🗄️ MS Access Data Processor

Automated MS Access Data Processing with Intelligent ID Mapping

Professional Python tool for bulk processing MS Access database files, creating correspondence tables, and generating complex identifiers according to specified rules.

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📋 Table of Contents

• Features
• Quick Start
• Installation
• Usage
• Project Structure
• How It Works
• Examples
• Technologies
• Author

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✨ Features

✅ Bulk Processing - Simultaneous processing of multiple Access files
✅ Intelligent Mapping - Automatic ID matching using correspondence tables
✅ ID Generation - Creating complex identifiers according to rules
✅ Duplicate Detection - Excluding duplicate records
✅ Detailed Logging - Tracking every processing step
✅ Python 3.6.8+ Compatibility - Works on older Python versions
✅ Clean Code - Fully documented and readable code

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🚀 Quick Start

1. Clone the repository

git clone https://github.com/palagina00/ms-access-data-processor.git
cd ms-access-data-processor

2. Install dependencies

pip install -r requirements.txt

3. Generate test data

python tests/generate_test_data.py

4. Run processing

python src/access_processor.py

5. Check results

cat data/output/result.csv

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📦 Installation

System Requirements

• Python: 3.6.8 or higher
• OS: Windows, Linux, macOS
• MS Access Driver: for working with .mdb files (optional)

Step-by-Step Installation

Windows:

# 1. Install Python 3.6.8+
# Download from https://www.python.org/downloads/

# 2. Create virtual environment
python -m venv venv
venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Install MS Access Driver (if needed)
# Download: https://www.microsoft.com/en-us/download/details.aspx?id=13255

Linux/macOS:

# 1. Create virtual environment
python3 -m venv venv
source venv/bin/activate

# 2. Install dependencies
pip install -r requirements.txt

Detailed instructions: INSTALLATION.md

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💻 Usage

Basic Usage

from src.access_processor import AccessDataProcessor

# Create processor
processor = AccessDataProcessor(
    input_dir='data/input',
    correspondence_file='data/correspondence.csv',
    codes_file='data/filename_codes.csv'
)

# Process all files
processor.process_all_files('data/output/result.csv')

Command Line Usage

python src/access_processor.py

Data Examples

Input file (input/18%Ese21.csv):

RecordID;ID;SomeData
1;8d 7d 2c_Ah9h;Data_1
2;3f 2a 1b_Xk5l;Data_2

Correspondence table (correspondence.csv):

id;ID2
8d 7d 2c_Ah9h;8d 7d 2c_P000
3f 2a 1b_Xk5l;3f 2a 1b_P000

Filename codes (filename_codes.csv):

filename;code
18%Ese21.csv;AF21

Result (output/result.csv):

ID3;ID4
AF21_8d 7d 2c_P000;AF21_8d 7d 2c_Ah9h
AF21_3f 2a 1b_P000;AF21_3f 2a 1b_Xk5l

More examples: USAGE.md

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📁 Project Structure

ms-access-data-processor/
│
├── data/                       # Data files
│   ├── input/                  # Input files
│   ├── output/                 # Output files
│   ├── correspondence.csv      # ID → ID2 mapping table
│   └── filename_codes.csv      # Filename → Code mapping
│
├── src/                        # Source code
│   ├── __init__.py
│   └── access_processor.py     # Main processor
│
├── tests/                      # Tests and utilities
│   └── generate_test_data.py   # Test data generator
│
├── docs/                       # Documentation
│   ├── INSTALLATION.md         # Installation guide
│   └── USAGE.md                # User guide
│
├── requirements.txt            # Python dependencies
├── .gitignore                  # Git ignore files
├── LICENSE                     # MIT License
└── README.md                   # This file

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🔧 How It Works

Processing Algorithm:

Step 1: Create output CSV file
Step 2: Read all input files
Step 3: For each ID → find corresponding ID2
Step 4: Generate ID3 = CODE + "_" + ID2
Step 5: Check for ID3 duplicates
Step 6: Generate ID4 = ID3[:14] + original_ID[-4:]
Step 7: Write to output file

Transformation Example:

Input:
  Filename: 18%Ese21.csv
  ID: "8d 7d 2c_Ah9h"

Processing:
  1. Code = "AF21" (from filename_codes.csv)
  2. ID2 = "8d 7d 2c_P000" (from correspondence.csv)
  3. ID3 = "AF21_8d 7d 2c_P000"
  4. ID4 = "AF21_8d 7d 2c_" + "Ah9h" = "AF21_8d 7d 2c_Ah9h"

Output:
  ID3;ID4
  AF21_8d 7d 2c_P000;AF21_8d 7d 2c_Ah9h

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🛠️ Technologies

• Python 3.6.8+ - Main programming language
• pyodbc - Working with MS Access databases
• CSV - CSV file processing
• Logging - Detailed process logging
• Pathlib - Modern path handling

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📊 Performance

• ✅ Speed: ~1000 records/sec
• ✅ Memory: Minimal usage (stream processing)
• ✅ Scalability: Support for files of any size
• ✅ Reliability: Complete error handling

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🎯 Use Cases

Perfect for:

• ✅ Data migration between systems
• ✅ Creating lookup tables
• ✅ Generating unique identifiers
• ✅ Bulk database processing
• ✅ ETL processes (Extract, Transform, Load)

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📈 Roadmap

• Basic CSV file processing
• Intelligent ID mapping
• Process logging
• Test data generator
• Real .mdb file support via pyodbc
• GUI interface
• Excel export with formatting
• Parallel file processing

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🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss.

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📄 License

This project is licensed under MIT License.

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👤 Author

Palagina Ekaterina

• 📧 Email: palagina00@gmail.com
• 🐙 GitHub: @palagina00
• 💼 Portfolio: github.com/palagina00

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🌟 Support

If this project was helpful, please give it a ⭐ on GitHub!

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📞 Contact & Support

Have questions or suggestions?

• 📧 Email: palagina00@gmail.com
• 🐛 Report Bug: Issues
• 💡 Request Feature: Issues

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Made with ❤️ by Palagina Ekaterina

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