Function Contracts

Use docstrings and type hints to communicate how functions should be used.

• Level: Beginner
• Estimated time: 35–50 minutes
• You will learn: Use docstrings and type hints to communicate how functions should be used.
• Practice in: Google Colab, Jupyter, or VS Code

Open this lesson in Google Colab

Questions

• What problem does Function Contracts 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 function contracts 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: Function Contracts

Docstrings, type hints, clear names, and examples communicate a function’s contract. They do not replace tests, but they make the function easier to read and safer to change.

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

Read the contract before reading the body, then predict what the function should return.

def add_tax(price: float, tax_rate: float) -> float:
"""Return price after applying tax_rate as a decimal."""
return price * (1 + tax_rate)

print(add_tax(100, 0.07))

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 name add_tax says the purpose in a verb phrase.
• The type hints say both inputs should be numbers that may contain decimals.
• The docstring explains that 0.07 means seven percent, which avoids a common ambiguity.
• The return annotation says callers should expect a floating-point number back.

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

NoteChallenge

Call add_tax(100, 7) and notice the result is much too large. The type is still allowed, but the meaning violates the contract. This is why examples and assumptions matter.

Show a safe way to approach the challenge

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

Suggested first move: Call add_tax(100, 7) and notice the result is much too large.

Debugging checkpoint 1.1

WarningDebugging checkpoint

Type hints do not convert values. If price is the string "100", the annotation price: float will not magically turn it into a float at runtime. Convert and validate data before calling the function.

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

Add a docstring and type hints to three simple functions: one that formats a name, one that computes an average, and one that checks whether a score passes.

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

Use docstrings and type hints to communicate how functions should be used.

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.

NoteGuiding questions

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

• How do the sections in Function Contracts fit together?
• Which small example demonstrates each section?
• Which debugging clue should I check first for each section?

NoteLearning 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. Docstrings — Document what functions are for, what inputs they expect, and what they return.
• 2. Type Hints — Add optional type information to make function contracts clearer.

1. Docstrings

Document what functions are for, what inputs they expect, and what they return.

TipAnalogy

A docstring is a label on a tool drawer: it tells the next person what the tool is for before they open it.

What this means

A docstring is the first string inside a function and explains its purpose to humans and tools.

Example 1

Predict what will happen before you run the code.

def area(width, height):
    """Return the area of a rectangle."""
    return width * height

Step-by-step explanation

1. def area(width, height): — pause here and say what this line reads, creates, changes, or displays.
2. """Return the area of a rectangle.""" — pause here and say what this line reads, creates, changes, or displays.
3. return width * height — 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

NotePractice

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

Show one possible solution path

1. Copy Example 1 into Colab, Jupyter, or a .py file.
2. Mark the line you plan to change.
3. Write a one-sentence prediction.
4. Run the changed code.
5. 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

WarningCommon mistake

A docstring should explain why and what, not repeat every obvious line of code.

When you get stuck, use this debugging routine:

1. Read the last line of the error message or inspect the unexpected output.
2. Find the smallest line of code that could be responsible.
3. Print or inspect the value and type at that point.
4. Change one thing.
5. 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. Type Hints

Add optional type information to make function contracts clearer.

TipAnalogy

Type hints are labels on electrical outlets: they tell you what fits safely before you plug it in.

What this means

Type hints document expected value types and help tools catch mistakes before runtime.

Example 2

Predict what will happen before you run the code.

def repeat(word: str, times: int) -> str:
    return word * times

print(repeat("ha", 3))

Step-by-step explanation

1. def repeat(word: str, times: int) -> str: — pause here and say what this line reads, creates, changes, or displays.
2. return word * times — pause here and say what this line reads, creates, changes, or displays.
3. print(repeat("ha", 3)) — 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

NotePractice

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

Show one possible solution path

1. Copy Example 1 into Colab, Jupyter, or a .py file.
2. Mark the line you plan to change.
3. Write a one-sentence prediction.
4. Run the changed code.
5. 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

WarningCommon mistake

Type hints are not automatic runtime conversions. Passing “3” to an int parameter does not magically become 3.

When you get stuck, use this debugging routine:

1. Read the last line of the error message or inspect the unexpected output.
2. Find the smallest line of code that could be responsible.
3. Print or inspect the value and type at that point.
4. Change one thing.
5. 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

TipKey points

• Docstrings: A docstring is the first string inside a function and explains its purpose to humans and tools.
• Type Hints: Type hints document expected value types and help tools catch mistakes before runtime.
• Use the section quizzes as gates: review before moving on if a quiz feels uncertain.

References

• Defining functions: https://docs.python.org/3/tutorial/controlflow.html#defining-functions
• 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/