Exceptions and Validation

files-and-data

Handle expected failures and validate user input before trusting it.

Level: Beginner to intermediate
Estimated time: 35–50 minutes
You will learn: Handle expected failures and validate user input before trusting it.
Practice in: VS Code, Jupyter, and local files

Open this lesson in Google Colab

Questions

What problem does Exceptions and Validation help us solve in a small Python program?
What should we predict before running the example?
What value, output, or error should we inspect after changing one line?

Objectives

Run a complete example for exceptions and validation in Colab.
Explain the example line by line using plain language.
Change one part of the code and predict the result before running it.
Recognize one common mistake and use the error message as evidence.

Hands-on episode: Exceptions and Validation

Validation checks data before using it. try and except let you recover from specific errors you understand and can handle.

We will learn this by running code, not by memorizing a definition first. Open the Colab notebook from the button above, find this section, and run each cell in order. Keep a small note beside the notebook with three columns: prediction, actual result, and what changed.

Example 1.1

Predict which inputs become numbers and which ones need a helpful message.

text = "12"
try:
age = int(text)
print(age + 1)
except ValueError:
print("Please enter a whole number.")

Run the cell once without editing it. If the result is different from your prediction, leave the prediction visible and write one sentence about the difference. That sentence is more useful than a perfect first guess.

Explain Example 1.1

The risky operation is int(text) because not every string can become an integer.
If conversion works, Python continues inside the try block and prints the next age.
If conversion fails with ValueError, Python jumps to the matching except block.

Now explain the example out loud or in a Markdown cell. Use short sentences: “this line creates…”, “this name stores…”, “this output appears because…”. If you cannot explain a line yet, run only the lines above it and inspect the values that exist at that moment.

Challenge 1.1

Challenge

Change text to "twelve". The program should not crash; it should print the planned recovery message. That is different from hiding every possible error.

Show a safe way to approach the challenge

Copy Example 1.1 into a new Colab cell.
Change exactly one value, name, condition, or line.
Write the expected output before running the cell.
Run the cell and compare the actual result with your prediction.
If the result surprises you, undo the change and try a smaller one.

Suggested first move: Change text to "twelve".

Debugging checkpoint 1.1

Debugging checkpoint

Avoid a bare except: because it can hide mistakes you did not intend to handle. Catch the specific error you understand, keep the recovery message honest, and let unknown bugs remain visible.

Do not debug by rewriting the whole example. Read the error type or surprising output, inspect the closest value with print(...) or type(...), then change one thing. This is the same routine you will use in larger projects.

Apply it

Write a loop that asks for a price, converts it with float, rejects negative numbers, and keeps asking until the input is valid. Test it with good input, text input, and negative input.

Finish by adding a Markdown cell that answers: What did this example teach me that I can reuse in a project?

Key points

Learn the concept by running a complete, small example first.
Predict before execution so your thinking becomes visible.
Change one thing at a time so cause and effect stay clear.
Treat errors as clues about the exact line or value Python could not handle.

Why this matters

Handle expected failures and validate user input before trusting it.

This lesson combines related subtopics that belong together in one learning conversation. You will still pause for a quiz after each section, but you do not need to jump between separate pages while building one clear explanation.

Guiding questions

By the end of this lesson, you should be able to answer:

How do the sections in Exceptions and Validation fit together?
Which small example demonstrates each section?
Which debugging clue should I check first for each section?

Learning objectives

You will practice how to:

explain the shared concept for this lesson;
use each section as one step in a larger workflow;
complete 2 short section quizzes before moving on;
connect examples, mistakes, and debugging routines.

Lesson map

1. Exceptions — Handle expected failures with try and except.
2. Validating User Input — Check user input before trusting it in calculations or file operations.

How validation handles unsafe input

Validation prevents many errors. Exception handling gives a planned response for failures you still expect might happen.

flowchart TD
  input["User input"] --> valid{"Looks valid?"}
  valid -- No --> message["Show helpful message"]
  valid -- Yes --> tryblock["try risky conversion or file operation"]
  tryblock --> success["Use result"]
  tryblock --> except["except expected error"]
  except --> recover["Explain and recover safely"]
  success --> continue([Continue])
  recover --> continue

Use validation for checks you can make before the risky operation, and except for failures raised during the operation.

1. Exceptions

Handle expected failures with try and except.

Analogy

An exception handler is like a safety net under a tightrope: it does not remove risk, but it gives a planned response.

What this means

An exception interrupts normal flow when something goes wrong; handling catches a specific expected problem.

Example 1

Predict what will happen before you run the code.

text = "abc"
try:
    number = int(text)
except ValueError:
    print("Please enter digits only.")

Step-by-step explanation

text = "abc" — pause here and say what this line reads, creates, changes, or displays.
try: — pause here and say what this line reads, creates, changes, or displays.
number = int(text) — pause here and say what this line reads, creates, changes, or displays.
except ValueError: — pause here and say what this line reads, creates, changes, or displays.
print("Please enter digits only.") — pause here and say what this line reads, creates, changes, or displays.

After running the example, compare the actual output with your prediction. If they differ, do not erase your prediction. The difference is the part that can teach you the most.

Challenge

Practice

Change one input value, predict the new output, run the code, and explain the difference in one sentence.

Show one possible solution path

Copy Example 1 into Colab, Jupyter, or a .py file.
Mark the line you plan to change.
Write a one-sentence prediction.
Run the changed code.
If the result surprises you, restore the original and change a smaller part.

The goal is not to find the only correct answer. The goal is to create a small experiment where you can explain cause and effect.

Common mistakes

Common mistake

Catching every exception with bare except hides real bugs. Catch the specific error you expect.

When you get stuck, use this debugging routine:

Read the last line of the error message or inspect the unexpected output.
Find the smallest line of code that could be responsible.
Print or inspect the value and type at that point.
Change one thing.
Run again and record what changed.

Check your understanding

This quiz checks the ideas in this section before you move on.

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2. Validating User Input

Check user input before trusting it in calculations or file operations.

Analogy

Validation is a door checklist: do you have a ticket, is it for today, and is it for this room?

What this means

Validation confirms that input has the shape and meaning your program needs.

Example 2

Predict what will happen before you run the code.

age_text = input("Age: ")
if age_text.isdigit():
    age = int(age_text)
    print(age + 1)
else:
    print("Use digits only.")

Step-by-step explanation

age_text = input("Age: ") — pause here and say what this line reads, creates, changes, or displays.
if age_text.isdigit(): — pause here and say what this line reads, creates, changes, or displays.
age = int(age_text) — pause here and say what this line reads, creates, changes, or displays.
print(age + 1) — pause here and say what this line reads, creates, changes, or displays.
else: — pause here and say what this line reads, creates, changes, or displays.
Continue the same process for the remaining lines, one line at a time.

After running the example, compare the actual output with your prediction. If they differ, do not erase your prediction. The difference is the part that can teach you the most.

Challenge

Practice

Change one input value, predict the new output, run the code, and explain the difference in one sentence.

Show one possible solution path

Copy Example 1 into Colab, Jupyter, or a .py file.
Mark the line you plan to change.
Write a one-sentence prediction.
Run the changed code.
If the result surprises you, restore the original and change a smaller part.

The goal is not to find the only correct answer. The goal is to create a small experiment where you can explain cause and effect.

Common mistakes

Common mistake

Validation should explain what went wrong and how to fix it, not only say invalid.

When you get stuck, use this debugging routine:

Read the last line of the error message or inspect the unexpected output.
Find the smallest line of code that could be responsible.
Print or inspect the value and type at that point.
Change one thing.
Run again and record what changed.

Check your understanding

This quiz checks the ideas in this section before you move on.

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Notebook and Colab practice

Open a blank notebook at https://colab.new. Use one section at a time: copy the Example 1, predict the result, run it, answer the section quiz, and then move to the next section. This is better than copying the entire page at once.

Instructor note

Teaching notes

Treat each section as a short teaching episode.
Pause for the section quiz before introducing the next section.
Ask learners to compare sections: what stayed the same, and what changed?
If time is short, teach the first two sections live and assign the rest as practice.

Key points

Exceptions: An exception interrupts normal flow when something goes wrong; handling catches a specific expected problem.
Validating User Input: Validation confirms that input has the shape and meaning your program needs.
Use the section quizzes as gates: review before moving on if a quiz feels uncertain.

References

pathlib documentation: https://docs.python.org/3/library/pathlib.html
csv documentation: https://docs.python.org/3/library/csv.html
json documentation: https://docs.python.org/3/library/json.html
Python Tutorial: https://docs.python.org/3/tutorial/
Quarto OJS documentation: https://quarto.org/docs/interactive/ojs/
ipywidgets documentation: https://ipywidgets.readthedocs.io/en/stable/