CODING · Dev & Data
Reconcile 100k-Row E-Commerce Orders & Bank Statements in Seconds with AI-Generated Pandas Scripts
Excel freezing on 100,000 rows of order vs. payout logs? Have AI write a local Python Pandas script tailored to your column headers to flag missing payouts and fee discrepancies in 3 seconds.
CORE TOOLS
Python Pandas (Open Source), DeepSeek / Claude
BEST FOR
E-commerce accountants, store operators, auditors, analysts
REQUIREMENTS
Local Python installation with `pandas` and `openpyxl`
✦ WHY THIS WORKFLOW
Multi-line payout reconciliation at 100k+ rows breaks Excel formulas. A local Pandas script runs in 3 seconds, keeps financial data offline, and works every month.
✦
Interactive Workflow Animation Preview
Step-by-Step Action Checklist
0 / 4 Completed
READY-TO-USE PROMPT TEMPLATE
Write a local **Python (`pandas` + `openpyxl`) reconciliation script** to match two large spreadsheets without freezing Excel: - Table A (`orders.xlsx`) columns: [e.g., Order_ID, Ship_Date, Expected_Net_Amount] - Table B (`settlement.csv`) columns: [e.g., External_Order_ID, Transaction_Type, Amount, Fee] Requirements: 1) Read ID columns strictly as strings (`dtype=str`), strip whitespace and leading quotes; 2) Group Table B by `External_Order_ID` to aggregate split rows (gross sale, platform fee, partial refund) into net payout; 3) Perform an outer merge with a `0.01` floating-point tolerance and export `reconciliation_report.xlsx` with 4 sheets: `Matched`, `Missing_Payout`, `Amount_Mismatch`, and `Orphan_Deductions`.