Today, let's talk about a particularly practical but lesser-known Python library—Loguru. This is a tool that makes logging incredibly simple! Whether you're a beginner or a veteran, using it can make your logging work much more efficient. Why use Loguru? To be honest, Python's built-in logging module is like playing with building blocks—it's complex to configure and prone to errors. But Loguru is different; it's like a "point-and-shoot camera," ready to use out of the box and incredibly powerful.
Installation
Installing Loguru is super simple, just one command:
pip install loguru
Basic Usage Let's look at the most basic usage, I guarantee you'll get it in one read:
from loguru import logger
# The simplest logging
logger.debug("Debug info: The program is executing...")
logger.info("General info: Task completed!")
logger.warning("Warning info: High memory usage")
logger.error("Error info: Failed to connect to the database")
Isn't it super simple? No configuration needed, just import and use!
Advanced Features
Output to a file
from loguru import logger
# Output to a file with log rotation
logger.add("app.log", rotation="500 MB", compression="zip")
def process_data():
logger.info("Starting to process data...")
try:
result = 10 / 0 # Intentionally causing an error
except Exception as e:
logger.exception(f"Data processing error: {e}")
process_data()
This example will automatically create a log file, and when the file size exceeds 500MB, it will automatically compress and archive, which is great for long-running programs!
Customize log format
from loguru import logger
import sys
# Customize log format
logger.remove() # Remove default handler
logger.add(sys.stderr, format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>")
def check_status():
logger.info("Checking system status")
logger.warning("Potential issue detected")
check_status()
Look at this colorful output, isn't it especially cool? And the information is clear at a glance!
Capture detailed exceptions
from loguru import logger
@logger.catch
def calculate_average(numbers):
total = sum(numbers)
average = total / len(numbers)
return average
# Test the function
try:
result = calculate_average([]) # An empty list will cause an error
except Exception as e:
logger.error("Error occurred while calculating the average")
Using the @logger.catch decorator, you can automatically capture and log detailed error information, including the complete stack trace, which is incredibly useful for debugging!
Real-world example: Monitoring a file processing program Let's look at a practical example, suppose we need to monitor a file processing program:
from loguru import logger
import time
# Configure logging
logger.add(
"file_processor.log",
rotation="1 day",
retention="7 days",
level="INFO"
)
class FileProcessor:
def __init__(self):
logger.info("File processor initialized")
def process_file(self, filename):
logger.info(f"Starting to process file: {filename}")
try:
# Simulate file processing
time.sleep(1)
if "test" in filename:
raise ValueError("Test files are not allowed to be processed")
logger.success(f"File {filename} processed successfully")
except Exception as e:
logger.error(f"Error processing file {filename}: {str(e)}")
# Usage example
processor = FileProcessor()
processor.process_file("document.txt")
processor.process_file("test.txt")
Tips
Loguru's log levels: trace: most detailed logs, debug: debug information, info: general information, success: success information, warning: warning information, error: error information, critical: critical errors. Log file rotation supports various methods: by size: rotation="100 MB", by time:rotation="00:00", by day:rotation="1 day".Do not frequently create log files in loops. Remember to set the appropriate log level. Pay attention to permission issues with log files. Use the exception capture decorator reasonably.
Folks, that's it for today's Python logging tool study!