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how to handle assertion errors properly

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## Handling Assertion Errors Properly: A Comprehensive Guide

Assertion errors are a crucial part of defensive programming and testing. They act as sanity checks, helping you verify that your code is behaving as expected. However, a poorly handled assertion error can be worse than no assertion at all. This tutorial provides a detailed look at how to use assertions effectively and what to do when they fail, covering various aspects from best practices to advanced techniques.

**1. What are Assertions?**

At its core, an assertion is a statement that evaluates to a boolean expression. You "assert" that a condition is true. If the condition is true, the program continues execution. If the condition is false, an `AssertionError` exception is raised, halting the program (by default).

**Why use assertions?**

* **Debugging:** They quickly expose unexpected states and incorrect logic.
* **Documentation:** They clarify assumptions about the code and its data. A failed assertion acts as a strong signal of a violation of that documented assumption.
* **Testing:** They are fundamental to unit testing and integration testing.
* **Defensive Programming:** They help catch errors early in development, preventing them from causing more significant problems later on. They act as guardrails.

**2. The `assert` Statement in Python (and similar constructs in other languages)**

In Python, the `assert` statement has the following syntax:
* `expression`: A boolean expression that is expected to be true.
* `optional_message`: An optional string message that is displayed when the assertion fails. This message helps in understanding the reason for the failure.

**Example:**
**Explanation:**

1. The `divide` function uses `assert y != 0` to check if the divisor is zero.
2. If `y` is zero, the assertion fails, and an `AssertionError` is raised with the message "Cannot divide by zero!".
3. If `y` is not zero, the division proceeds normally.
4. The `try...except` block demons ...

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