Enhanced CSV Ledger Transformation and Reconciliation

📅 2025-11-10 — Session: Enhanced CSV Ledger Transformation and Reconciliation

🕒 16:40–17:10
🏷️ Labels: CSV, Data Transformation, Python, Reconciliation, SQL, Pandas
📂 Project: Dev

Session Goal:

The session aimed to transform a wide CSV ledger into a normalized transactions table and develop a comprehensive data management strategy for CSV files, focusing on maintaining data integrity and enhancing reporting capabilities.

Key Activities:

1. CSV Ledger Transformation: Developed a schema design and transformation rules to convert a wide CSV ledger into a normalized transactions table using SQL and pandas, ensuring data integrity through validation checks.
2. Data Management Plan: Outlined a low-friction data management strategy for CSV files, including five guardrails to enhance data integrity and a pandas script for generating monthly aggregates.
3. Reconciliation Script: Created a Python script to process CSV files for monthly financial reporting and internal reconciliation, ensuring legacy data remains intact.
4. Function Updates: Updated the greedy_pair_match function to include ‘lax’ and ‘tight’ modes with adjustable tolerances, and improved the normalize_df function for better timezone handling.

Achievements:

• Successfully designed a transformation process for CSV ledgers that ensures data integrity.
• Implemented a low-friction data management plan with minimal changes to existing CSV structures.
• Developed a robust reconciliation script for monthly financial reporting.
• Enhanced existing functions to improve data processing and error handling.

Pending Tasks:

• Further testing and validation of the new transformation and reconciliation processes.
• Implementation of the updated functions in the production environment.