8 Python Error Handling Best Practices
Python is a great language for programming, but it's not perfect. Here are eight best practices for error handling in Python.
Python’s simple, easy-to-learn syntax can mislead Python beginners – especially those who are new to programming – into thinking that Python is not a very powerful language.
But don’t be fooled!
Python is actually a very versatile language that can be used for everything from web development to scientific computing. And while it is easy to get started with, there are still some best practices to follow when it comes to error handling.
In this article, we will go over 8 best practices for error handling in Python. By following these best practices, you can avoid common mistakes and write more robust and error-resistant code.
1. Use try-except blocks
When you use a try-except block, you are essentially telling Python to “try” running a certain piece of code. If it succeeds, great! If not, then the “except” clause will be executed, which usually contains some sort of error message or recovery code.
This is important because it means that your code will still run even if there are errors, which is crucial in production environments where things are constantly changing and breaking.
It also makes your code more readable and easier to debug, since you can see exactly what is supposed to happen and what to do if something goes wrong.
2. Don’t catch too broad exceptions
Broad exceptions are those that catch a large number of potential errors, such as ValueError or Exception. The problem with these is that they can mask real errors that you didn’t anticipate. For example, if you have a function that expects an integer and someone passes in a string, catching a broad exception will prevent the more specific TypeError from being raised.
It’s better to be specific when catching exceptions. For example, if you’re expecting an integer, catch the ValueError exception. This way, you can be sure that any other exceptions that occur are real errors that need to be addressed.
3. Catch specific exceptions only
When you catch a general exception, such as Exception, you are catching all exceptions. This might not be what you want. For example, if you’re trying to open a file, you only want to catch the FileNotFoundError exception.
Catching specific exceptions is more efficient because it doesn’t require try/except blocks for all of your code. It also makes your code easier to read and understand because it’s more explicit about what could go wrong.
4. Avoid empty except and finally clauses
When an exception is raised, the Python interpreter will first look for an except clause that matches the exception. If it finds one, it will execute the code in that clause. If it doesn’t find a matching except clause, it will look for a finally clause. If it finds one, it will execute the code in that clause and then re-raise the exception (if it wasn’t handled).
If there are no except or finally clauses, the exception will be unhandled and will propagate up to the caller.
An empty except clause will catch all exceptions and do nothing with them. This is generally not what you want, as it means that any unexpected exceptions will go unnoticed. It’s better to be explicit about which exceptions you’re catching, so that you can handle them appropriately.
An empty finally clause is also generally not what you want. A finally clause is typically used to clean up resources that were acquired in the try block, such as closing files or database connections. If the finally clause is empty, there’s nothing to clean up!
5. Raise errors when necessary
If you don’t raise errors when they occur, your program will continue to run even though there’s a problem. This can lead to unexpected results or data loss.
Additionally, if you don’t raise errors, it can be difficult to debug your code because you won’t know where the problem occurred. By raising errors, you can pinpoint the exact line of code that caused the error, which makes debugging much easier.
Finally, raising errors is a good way to communicate with other developers who are working on your code. If you raise an error, they’ll know that something went wrong and they can investigate the issue further.
6. Provide meaningful error messages
When an error occurs, the Python interpreter will print out a traceback. This traceback includes information about the location of the error and the values of the variables at that point in the code. However, these tracebacks can be difficult to understand, especially for beginners.
This is why it’s important to provide meaningful error messages. By doing so, you can make it easier for yourself and others to debug your code.
Here’s an example of a helpful error message:
python<br>def divide(a, b):<br> try:<br> return a / b<br> except ZeroDivisionError:<br> print("ERROR: Cannot divide by zero.")<br>
This error message is clear and concise. It tells us what went wrong and how to fix it.
On the other hand, here’s an example of an unhelpful error message:
python<br>def divide(a, b):<br> try:<br> return a / b<br> except ZeroDivisionError:<br> print("An error occurred.")<br>
This error message is vague and doesn’t give us any useful information. We have no idea what went wrong or how to fix it.
As you can see, providing meaningful error messages is a valuable best practice in Python error handling. Not only will it make your life easier, but it will also make it easier for others to use and understand your code.
7. Document your code
When you’re writing code, it’s important to think about how someone else might use it. If your code is well-documented, it will be much easier for someone else to understand and use.
Documentation can take many forms, but a few of the most important things to document are:
– The purpose of the code
– Any assumptions that are made
– How to use the code
– Any known issues
By taking the time to document your code, you’ll make it much easier for others to use and maintain.
8. Log errors for later review
If you don’t log errors, you’ll never know about them. And if you don’t know about them, you can’t fix them. Logging is essential for monitoring the health of your Python application and ensuring that it’s running smoothly.
There are a few different ways to log errors in Python. The most common way is to use the logging module. This module provides a flexible and powerful logging system that is widely used in the Python community.
Another popular way to log errors is to use a third-party service like Sentry. Sentry is a cloud-based error tracking service that makes it easy to collect, track, and fix errors in your Python applications.
Finally, you can also log errors to a file. This is often the simplest and most effective way to log errors, especially if you’re not using a third-party service.
No matter which method you choose, make sure that you log all errors, including both fatal and non-fatal errors. By doing so, you’ll have a complete record of all the errors that have occurred in your application, which will make it much easier to debug and fix them.
