Gain insights into array-based and linked list-based data structures, explore advanced structures like skiplists and hashing, and discover template-based collections for efficient data storage and retrieval.

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Data structures and algorithms are essential in computer science since they play a crucial role in efficient information retrieval and processing, dealing with files, storing contacts on phones, social networks and web searches.

In this course, you’ll learn about the array-based implementation of various linear data structures, stack, and queues. You’ll also learn about linked list-based implementation. Next, you’ll explore advanced data structures like skiplists and hashing. You’ll learn how to implement a variety of trees and graphs, and data structures related to bits of an integer. Toward the end of the course, you’ll learn the implementation of structures based on external storage.

After completing this course, you’ll be able to create reusable programs with template-based collections that can efficiently analyze how to optimize the storage and retrieval of very large amounts of data. Overall, this course will enhance your productivity and performance as a software developer.

Data Structures with Generic Types in Python

# Task
Here is the solution that implements the `List` method `addAll(i,c)` inserts all elements of the `Collection`, `c`, into the list at position `i`. (The `add(i, x)` method is a special case where `c = {x}.`) Explain why, for the data structures in this chapter, it is not efficient to implement `addAll(i,c)` by repeated calls to `add(i,x)`. Design and implement a more efficient implementation.


# Solution



The reason why implementing `addAll(i, c)` by repeated calls to `add(i, x)` is not efficient is the following: 

If we consider an `ArrayList` as the underlying data structure, the `add(i, x)` operation has a time complexity of $O(n)$ because it requires shifting all the elements after the insertion point to make room for the new element. It also checks and calls the `resize()` operation that adds one more memory cell and copies the whole existing array on each call to `add(i,x)`. Therefore, if we use `add(i, x)` in a loop to add each element from the collection `c`, the time complexity would be $O(n^2)$, as we would have to shift elements and copy the whole array multiple times for each insertion.

To design a more efficient implementation for `addAll(i, c)`, we can add the efficiency in the following way: adding all needed cells in a single call to the modified `resize()` and allowing efficient insertion by inserting multiple elements at a specific position shifting all affected elements only once.

We have implemented the conventional `addAll()` as `addAll1()` and the efficient version as `addAll2()`. We have also implemented the efficient `resize2()` method to replace the conventional `resize()` to avoid multiple calls of the same.

Review the solution to a more efficient implementation of the addAll() method in array-based lists.

Overview

Array-Based Lists

Linked Lists

Skiplists

Hash Tables

Binary Trees

Random Binary Search Trees

Scapegoat Trees

Red-Black Trees

Heaps

Sorting Algorithms

Graphs

Data Structures for Integers

External Memory Searching

Wrap Up

Solution: Array-Based Lists

Task

Solution