Like a lot of people, I'm guilty of using print() to debug my code. While it's a good idea to use a proper debugger, it can also be good to have your programs log useful information as they run, particularly if they're going to run on a server relatively unsupervised. That's where Python's logging module comes in. It's best to use logging instead of print right from the start of a project, so keep it in mind for your next one!

At the most basic level, logging lets you set the importance level of each message you're sending. The levels (in order of importance) are:

• DEBUG
• INFO
• WARNING
• ERROR
• CRITICAL

To log each kind of message, just use the equivalent function from logging:

logging.debug("Log a debug message")
logging.warning("Log a warning")
# etc.

When running your code, you can set the log level (using a command line argument, environment variable, config file, etc.), and only messages of that importance or higher will be shown:

logging.basicConfig(level=logging.INFO)
logging.debug("This won't be shown")
logging.info("Anything at info level or higher will be logged")

The benefit of this is you can have your programs show a lot of information on what they're doing through debug messages, without making the output too noisy for regular use. If you find yourself going back through your code removing print() statements once it's finished, consider using logging instead.

Going further, the logging module has lots of useful functionality like:

• Logging to a file instead of/as well as the console - and easily switching between these
• Automatically including context like the current Python module and line number in messages
• Running multiple loggers with different settings.

See this RealPython tutorial for info on some of these.

The closest equivalent in R seems to be the logger package, which is inspired by Python's logging.