📅 2025-01-03 — Session: Comprehensive Analysis and Correction of Debt Management Functions
🕒 18:30–23:10
🏷️ Labels: Debt Management, Python Functions, Data Processing, Financial Analysis
📂 Project: Accounting
⭐ Priority: MEDIUM
Session Goal
The session aimed to analyze and correct various Python functions related to financial data processing, specifically focusing on calculating outstanding debts and ensuring data integrity.
Key Activities
- Conducted detailed analysis of song themes, including “A Rainy Night in Soho” and a Hindi song, to explore emotional tones and cultural contexts.
- Developed and corrected Python functions for financial calculations, including future value and outstanding debt.
- Addressed data type mismatches in payment processing by ensuring datetime conversions.
- Inspected and adjusted DataFrame structures for accurate groupby operations.
- Summarized chronological payment processing and ensured valid datetime objects in ledger data.
Achievements
- Successfully loaded Google Sheets data into Pandas DataFrames using Python.
- Corrected Python float rounding issues and ensured accurate future value calculations.
- Developed a comprehensive time series analysis of outstanding debt from 2022 to 2025.
- Prepared a memo summarizing the session’s outcomes and outlined next steps for ledger reconciliation.
Pending Tasks
- Review and correct the
calculate_outstandingfunction to ensure it returns the expected tuple. - Continue error correction and data consolidation in the next session, focusing on ledger reconciliation and payment grouping.
Session Summary
The session was productive in addressing key issues in financial data processing, achieving significant progress in function development and data analysis while identifying areas for further improvement.