Logging and Configuration

Record useful program events and separate settings from code.

• Level: Intermediate
• Estimated time: 35–50 minutes
• You will learn: Record useful program events and separate settings from code.
• Practice in: VS Code, Jupyter, or Colab

Open this lesson in Google Colab

Questions

• What problem does Logging and Configuration 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 logging and configuration 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: Logging and Configuration

Logging records events with levels such as debug, info, warning, and error. Configuration stores choices such as file paths, thresholds, and modes outside the main logic.

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 message is routine information and which one asks for attention.

import logging

logging.basicConfig(level=logging.INFO)
limit = 10
logging.info("Using limit %s", limit)
logging.warning("Demo warning: check your input data")

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

• basicConfig sets a simple logging threshold for the program.
• logging.info records ordinary progress information.
• logging.warning records something that may need attention but does not necessarily stop the program.

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

Change the level to logging.WARNING. The info message disappears, but the warning remains. This shows how levels control the amount of detail.

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: Change the level to logging.WARNING.

Debugging checkpoint 1.1

WarningDebugging checkpoint

Do not log secrets such as tokens, passwords, or private data. Also avoid using print as the only diagnostic tool in long-running programs where you need timestamps and severity levels.

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 logging to a small script with one info message, one warning, and one error-handling path. Move one setting, such as an input file name, into a configuration variable.

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

Record useful program events and separate settings from code.

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 Logging and Configuration 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. Logging — Record program events at useful levels instead of relying only on print.
• 2. Configuration — Separate settings from code so programs can change behavior safely.

1. Logging

Record program events at useful levels instead of relying only on print.

TipAnalogy

Logging is a ship captain’s log: record important events so future you can understand the journey.

What this means

Logging writes structured messages about what a program is doing.

Example 1

Predict what will happen before you run the code.

import logging

logging.basicConfig(level=logging.INFO)
logging.info("Starting analysis")

Step-by-step explanation

1. import logging — pause here and say what this line reads, creates, changes, or displays.
2. logging.basicConfig(level=logging.INFO) — pause here and say what this line reads, creates, changes, or displays.
3. logging.info("Starting analysis") — 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

print is fine for quick learning, but logging is better for long-running programs and libraries.

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. Configuration

Separate settings from code so programs can change behavior safely.

TipAnalogy

Configuration is a control panel: change settings without rewiring the machine.

What this means

Configuration stores values such as paths, thresholds, and modes outside core logic.

Example 2

Predict what will happen before you run the code.

from pathlib import Path

DATA_DIR = Path("data")
MAX_ROWS = 100
print(DATA_DIR, MAX_ROWS)

Step-by-step explanation

1. from pathlib import Path — pause here and say what this line reads, creates, changes, or displays.
2. DATA_DIR = Path("data") — pause here and say what this line reads, creates, changes, or displays.
3. MAX_ROWS = 100 — pause here and say what this line reads, creates, changes, or displays.
4. print(DATA_DIR, MAX_ROWS) — 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

Hard-coded secrets or machine-specific paths make code unsafe and difficult to share.

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

• Logging: Logging writes structured messages about what a program is doing.
• Configuration: Configuration stores values such as paths, thresholds, and modes outside core logic.
• Use the section quizzes as gates: review before moving on if a quiz feels uncertain.

References

• logging documentation: https://docs.python.org/3/library/logging.html
• Python functional tools: https://docs.python.org/3/howto/functional.html
• contextlib documentation: https://docs.python.org/3/library/contextlib.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/