When are arrays inappropriate to use?

Limitations of arrays include: unsuitable for storing heterogeneous data, difficulty in sizing, low search efficiency, and memory waste. Alternatives are: Dictionaries/Hashtables: for storing heterogeneous data and efficient lookups. Linked lists: dynamic resizing and efficient insertion/deletion. Trees: Hierarchical data storage and efficient lookup.

Limitations of Arrays and Alternatives

Introduction
Arrays are a are powerful data structures, but they also have some limitations. Understanding these limitations is critical to choosing the data structure that best suits your specific needs.

Situations not suitable for using arrays

1. Storage of heterogeneous data types
Arrays can only store elements of the same data type. Arrays are not appropriate if you need to store different types of data (such as text, numbers, and Boolean values).

2. Dynamic Sizing
The size of an array is fixed when it is created, which means that elements cannot be easily added or removed from the array. Arrays can become inefficient for collections that change size frequently.

3. Efficiency of finding specific elements
Finding a specific element in an array requires traversing the entire array, and the time complexity is O(n), where n is the length of the array. For large arrays this can be very time consuming.

4. Memory waste
When the array size is larger than required, it will cause memory waste.

Alternatives

1. Dictionary/hash table: store heterogeneous data and fast lookup
Dictionaries (hash tables) use keys Value pairs are used to store data, allowing multiple types of values. They also provide fast lookups with time complexity of O(1).

2. Linked Lists: Dynamic Sizing and Efficient Insertion/Deletion
Linked lists store data in interconnected nodes, allowing dynamic resizing and efficient insertion and deletion of elements.

3. Tree: hierarchical data storage and efficient search
Tree is a hierarchical data structure that can store and search data efficiently. They are very useful when working with large data sets.

Practical Case

Suppose we need to store student information, including their name, age and test scores. We use an array to store information:

students = [
    ["John", 20, 85],
    ["Mary", 21, 90],
    ["Bob", 19, 80]
]

However, if we need to add a new student to the array or delete an existing student, this will be difficult and inefficient. Using a dictionary would be better:

students = {
    "John": {"age": 20, "score": 85},
    "Mary": {"age": 21, "score": 90},
    "Bob": {"age": 19, "score": 80}
}

This not only allows for storing heterogeneous data but also allows for easy addition, deletion and lookup of student information using the student's name as the key.

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