Lesson Notes By Weeks and Term v5 - Grade 12

Lesson Video

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Performance objectives

• Define and explain complex data structures like arrays, records, and lists.
• Implement basic operations on arrays and records.
• Read and write data to text files and handle exceptions.
• Implement basic data validation when reading from files.
• Explain data persistence and file organization.

Lesson summary

Welcome to the exciting world of advanced solution development! This week, we'll dive into complex data structures and files, a crucial aspect of Information Technology that directly impacts the apps we use, the websites we browse, and even the systems that power our economy. Understanding these concepts allows you to build more efficient, reliable, and scalable software solutions. Imagine being able to develop a system that efficiently manages learner records in a large school district, or a mobile application that provides real-time traffic updates for commuters during rush hour in Johannesburg – that's the power of complex data structures and efficient file handling.

Lesson notes

2.1 Complex Data Structures Complex data structures allow us to organize and manage data in more sophisticated ways than simple variables. They enable us to represent relationships between data items and perform operations more efficiently.

Arrays: Arrays are fundamental data structures that store a fixed-size sequence of elements of the same data type. Think of an array as a numbered row of lockers in a school, each locker holding one specific item.

Example: An array to store the marks of 10 students in a class.

Advantages: Fast access to elements using their index (position).

Disadvantages: Fixed size; insertion and deletion can be slow if they require shifting elements.

South African Context: Useful for storing examination marks, population data for different provinces, or rainfall data collected over a period.

Code Example (Python): ```python Creating an array (list in Python) marks = [75, 80, 68, 92, 88, 70, 95, 65, 78, 82] Accessing an element print(marks[0]) # Output: 75 Modifying an element marks[3] = 90 print(marks[3]) # Output: 90 Finding the length of the array print(len(marks)) # Output: 10 ``` Records/Structs (or Classes): Records (also known as structs in some languages like C) and Classes allow us to group together related data items of different data types. In object-oriented languages, classes add behaviour(methods/functions) to data. Imagine a learner's record containing their name, ID number, grade, and address.

Example: A record to store information about a student (name, ID, grade, subjects).

Advantages: Organizes related data; improves code readability.

Disadvantages: Requires defining the structure beforehand.

South African Context: Suitable for managing employee records, patient information in hospitals, or inventory details in a retail store.

Code Example (Python - using a Class): ```python class Student: def __init__(self, name, id_number, grade, subjects): self.name = name self.id_number = id_number self.grade = grade self.subjects = subjects # subjects is a list def display_info(self): print(f"Name: {self.name}") print(f"ID: {self.id_number}") print(f"Grade: {self.grade}") print(f"Subjects: {', '.join(self.subjects)}") # Join the subjects list into a string Creating a Student object student1 = Student("Zanele Mbeki", "1234567890", 12, ["Maths", "Science", "English"]) Accessing data members print(student1.name) # Output: Zanele Mbeki Calling a method student1.display_info() ``` Lists/Linked Lists: Lists (and their more complex variations like linked lists) are dynamic data structures that can grow or shrink in size. Each element (node) contains data and a pointer (link) to the next element in the list. Think of a train where each carriage is connected to the next.

Example: A list of students in a queue.

Advantages: Dynamic size; efficient insertion and deletion.

Disadvantages: Slower access to elements compared to arrays (requires traversing the list).

South African Context: Useful for managing waiting lists at clinics, queues at government service offices, or a playlist of songs in a music application. Linked Lists are usually implemented from scratch and are more memory efficient compared to python Lists. Code Example (Python - using a List which is a dynamic array): ```python Creating a list students = ["Thabo", "Nomusa", "Sipho"] Adding an element students.append("Lerato") print(students) # Output: ['Thabo', 'Nomusa', 'Sipho', 'Lerato'] Inserting an element at a specific position students.insert(1, "Mandla") print(students) # Output: ['Thabo', 'Mandla', 'Nomusa', 'Sipho', 'Lerato'] Removing an element students.remove("Nomusa") print(students) # Output: ['Thabo', 'Mandla', 'Sipho', 'Lerato'] ``` 2.2 File Handling File handling involves reading data from and writing data to files. This allows us to store data persistently, meaning the data remains available even after the program has finished running. This is critical for almost all modern applications.

Opening a file: Before reading or writing, you must open a file. The `open()` function typically takes the file name and mode (read, write, append) as arguments.

Reading from a file: Use functions like `read()`, `readline()`, or `readlines()` to read data from the file.

Writing to a file: Use functions like `write()` or `writelines()` to write data to the file.

Closing a file: Always close the file after you're done with it using the `close()` function to release system resources and ensure data is saved correctly. Using `with open()` simplifies file handling, handling automatic closing even if errors occur.

Exception Handling: It’s crucial to handle potential errors like `FileNotFoundError` when opening files.

Data Validation: Verify that the data read from a file is in the expected format and range. This helps prevent errors and ensures data integrity.