In Python data analysis, how do I log effectively?

In Python data analysis, effective logging is crucial for tracking the behavior of your applications, debugging issues, and monitoring the performance of your data processing tasks. By implementing a well-structured logging system, you can gain insights into your data analysis pipeline and make informed decisions.

The built-in `logging` library in Python provides a flexible framework for emitting log messages from your programs. It is recommended to set up logging at the beginning of your scripts or modules. Below is an example of how to effectively log in a data analysis application:

import logging # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') def analyze_data(data): logging.info("Starting data analysis") # Process data try: result = process_data(data) logging.info("Data analysis completed successfully") return result except Exception as e: logging.error(f"Error occurred during data analysis: {e}") def process_data(data): # Simulate data processing if not data: raise ValueError("No data provided") return sum(data) / len(data) # Example usage if __name__ == "__main__": sample_data = [1, 2, 3, 4, 5] analyze_data(sample_data)

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