Which Data Structure Is Used to Implement Dictionary in Python?

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Larry Thompson

When working with Python, dictionaries are an essential data structure that allows us to store and retrieve data efficiently. But have you ever wondered how dictionaries are implemented under the hood? In this article, we will explore the data structure used to implement dictionaries in Python.

Hash Tables

Python dictionaries are implemented using a data structure called hash tables. Hash tables are a type of associative array that store key-value pairs. They provide fast insertion, deletion, and lookup operations.

The underlying concept behind hash tables is hashing. A hash function takes an input (in our case, a key) and maps it to an index in an array (also known as the hash table). The goal of a good hash function is to distribute the keys uniformly across the array.

Hash Function

A crucial component of implementing hash tables is the hash function. Python uses a built-in hash() function to generate a unique integer value for each object.

This value acts as the index for storing and retrieving values in the dictionary. The hash() function uses different algorithms depending on the type of object being hashed.

For example, when hashing strings, Python uses the djb2 algorithm. This algorithm calculates the hash value by multiplying each character’s ASCII value by a prime number and summing them up.

Buckets and Collision Resolution

In practice, there might be cases where two different keys result in the same index after applying the hash function. This is known as a collision. To handle collisions efficiently, Python uses a technique called open addressing.

In open addressing, each index in the hash table is not limited to storing only one key-value pair. Instead, it stores a bucket that can hold multiple entries. When a collision occurs, Python searches for the next available bucket in a linear fashion until an empty bucket is found.

Load Factor and Rehashing

As the number of key-value pairs stored in a dictionary increases, the number of collisions also increases. To maintain efficient performance, Python dynamically adjusts the size of the hash table when certain conditions are met.

The load factor is a measure of how full the hash table is. It calculates the ratio of occupied buckets to total buckets. When the load factor exceeds a threshold (usually 0.66), Python triggers a process called rehashing.

During rehashing, Python creates a new hash table with a larger size and reinserts all key-value pairs into the new table. This process reduces collisions and improves overall performance.

Conclusion

In conclusion, dictionaries in Python are implemented using hash tables, which provide fast insertion, deletion, and lookup operations. The underlying hash function maps keys to indices in an array (hash table).

In case of collisions, open addressing with buckets is used to efficiently resolve them. Additionally, dynamic resizing through rehashing ensures optimal performance as the size of the dictionary grows.

Understanding how dictionaries are implemented can help you write more efficient code by leveraging their strengths and avoiding potential pitfalls.

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