Data Structures- Hashmaps, Sets, Hash Tables, Hashing and Collisions
Observing hashmaps with python dictionaries
- What is a Hashtable/Hashmap?
- What is Hashing and Collision?
- What is a Set?
- Dictionary Example
- Hacks
- Hacks
What is a Hashtable/Hashmap?
A hashtable is a data structure that with a collection of key-value pairs, where each key maps to a value, and the keys must be unique and hashable.
- In Python there is a built in hashtable known as a hash tables.
The primary purpose of a hashtable is to provide efficient lookup, insertion, and deletion operations. When an element is to be inserted into the hashtable, a hash function is used to map the key to a specific index in the underlying array that is used to store the key-value pairs. The value is then stored at that index. When searching for a value, the hash function is used again to find the index where the value is stored.
The key advantage of a hashtable over other data structures like arrays and linked lists is its average-case time complexity for lookup, insertion, and deletion operations.
- The typical time complexity of a hashtable is 0(1) .
What is Hashing and Collision?
Hashing is the process of mapping a given key to a value in a hash table or hashmap, using a hash function. The hash function takes the key as input and produces a hash value or hash code, which is then used to determine the index in the underlying array where the value is stored. The purpose of hashing is to provide a quick and efficient way to access data, by eliminating the need to search through an entire data structure to find a value.
However, it is possible for two different keys to map to the same hash value, resulting in a collision. When a collision occurs, there are different ways to resolve it, depending on the collision resolution strategy used.
Python's dictionary implementation is optimized to handle collisions efficiently, and the performance of the dictionary is generally very good, even in the presence of collisions. However, if the number of collisions is very high, the performance of the dictionary can degrade, so it is important to choose a good hash function that minimizes collisions when designing a Python dictionary.
What is a Set?
my_set = set([1, 2, 3, 2, 1])
print(my_set)
# What do you notice in the output?
#set only contains unique elements, even though the input list had duplicate values.
#
# Why do you think Sets are in the same tech talk as Hashmaps/Hashtables?
#In Python, sets are implemented using dictionaries (which are similar to hashmaps)
# with dummy values for the keys. The hashing mechanism in sets allows for quick membership testing and eliminates duplicate values.
#
lover_album = {
"title": "Lover",
"artist": "Taylor Swift",
"year": 2019,
"genre": ["Pop", "Synth-pop"],
"tracks": {
1: "I Forgot That You Existed",
2: "Cruel Summer",
3: "Lover",
4: "The Man",
5: "The Archer",
6: "I Think He Knows",
7: "Miss Americana & The Heartbreak Prince",
8: "Paper Rings",
9: "Cornelia Street",
10: "Death By A Thousand Cuts",
11: "London Boy",
12: "Soon You'll Get Better (feat. Dixie Chicks)",
13: "False God",
14: "You Need To Calm Down",
15: "Afterglow",
16: "Me! (feat. Brendon Urie of Panic! At The Disco)",
17: "It's Nice To Have A Friend",
18: "Daylight"
}
}
# What data structures do you see?
# Dictionaries
#
# Printing the dictionary
print(lover_album)
print(lover_album.get('tracks'))
# or
print(lover_album['tracks'])
print(lover_album.get('tracks')[4])
# or
print(lover_album['tracks'][4])
lover_album["producer"] = ['Taylor Swift', 'Jack Antonoff', 'Joel Little', 'Taylor Swift', 'Louis Bell', 'Frank Dukes']
# What can you change to make sure there are no duplicate producers?
# You can add a set around the list so that there are no duplicates.
#
# Printing the dictionary
print(lover_album)
lover_album["tracks"].update({19: "All Of The Girls You Loved Before"})
# How would add an additional genre to the dictionary, like electropop?
#
#
# Printing the dictionary
print(lover_album)
for k,v in lover_album.items(): # iterate using a for loop for key and value
print(str(k) + ": " + str(v))
# Write your own code to print tracks in readable format
tracks = lover_album['tracks'] #
#
def search():
search = input("What would you like to know about the album?")
if lover_album.get(search.lower()) == None:
print("Invalid Search")
else:
print(lover_album.get(search.lower()))
search()
# This is a very basic code segment, how can you improve upon this code?
#
#
def search(lover_album):
valid_keys = ", ".join(lover_album.keys())
print(f"Valid search options: {valid_keys}")
while True:
search_query = input("\nWhat would you like to know about the album? (Type 'quit' to exit) ").lower()
if search_query == 'quit':
break
try:
result = lover_album[search_query]
print(f"{search_query.capitalize()}: {result}")
except KeyError:
print("Invalid search. Please try again.")
search(lover_album)
Hacks
- Answer ALL questions in the code segments
- Create a diagram or comparison illustration (Canva).
- What are the pro and cons of using this data structure?
- Dictionary vs List
- Expand upon the code given to you, possible improvements in comments
-
Build your own album showing features of a python dictionary
-
For Mr. Yeung's class: Justify your favorite Taylor Swift song, answer may effect seed
Pros and cons of using a dictionary:
Pros:
Fast key-based lookups, insertions, and deletions due to hashing. Keys can be of any hashable data type, not limited to integers. Allows organizing data in key-value pairs, providing a clear relationship between related data.
Cons:
Consumes more memory compared to lists as it needs to store both keys and values. Dictionaries are unordered, so you cannot rely on the order of elements. Keys must be unique and hashable, which means mutable data types like lists cannot be used as keys.
Dictionary vs List:
Dictionaries are suitable for situations where you need to associate keys with values and quickly look up values based on keys. Lists, on the other hand, are more appropriate when you need an ordered collection of elements, and the position of elements in the collection is important.
The main differences between dictionaries and lists are:
Dictionaries store key-value pairs, while lists store elements only. Dictionaries are unordered, whereas lists are ordered. Dictionary keys must be unique and hashable, whereas lists can have duplicate elements. Dictionary lookups are faster than lists for large datasets due to hashing.
album = {
"title": "Example Album",
"artist": "Example Artist",
"year": 2023,
"genre": ["Rock", "Alternative"],
"tracks": {
1: "Intro",
2: "First Hit",
3: "Midnight Drive",
4: "Lost and Found",
5: "Summer Days",
6: "Fading Memories",
7: "Outro"
},
"producers": ["Producer A", "Producer B"],
"label": "Example Record Label",
"duration_minutes": 42
}
# Example of adding a new key-value pair
album["rating"] = 4.5
# Example of updating a key-value pair
album["year"] = 2024
# Example of deleting a key-value pair
del album["duration_minutes"]
# Example of checking if a key exists
if "label" in album:
print(f"Label: {album['label']}")
# Example of iterating through the dictionary keys and values
for key, value in album.items():
print(f"{key.capitalize()}: {value}")