What is Hashmap in Python?

Guide to Hashmap in Python – Data must be stored and retrieved in a number of different ways. Hash Tables are one of the most essential implementations. These Hash tables are implemented in Python using the built-in data type dictionary. In this post, you’ll learn about Hash Tables and Hashmaps in Python, as well as how to use dictionaries to construct them.

What is a Hash table or a Hashmap in Python?

A Hash table, also known as a Hashmap, is a form of data structure in computer science that maps keys to value pairs (implement abstract array data types). It works by using a function to calculate an index value, which then retains the elements to be searched, inserted, removed, and so on. This makes data access simple and quick. Hash tables are used to hold key-value pairs, with the key being produced via a hash function.

The built-in dictionary data type in Python is used to implement hash tables or has maps. A hashing function in Python generates the keys of a dictionary. A dictionary’s elements are not ordered and can be altered.

A mapping of employee names and employee IDs, or the names of students and their student IDs, is an example of a dictionary.

Let’s look at the differences between a hash table and a hashmap in Python.

Understading of Hashmap in Java

The HashMap class in Java is available in java.util package. To further understand how Hashmap works in Java, let’s look at a simple example using java then we will move to python. We’ll look at a Hashmap with the keys representing the animal’s name and the values reflecting the animal’s kind. And the data type for both of these will be a string.

We’ll use the following command to create a Hashmap in Java:

HashMap<String, String> bio_data = new HashMap<String, String>();

The preceding piece of code builds a hashmap with two string data type entries.

The following commands can be used to add keys and values:

bio_data.put(“Name”, “John”);
bio_data.put(“Age”, “24”);
bio_data.put(“City”, “Paris”);

The key is the first string we type in, and the value is the second string.

Hashmaps can be used for a variety of purposes, including:

By typing animals, you may see the value of the key. get(“Name”). This will return the value John as a string. Similarly, the command animals can be used to remove an element from the hashmap. remove(“Name”). The Name, John pair will be removed from the hashmap as a result of this. The clean() method can be used to clear the entire hashmap or to remove all of the pairings. bio_data, for example. clear(). Finally, bio_data.size() can be used to get the size of the hashmap.

Then there’s Python.

Understading of Hashmap in Python

So far, we’ve looked at how hashmaps work and how they’re implemented in Java. Now we’ll move on to Python. Hash Tables and Hashmaps are implementes in Python using a built-in dictionary. A dictionary is made up of various (key, value) pair items, as the name implies. Every term in a dictionary has a definition, and dictionaries in Python are no different. A dictionary can be as simple as a mapping of student names to their roll numbers or employee names to their associated employee IDs. In addition, the dictionary’s elements are not in order and are mutable. That is, they can be inserted, deleted, or cleared.

In Python, we can create and initialize dictionaries in two different methods. The following are some of them:

  • Using curly braces ({})
  • Using dict() function

Let’s have a look at the first approach.

Bio_data={‘Name : ‘John’ , ‘Age: ‘24 , ‘City: ‘Paris}

The command above generates the dictionary bio_data, which has the above entries. The dictionary is three words long and has the type of a dictionary (which can be found using the command — type(bio_data)).

A dictionary can also be initialize with the dict() command. As an example

bio_data = dict()
type(bio_data)

The code above builds a new dict dictionary and returns a type dictionary.

Simply write – bio_data =dict(Name = ‘John’, Age =’24’, City = ‘Paris’) to create a dictionary similar to the one created with the previous technique.

The command above builds a dictionary that is comparable to

bio_data ={‘Name : ‘John , ‘Age’: ‘24’ , ‘City’: ‘Paris’}

Isn’t it simple? Now that we’ve covered how to create or instantiate a dictionary, let’s look at some of the actions that dictionaries can execute. A dictionary’s values can be access.

How to Use Key Values

A dictionary’s values can be retrieved by typing the dictionary’s name followed by the key name into a square box. bio_data [Name] will, for example, yield the value, John. In the same way, bio_data [Age] will return a value of 24, and so on.

Functions to be used

Using numerous built-in functions is another technique to get the values of a dictionary. As an example: Below is a dictionary.

bio_data ={‘Name’ : ‘John’ , ‘Age: ‘24’ , ‘City: ‘Paris’}

The command bio_data.keys() prints all of the dictionary’s current keys. In this scenario, the output will be Name, Age, and City.

Similarly, bio_data.values() returns all of the dictionary’s values for each key. In this scenario, the result will be John, 24, and Paris.

Finally, the get() command can be use to get values. bio_data.get(Name), for example, will return the result John.

Making use of loops

Iterating through values in a dictionary is another technique to retrieve them using loops. Let’s look at an example from the previous dictionary, namely

bio_data ={‘Name : ‘John , ‘Age’: ‘24’ , ‘City’: ‘Paris’}

Let’s iterate over this dictionary

for i in bio_data:
print(i)

The lines of code above print out all of the keys in the dictionary.

for i in bio_data.values():
print(i)

The code above prints all of the values associated with the dictionary’s keys. And lastly:

for i,j in bio_data.items():
print(i,j)

All of the items in the dictionary’s key and value are prints in the above lines of code.

Dictionary values can be change Dictionaries are changeable, which means that their entries can be alter as needed. If I wish to alter Name’s bio_data type from John to Peter, for example, I can do so in the following ways:

bio_data [‘Name’] = ‘Peter’

In addition, the following method can be use to create a completely new entry.

bio_data [‘Religion’] = ‘Humanity`

This is what the newly modified my dict will look like now:

{‘Name : ‘John , ‘Age’: ‘24’ , ‘City’: ‘Paris’,’Religion’:’Humanity’}

Delete entries of a dictionary

There are several methods for deleting entries from a dictionary. For example, we may use the following line of code to delete a complete item or a key and value pair.

del bio_data [‘Name]

The preceding line removes Name and John from the vocabulary.

The pop() function can be use to delete only the value and not the key.

bio_data.pop(‘Name’)

The preceding code pops the value of the Name key while keeping the key element. You’re probably wondering what value Name’s key has now. At the moment, it maps to a null value.

There’s also a mechanism to delete the dictionary’s most recently entered element. The popitem() function can be use to accomplish this.

Finally, we can use the clear() function to remove all entries from the dictionary. Clearing the dictionary with bio_data.clear() removes all entries from the dictionary.

Code Example of Hashmap in Python

Hash maps are data structures that are index. Using a key, a hash map computes an index into an array of buckets or slots using a hash function. The bucket with the relevant index receives its value. The key is one-of-a-kind and unchangeable. Consider a hash map as a cabinet with drawers labeled with the items stored inside. Consider storing user information: using email as the key, we may map values pertaining to that user to a bucket, such as first name, last name, and so on.

Implementing a hash map relies on the hash function. It takes the key and converts it to a bucket index in the bucket list. For each key, ideal hashing should give a separate index. Collisions, on the other hand, are possible. By appending a list or rehashing, we may simply use a bucket for multiple values when hashing returns an existing index.

Dictionaries are a type of hash map in Python. We’ll walk through the creation of a hash map from the ground up in order to understand how to create and adapt such data structures for search optimization.

The following functions will be include in the hash map design:

  • set val(key, value) is a function that allows you to change the value of a variable. A key-value pair is adds to the hash map. Update the value if it already exists in the hash map.
  • get val(key): Returns the value to which the supplies key is map, or “No record found” if no mapping for the key exists in this map.
  • If the hash map contains the mapping for the key, delete val(key) removes the mapping for that key.

Example:

class HashTable:

	# Create empty bucket list of given size
	def __init__(self, size):
		self.size = size
		self.hash_table = self.create_buckets()

	def create_buckets(self):
		return [[] for _ in range(self.size)]

	# Insert values into hash map
	def set_val(self, key, val):
		
		# Get the index from the key
		# using hash function
		hashed_key = hash(key) % self.size
		
		# Get the bucket corresponding to index
		bucket = self.hash_table[hashed_key]

		found_key = False
		for index, record in enumerate(bucket):
			record_key, record_val = record
			
			# check if the bucket has same key as
			# the key to be inserted
			if record_key == key:
				found_key = True
				break

		# If the bucket has same key as the key to be inserted,
		# Update the key value
		# Otherwise append the new key-value pair to the bucket
		if found_key:
			bucket[index] = (key, val)
		else:
			bucket.append((key, val))

	# Return searched value with specific key
	def get_val(self, key):
		
		# Get the index from the key using
		# hash function
		hashed_key = hash(key) % self.size
		
		# Get the bucket corresponding to index
		bucket = self.hash_table[hashed_key]

		found_key = False
		for index, record in enumerate(bucket):
			record_key, record_val = record
			
			# check if the bucket has same key as
			# the key being searched
			if record_key == key:
				found_key = True
				break

		# If the bucket has same key as the key being searched,
		# Return the value found
		# Otherwise indicate there was no record found
		if found_key:
			return record_val
		else:
			return "No record found"

	# Remove a value with specific key
	def delete_val(self, key):
		
		# Get the index from the key using
		# hash function
		hashed_key = hash(key) % self.size
		
		# Get the bucket corresponding to index
		bucket = self.hash_table[hashed_key]

		found_key = False
		for index, record in enumerate(bucket):
			record_key, record_val = record
			
			# check if the bucket has same key as
			# the key to be deleted
			if record_key == key:
				found_key = True
				break
		if found_key:
			bucket.pop(index)
		return

	# To print the items of hash map
	def __str__(self):
		return "".join(str(item) for item in self.hash_table)


hash_table = HashTable(50)

# insert some values
hash_table.set_val('softhunt.net', 'some value')
print(hash_table)
print()

hash_table.set_val('[email protected]', 'some other value')
print(hash_table)
print()

# search/access a record with key
print(hash_table.get_val('softhunt.net.com'))
print()

# delete or remove a value
hash_table.delete_val('softhunt.net.com')
print(hash_table)

Output:

img1

Time Complexity

Hashing and accessing memory indexes require the same amount of time. As a result, a hash map’s search complexity is also constant time, i.e. O (1).

That’s all for this article. If you have any confusion contact us through our website or email us at [email protected] or by using LinkedIn.

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