Grebes

🕵️‍♂️ Grebes — A lightweight, nature-inspired data quality auditor for structured datasets.

🚀 Features

• Fast, zero-config audit of CSV, Excel (.xls/.xlsx), JSON and JSON-Lines files
• Rich CLI output with colored panels, sparklines, and warnings (powered by Rich)
• Key data quality checks:
  • Missing value counts & percentages
  • Unique-value ratio & (optional) samples
  • Numeric statistics (mean, std, min, max) & IQR-based outlier counts
  • Inline histograms (sparklines) for numeric distributions
  • Date-range for datetime columns
  • Top-N frequencies for low-cardinality text/categorical columns
  • Mixed-type detection & duplicate-row warnings
• Two modes:
  • CLI: grebes data.csv → instant terminal report
  • Python API: import GrebesAuditor into notebooks or scripts

💾 Installation

# From PyPI (when published)
pip install grebes

# Or install your local copy in editable mode for development
git clone https://github.com/yourusername/grebes.git
cd grebes
pip install -e .

Requires Python ≥ 3.7 and the following packages: pandas, numpy, openpyxl (for Excel), and rich.

⚡ CLI Usage

# Basic audit of a CSV file
grebes data.csv

# Audit an Excel sheet
grebes report.xlsx

# Audit a JSON-Lines file
grebes records.jsonl

# Show help / available options
grebes --help

Sample Output

╭────────────────────────────── 🧠 GREBES DIAGNOSTIC REPORT ──────────────────────────────╮
│ Rows: 1,000   Cols: 5   Mem: 180.21 KB                                                  │
╰─────────────────────────────────────────────────────────────────────────────────────────╯

╭──── id ─────────────────────────────────────────────────────────────────────────╮
│ Type    int64                                                                   │
│ Missing 0 (0.0%)                                                                │
│ Unique  1000                                                                    │
│ Stats   μ=500.5,σ=288.8,min=1.0,max=1000.0,out=0                                │
│ Dist    █▁▂▃▄▅▆▇█
╰─────────────────────────────────────────────────────────────────────────────────╯

╭─── amount ───────────────────────────────────────────────────────────────────────╮
│ Type    float64                                                                  │
│ Missing 0 (0.0%)                                                                 │
│ Stats   μ=495.4,σ=289.2,min=14.6,max=999.7,out=0                                 │
│ Dist    ▁▃▄▅▇▆▇▅                                                               │
╰──────────────────────────────────────────────────────────────────────────────────╯

… and so on for each column …

📦 Python API

import pandas as pd
from grebes.auditor import GrebesAuditor

df = pd.read_csv("data.csv")
auditor = GrebesAuditor(df)
auditor.print_report()

📝 How It Works

1. Reads your file (CSV, Excel, JSON(.l)) into a pandas.DataFrame.

2. Computes column-wise metrics:

   • Missing values
   • Unique ratio (and optional sample values for low-cardinality columns)
   • Descriptive stats & outlier count for numerics
   • Date ranges for datetimes
   • Top frequencies for text/categorical

3. Renders an interactive, colorized report with:

   • Panels per column
   • Sparklines for quick distribution glance
   • Warnings for mixed-type columns & duplicates

4. Zero external calls — all local, so safe on private data.

🤝 Contributing

1. Fork the repo
2. Create a feature branch: git checkout -b feat/my-awesome-feature
3. Commit your changes: git commit -m "Add feature X"
4. Push to your branch: git push origin feat/my-awesome-feature
5. Open a Pull Request

Please follow the existing code style and add tests for new functionality.

📜 License

MIT License © Your Name See LICENSE for details.

Built with 💙 and inspired by nature’s grace—light as air, sharp as a grebe’s dive.