dataset profiling
• reporting systems
• configuration workflows
• modular architecture
• developer-first UX

v0.2.x - Better User Experience

Planned features:

• Better HTML with charts.
• Better report templates.
• Report modes and presets.
• Better output management.
• Metadata
• Incldue & exclude sections.
• Config file supports (yml, json & toml).
• Improved documentation.

v0.3.x - Intelligence

Planned features:

• correlation analysis
• anomaly detection
• semantic profiling
• improved insights

v0.4.x - Universal Connectivity

Planned features:

• PostgreSQL support
• MySQL support
• DuckDB support
• BigQuery support
• profiling history
• snapshot management

v0.5.x - Extensibility

Planned features:

• plugin system
• custom profiling modules
• community extensions

v0.6.x - AI Intelligence

Planned features:

• dataset summarization
• semantic understanding
• AI-assisted recommendations
• anomaly explanations

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Philosophy

Aniwa is built around several core principles:

• universal
• developer-first
• fast
• modular
• intelligent
• beautiful
• automation-friendly

The long-term goal is to build:

universal data intelligence infrastructure

For deeper architectural and ecosystem thinking, see:

docs/philosophy.md
docs/roadmap.md

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Contributing

Contributions are welcome.

See:

• CONTRIBUTING.md
• SPRINT.md
• docs/developer-guide/

for:

• local development
• testing workflows
• architecture guidance
• release workflows
• contributor standards

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Support

If you find this project useful:

• star the repository
• Share repo with friends
• Contribute to making this project better solving open issues
• Recommendations can also be sent to maintainer

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License

Aniwa is released under the MIT License.

See LICENSE for details.