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Python Scripts for Data Transformation

Python scripts enable complex data transformations that go beyond simple field mapping.

Script Editor Interface​

Python Script Editor Python Script editor with context variables and examples

Script Manual and Documentation​

Python Script Manual Built-in manual showing available variables and usage examples

Available Context Variables​

When writing Python scripts, you have access to these variables:

Core Objects​

env          # Odoo environment for database access
model        # Target Odoo model being synchronized
records      # Current recordset being processed
request_data # JSON payload from external API

Utility Libraries​

# Date/Time Operations
time, datetime, dateutil, timezone

# Encoding Utilities
b64encode, b64decode  # Base64 encoding/decoding

# Error Handling
UserError  # User-facing error exceptions

# Record Manipulation
Command  # Record manipulation commands

Script Execution Modes​

Evaluate Mode​

Returns computed values for field assignment:

# Return a calculated value
record.price * 1.2  # Add 20% markup

Execute Mode​

Performs complex operations without return values:

# Create related records
env['product.pricelist.item'].create({
    'product_id': record.id,
    'price': request_data.get('special_price')
})

Common Use Cases​

1. Data Validation​

Validate incoming data before processing:

# Validate email format
import re

email = request_data.get('email', '')
if email and not re.match(r'^[\w\.-]+@[\w\.-]+\.\w+$', email):
    raise UserError(f"Invalid email format: {email}")

# Validate required fields
required_fields = ['name', 'email', 'phone']
missing = [f for f in required_fields if not request_data.get(f)]
if missing:
    raise UserError(f"Missing required fields: {', '.join(missing)}")

2. Format Conversion​

Convert between different data formats:

# Date format conversion
from datetime import datetime

# Convert from MM/DD/YYYY to YYYY-MM-DD
datetime.strptime(request_data.get('birth_date', ''), '%m/%d/%Y').strftime('%Y-%m-%d')

# Phone number formatting
''.join(filter(str.isdigit, request_data.get('phone', '')))

3. Conditional Logic​

Apply business rules based on data values:

# Customer categorization based on order value
total_orders = request_data.get('total_orders', 0)

if total_orders > 100000:
    category = 'platinum'
    discount = 0.20
elif total_orders > 50000:
    category = 'gold'
    discount = 0.15
elif total_orders > 10000:
    category = 'silver'
    discount = 0.10
else:
    category = 'bronze'
    discount = 0.05

# Update customer record
record.write({
    'customer_category': category,
    'discount_percentage': discount * 100
})

4. Relational Processing​

Handle complex relationships between records:

# Find or create related partner
partner_data = request_data.get('company', {})
if partner_data:
    # Search for existing partner
    partner = env['res.partner'].search([
        ('name', '=', partner_data.get('name')),
        ('is_company', '=', True)
    ], limit=1)
    
    if not partner:
        # Create new partner
        partner = env['res.partner'].create({
            'name': partner_data.get('name'),
            'is_company': True,
            'email': partner_data.get('email'),
            'phone': partner_data.get('phone'),
        })

5. Data Aggregation​

Calculate summary values from multiple sources:

# Calculate total from line items
line_items = request_data.get('items', [])
total = 0
tax_total = 0

for item in line_items:
    quantity = item.get('quantity', 0)
    price = item.get('price', 0)
    tax_rate = item.get('tax_rate', 0)
    
    subtotal = quantity * price
    tax = subtotal * (tax_rate / 100)
    
    total += subtotal
    tax_total += tax

# Create invoice lines
for item in line_items:
    env['account.move.line'].create({
        'move_id': record.id,
        'product_id': item.get('product_id'),
        'quantity': item.get('quantity'),
        'price_unit': item.get('price'),
    })

record.write({'amount': total + tax_total})

Advanced Techniques​

Error Handling​

try:
    # Risky operation
    value = int(request_data.get('quantity'))
    if value < 0:
        raise ValueError("Quantity cannot be negative")
except ValueError as e:
    # Log error and use default
    env['bj.api.log'].create({
        'name': f"Data transformation error: {str(e)}",
        'request_data': str(request_data),
    })

Logging and Debugging​

# Debug logging
import logging
_logger = logging.getLogger(__name__)

_logger.info(f"Processing record: {record.id}")
_logger.debug(f"Request data: {request_data}")

# Create audit log
env['bj.api.log'].create({
    'config_id': config.id,
    'record_id': record.id,
    'action': 'transform',
    'details': f"Transformed {len(request_data)} fields",
})

Performance Optimization​

# Batch operations for better performance
record_ids = request_data.get('record_ids', [])

# Bad: Individual queries
# for rid in record_ids:
#     rec = env['model'].browse(rid)
#     rec.write({'field': value})

# Good: Batch operation
if record_ids:
    records = env['model'].browse(record_ids)
    records.write({'field': value})

Best Practices​

  1. Always validate input data before processing
  2. Use try-except blocks for error handling
  3. Log important operations for debugging
  4. Optimize database queries with batch operations
  5. Document complex logic with comments
  6. Test scripts thoroughly before production
  7. Handle None/null values gracefully
  8. Use type conversion carefully

Common Pitfalls​

Avoid These Mistakes​

# ❌ Bad: Modifying request_data directly
request_data['field'] = 'value'  # Don't do this

# ✅ Good: Create new variables
modified_value = request_data.get('field', '') + '_suffix'

# ❌ Bad: Using undefined variables
undefined_variable  # Will cause error

# ✅ Good: Check variable existence
locals().get('variable_name', default_value)

# ❌ Bad: Infinite loops
while True:  # Dangerous
    process_data()

# ✅ Good: Bounded loops
for i in range(max_iterations):
    if condition_met:
        break
    process_data()

Testing Your Scripts​

Test Data Examples​

# Test with various input scenarios
test_cases = [
    {'name': 'Test Customer', 'email': 'test@example.com'},  # Valid
    {'name': 'No Email'},  # Missing email
    {'email': 'invalid-email'},  # Invalid format
    {},  # Empty data
]

for test_data in test_cases:
    try:
        # Your script logic here
        result = transform_data(test_data)
        print(f"Success: {result}")
    except Exception as e:
        print(f"Error with {test_data}: {str(e)}")