Modules and Packages

Organize reusable code into Python files and package folders.

• Level: Beginner to intermediate
• Estimated time: 25–40 minutes
• You will learn: Organize reusable code into Python files and package folders.
• Practice in: VS Code and the terminal

Open this lesson in Google Colab

Questions

• What problem does Modules and Packages 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 modules and packages 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: Modules and Packages

A module is a Python file that can be imported. A package is a folder of modules. Imports connect names from one file to code in another file.

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

Imagine this helper function lives in temperature.py; the script imports it and calls it.

# temperature.py
def c_to_f(celsius):
return celsius * 9 / 5 + 32

# app.py
from temperature import c_to_f
print(c_to_f(20))

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 function definition belongs in a module whose name communicates its topic.
• from temperature import c_to_f loads the function name into the script.
• The script can call the function without copying its body.

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

Rename the file to math.py and import the standard math module elsewhere. This can shadow the real library and create confusing import behavior, which is why file names 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: Rename the file to math.py and import the standard math module elsewhere.

Debugging checkpoint 1.1

WarningDebugging checkpoint

If an import fails, check the file name, current folder, spelling, and whether you are running the command from the project root. Circular imports can happen when two modules need each other at import time; move shared logic to a third module or simplify the dependency.

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

Split a small calculator into operations.py and app.py. Import two functions, call them, and then run the script from the project root.

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

Organize reusable code into Python files and package folders.

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 Modules and Packages 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. Modules — Put reusable Python code in .py files and import it elsewhere.
• 2. Packages — Group related modules into folders with importable names.

1. Modules

Put reusable Python code in .py files and import it elsewhere.

TipAnalogy

A module is like a toolbox drawer: open it when you need a tool stored there.

What this means

A module is a Python file that can define functions, classes, and constants for reuse.

Example 1

Predict what will happen before you run the code.

import math
print(math.sqrt(16))

Step-by-step explanation

1. import math — pause here and say what this line reads, creates, changes, or displays.
2. print(math.sqrt(16)) — 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

Naming your file the same as a standard library module, such as math.py, can shadow the real module.

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. Packages

Group related modules into folders with importable names.

TipAnalogy

A package is a labeled binder with several sections, not a pile of loose papers.

What this means

A package is a folder of modules that belong together.

Example 2

Predict what will happen before you run the code.

from pathlib import Path
print(Path("src") / "my_project")

Step-by-step explanation

1. from pathlib import Path — pause here and say what this line reads, creates, changes, or displays.
2. print(Path("src") / "my_project") — 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

Imports depend on project layout. Run code from the project root while learning to reduce path surprises.

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

• Modules: A module is a Python file that can define functions, classes, and constants for reuse.
• Packages: A package is a folder of modules that belong together.
• Use the section quizzes as gates: review before moving on if a quiz feels uncertain.

References

• Python modules tutorial: https://docs.python.org/3/tutorial/modules.html
• venv documentation: https://docs.python.org/3/library/venv.html
• argparse documentation: https://docs.python.org/3/library/argparse.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/