copilot-api

Based on your Python project 'DataProcessor' and the provided code snippet for the 'data_cleaner.py' module, here are my suggestions:

1. **Vectorization Suggestion**: The current loop for data cleaning can be vectorized using pandas' built-in functions. This will significantly improve performance for large datasets. Here's the suggested code:
```python
# Original code
for index, row in df.iterrows():
    if pd.isna(row['value']):
        df.at[index, 'value'] = row['default_value']

# Suggested vectorized version
df['value'] = df.apply(lambda row: row['default_value'] if pd.isna(row['value']) else row['value'], axis=1)
```

2. **Error Handling**: Add comprehensive error handling for file operations. The current implementation lacks proper exception handling for file I/O operations, which could lead to crashes or data corruption.

3. **Type Hints**: Add type hints to the function signatures. This will improve code readability and enable better IDE support. The Copilot API suggests the following additions:
```python
from typing import Dict, Any

def load_config(file_path: str) -> Dict[str, Any]:
    ...
```

4. **Logging**: Implement logging for critical operations. This will help with debugging and monitoring the application's behavior in production.