"""

Demonstration: Analyzing Historical Transaction Data

This script demonstrates how to analyze the historical transaction dataset

and provides summary statistics and insights.

"""

import csv

from decimal import Decimal

from datetime import datetime

from collections import defaultdict, Counter

def analyze_transactions(filename, max_display=20):

"""

Analyze transactions from CSV

Args:

filename: Path to the CSV file

max_display: Maximum number of sample transactions to display

"""

print(f"\nAnalyzing transactions from {filename}...\n")

# Statistics collectors

total_volume = Decimal('0')

by_type = defaultdict(lambda: {'count': 0, 'volume': Decimal('0')})

by_branch = defaultdict(lambda: {'count': 0, 'volume': Decimal('0')})

by_year = defaultdict(lambda: {'count': 0, 'volume': Decimal('0')})

all_transactions = []

transaction_count = 0

with open(filename, 'r', encoding='utf-8') as f:

reader = csv.DictReader(f)

for row in reader:

transaction_count += 1

all_transactions.append(row)

# Extract data

trans_type = row['type']

branch = row['branch']

year = row['date'][:4]

amount = Decimal(row['debit_amount'])

# Accumulate statistics

total_volume += amount

by_type[trans_type]['count'] += 1

by_type[trans_type]['volume'] += amount

by_branch[branch]['count'] += 1

by_branch[branch]['volume'] += amount

by_year[year]['count'] += 1

by_year[year]['volume'] += amount

return {

'total_count': transaction_count,

'total_volume': total_volume,

'by_type': dict(by_type),

'by_branch': dict(by_branch),

'by_year': dict(by_year),

'transactions': all_transactions[:max_display]

}

def print_analysis(stats):

"""Print analysis results"""

print("="*70)

print("OVERALL STATISTICS")

print("="*70)

print(f"Total Transactions: {stats['total_count']:,}")

print(f"Total Volume: {stats['total_volume']:,.2f} florins")

print(f"Average Transaction: {stats['total_volume'] / stats['total_count']:,.2f} florins")

print("\n" + "="*70)

print("TRANSACTIONS BY TYPE")

print("="*70)

print(f"{'Type':<25} {'Count':<10} {'Volume (florins)':<20} {'Avg':<15}")

print("-"*70)

for t_type, data in sorted(stats['by_type'].items(),

key=lambda x: x[1]['count'], reverse=True):

avg = data['volume'] / data['count']

pct = (data['count'] / stats['total_count']) * 100

print(f"{t_type:<25} {data['count']:<10} {data['volume']:>18,.2f} "

f"{avg:>13,.2f}")

print("\n" + "="*70)

print("TRANSACTIONS BY BRANCH")

print("="*70)

print(f"{'Branch':<15} {'Count':<10} {'% of Total':<12} {'Volume (florins)':<20}")

print("-"*70)

for branch, data in sorted(stats['by_branch'].items(),

key=lambda x: x[1]['count'], reverse=True):

pct = (data['count'] / stats['total_count']) * 100

print(f"{branch:<15} {data['count']:<10} {pct:>10.2f}% "

f"{data['volume']:>18,.2f}")

print("\n" + "="*70)

print("SAMPLE TRANSACTIONS (First 20)")

print("="*70)

print(f"{'Date':<12} {'Branch':<10} {'Type':<20} {'Amount':<15}")

print("-"*70)

for trans in stats['transactions']:

amount = float(trans['debit_amount'])

print(f"{trans['date']:<12} {trans['branch']:<10} "

f"{trans['type']:<20} {amount:>13,.2f}")

def find_significant_events(filename):

"""Find and display significant historical events"""

print("\n" + "="*70)

print("SIGNIFICANT HISTORICAL EVENTS")

print("="*70)

events_found = []

with open(filename, 'r', encoding='utf-8') as f:

reader = csv.DictReader(f)

for row in reader:

# Look for the ransom payment

if 'ransom' in row['type'].lower() or 'John XXIII' in row.get('description', ''):

events_found.append({

'name': 'Council of Constance Ransom',

'date': row['date'],

'amount': float(row['debit_amount']),

'description': row['description']

})

# Look for very large transactions (>500,000 florins)

elif float(row['debit_amount']) > 500000:

events_found.append({

'name': 'Major Transaction',

'date': row['date'],

'amount': float(row['debit_amount']),

'description': row['description'][:60] + '...' if len(row['description']) > 60 else row['description']

})

# Sort by amount descending

events_found.sort(key=lambda x: x['amount'], reverse=True)

print("\nTop 10 Largest Transactions:\n")

for i, event in enumerate(events_found[:10], 1):

print(f"{i}. {event['name']}")

print(f" Date: {event['date']}")

print(f" Amount: {event['amount']:,.2f} florins")

print(f" {event['description']}")

print()

def main():

"""Main demonstration function"""

print("="*70)

print("MEDICI BANK - HISTORICAL TRANSACTION DATA ANALYSIS")

print("="*70)

print("\nThis analysis examines the full dataset of 20,000 historical")

print("transactions from the Medici Bank operations (1390-1440).\n")

try:

# Analyze the data

stats = analyze_transactions('medici_transactions.csv', max_display=20)

# Print analysis

print_analysis(stats)

# Find significant events

find_significant_events('medici_transactions.csv')

print("\n" + "="*70)

print("ANALYSIS COMPLETE")

print("="*70)

print("\nFor more details, see:")

print(" - medici_transactions.csv (full dataset)")

print(" - medici_transactions.json (JSON format)")

print(" - TRANSACTION_DATA.md (documentation)")

print(" - validate_transactions.py (data validation)")

print("="*70)

return 0

except FileNotFoundError:

print("\n❌ Error: medici_transactions.csv not found.")

print("Please run generate_historical_data.py first.")

return 1

except Exception as e:

print(f"\n❌ Error: {e}")

return 1

if __name__ == "__main__":

exit(main())