I'm working on a Django project and encountering an issue with Django admin. Here's my current implementation:

# models.py
class Article(models.Model):
    title = models.CharField(max_length=200)
    author = models.ForeignKey(User, on_delete=models.CASCADE)
    
    def save(self, *args, **kwargs):
        # This is causing issues
        super().save(*args, **kwargs)

The specific error I'm getting is: "django.core.exceptions.ValidationError: Enter a valid email address"

I've already tried the following approaches:

• Checked Django documentation and Stack Overflow
• Verified my database schema and migrations
• Added debugging prints to trace the issue
• Tested with different data inputs

Environment details:

• Django version: 5.0.1
• Python version: 3.11.0
• Database: PostgreSQL 15
• Operating system: Windows 11

Has anyone encountered this before? Any guidance would be greatly appreciated!

The choice between Django signals and overriding save() depends on your use case:

Use save() method when:

• The logic is directly related to the model
• You need to modify the instance before saving
• The operation is essential for data integrity

class Article(models.Model):
    title = models.CharField(max_length=200)
    slug = models.SlugField(unique=True)
    
    def save(self, *args, **kwargs):
        if not self.slug:
            self.slug = slugify(self.title)
        super().save(*args, **kwargs)

Use signals when:

• You need decoupled logic
• Multiple models need the same behavior
• You're working with third-party models

from django.db.models.signals import post_save
from django.dispatch import receiver

@receiver(post_save, sender=User)
def create_user_profile(sender, instance, created, **kwargs):
    if created:
        UserProfile.objects.create(user=instance)

The RecursionError occurs when Python's recursion limit is exceeded. Here are several solutions:

1. Increase recursion limit (temporary fix):

import sys
sys.setrecursionlimit(10000)  # Default is usually 1000

2. Convert to iterative approach (recommended):

# Recursive (problematic for large inputs)
def factorial_recursive(n):
    if n <= 1:
        return 1
    return n * factorial_recursive(n - 1)

# Iterative (better)
def factorial_iterative(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

3. Use memoization for recursive algorithms:

from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

4. Tail recursion optimization (manual):

def factorial_tail_recursive(n, accumulator=1):
    if n <= 1:
        return accumulator
    return factorial_tail_recursive(n - 1, n * accumulator)

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}')