12-Month Payment File Consolidation

Vertically stacks 12 monthly payment files with identical schemas for annual reporting. Validates schema consistency, checks for duplicates, and provides monthly statistics with payment method and status breakdowns.

Input

What you provide to the skill

Stack all 12 monthly payment files (jan_payments.csv through dec_payments.csv) with identical schemas for annual financial reporting.

Output

What the skill generates for you

MERGE REPORT - Annual Payment Consolidation

Generated: 2025-12-04 14:32:18

OPERATION SUMMARY

Type: Vertical concatenation (stack identical schemas)
Strategy: Union all 12 monthly payment files into single annual dataset

INPUT FILES

12 monthly payment files with identical schemas:

File	Rows	Columns
jan_payments.csv	1,847	8
feb_payments.csv	1,653	8
mar_payments.csv	1,924	8
apr_payments.csv	1,789	8
may_payments.csv	1,912	8
jun_payments.csv	1,856	8
jul_payments.csv	1,734	8
aug_payments.csv	1,801	8
sep_payments.csv	1,888	8
oct_payments.csv	2,045	8
nov_payments.csv	2,134	8
dec_payments.csv	2,267	8

Total input records: 22,850

Shared schema:

payment_id (string)
payment_date (date)
customer_id (string)
invoice_number (string)
amount (float)
payment_method (string)
status (string)
processed_by (string)

MERGE EXECUTION

Method: pd.concat() with vertical stacking
Deduplication check: Applied on payment_id
Sort order: payment_date ascending

Processing Steps:

Loaded all 12 files and validated schema consistency ✓
Concatenated vertically preserving all columns ✓
Checked for duplicate payment_ids ✓
Sorted by payment_date ✓
Validated date continuity ✓

FINAL DATASET

File: annual_payments_2024.csv

Dimensions:

Total rows: 22,850
Total columns: 8
Date range: 2024-01-02 to 2024-12-31
File size: 1.87 MB

Column Details:

Column	Type	Non-Null Count	Null %
payment_id	object	22,850	0.0%
payment_date	datetime64	22,850	0.0%
customer_id	object	22,850	0.0%
invoice_number	object	22,837	0.06%
amount	float64	22,850	0.0%
payment_method	object	22,850	0.0%
status	object	22,850	0.0%
processed_by	object	22,843	0.03%

DATA QUALITY FINDINGS

✓ No Critical Issues

Payment ID Uniqueness:

All 22,850 payment_ids are unique
No duplicates detected across monthly files
ID format consistent: PAY-YYYYMMDD-NNNN

Schema Consistency:

All 12 files have identical column names, order, and data types
No structural mismatches detected

⚠️ Minor Observations

Missing Values:

invoice_number: 13 missing values (0.06%)
- Likely cash/walk-in payments without formal invoices
processed_by: 7 missing values (0.03%)
- Automated/system-processed payments

Payment Status Distribution:

completed: 22,456 (98.3%)
refunded: 287 (1.3%)
pending: 78 (0.3%)
failed: 29 (0.1%)

Payment Method Breakdown:

credit_card: 13,874 (60.7%)
bank_transfer: 5,912 (25.9%)
paypal: 2,134 (9.3%)
check: 687 (3.0%)
cash: 243 (1.1%)

MONTHLY SUMMARY STATISTICS

Month	Records	Total Amount	Avg Amount	Max Amount
January	1,847	$412,338.92	$223.27	$12,450.00
February	1,653	$389,127.15	$235.44	$9,875.50
March	1,924	$441,289.33	$229.38	$11,200.00
April	1,789	$398,562.48	$222.80	$10,550.75
May	1,912	$429,873.21	$224.78	$13,125.00
June	1,856	$418,945.67	$225.72	$10,875.25
July	1,734	$382,156.89	$220.40	$9,650.00
August	1,801	$407,234.55	$226.11	$11,450.50
September	1,888	$435,678.22	$230.76	$12,800.00
October	2,045	$478,923.16	$234.20	$14,230.75
November	2,134	$512,389.44	$240.12	$15,675.00
December	2,267	$548,912.37	$242.15	$16,450.25

Annual Totals:

Total payments: 22,850
Total revenue: $5,255,430.39
Average payment: $230.02
Peak month: December (2,267 payments, $548,912.37)
Lowest month: February (1,653 payments, $389,127.15)

VALIDATION CHECKS

✓ Date Continuity: All dates fall within expected 2024 calendar year
✓ Amount Validation: All amounts > $0, no negative values detected
✓ ID Format: Consistent PAY-YYYYMMDD-NNNN pattern across all records
✓ Customer IDs: 4,832 unique customers (average 4.7 payments per customer)
✓ No Temporal Gaps: All business days represented (accounting for weekends/holidays)

RECOMMENDATIONS

Ready for Analysis

The merged dataset is clean and ready for annual financial reporting:

Revenue Analysis: Month-over-month trends, seasonality patterns
Customer Behavior: Payment frequency, preferred methods, customer lifetime value
Collections Performance: Status tracking, refund analysis, failed payment follow-up
Payment Operations: Processing staff workload, method cost analysis

Optional Enhancements

Invoice Reconciliation: Cross-reference the 13 missing invoice_numbers against invoice master file
Failed Payment Investigation: Review 29 failed payments for recovery opportunities
Processor Attribution: Fill 7 missing processed_by values from system logs if needed for audit compliance

Data Governance

Archival: Original monthly files preserved for audit trail
Version Control: Merged file tagged with generation timestamp
Access: Recommend restricting PII (customer_id, processed_by) based on compliance requirements

OUTPUT DELIVERED

Primary Deliverable:

annual_payments_2024.csv (22,850 rows × 8 columns, 1.87 MB)

Supporting Documentation:

This merge report (JSON and markdown formats)
Data dictionary with column definitions
Monthly file manifest with checksums

Merge completed successfully. Dataset ready for annual financial analysis.