I'm working on a Python application and running into an issue with Python optimization. Here's the problematic code:

# Current implementation
def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

# This causes RecursionError for large n
result = fibonacci(1000)

The error message I'm getting is: "KeyError: 'missing_key'"

What I've tried so far:

• Used pdb debugger to step through the code
• Added logging statements to trace execution
• Checked Python documentation and PEPs
• Tested with different Python versions
• Reviewed similar issues on GitHub and Stack Overflow

Environment information:

• Python version: 3.11.0
• Operating system: Windows 11
• Virtual environment: venv (activated)
• Relevant packages: django, djangorestframework, celery, redis

Any insights or alternative approaches would be very helpful. Thanks!

To handle Django database transactions properly and avoid data inconsistency, use Django's transaction management:

from django.db import transaction

# Method 1: Decorator
@transaction.atomic
def transfer_money(from_account, to_account, amount):
    from_account.balance -= amount
    from_account.save()
    
    to_account.balance += amount
    to_account.save()

# Method 2: Context manager
def complex_operation():
    with transaction.atomic():
        # All operations in this block are atomic
        user = User.objects.create(username='test')
        profile = UserProfile.objects.create(user=user)
        # If any operation fails, all are rolled back

For more complex scenarios with savepoints:

def nested_transactions():
    with transaction.atomic():
        # Outer transaction
        user = User.objects.create(username='test')
        
        try:
            with transaction.atomic():
                # Inner transaction (savepoint)
                risky_operation()
        except Exception:
            # Inner transaction rolled back, outer continues
            handle_error()

Python decorators with arguments require a three-level nested function. Here's the proper implementation:

import functools

# Decorator with arguments
def retry(max_attempts=3, delay=1):
    def decorator(func):
        @functools.wraps(func)  # Preserves function metadata
        def wrapper(*args, **kwargs):
            for attempt in range(max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == max_attempts - 1:
                        raise e
                    time.sleep(delay)
        return wrapper
    return decorator

# Usage
@retry(max_attempts=5, delay=2)
def unreliable_function():
    # Function that might fail
    pass

Class-based decorator (alternative approach):

class Retry:
    def __init__(self, max_attempts=3, delay=1):
        self.max_attempts = max_attempts
        self.delay = delay
    
    def __call__(self, func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(self.max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if attempt == self.max_attempts - 1:
                        raise e
                    time.sleep(self.delay)
        return wrapper

# Usage
@Retry(max_attempts=5, delay=2)
def another_function():
    pass

Here's how to optimize Python code performance using profiling tools:

1. Use cProfile for function-level profiling:

import cProfile
import pstats

# Profile your code
cProfile.run('your_function()', 'profile_output.prof')

# Analyze results
stats = pstats.Stats('profile_output.prof')
stats.sort_stats('cumulative')
stats.print_stats(10)  # Top 10 functions

2. Use line_profiler for line-by-line analysis:

# Install: pip install line_profiler
# Add @profile decorator to functions
@profile
def slow_function():
    # Your code here
    pass

# Run: kernprof -l -v script.py

3. Memory profiling with memory_profiler:

# Install: pip install memory_profiler
from memory_profiler import profile

@profile
def memory_intensive_function():
    # Your code here
    pass

# Run: python -m memory_profiler script.py

4. Use timeit for micro-benchmarks:

import timeit

# Compare different approaches
time1 = timeit.timeit('sum([1,2,3,4,5])', number=100000)
time2 = timeit.timeit('sum((1,2,3,4,5))', number=100000)
print(f'List: {time1}, Tuple: {time2}')