Sets

Store unique values, test membership, and compare groups with set operations.

• Level: Beginner
• Estimated time: 35–50 minutes
• You will learn: Store unique values, test membership, and compare groups with set operations.
• Practice in: Google Colab, Jupyter, or VS Code

Open this lesson in Google Colab

Questions

• What problem does Sets 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 sets 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: Sets

A set is an unordered collection of unique, hashable values. It is ideal for membership tests and comparisons between groups.

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 the unique names and then the overlap between two groups.

python_group = {"Ada", "Grace", "Ada"}
data_group = {"Grace", "Katherine"}
print(python_group)
print(python_group & data_group)

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 duplicate Ada is stored only once because sets keep unique values.
• The printed order may differ because sets are unordered.
• The & operator computes the intersection: values that appear in both sets.

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

Try python_group | data_group for the union and python_group - data_group for the difference. Read each operation as a question about groups, not as punctuation to memorize.

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: Try python_group | data_group for the union and python_group - data_group for the difference.

Debugging checkpoint 1.1

WarningDebugging checkpoint

Do not index a set with [0]; sets do not promise positions. If order matters, use a list. If uniqueness and membership matter, use a set. If a set operation surprises you, write the two groups on paper and circle the members that answer the question.

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

Create two attendance lists in Colab, convert them to sets, find who attended both sessions, who attended only the first, and how many unique learners attended either session.

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

Store unique values, test membership, and compare groups with set operations.

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 Sets 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. Sets — Store unique unordered values and test membership quickly.
• 2. Set Operations — Combine sets with union, intersection, difference, and symmetric difference.

1. Sets

Store unique unordered values and test membership quickly.

TipAnalogy

A set is like a guest list at the door: each name appears once, and membership matters more than position.

What this means

A set keeps one copy of each value and ignores order.

Example 1

Predict what will happen before you run the code.

names = {"Ada", "Grace", "Ada"}
print(names)
print("Ada" in names)

Step-by-step explanation

1. names = {"Ada", "Grace", "Ada"} — pause here and say what this line reads, creates, changes, or displays.
2. print(names) — pause here and say what this line reads, creates, changes, or displays.
3. print("Ada" in names) — 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

Sets do not preserve positions. You cannot reliably ask for set_item[0].

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. Set Operations

Combine sets with union, intersection, difference, and symmetric difference.

TipAnalogy

Set operations are like comparing club rosters: who is in either club, both clubs, or only one club?

What this means

Set operations answer questions about overlap and difference.

Example 2

Predict what will happen before you run the code.

python = {"Ada", "Grace", "Linus"}
data = {"Grace", "Katherine"}
print(python | data)
print(python & data)
print(python - data)

Step-by-step explanation

1. python = {"Ada", "Grace", "Linus"} — pause here and say what this line reads, creates, changes, or displays.
2. data = {"Grace", "Katherine"} — pause here and say what this line reads, creates, changes, or displays.
3. print(python | data) — pause here and say what this line reads, creates, changes, or displays.
4. print(python & data) — pause here and say what this line reads, creates, changes, or displays.
5. print(python - data) — 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

Use set operators on sets, not lists. Convert with set(items) when needed.

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

• Sets: A set keeps one copy of each value and ignores order.
• Set Operations: Set operations answer questions about overlap and difference.
• Use the section quizzes as gates: review before moving on if a quiz feels uncertain.

References

• Python data structures tutorial: https://docs.python.org/3/tutorial/datastructures.html
• Built-in types: https://docs.python.org/3/library/stdtypes.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/