Build your intuition. Fill in the missing part by typing it in.

Hashmaps, also known as hash tables, are a fundamental data structure in computer science. They provide efficient retrieval, insertion, and deletion of elements by using a technique called ___.

In Python, we can implement a hashmap using a dictionary. Let's take a look at some basic hashmap operations:

1if __name__ == "__main__":
2    # Python code here
3    hashmap = {}
4    
5    # Inserting key-value pairs
6    hashmap["Kobe Bryant"] = 24
7    hashmap["LeBron James"] = 23
8    
9    # Retrieving values using keys
10    print(hashmap["Kobe Bryant"])  # Output: 24
11    print(hashmap["LeBron James"])  # Output: 23
12    
13    # Deleting key-value pairs
14    del hashmap["LeBron James"]
15    
16    # Updating values
17    hashmap["Kobe Bryant"] = 8
18    
19    # Retrieving updated value
20    print(hashmap["Kobe Bryant"])  # Output: 8

Different keys may produce the same hash code, leading to a ____. One common approach to handle collisions is ____, where each bucket of the hash table contains a linked list of key-value pairs.

The average and worst-case time complexities for hashmap operations depend on the quality of the hash function and the collision handling technique used:

• Retrieval: ___
• Insertion: ___
• Deletion: ___

Fill in the blanks:

1. The technique used in hashmaps for efficient retrieval, insertion, and deletion of elements is called ___.

2. Different keys producing the same hash code is known as ____.

3. One common approach to handle collisions in hashmaps is called ____.

4. The average and worst-case time complexities for retrieval operation on hashmaps is ___.

5. The average and worst-case time complexities for insertion operation on hashmaps is ___.

6. The average and worst-case time complexities for deletion operation on hashmaps is ___.