how to find load factor of hash table

Hash table is one of the important data structures in computer science. It provides a way for efficient storage and retrieval of data. In order to make a hash table work effectively, we must ensure that it does not become too crowded or too sparse. To keep track of the hash table occupancy, we need to compute the load factor. In this article, we will look at how to calculate the load factor of a hash table.

What is Load Factor?

Load factor is a measure of the occupancy of a hash table. It is defined as the ratio of the number of elements in the hash table to the total number of slots available in the table. Suppose we have a hash table with 'n' slots and 'm' elements stored in it. Then, the load factor 'α' is calculated as:α = m/nThe load factor gives us an idea of how efficiently the hash table is being utilized. If the load factor is too high, it means that the table is overcrowded and searching for an element will take more time. On the other hand, if the load factor is too low, it means that the table is wasting memory as many slots are unused.

How to Find Load Factor?

To find the load factor, we need to count the number of elements stored in the hash table and the total number of slots available in the table. To do this, we can loop through each slot in the table and count the number of elements stored in it. This is illustrated in the following pseudocode:```count = 0for i in range(n): if table[i] is not empty: count = count 1load_factor = count/n```Here, 'n' is the size of the hash table, and 'table[i]' refers to the ith slot in the table. We check if the slot is empty or not by using the 'is not empty' condition. If the slot is not empty, it means that an element is stored in it, and we increment the 'count' variable. Finally, we divide the count by the total number of slots to obtain the load factor.

Impact of Load Factor on Hash Table Performance

As mentioned earlier, the load factor determines the efficiency of a hash table. When the load factor is high, the hash table becomes crowded, and the time taken for searching an element increases. This is because multiple elements are stored in the same slot, and we need to traverse through all of them to find the required element. On the other hand, when the load factor is low, many slots are left unused, and the hash table takes up unnecessary memory. Thus, it is important to maintain a balanced load factor to ensure optimal hash table performance.

Conclusion

In conclusion, load factor is an important metric for measuring the occupancy of a hash table. It helps us to determine if the table is being utilized efficiently or not. We can find the load factor by counting the number of elements stored in the table and dividing it by the total number of slots. A high load factor leads to slower performance due to overcrowding, while a low load factor leads to memory wastage. Therefore, it is essential to maintain an optimal load factor for a hash table to work efficiently.