What Data Structure Does a Python Dictionary Implement?
Python dictionaries are widely used data structures that allow you to store and retrieve key-value pairs efficiently. Behind the scenes, a Python dictionary uses a hash table data structure to implement its functionality.
A hash table is a data structure that provides constant-time average-case performance for insertions, deletions, and lookups. It achieves this by using a hash function to compute an index into an array of buckets or slots where the key-value pairs are stored.
The hash function takes the key as input and computes an integer value, which is used to determine the index in the array. Ideally, the hash function should distribute the keys uniformly across all possible indices to minimize collisions.
A Python dictionary stores key-value pairs in its underlying hash table. Each key is unique and maps to a specific value. When you insert a key-value pair into a dictionary or update an existing one, Python computes the hash value of the key and uses it to determine the index where the corresponding value will be stored.
In some cases, different keys may have the same hash value, resulting in what is called a collision. To handle collisions, Python uses open addressing with probing or separate chaining.
- Open addressing with probing: When a collision occurs, Python finds an alternative empty slot in the hash table by probing sequentially until it finds one.
- Separate chaining: In this approach, each bucket in the hash table contains a linked list of key-value pairs. When a collision occurs, new elements are added to this list.
Python dictionaries provide constant-time average-case performance for accessing, inserting, and deleting key-value pairs. However, in the worst case scenario, when there are many collisions and the hash table needs to be resized or rehashed, these operations may take linear time.
In conclusion, Python dictionaries implement a hash table data structure to efficiently store and retrieve key-value pairs. The use of a hash function allows for constant-time average-case performance for most operations. Understanding the underlying data structure helps you make informed decisions when working with dictionaries in Python.