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}Structured Files
files-and-data
Read and write CSV and JSON data formats.
Questions
- What problem does Structured Files 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 CSV and JSON structured files 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: Structured Files
CSV represents table-like data; JSON represents nested data built from dictionaries, lists, strings, numbers, booleans, and null-like values.
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 JSON example as nested Python-style data before thinking about files.
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 profile dictionary contains a string and a list of numbers.
json.dumpsconverts the Python data structure into JSON text.json.loadsconverts JSON text back into Python data.- The final line uses a dictionary key and then a list index to reach the first score.
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
Add a new key such as "city" and inspect the JSON text. Notice that the file format preserves labels, not just values.
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: Add a new key such as "city" and inspect the JSON text.
Debugging checkpoint 1.1
WarningDebugging checkpoint
Do not parse CSV by splitting every line on commas unless you know the data is extremely simple. Real CSV files can contain commas inside quoted values. Use the csv module or pandas when appropriate. For JSON, check brackets and quotes when a decoder error appears.
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 a tiny contacts dataset as a list of dictionaries, save it as JSON, load it again, and print one contact’s email. Then describe whether CSV or JSON would fit the same data better.
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
Read and write CSV and JSON data formats.
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 Structured Files 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. CSV Files — Read and write tabular data in comma-separated value files.
- 2. JSON Files — Use JSON for nested data that can be shared between programs.
1. CSV Files
Read and write tabular data in comma-separated value files.
TipAnalogy
A CSV file is a plain-text spreadsheet: each line is a row and commas separate cells.
What this means
CSV stores rows and columns as text, making it common for spreadsheets and simple datasets.
Example 1
Predict what will happen before you run the code.
Step-by-step explanation
import csv— pause here and say what this line reads, creates, changes, or displays.with open("scores.csv", newline="", encoding="utf-8") as file:— pause here and say what this line reads, creates, changes, or displays.reader = csv.DictReader(file)— pause here and say what this line reads, creates, changes, or displays.for row in reader:— pause here and say what this line reads, creates, changes, or displays.print(row["name"], row["score"])— 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
- Copy Example 1 into Colab, Jupyter, or a
.pyfile. - 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
WarningCommon mistake
CSV values are text when read. Convert numeric columns before arithmetic.
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.
2. JSON Files
Use JSON for nested data that can be shared between programs.
TipAnalogy
JSON is like a labeled shipping box: programs in different places can read the labels and contents.
What this means
JSON stores dictionaries, lists, strings, numbers, booleans, and null in a language- independent text format.
Example 2
Predict what will happen before you run the code.
Step-by-step explanation
import json— pause here and say what this line reads, creates, changes, or displays.text = '{"name": "Ada", "scores": [90, 95]}'— pause here and say what this line reads, creates, changes, or displays.data = json.loads(text)— pause here and say what this line reads, creates, changes, or displays.print(data["scores"][0])— 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
- Copy Example 1 into Colab, Jupyter, or a
.pyfile. - 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
WarningCommon mistake
JSON uses true/false/null, while Python uses True/False/None. Use json.loads instead of hand-parsing.
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.
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
- CSV Files: CSV stores rows and columns as text, making it common for spreadsheets and simple datasets.
- JSON Files: JSON stores dictionaries, lists, strings, numbers, booleans, and null in a language- independent text format.
- Use the section quizzes as gates: review before moving on if a quiz feels uncertain.
References
- csv documentation: https://docs.python.org/3/library/csv.html
- pathlib documentation: https://docs.python.org/3/library/pathlib.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/