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 an `addSlow()` method for adding an element `x` to a `LinearHashTable`, which simply stores `x` in the first null array entry it finds.

# Solution

The `addSlow()` method uses linear probing to handle collisions by sequentially checking the next available slot until an empty slot is found.

"""Some base classes inherited by others"""

class BaseCollection(object):
    """Base class for everything"""
    def __init__(self):
        super(BaseCollection, self).__init__()
    
    def size(self):
        """This implementation works for almost every class in ODS"""
        return self.n
    
    def __len__(self):
        return self.size()
    
    def __str__(self):
        return "[" + ",".join([str(x) for x in self]) + "]"
    
    def __repr__(self):
        return self.__class__.__name__ \
            + "(["+ ",".join([repr(x) for x in self]) +"])"


class BaseSet(BaseCollection):
    """Base class for Set implementations"""
    def __init__(self):
        super(BaseSet, self).__init__()
                
    def add_all(self, a):
        for x in a:
            self.add(x)
            
    def __in__(self, x):
        return self.find(x) is not None

    def __eq__(self, a):
        if len(a) != len(self): return False
        for x in self:
            if not x in a: return False
        for x in a:
            if not x in self: return False
        return True

    def __ne__(self, a):
        return not self == a


class BaseList(BaseCollection):
    """Base class for List implementations"""
    def __init__(self):
        super(BaseList, self).__init__()
        
    def append(self, x):
        self.add(self.size(), x)

    def add_all(self, iterable):
        for x in iterable:
            self.append(x)

    def clear(self):
        """This can be overridden with more efficient implementations"""
        while self.size() > 0:
            self.remove(self.size()-1)

    def add_first(self, x):
        return self.add(0, x)
        
    def remove_first(self):
        return self.remove(0)
        
    def add_last(self, x):
        return self.add(self.size(), x)
    
    def remove_last(self):
        return self.remove(self.size()-1)
        
    def insert(i, x):
        self.add(i, x)
 
    def __iter__(self):
        """This implementation is good enough for array-based lists"""
        for i in range(len(self)):
            yield(self.get(i))

    def __eq__(self, a):
        if len(a) != len(self): return False
        it1 = iter(a)
        it2 = iter(self)
        for i in range(len(a)):
            if it1.next() != it2.next(): return False
        return True

    def __ne__(self, a):
        return not self == a
    
    def index(self, x):
        i = 0
        for y in self:
            if y == x:
                return i
            i += 1
        raise ValueError('%r is not in the list' % x)
    
    def remove_value(self, x):
        try:
            return self.remove(self.index(x))
        except ValueError:
            return False

    def __getitem__(self, key):
        return self.get(key)

    def __setitem__(self, key, value):
        return self.set(key, value)

    def __delitem__(self, i):
       self.remove(i)
       
       


import numpy

w = 32

def new_array(n, dtype=numpy.object):
    return numpy.empty(n, dtype)

def _new_array(n):
    return [None]*n
    
def new_zero_array(n):
    return numpy.zeros(n)
    
def new_boolean_matrix(n, m):
    return numpy.zeros([n, n], numpy.bool_)
    
def new_boolean_array(n):
    return numpy.zeros(n, numpy.bool_)

def new_int_array(n, init=0):
    a = numpy.empty(n, numpy.int32)
    a.fill(init)
    return a

def binfmt(n):
    return "{0:012b}".format(n)

# Explanation

This code snippet defines a method `addSlow` within a class. Here's an explanation of each part:

- **Lines 58–59:** It checks if the element `x` already exists in the data structure by calling the `find()` method of the class. If `find()` returns a non-`None` value, the element is found, and the method returns `False` indicating that the element was not added.

- **Lines 60–61:** It checks if the table needs to be resized. It compares twice the quantity of `(self.q + 1)` (the number of elements in the table plus one) with the length of the table `self.t`. If the condition is true (occupancy is over 50%), the `_resize()` method is called to increase the table size.

- **Line 62:** It calculates the index `i` for the element `x` using the `_hash()` method of the class.

- **Line 63:** It enters a loop that iterates as long as the current index `i` in the table `self.t` is not empty (i.e., `None`).

- **Lines 64–65:** It checks if the element at index `i` is not marked for deletion (`self.t[i] != self.dl`) and if it matches the element `x`. If both conditions are true, the element already exists in the table, and the method returns `False`.

- **Line 66:** It increments the index `i` by `1` and wraps it around to the beginning of the table if it reaches the end (`len(self.t)`).

- **Line 67:** It checks if the current index `i` is empty (i.e., `None`).

- **Line 68:** It increments the number of elements `self.q` in the table.

- **Line 69:** It increments the total count `self.n` of elements added to the table.

- **Line 70:** It assigns the element `x` to the table at index `i`.

- **Line 71:** It returns `True` to indicate that the element `x` was successfully added to the table.

Review the solution that implements the addSlow() method in LinearHashTable.

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: Hash Tables