Difference between Hash Table and Dictionary

Hi coders! In this article we would see their major differences between a hash table and a dictionary.

A hash table and a dictionary are both data structures that are used to store key-value pairs. The main difference between the two is the way they are implemented.

A hash table uses a hash function to map keys to indexes in an array, where the values are stored. This allows for efficient lookups and insertions, as the keys can be quickly hashed and the corresponding values found in constant time. However, hash tables do not maintain the order of the elements and can also have collisions when two keys are mapped to the same index.

A dictionary, on the other hand, is typically implemented as a balanced tree, such as a red-black tree or a AVL tree. This allows for keys to be stored in a sorted order and for efficient lookups, insertions, and deletions. However, the operations on a dictionary are generally slower than those on a hash table.

Example for Hash Table and Dictionary

In Python, the built-in dict type is implemented as a hash table, while the collections.OrderedDict is implemented as a dictionary-like data structure that maintains the order of the elements.

Here is an example of creating and using a Python dictionary:

Python

# Creating a dictionary
my_dict = {'apple': 0.5, 'banana': 0.25, 'orange': 0.75}
# Accessing a value using its key
print(my_dict['apple'])  # Output: 0.5
# Updating a value using its key
my_dict['apple'] = 0.6
# Adding a new key-value pair
my_dict['pear'] = 0.35
# Removing a key-value pair using the del keyword
del my_dict['banana']
# Checking if a key is in the dictionary
print('apple' in my_dict)  # Output: True

Here is an example of creating and using a Python collections.OrderedDict:

Python

from collections import OrderedDict
# Creating an ordered dictionary
my_dict = OrderedDict([('apple', 0.5), ('banana', 0.25), ('orange', 0.75)])
# Accessing a value using its key
print(my_dict['apple'])  # Output: 0.5
# Updating a value using its key
my_dict['apple'] = 0.6
# Adding a new key-value pair
my_dict['pear'] = 0.35
# Removing a key-value pair using the del keyword
del my_dict['banana']
# Checking if a key is in the dictionary
print('apple' in my_dict)  
# Output: True

Both examples create a dictionary and perform operations such as accessing values using keys, updating values, adding new key-value pairs, removing key-value pairs, and checking if keys are in the dictionary. The main difference between the two examples is that the collections.OrderedDict maintains the order of the elements, while the built-in dict does not.

Where to use each one of them?

The choice of whether to use a hash table (Python’s dict) or a dictionary (Python’s collections.OrderedDict) depends on the specific requirements of the problem you are trying to solve.

Here are some general guidelines on when to use each data structure:

Use a hash table (dict) when:
- You need to perform fast lookups, insertions, and deletions. Hash tables have constant-time complexity for these operations on average.
- The order of the elements does not matter. Hash tables do not maintain the order of the elements.
- The keys are simple and can be easily hashed.
Use a dictionary (collections.OrderedDict) when:
- The order of the elements matters. Dictionaries maintain the order of the elements based on the order they were added.
- You need to iterate over the elements in a specific order.
- The keys are more complex and may not be easily hashed.

It’s worth noting that, in practice, the difference in performance between a dict and an collections.OrderedDict is not very significant unless you are working with large amounts of data and you have a very specific use case.

Conclusion

In summary, if you need to maintain the order of the elements, use an collections.OrderedDict otherwise use a dict for the fast lookups and insertions.