- When working with collections of data, it's important to group related data items in a way that makes sense for the problem you're trying to solve.
- Grouping data items helps you organise and access information efficiently.
Reason Behind Grouping Data
- Organise related information: Keep data that belongs together in one place.
- Access data efficiently: Retrieve specific information quickly.
- Maintain data integrity: Ensure that related data items stay together.
Examples of Sensible Grouping
Example
- Tabular Representation:
- One common way to group data is using a table, where each row represents a record and each column represents a field.
- Consider a table storing information about students:
- Each row represents a student.
- Each column represents a specific attribute (Student ID, Name, Age, Grade).
| Student ID | Name | Age | Grade |
|---|---|---|---|
| 001 | Alice | 16 | 10 |
| 002 | Bob | 17 | 11 |
| 003 | Carol | 16 | 10 |
Example
- Parallel Lists:
- Another approach is to use parallel lists, where each list stores a specific attribute, and the same index across lists represents a single record.
- Using the student example above, you could have:
- student_ids = [001, 002, 003]
- names = ["Alice", "Bob", "Carol"]
- ages = [16, 17, 16]
- grades = [10, 11, 10]
- Here, names[0] corresponds to student_ids[0], ages[0], and grades[0], representing the same student.
Common Mistake
- We often forget to keep the lists synchronized.
- If you add or remove an item from one list, you must do the same for all other lists to maintain data integrity.
Example
- Dictionaries or Maps:
- Dictionaries (or maps) allow you to group data using key-value pairs.
- For the same student example, you could use:
- students = {001: {"name": "Alice", "age": 16, "grade": 10}, 002: {"name": "Bob", "age": 17, "grade": 11}, 003: {"name": "Carol", "age": 16, "grade": 10}}
- Here, each student ID is a key, and the value is another dictionary containing the student's attributes.
Example
- Let's look at a more detailed example of grouping data for a library system.
- Suppose you want to store information about books, including the title, author, publication year, and whether the book is checked out.
- Using a table:
| Title | Author | Year | Checked Out |
|---|---|---|---|
| 1984 | George Orwell | 1949 | No |
| To Kill a Mockingbird | Harper Lee | 1960 | Yes |
| The Great Gatsby | F. Scott Fitzgerald | 1925 | No |
- Using parallel lists:
- titles = ["1984", "To Kill a Mockingbird", "The Great Gatsby"]
- authors = ["George Orwell", "Harper Lee", "F. Scott Fitzgerald"]
- years = [1949, 1960, 1925]
- checked_out = [False, True, False]
- Using a dictionary:
- books = {"1984": {"author": "George Orwell", "year": 1949, "checked_out": False}, "To Kill a Mockingbird": {"author": "Harper Lee", "year": 1960, "checked_out": True}, "The Great Gatsby": {"author": "F. Scott Fitzgerald", "year": 1925, "checked_out": False}}
Choosing the Right Grouping Method
- The best way to group data depends on:
- The type of data: Is it structured (like a table) or unstructured?
- How you access the data: Do you need to search by a key, or do you access data by index?
- The operations you perform: Do you frequently add or remove items?
Hint
- Tables are great for structured data with fixed attributes.
- Parallel lists work well for simple datasets but require careful synchronization.
- Dictionaries are ideal for key-based access and when attributes may vary.
