Script Meta-Fields Extractor

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Table of Contents

• Description
• Getting Started
• Prerequisites
• Installation
• Usage
• Project Structure
• Contributing
• License
• Acknowledgements

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Description

The Script Meta-Fields Extractor is a Python-based tool designed to extract metadata (field names, data types, and example values) from a variety of file formats, including:

• CSV
• Excel (.xls, .xlsx)
• JSON
• XML
• Parquet
• QVD (with the version 1.1)

The tool processes data files located in the inputs directory and generates metadata reports saved in the outputs directory.

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Getting Started

Prerequisites

Ensure you have the following installed:

1. Python 3.6+
2. pip (Python package manager)

Installation

1. Clone this repository:

   git clone <repository_url>
   cd script-meta-fields-extractor
   

2. Install the required Python libraries:

   pip install -r requirements.txt
   

3. Set up the folder structure:

   • Ensure the inputs folder exists and place your data files inside it.
   • The script will automatically create the outputs folder if it doesn't exist.

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Usage

1. Place your data files (e.g., sample.csv, data.json) in the inputs folder.
2. Run the script:

   python data_info_extractor.py
   

3. Follow the prompts to select a file for analysis.
4. The tool will display the metadata (field names, types, examples) in the terminal and save the output to the outputs directory.

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Project Structure

SCRIPT-META-FIELDS-EXTRACTOR/
├── inputs/                 # Input folder containing data files (CSV, JSON, etc.)
│   ├── sample.csv
│   ├── sample.json
│   ├── sample.parquet
│   ├── sample.xls
│   └── sample.xml
├── outputs/                # Output folder for processed metadata reports
│   ├── .gitignore          # Ignores unnecessary files
├── data_info_extractor.py  # Main Python script for metadata extraction
├── LICENSE                 # License information
├── README.md               # Project documentation
├── requirements.txt        # Python dependencies

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Contributing

We welcome contributions to improve this project! To contribute:

1. Fork the repository.
2. Create a new branch (feature/your-feature-name).
3. Commit your changes with clear and concise messages.
4. Push to your branch and open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Acknowledgements

• OpenAI's o1 for assisting with the refactoring and creating this README.