Module 13: Error Handling

Even when we write code carefully, unexpected situations can cause a program to crash during execution. For example:

• An internet connection drops.
• A file we want to open does not exist on the system.
• A user enters text when a number is expected.

In such situations, we want to prevent the program from stopping abruptly (crashing) and instead handle the issue gracefully so the execution can continue. This process is called Error Handling.

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Real-world Analogy

Think of a trapeze artist performing in a circus. Even though they practice extensively, there is always a risk of falling. To prevent injury, a safety net is installed below them. If they fall, the safety net catches them safely.

In Python, Error Handling acts as a safety net for your code.

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Importance in Data Science and AI

In Data Science and AI workflows, we frequently automate tasks like processing thousands of files or scraping data from websites.

• If the 500th file out of 1000 files is corrupted, we do not want the entire program to stop.
• Instead, we want to log the error, skip that file, and continue processing the remaining files.
• Error handling is crucial for building robust applications that can handle real-world data issues.

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What we will learn in this module:

In this module, we will learn:

1. What are Errors?: Understanding the basics of errors and their types.
2. Syntax Errors: Grammatical mistakes in the code that prevent execution.
3. Runtime Errors (Exceptions): Errors that occur during code execution (e.g., ZeroDivisionError, FileNotFoundError).
4. Using try and except: How to catch and handle exceptions to prevent crashes.
5. The finally Block: Code block that runs regardless of whether an error occurred (e.g., closing file streams).
6. Raising Exceptions: Using the raise keyword to trigger custom errors.
7. Practice Exercises: Interview questions and practical problems.