Hashmap in python

Hash maps are indexed data structures. A hash map makes use of a Hash function is the core of implementing a hash map. It takes in the key and translates it to the index of a bucket in the bucket list. Ideal hashing should produce a different index for each key. However, collisions can occur. When hashing gives an existing index, we can simply use a bucket for multiple values by appending a list or by rehashing. • set_val(key, value): Inserts a key-value pair into the hash map. If the value already exists in the hash map, update the value. • get_val(key): Returns the value to which the specified key is mapped, or “No record found” if this map contains no mapping for the key. • delete_val(key): Removes the mapping for the specific key if the hash map contains the mapping for the key. Below is the implementation. Time Complexity: Memory index access takes constant time and hashing takes constant time. Hence, the search complexity of a hash map is also constant time, that is, O(1). Advantages of HashMaps ● Fast random memory access through hash functions ● Can use negative and non-integral values to access the values. ● Keys can be stored in sorted order hence can iterate over the maps easily. Disadvantages of HashMaps ● Collisions can cause large penalties and can blow up the time complexity to linear. ● When the number of keys is large, a single hash function often causes collisions. Applications of HashMaps ● These have applications in implementations of Cache ...

Data requires multiple ways to be accessed or stored. Hash Tables and Hashmaps happen to be the best data structures to implement this in Python via the built-in data type known as a dictionary. A Hashmap or a Hash table in data structure maps keys to its value pairs and uses a function that computes any index value holding the elements to be inserted, searched, or removed. This helps make data access easier and faster. Hash tables generally store key-value pairs and use the hash function for generating a key. In this article, you will learn what Hash Tables and Hashmaps are and how they are implemented in Python. What is a Hash table or a Hashmap Python? A hash table or hashmap Python is an indexed data structure. It uses hash functions to compute an index by using a key into an array of slots or buckets. You can map its value to a bucket using the corresponding index, and the key is immutable and unique. Hashmaps are similar to a cabinet of drawers labeled with the things they store. For instance, hashmaps can store user information like the first and last name, etc., in the bucket. The hash function is integral for the implementation of a hashmap. It uses the key and translates it to the bucket’s index in the bucket list. Ideal hashing produces a separate index for every key. However, keep in mind that collisions can occur. When hashing produces an already existing index, a bucket for multiple values can be easily used by rehashing or appending a list. In Python, an exa...

• DSA Introduction • What is an algorithm? • Data Structure and Types • Why learn DSA? • Asymptotic Notations • Master Theorem • Divide and Conquer Algorithm • Data Structures (I) • Stack • Queue • Types of Queue • Circular Queue • Priority Queue • Deque • Data Structures (II) • Linked List • Linked List Operations • Types of Linked List • Hash Table • Heap Data Structure • Fibonacci Heap • Decrease Key and Delete Node Operations on a Fibonacci Heap • Tree based DSA (I) • Tree Data Structure • Tree Traversal • Binary Tree • Full Binary Tree • Perfect Binary Tree • Complete Binary Tree • Balanced Binary Tree • Binary Search Tree • AVL Tree • Tree based DSA (II) • B Tree • Insertion in a B-tree • Deletion from a B-tree • B+ Tree • Insertion on a B+ Tree • Deletion from a B+ Tree • Red-Black Tree • Red-Black Tree Insertion • Red-Black Tree Deletion • Graph based DSA • Graph Data Structure • Spanning Tree • Strongly Connected Components • Adjacency Matrix • Adjacency List • DFS Algorithm • Breadth-first Search • Bellman Ford's Algorithm • Sorting and Searching Algorithms • Bubble Sort • Selection Sort • Insertion Sort • Merge Sort • Quicksort • Counting Sort • Radix Sort • Bucket Sort • Heap Sort • Shell Sort • Linear Search • Binary Search • Greedy Algorithms • Greedy Algorithm • Ford-Fulkerson Algorithm • Dijkstra's Algorithm • Kruskal's Algorithm • Prim's Algorithm • Huffman Coding • Dynamic Programming • Dynamic Programming • Floyd-Warshall Algorithm • Longest Common Seque...

Closed 9 years ago. I'm new to python. I have a directory which has many subfolders and files. So in these files I have to replace some specified set of strings to new strings. In java I have done this using HashMap. I have stored the old strings as keys and new strings as their corresponding values. I searched for the key in the hashMap and if I got a hit, I replaced with the corresponding value. Is there something similar to hashMap in Python or can you suggest how to go about this problem. To give an example lets take the set of strings are Request, Response. I want to change them to MyRequest and MyResponse. My hashMap was Key -- value Request -- MyRequest Response -- MyResponse I need an equivalent to this. You need a dict: my_dict = ) >>> a == b == c == d == e True You can read more about dictionaries

I attempted making a hashmap in python, and it was harder, due to some limitations but this is my version of dictionaries in python. Are there any way to simplify or do the same thing in less code along with tips and tricks? class HashMap: def __init__(self, memory): # refers to the length of the bucket self.data = [None] * memory self.memory = memory def _hash(self, key): hashed_value = 0 bucket_length = self.memory string_length = len(key) i = 0 while i bucket_length-1: hashed_value %= bucket_length-1 i += 1 return hashed_value def set(self, key, value): address = self._hash(key) bucket = self.data[address] if not bucket: self.data[address] = [key, value, None] # None refers to next pointer else: while bucket[2] != None: bucket = bucket[2] bucket[2] = [key, value, None] def get(self, key): address = self._hash(key) bucket = self.data[address] if bucket: while bucket[2] != None or key != bucket[0]: bucket = bucket[2] if bucket: return bucket[1] raise KeyError def keys(self): keys_list = [] bucket_list = self.data for bucket in bucket_list: current_bucket = bucket if bucket: while current_bucket != None: keys_list.append(current_bucket[0]) current_bucket = current_bucket[2] return keys_list Your implementation of get is wrong. The code is: def get(self, key): address = self._hash(key) bucket = self.data[address] if bucket: while bucket[2] != None or key != bucket[0]: bucket = bucket[2] if bucket: return bucket[1] raise KeyError The line while bucket[2] != None or key != b...

Have you ever heard of hash maps in programming? They’re a super useful data structure that allows you to store and retrieve key-value pairs efficiently. And guess what? Python’s built-in dictionary is implemented using hash maps. In this article, we will dive into hash maps and explore how they work, how to implement them in Python, and what some common use cases are. We’ll also look at the performance of hash maps compared to other data structures and discuss some advantages and disadvantages of using them. So whether you’re new to programming or just looking to expand your knowledge, let’s get started. Table of Contents • • • • • • • How Hash Map Works Alright, let’s dive into how hash maps work. A hash map is a data structure allowing you to store key-value pairs. The key is used to look up the corresponding value, much like a word in a dictionary. Behind the scenes, hash maps use something called a hash function. This function takes the key as input and returns a hash code, a unique numeric value representing the key. The hash code is then used to determine where the key-value pair should be stored in the When adding a key-value pair to a hash map, the hash code is used to find the appropriate location in the hash map’s underlying array. A hash collision occurs if another key has already been stored at that location. In this case, the hash map uses a collision resolution technique, such as chaining or linear probing, to handle the collision and store the new key-value...