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}