Data Visualization with Python and Matplotlib

Create stunning data visualizations using Python libraries like Matplotlib and Seaborn.

## Advanced Python Programming

### Decorators and Context Managers
```python
from functools import wraps
import time

def timing_decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
end = time.time()
print(f"{func.__name__} took {end - start:.2f} seconds")
return result
return wrapper

class DatabaseConnection:
def __enter__(self):
self.connection = create_connection()
return self.connection

def __exit__(self, exc_type, exc_val, exc_tb):
self.connection.close()
```

### Async Programming with asyncio
```python
import asyncio
import aiohttp

async def fetch_data(session, url):
async with session.get(url) as response:
return await response.json()

async def main():
async with aiohttp.ClientSession() as session:
tasks = [fetch_data(session, url) for url in urls]
results = await asyncio.gather(*tasks)
return results
```

### Data Processing with Pandas
- Efficient data manipulation and analysis
- Handling missing data and outliers
- Data visualization with matplotlib and seaborn
- Integration with databases and APIs

### Best Practices
- Follow PEP 8 style guidelines
- Use type hints for better code documentation
- Implement proper error handling
- Write comprehensive unit tests