Job Title

Scope of work: Visual Basics Script to Python 1. Planning1.1 ObjectiveRecreate the functionality of the original script using Python to perform normality checks, automate email reporting, and log processing.1.2 Requirement Analysis1.2.1 Input/Output Requirements Input: CSV files, system configurations, or other data sources.Output: Generated reports, emails, and logs.1.2.2 System Requirements Python 3.xDependencies: pandas, openpyxl, smtplib, os, etc.1.3 MilestonesScript conversion to Python.Functional testing of email integration and report generation.Deployment and maintenance.2. Design2.1 System ArchitectureCore functionality divided into modules:Data Processing Module: Parses input files and validates data.Report Generation Module: Generates normality check reports.Email Automation Module: Sends reports via email.Error Logging Module: Logs errors for troubleshooting.2.2 User Interface• Console-based interaction for configuration, with optional integration into a web-based dashboard.3. Technology Stack3.1 Python LibrariesData handling: pandas, os, csv.Email: smtplib, email.File processing: openpyxl, xlsxwriter.4. Develoment4.1 Setup and ConfigurationInitialize the Python project with appropriate file structure.Create a configuration file (config.json or .env) for constants like email credentials and paths.4.2 Script Development4.2.1 Data Handling Read and validate input files (e.g., CSV/Excel).Handle missing or malformed data.4.2.2 Report Generation Use pandas to create data summaries or normality checks.Save output as .xlsx or .pdf.4.2.3 Email Automation Integrate smtplib to send emails with attachments.Ensure secure authentication (e.g., TLS/SSL).4.2.4 Error Handling and Logging 1. Implement structured logging using logging module.5. Testing5.1 Unit Testing Test individual functions for data processing, report generation, and email sending.5.2 Integration Testing Verify end-to-end functionality, ensuring compatibility between modules.5.3 Environment Compatibility Testing Validate functionality across different OS environments (Windows, Linux).5.4 Performance Testing Ensure the script handles large datasets efficiently.6. Deployment6.1 DocumentationCreate a comprehensive user manual with steps for installation and execution.Document code using inline comments and docstrings.6.2 Environment SetupProvide a requirements file (requirements.txt) for dependencies.Package the script for easy deployment using PyInstaller or similar tools.7. Maintenance and Support7.1 Version Control Use Git for version tracking and collaborative development.7.2 Updates and Bug Fixes Periodically review the script for improvements and new features.7.3 Support• Provide ongoing support for troubleshooting and issue resolution.