4 Common Data Structures

Originally posted on my blog

1.) Arrays

• A collection of elements identified by an index or a key

example:

ex_arr = [1, 'string', 3, 'four']
print(ex_arr[3])

answer:

four

2.) Linked Lists

• A collection of data elements, called nodes that contain a reference to the next node in the list and holds whatever data the application needs

examples:

the node class

class Node(object):
    def __init__(self, val):
        self.val = val
        self.next = None

    def get_data(self):
        return self.val

    def set_data(self, val):
        self.val = val

    def get_next(self):
        return self.next

    def set_next(self, next):
        self.next = next

the linkedList class

class LinkedList(object):
    def __init__(self, head=None):
        self.head = head
        self.count = 0

    def get_count(self):
        return self.count

    def insert(self, data):
        new_node = Node(data)
        new_node.set_next(self.head)
        self.head = new_node
        self.count += 1

    def find(self, val):
        item = self.head
        while (item != None):
            if item.get_data() == val:
                return item
            else:
                item = item.get_next()
        return None

    def deleteAt(self, idx):
        if idx > self.count:
            return
        if self.head == None:
            return
        else:
            tempIdx = 0
            node = self.head
            while tempIdx < idx-1:
                node = node.get_next()
                tempIdx += 1
            node.set_next(node.get_next().get_next())
            self.count -= 1

    def dump_list(self):
        tempnode = self.head
        while (tempnode != None):
            print("Node: ", tempnode.get_data())
            tempnode = tempnode.get_next()

create a linked list and insert some items

itemlist = LinkedList()
itemlist.insert(38)
itemlist.insert(49)
itemlist.insert(13)
itemlist.insert(15)

itemlist.dump_list()

exercise the list

print("Item count: ", itemlist.get_count())
print("Finding item: ", itemlist.find(13))
print("Finding item: ", itemlist.find(78))

delete an item

itemlist.deleteAt(3)
print("Item count: ", itemlist.get_count())
print("Finding item: ", itemlist.find(38))
itemlist.dump_list()

answer:

Node:  15
Node:  13
Node:  49
Node:  38
Item count:  4
Finding item:  <__main__.Node object at 0x106568990>
Finding item:  None
Item count:  3
Finding item:  None
Node:  15
Node:  13
Node:  49

3.) Stacks and Queues

• Stacks is a collection of operations that supports push and pop operations. The last item pushed is the first one popped.

example:

create a new empty stack

stack = []

push items onto the stack

stack.append(1)
stack.append(2)
stack.append(3)
stack.append(4)

print the stack contents

print(stack)

pop an item off the stack

x = stack.pop()
print(x)
print(stack)

answer:

[1, 2, 3, 4]
4
[1, 2, 3]

• A Stack is a collection of operations that supports push and pop operations. The last item pushed is the first one popped.

example:

from collections import deque

create a new empty deque object that will function as a queue

queue = deque()

add some items to the queue

queue.append(1)
queue.append(2)
queue.append(3)
queue.append(4)

print the queue contents

print(queue)

pop an item off the front of the queue

x = queue.popleft()
print(x)
print(queue)

answer:

deque([1, 2, 3, 4])
1
deque([2, 3, 4])

4.) Hash Tables (Dictionary)

• A data structure that maps keys to its associated values

Benefits:

• Key-to-value maps are unique
• Hash tables are very fast
• For small datasets, arrays are usually more efficient
• Hash tables don't order entries in a predictable way

example:

create a hashtable all at once

items1 = dict(
        {
            "key1": 1, 
            "key2": 2, 
            "key3": "three"
        }
    )
print(items1)

create a hashtable progressively

items2 = {}
items2["key1"] = 1
items2["key2"] = 2
items2["key3"] = 3
print(items2)

replace an item

items2["key2"] = "two"
print(items2)

iterate the keys and values in the dictionary

for key, value in items2.items():
    print("key: ", key, " value: ", value)

Answer:

{'key1': 1, 'key2': 2, 'key3': 'three'}
{'key1': 1, 'key2': 2, 'key3': 3}
{'key1': 1, 'key2': 'two', 'key3': 3}
key:  key1  value:  1
key:  key2  value:  two
key:  key3  value:  3

Real World Examples:

Filter out duplicate items

define a set of items that we want to reduce duplicates

items = ["apple", "pear", "orange", "banana", "apple",
         "orange", "apple", "pear", "banana", "orange",
         "apple", "kiwi", "pear", "apple", "orange"]

create a hashtable to perform a filter

filter = dict()

loop over each item and add to the hashtable

for item in items:
    filter[item] = 0

create a set from the resulting keys in the hashtable

result = set(filter.keys())
print(result)

output:

{
    'kiwi',
    'apple',
    'pear',
    'orange',
    'banana'
}

Find a maximum value

declare a list of values to operate on

items = [6, 20, 8, 19, 56, 23, 87, 41, 49, 53]

def find_max(items):
    # breaking condition: last item in list? return it
    if len(items) == 1:
        return items[0]

    # otherwise get the first item and call function
    # again to operate on the rest of the list
    op1 = items[0]
    print(op1)
    op2 = find_max(items[1:])
    print(op2)

    # perform the comparison when we're down to just two
    if op1 > op2:
        return op1
    else:
        return op2

test the function

print(find_max(items))

output:

6
20
8
19
56
23
87
41
49
53
53
53
87
87
87
87
87
87
87

Counting Items

define a set of items that we want to count

items = ["apple", "pear", "orange", "banana", "apple",
         "orange", "apple", "pear", "banana", "orange",
         "apple", "kiwi", "pear", "apple", "orange"]

create a hashtable object to hold the items and counts

counter = dict()

iterate over each item and increment the count for each one

for item in items:
    if item in counter.keys():
        counter[item] += 1
    else:
        counter[item] = 1

print the results

print(counter)

output:

{'apple': 5, 'pear': 3, 'orange': 4, 'banana': 2, 'kiwi': 1}

Comments

[Aurelia Naiyoma]

Thanks your article was very insightful

[Rodrigo Suárez]

Very useful article. Is very practical to review concepts and get the mind fresh instead of just use one of these data structures. Thanks for sharing.