Product Requirements Document (PRD)

Location Converter: Excel to CSV to PostgreSQL Pipeline

Project Overview

Project Name: location-converter-xlsx-csv-pgtable
Version: 1.0
Date: December 2024
Status: Development Phase

1. Executive Summary

This project aims to create a robust data pipeline that processes farmer location data from Excel files, performs data cleaning and analysis, converts the data to CSV format, and finally loads it into a PostgreSQL database table called bellary_farmers.

2. Project Objectives

Primary Goals

• Extract and analyze data from multi-sheet Excel files (Bellary.xlsx)
• Clean and standardize farmer location data
• Convert processed data to CSV format
• Load data into PostgreSQL database for further analysis
• Provide data insights through Jupyter notebook analysis

Success Metrics

• Successfully process all 5 sheets from the Excel file
• Achieve 100% data integrity during conversion
• Complete data loading into PostgreSQL without errors
• Generate comprehensive data analysis report

3. Data Schema Definition

Based on the provided parameters, the following data structure will be implemented:

Source Data Fields

Field Name	Data Type	Description	Constraints
S.no	Integer	Serial number/Record ID	Primary identifier
Status	String	Current status of farmer record	Enum values
Farmer Code	String	Unique farmer identifier	Unique, Not Null
Farmer Name	String	Full name of the farmer	Not Null
Govt id num	String	Government issued ID number	Unique
Primary Contact	String	Phone/contact information	Format validation
Farmer Created Date	DateTime	Date when farmer record was created	ISO format
Plot Created Date	DateTime	Date when plot was registered	ISO format
Sync Date	DateTime	Last synchronization date	ISO format
Updation Date	DateTime	Last update timestamp	ISO format
Area	Float	Plot area measurement	Positive value
GPS Area	Float	GPS-calculated area	Positive value
Village	String	Village name	Not Null

Target PostgreSQL Schema

CREATE TABLE bellary_farmers (
    id SERIAL PRIMARY KEY,
    serial_number INTEGER,
    status VARCHAR(50),
    farmer_code VARCHAR(100) UNIQUE NOT NULL,
    farmer_name VARCHAR(255) NOT NULL,
    govt_id_number VARCHAR(50) UNIQUE,
    primary_contact VARCHAR(20),
    farmer_created_date TIMESTAMP,
    plot_created_date TIMESTAMP,
    sync_date TIMESTAMP,
    updation_date TIMESTAMP,
    area DECIMAL(10,4),
    gps_area DECIMAL(10,4),
    village VARCHAR(100) NOT NULL,
    source_sheet VARCHAR(50),
    imported_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

4. Technical Requirements

4.1 Environment Setup

• Python Version: 3.11+
• Package Manager: uv
• Virtual Environment: .venv

4.2 Core Dependencies

jupyterlab>=4.0.0
pandas>=2.0.0
numpy>=1.24.0
openpyxl>=3.1.0
psycopg2-binary>=2.9.0
sqlalchemy>=2.0.0
python-dotenv>=1.0.0

4.3 Development Dependencies

pytest>=7.0.0
black>=23.0.0
flake8>=6.0.0
pre-commit>=3.0.0

5. Functional Requirements

5.1 Data Extraction

• FR-001: System shall read Excel files with multiple sheets
• FR-002: System shall identify and process exactly 5 sheets from Bellary.xlsx
• FR-003: System shall handle different column naming conventions across sheets
• FR-004: System shall preserve data integrity during extraction

5.2 Data Analysis & Cleaning

• FR-005: System shall provide comprehensive data profiling in Jupyter notebook
• FR-006: System shall identify and report data quality issues
• FR-007: System shall standardize column names across all sheets
• FR-008: System shall validate data types and formats
• FR-009: System shall handle missing values appropriately
• FR-010: System shall detect and report duplicate records

5.3 Data Transformation

• FR-011: System shall merge data from all 5 sheets into a single dataset
• FR-012: System shall apply consistent data formatting rules
• FR-013: System shall add metadata columns (source_sheet, imported_at)
• FR-014: System shall export cleaned data to CSV format

5.4 Database Integration

• FR-015: System shall connect to PostgreSQL database
• FR-016: System shall create bellary_farmers table if not exists
• FR-017: System shall load CSV data into PostgreSQL table
• FR-018: System shall handle database connection errors gracefully
• FR-019: System shall provide data loading progress feedback

6. Non-Functional Requirements

6.1 Performance

• NFR-001: Data processing shall complete within 5 minutes for files up to 10MB
• NFR-002: Database loading shall handle up to 100,000 records efficiently
• NFR-003: Memory usage shall not exceed 2GB during processing

6.2 Reliability

• NFR-004: System shall have 99% success rate for data conversion
• NFR-005: System shall provide detailed error logging
• NFR-006: System shall support transaction rollback on failures

6.3 Usability

• NFR-007: System shall provide clear progress indicators
• NFR-008: System shall generate comprehensive data quality reports
• NFR-009: System shall include detailed documentation and examples

7. Project Structure

location-converter-xlsx-csv-pgtable/
├── .venv/                          # Virtual environment
├── .cursor/                        # Cursor IDE configuration
├── data/
│   ├── raw/                        # Original Excel files
│   ├── processed/                  # Cleaned CSV files
│   └── reports/                    # Data quality reports
├── notebooks/
│   ├── 01_data_exploration.ipynb   # Initial data analysis
│   ├── 02_data_cleaning.ipynb      # Data cleaning process
│   └── 03_data_validation.ipynb    # Final validation
├── src/
│   ├── __init__.py
│   ├── data_extractor.py           # Excel reading functionality
│   ├── data_cleaner.py             # Data cleaning utilities
│   ├── data_validator.py           # Data validation rules
│   └── db_loader.py                # Database operations
├── scripts/
│   ├── setup_environment.py        # Environment setup
│   ├── run_pipeline.py             # Main pipeline execution
│   └── create_database.py          # Database schema creation
├── tests/
│   ├── test_data_extractor.py
│   ├── test_data_cleaner.py
│   └── test_db_loader.py
├── config/
│   ├── database.yaml               # Database configuration
│   └── data_mapping.yaml           # Column mapping rules
├── .env.example                    # Environment variables template
├── .gitignore
├── requirements.txt
├── README.md
├── PRD.md                          # This document
└── Makefile                        # Build automation

8. Implementation Phases

Phase 1: Project Setup (Week 1)

• Initialize uv environment
• Install required dependencies
• Set up project structure
• Create basic documentation

Phase 2: Data Exploration (Week 1-2)

• Analyze Excel file structure
• Identify data quality issues
• Create data profiling notebook
• Define cleaning strategies

Phase 3: Data Processing Pipeline (Week 2-3)

• Implement data extraction module
• Develop data cleaning utilities
• Create validation framework
• Build CSV export functionality

Phase 4: Database Integration (Week 3-4)

• Set up PostgreSQL connection
• Create database schema
• Implement data loading pipeline
• Add error handling and logging

Phase 5: Testing & Documentation (Week 4)

• Write comprehensive tests
• Create user documentation
• Perform end-to-end testing
• Optimize performance

9. Risk Assessment

High Risk

• Data Quality Issues: Inconsistent data formats across sheets
• Database Connectivity: Network or authentication failures
• Memory Constraints: Large file processing limitations

Medium Risk

• Column Mapping: Variations in column names across sheets
• Data Type Conversion: Invalid data format handling
• Performance: Slow processing for large datasets

Low Risk

• Environment Setup: Dependency installation issues
• File Access: Permission or path-related problems

10. Success Criteria

Technical Success

• All 5 Excel sheets successfully processed
• Data quality report generated with <5% error rate
• CSV file created with all required columns
• PostgreSQL table populated with complete data
• Pipeline execution time under 5 minutes

Business Success

• Farmer data accessible for analysis
• Data integrity maintained throughout pipeline
• Reproducible process for future data updates
• Comprehensive documentation for maintenance

11. Deliverables

1. Working Data Pipeline

   • Complete Python application
   • Jupyter notebooks for analysis
   • Database schema and loading scripts

2. Documentation

   • Technical documentation
   • User guide
   • API documentation
   • Data dictionary

3. Testing Suite

   • Unit tests
   • Integration tests
   • Data validation tests

4. Configuration Files

   • Environment setup
   • Database configuration
   • Data mapping rules

12. Maintenance & Support

Ongoing Requirements

• Regular data quality monitoring
• Database performance optimization
• Documentation updates
• Bug fixes and enhancements

Support Model

• Self-service documentation
• Issue tracking system
• Regular maintenance schedule

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Document Version: 1.0
Last Updated: December 2024
Next Review: January 2025