Enhanced Data Processing with Logging

Code Analysis

Existing Code Snippet

• Language: Python
• Functionality: The current code implements a basic data processing function that reads a CSV file and performs basic analysis.

Structure

• The code consists of:
  • Import statements
  • A function definition for data loading and preprocessing
  • Basic data operations, possibly involving pandas

Requirements for Extension

[Instruction]

Integrate a logging feature that tracks the execution of the data loading and processing functions. The logs should record:

• Start and end times of the process
• Number of rows processed
• Any exceptions encountered during the file reading.

Code Extension

Extended Code Snippet

import pandas as pd
import logging
from datetime import datetime

# Set up logging configuration
logging.basicConfig(
    filename='data_processing.log',
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

def load_and_process_data(file_path):
    """
    Load data from a CSV file and perform basic preprocessing.
    
    Parameters:
    file_path (str): The path to the CSV file.
    
    Returns:
    pd.DataFrame: A DataFrame containing the processed data.
    """
    logging.info("Data loading and processing started.")
    start_time = datetime.now()  # Start time tracking

    try:
        # Read data from the CSV file
        data = pd.read_csv(file_path)
        logging.info("CSV file loaded successfully.")
        
        # Data preprocessing steps can be included here.
        rows_processed = len(data)
        logging.info(f"Number of rows processed: {rows_processed}")

    except FileNotFoundError as e:
        logging.error(f"File not found: {file_path}. Error: {str(e)}")
        raise
    except pd.errors.EmptyDataError as e:
        logging.error(f"No data: {file_path}. Error: {str(e)}")
        raise
    except Exception as e:
        logging.error(f"An error occurred: {str(e)}")
        raise
    finally:
        end_time = datetime.now()  # End time tracking
        duration = end_time - start_time
        logging.info(f"Data loading and processing completed in {duration}.")

    return data  # Return processed data

Key Points of Enhancement

1. Logging Configuration:

   • Integrated a logging system using Python's built-in logging module.
   • Configured to write logs to a file named data_processing.log.

2. Execution Time Tracking:

   • Captured start and end times to calculate and log the processing duration.

3. Error Handling:

   • Enhanced error handling routines that log specific errors for different exceptions related to file operations.

4. Seamless Integration:

   • The logging functionality is integrated without disrupting the existing flow of data processing.

Conclusion

The extended code snippet enhances the original functionality by introducing robust logging and error-handling features while maintaining the logical structure and purpose of the original function. This approach follows Python best practices, ensuring readability, maintainability, and a clear execution path.