Skip to main content

​
Overview

The TransactionsAPI provides methods to list and retrieve transaction information. All transaction operations are accessed through the client.transactions property.

​
Methods

​
list()

List transactions for a specific account with optional filtering. 
def list(
    self,
    account_id: str,
    expand: Optional[list[str]] = None,
    limit: Optional[int] = None,
    since: Optional[Union[datetime, str]] = None,
    before: Optional[datetime] = None,
) -> list[Transaction]:
    """List transactions for an account."""
Parameters:
  • account_id: The unique account identifier
  • expand (optional): List of fields to expand (e.g., ["merchant"])
  • limit (optional): Maximum number of transactions to return (default 100, max 500)
  • since (optional): Return transactions after this date/time or transaction ID
  • before (optional): Return transactions before this date/time
Returns: list[Transaction] - List of transaction objects Examples:
  • Basic Usage
  • Date Filtering
  • Expanded Data
from monzoh import MonzoClient

client = MonzoClient()

# Get accounts
accounts = client.accounts.list()
account_id = accounts[0].id

# Get recent transactions
transactions = client.transactions.list(
    account_id=account_id,
    limit=20
)

print(f"Found {len(transactions)} transactions")

for txn in transactions:
    amount = txn.amount / 100
    emoji = "💸" if amount < 0 else "💰"
    print(f"{emoji} {txn.description}: £{amount:.2f}")

​
get()

Retrieve a specific transaction by ID. 
def get(
    self,
    transaction_id: str,
    expand: Optional[list[str]] = None
) -> Transaction:
    """Get a specific transaction by ID."""
Parameters:
  • transaction_id: The unique transaction identifier
  • expand (optional): List of fields to expand
Returns: Transaction - The transaction object Example: 
# Get a specific transaction
transaction = client.transactions.get(
    transaction_id="tx_00009hjzAHHyUp1hAzuJUB",
    expand=["merchant"]
)

print(f"Transaction: {transaction.description}")
print(f"Amount: £{transaction.amount / 100:.2f}")
print(f"Date: {transaction.created}")

if transaction.merchant:
    print(f"Merchant: {transaction.merchant.name}")

​
Transaction Analysis Examples

​
Spending Analysis

Analyze spending patterns by category: 
from collections import defaultdict
from datetime import datetime, timedelta

def analyze_spending_by_category(account_id, days=30):
    """Analyze spending by category over the last N days."""
    
    client = MonzoClient()
    
    # Get transactions from the last N days
    since = datetime.now(tz=datetime.timezone.utc) - timedelta(days=days)
    transactions = client.transactions.list(
        account_id=account_id,
        since=since,
        limit=200
    )
    
    # Group spending by category
    spending_by_category = defaultdict(list)
    total_spending = 0
    
    for txn in transactions:
        if txn.amount < 0:  # Spending transactions
            amount = abs(txn.amount) / 100
            category = txn.category or "Other"
            
            spending_by_category[category].append({
                'description': txn.description,
                'amount': amount,
                'date': txn.created,
                'merchant': txn.merchant.name if txn.merchant else None
            })
            
            total_spending += amount
    
    # Print analysis
    print(f"Spending Analysis (Last {days} days)")
    print("=" * 40)
    print(f"Total Spent: £{total_spending:.2f}")
    print()
    
    for category, transactions in sorted(
        spending_by_category.items(), 
        key=lambda x: sum(t['amount'] for t in x[1]), 
        reverse=True
    ):
        category_total = sum(t['amount'] for t in transactions)
        percentage = (category_total / total_spending) * 100
        
        print(f"{category}: £{category_total:.2f} ({percentage:.1f}%)")
        print(f"  Transactions: {len(transactions)}")
        print(f"  Average: £{category_total / len(transactions):.2f}")
        
        # Show top transactions in this category
        top_transactions = sorted(
            transactions, 
            key=lambda x: x['amount'], 
            reverse=True
        )[:3]
        
        for txn in top_transactions:
            print(f"    • {txn['description']}: £{txn['amount']:.2f}")
        print()

# Usage
accounts = client.accounts.list()
analyze_spending_by_category(accounts[0].id, days=30)
Track spending trends over time: 
from datetime import datetime, timedelta
import calendar

def monthly_spending_trends(account_id, months=6):
    """Analyze spending trends over the last N months."""
    
    client = MonzoClient()
    
    # Get transactions from the last N months
    since = datetime.now(tz=datetime.timezone.utc) - timedelta(days=months * 30)
    transactions = client.transactions.list(
        account_id=account_id,
        since=since,
        limit=1000
    )
    
    # Group by month
    monthly_spending = defaultdict(float)
    
    for txn in transactions:
        if txn.amount < 0:  # Spending only
            date = datetime.fromisoformat(txn.created.replace('Z', '+00:00'))
            month_key = date.strftime('%Y-%m')
            monthly_spending[month_key] += abs(txn.amount) / 100
    
    # Print trends
    print("Monthly Spending Trends")
    print("=" * 30)
    
    sorted_months = sorted(monthly_spending.keys())
    
    for i, month in enumerate(sorted_months):
        spending = monthly_spending[month]
        year, month_num = month.split('-')
        month_name = calendar.month_name[int(month_num)]
        
        print(f"{month_name} {year}: £{spending:.2f}")
        
        # Show trend arrow
        if i > 0:
            prev_spending = monthly_spending[sorted_months[i-1]]
            change = ((spending - prev_spending) / prev_spending) * 100
            
            if change > 5:
                trend = "📈 ↗️"
            elif change < -5:
                trend = "📉 ↘️"
            else:
                trend = "➡️"
            
            print(f"    {trend} {change:+.1f}% from previous month")

# Usage
monthly_spending_trends(accounts[0].id, months=6)

​
Transaction Search and Filtering

Search transactions by various criteria: 
def search_transactions(account_id, **criteria):
    """Search transactions by various criteria."""
    
    client = MonzoClient()
    
    # Get recent transactions
    transactions = client.transactions.list(
        account_id=account_id,
        limit=500
    )
    
    filtered_transactions = []
    
    for txn in transactions:
        # Apply filters
        match = True
        
        # Description filter
        if 'description' in criteria:
            if criteria['description'].lower() not in txn.description.lower():
                match = False
        
        # Amount range filter
        if 'min_amount' in criteria:
            if abs(txn.amount) / 100 < criteria['min_amount']:
                match = False
        
        if 'max_amount' in criteria:
            if abs(txn.amount) / 100 > criteria['max_amount']:
                match = False
        
        # Category filter
        if 'category' in criteria:
            if txn.category != criteria['category']:
                match = False
        
        # Merchant filter
        if 'merchant' in criteria and txn.merchant:
            if criteria['merchant'].lower() not in txn.merchant.name.lower():
                match = False
        
        # Date range filter
        if 'after_date' in criteria:
            txn_date = datetime.fromisoformat(txn.created.replace('Z', '+00:00'))
            if txn_date < criteria['after_date']:
                match = False
        
        if match:
            filtered_transactions.append(txn)
    
    return filtered_transactions

# Usage examples
client = MonzoClient()
accounts = client.accounts.list()
account_id = accounts[0].id

# Find all coffee purchases
coffee_transactions = search_transactions(
    account_id,
    description='coffee'
)

# Find large transactions over £100
large_transactions = search_transactions(
    account_id,
    min_amount=100
)

# Find grocery spending
grocery_transactions = search_transactions(
    account_id,
    category='groceries'
)

# Find transactions at specific merchant
tesco_transactions = search_transactions(
    account_id,
    merchant='tesco'
)

print(f"Coffee purchases: {len(coffee_transactions)}")
print(f"Large transactions: {len(large_transactions)}")
print(f"Grocery transactions: {len(grocery_transactions)}")

​
Duplicate Transaction Detection

Find potential duplicate transactions: 
def find_duplicate_transactions(account_id):
    """Find potential duplicate transactions."""
    
    client = MonzoClient()
    
    # Get recent transactions
    transactions = client.transactions.list(
        account_id=account_id,
        limit=200
    )
    
    # Group by amount and description
    grouped = defaultdict(list)
    
    for txn in transactions:
        key = (txn.amount, txn.description.lower().strip())
        grouped[key].append(txn)
    
    # Find potential duplicates
    duplicates = []
    
    for (amount, description), txn_group in grouped.items():
        if len(txn_group) > 1:
            # Check if transactions are close in time (within 24 hours)
            dates = [
                datetime.fromisoformat(txn.created.replace('Z', '+00:00')) 
                for txn in txn_group
            ]
            dates.sort()
            
            for i in range(1, len(dates)):
                time_diff = dates[i] - dates[i-1]
                if time_diff.total_seconds() < 86400:  # 24 hours
                    duplicates.append({
                        'amount': amount / 100,
                        'description': description,
                        'transactions': txn_group,
                        'time_difference': time_diff
                    })
    
    return duplicates

# Usage
duplicates = find_duplicate_transactions(accounts[0].id)

print(f"Found {len(duplicates)} potential duplicates:")
for dup in duplicates:
    print(f"\n💰 £{dup['amount']:.2f} - {dup['description']}")
    print(f"   Time difference: {dup['time_difference']}")
    for txn in dup['transactions']:
        print(f"   • {txn.id} at {txn.created}")

​
Data Export

Export transaction data to various formats: 
import csv
import json
from datetime import datetime

def export_transactions_csv(account_id, filename="transactions.csv"):
    """Export transactions to CSV file."""
    
    client = MonzoClient()
    transactions = client.transactions.list(account_id=account_id, limit=500)
    
    with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
        fieldnames = [
            'id', 'date', 'description', 'amount_gbp', 'category',
            'merchant_name', 'merchant_category', 'notes'
        ]
        
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        
        for txn in transactions:
            writer.writerow({
                'id': txn.id,
                'date': txn.created,
                'description': txn.description,
                'amount_gbp': txn.amount / 100,
                'category': txn.category or '',
                'merchant_name': txn.merchant.name if txn.merchant else '',
                'merchant_category': txn.merchant.category if txn.merchant else '',
                'notes': txn.notes or ''
            })
    
    print(f"Exported {len(transactions)} transactions to {filename}")

def export_transactions_json(account_id, filename="transactions.json"):
    """Export transactions to JSON file."""
    
    client = MonzoClient()
    transactions = client.transactions.list(account_id=account_id, limit=500)
    
    # Convert to serializable format
    export_data = []
    for txn in transactions:
        txn_data = txn.model_dump()
        export_data.append(txn_data)
    
    with open(filename, 'w', encoding='utf-8') as jsonfile:
        json.dump(export_data, jsonfile, indent=2, default=str)
    
    print(f"Exported {len(transactions)} transactions to {filename}")

# Usage
accounts = client.accounts.list()
export_transactions_csv(accounts[0].id)
export_transactions_json(accounts[0].id)

​
Best Practices

  1. Use pagination: For large date ranges, use the limit parameter and implement pagination
  2. Filter at the API level: Use since and before parameters rather than filtering client-side
  3. Expand selectively: Only expand fields you actually need to reduce response size
  4. Cache results: Cache transaction data to avoid repeated API calls
  5. Handle rate limits: Implement retry logic for rate limit errors
The Transactions API provides powerful capabilities for analyzing spending patterns, detecting anomalies, and building financial insights into your Monzo account activity.
⌘I