Data Structures and Algorithms: Understanding Load Factor and Rehashing 🎯

Welcome to this comprehensive guide on Load Factor and Rehashing! In this lesson, we will delve into the intricacies of these concepts that are fundamental to understanding data structures and algorithms. By the end of this lesson, you'll have a solid grasp of these concepts, ready to apply them in your coding projects.

What is Load Factor? 💡

Load factor is a measure of how full a hash table is. It is defined as the ratio of the number of keys in the hash table to its capacity. In other words, it is the percentage of the hash table that is occupied by keys.

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Load Factor = Number of Keys / Hash Table Capacity

A common practice is to create hash tables with a load factor of around 0.75. This leaves enough room for adding new keys without causing hash collisions too frequently.

Why is Load Factor Important? 📝

The load factor determines the efficiency of a hash table. If the load factor is too high, there will be a significant number of hash collisions, making it difficult to find and retrieve keys quickly. On the other hand, if the load factor is too low, the hash table is underutilized, wasting memory.

What is Rehashing? 💡

Rehashing is the process of resizing a hash table when its load factor exceeds a certain threshold. When this happens, the hash table is recreated with a larger capacity, and the keys are redistributed to the new hash table.

Why is Rehashing Necessary? 📝

Rehashing is essential to maintain the efficiency of a hash table. As the number of keys increases, the load factor increases, leading to more hash collisions. Rehashing helps reduce hash collisions by providing more space for keys, thus improving the hash table's performance.

Example: Rehashing in Python 💡

Here's an example of rehashing in Python using the built-in dict class.

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# Original Hash Table
hash_table = {'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5}

# Set load factor threshold
load_factor = 0.75

# Current number of keys
current_keys = len(hash_table)

# Current capacity
current_capacity = len(hash_table)

# Check if load factor exceeds the threshold
if (current_keys / current_capacity) > load_factor:
    # Double the capacity of the hash table
    new_capacity = current_capacity * 2

    # Create a new hash table with the larger capacity
    new_hash_table = {}

    # Iterate over the keys in the original hash table
    for key, value in hash_table.items():
        # Calculate the new hash code for the key
        new_hash_code = hash(key) % new_capacity

        # Store the key-value pair in the new hash table
        new_hash_table[new_hash_code] = hash_table[key]

    # Replace the original hash table with the new one
    hash_table = new_hash_table

Quiz 📝

Keep learning, and happy coding! 💻💪