Arduino Sensor and Electronics Prototyping Course in Shinjuku

1. Introduction: Advancing Loops to Structured Data

After learning how to apply loops across fundamental sequences like strings, this module advances loop operations to more complex data structures: Python Dictionaries.

In real-world applications and artificial intelligence pipelines, data rarely exists as isolated variables or simple text strings. Instead, it is organized into structured formats. Looping through dictionaries is essential whenever a program needs to:

• Access structured data stored across key-value mappings.
• Modify dictionary data in-place during execution.
• Summarize data collected across multiple records.
• Filter out specific information from large collections to display or process only the required fields.

2. Dictionary Foundations: Key-Value Architecture

A Python dictionary stores data in key-value pairs, where each unique key serves as an index or label to retrieve its corresponding value.

Consider a dictionary storing data for a student named Ali:

• name → "Ali"
• age → 20
• grade → "A"

student = {
    "name": "Ali",
    "age": 20,
    "grade": "A"
}

3. Example 1: Looping Through and Printing Dictionary Keys

When you iterate directly over a dictionary using a for loop, Python defaults to iterating across its keys.

student = {"name": "Ali", "age": 20, "grade": "A"}

# Iterating over the dictionary to retrieve keys
for key in student:
    print(key)

Output & Execution Flow

Running this loop iterates through the student record and extracts each key sequentially on a new line:

name
age
grade

The dictionary contains three keys—name, age, and grade—and the loop prints all three.

4. Example 2: Looping to Extract and Print Values

In many scenarios, programs only need the underlying values rather than the labels. To access each value, iterate over the keys and use standard dictionary indexing (student[key]) inside the loop body:

student = {"name": "Ali", "age": 20, "grade": "A"}

# Looping through keys to index and display values
for key in student:
    print(student[key])

Output & Execution Flow

Ali
20
A

• On the first line, the value of name ("Ali") prints.
• On the second line, the value of age (20) prints.
• On the third line, the value of grade ("A") prints.

5. Example 3: Printing Keys and Values Together

When generating human-readable reports or debugging records, you often need both the key identifier and its corresponding value displayed side-by-side:

student = {"name": "Ali", "age": 20, "grade": "A"}

# Printing both the key and its corresponding value
for key in student:
    print(f"{key}: {student[key]}")

Output & Execution Flow

name: Ali
age: 20
grade: A

Each iteration pairs the attribute name with its data, producing a clean printout of the student's entire profile.

6. Example 4: Advanced Manipulation & Filtering Across Multiple Records

When managing multi-record datasets—such as multiple students within a collection—loops allow you to extract and filter out specific fields while ignoring others.

Scenario: Extracting Academic Records While Omitting Age

Consider a dataset containing information for two students:

1. Ali: Age 20, Grade "A"
2. Sara: Age 22, Grade "B"

students = {
    "student1": {"name": "Ali", "age": 20, "grade": "A"},
    "student2": {"name": "Sara", "age": 22, "grade": "B"}
}

# Looping to filter data: displaying name and grade while omitting age
for s_id in students:
    student_data = students[s_id]
    print(f"Name: {student_data['name']}, Grade: {student_data['grade']}")

Output & Execution Flow

Name: Ali, Grade: A
Name: Sara, Grade: B

• The loop iterates through each student entry one by one.
• Instead of printing every stored attribute, the program selectively filters the data: it omits age and prints only the name and grade.
• This demonstrates how loops can filter and present specific subsets of data from larger dictionaries.