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In this section, you’ll learn the basics of using the assert statement to introduce assertions in your code. This behavior can be a common source of confusion and issues, as you’ll learn later in this tutorial. First, note that Python implements assertions as a statement with the assert keyword rather than as a function. It’s time to learn the basics of writing your own assertions. Now you know what assertions are, what they’re good for, and when you shouldn’t use them in your code.
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In this regard, you shouldn’t write code that catches assertion errors using a regular try … except statement. The ultimate purpose of assertions isn’t to handle errors in production but to notify you during development so that you can fix them. Using assertions for data processing and validation is a common pitfall, as you’ll learn in Understanding Common Pitfalls of assert later in this tutorial.Īdditionally, assertions aren’t an error-handling tool. In general, you shouldn’t use assertions for data processing or data validation, because you can disable assertions in your production code, which ends up removing all your assertion-based processing and validation code. Now you’ll learn the basics of when you shouldn’t use assertions. You’ll learn more about these common use cases of assertions later in this tutorial. You can write concise and to-the-point test cases because assertions provide a quick way to check if a given condition is met or not, which defines if the test passes or not. In this context, their main advantage is their ability to take concrete action instead of being passive, as comments and docstrings are.įinally, assertions are also ideal for writing test cases in your code. That’s why they’re also an efficient tool for documenting code. In Python, they can also include an optional message to unambiguously describe the error or problem at hand. Programmers also place assertions before functions’ return values to check if the output is valid (postconditions).Īssertions make it clear that you want to check if a given condition is and remains true. For example, programmers often place assertions at the beginning of functions to check if the input is valid (preconditions). In practice, you can use assertions to check preconditions and postconditions in your programs at development time. In this context, assertions mean Make sure that this condition remains true. The primary role of assertions is to trigger the alarms when a bug appears in a program. These use cases include documenting and testing your code. However, they can have other interesting use cases within your development process. They’ll help you ensure that you don’t introduce new bugs while adding features and fixing other bugs in your code. What Are Assertions Good For?Īssertions are mainly for debugging. These kinds of checks can help you catch errors as soon as possible when you’re developing a program. For example, you can test conditions along the lines of This argument is not None or This return value is a string. By doing so, you can check assumptions like preconditions and postconditions. With assertions, you can set checks to make sure that invariants within your code stay invariant. If the condition turns out to be false, then the assertion raises an exception and terminates the execution of your program. The assertion condition should always be true unless you have a bug in your program. Assertions allow you to test the correctness of your code by checking if some specific conditions remain true, which can come in handy while you’re debugging code. In Python, assertions are statements that you can use to set sanity checks during the development process. Having a basic understanding of documenting, debugging, and testing Python code is also a plus. To get the most out of this tutorial, you should have previous knowledge of expressions and operators, functions, conditional statements, and exceptions.
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