Python: How to implement proper unit testing with mocking and fixtures?

Answered

Aug 30, 2025 432 views 2 answers

Django REST Framework Flask Database Design Data Science

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


# Current implementation
import threading
import time

def worker():
    global counter
    for _ in range(100000):
        counter += 1  # Race condition here

counter = 0
threads = [threading.Thread(target=worker) for _ in range(4)]
for t in threads:
    t.start()

The error message I'm getting is: "ImportError: cannot import name 'some_function' from 'module'"

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: Ubuntu 22.04
Virtual environment: venv (activated)
Relevant packages: django, djangorestframework, celery, redis

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

Asked by william

Newbie • 40 rep

Comments

michael_code: Could you provide the requirements.txt for the packages used in this solution? 2 months ago

lisa_data: Perfect! This JWT authentication setup works flawlessly with my React frontend. 2 months ago

abdullah3: How would you modify this approach for a high-traffic production environment? 2 months ago

2 Answers

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}')

Answered by jane_smith 2 months ago

Bronze • 60 rep

Here's a comprehensive approach to implementing JWT authentication in Django REST Framework:

# settings.py
INSTALLED_APPS = [
    'rest_framework',
    'rest_framework_simplejwt',
]

REST_FRAMEWORK = {
    'DEFAULT_AUTHENTICATION_CLASSES': (
        'rest_framework_simplejwt.authentication.JWTAuthentication',
    ),
    'DEFAULT_PERMISSION_CLASSES': [
        'rest_framework.permissions.IsAuthenticated',
    ],
}

from datetime import timedelta
SIMPLE_JWT = {
    'ACCESS_TOKEN_LIFETIME': timedelta(minutes=60),
    'REFRESH_TOKEN_LIFETIME': timedelta(days=7),
    'ROTATE_REFRESH_TOKENS': True,
}

# urls.py
from rest_framework_simplejwt.views import (
    TokenObtainPairView,
    TokenRefreshView,
)

urlpatterns = [
    path('api/token/', TokenObtainPairView.as_view()),
    path('api/token/refresh/', TokenRefreshView.as_view()),
]

# Custom serializer for additional user data
from rest_framework_simplejwt.serializers import TokenObtainPairSerializer

class CustomTokenObtainPairSerializer(TokenObtainPairSerializer):
    @classmethod
    def get_token(cls, user):
        token = super().get_token(user)
        token['username'] = user.username
        token['email'] = user.email
        return token

Answered by david_web 2 months ago

Bronze • 75 rep

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