How are Python's Built In Dictionaries Implemented?

How Are Python's Built-In Dictionaries Implemented?

Python's built-in dictionaries (dict) are implemented using a hash table. A hash table is a data structure that provides efficient mapping from keys to values, allowing for average-case constant-time complexity for insertions, deletions, and lookups. Below is a detailed explanation of how Python's dictionaries work under the hood.

Key Components of Python's Dictionary Implementation

1. Hash Table:

   • The core of the dictionary implementation is a hash table, which consists of an array of entries (also called buckets). Each entry stores a key-value pair.

2. Hash Function:

   • A hash function is used to convert the key into a hash value. This hash value determines the index at which the key-value pair should be stored in the hash table.

3. Handling Collisions:

   • Collisions occur when two keys hash to the same index. Python's dictionaries handle collisions using open addressing with quadratic probing. This means that if a collision occurs, the algorithm looks for the next available slot using a quadratic function of the number of collisions.

4. Dynamic Resizing:

   • The hash table dynamically resizes to maintain efficient operations. When the dictionary grows beyond a certain load factor (number of entries relative to the table size), the table is resized (usually doubled in size), and all existing entries are rehashed and inserted into the new table.

Dictionary Structure

In Python 3.6 and later, the dictionary implementation was optimized to maintain insertion order and improve memory efficiency. The new implementation is often referred to as the "compact dictionary."

• Array of Entries: Each entry in the hash table array stores references to keys and values.
• Index Arrays: Separate arrays store the indices of the keys and values, reducing the memory footprint and improving cache locality.

Example of How It Works

Here is a simplified version of how Python's dictionaries work:

1. Hashing the Key:

   def hash_function(key):
       # Simplified hash function
       return hash(key) % table_size

2. Inserting a Key-Value Pair:

   def insert(dictionary, key, value):
       index = hash_function(key)
       while dictionary[index] is not None:
           index = (index + 1) % table_size  # Quadratic probing for collision resolution
       dictionary[index] = (key, value)

3. Looking Up a Key:

   def lookup(dictionary, key):
       index = hash_function(key)
       while dictionary[index] is not None:
           if dictionary[index][0] == key:
               return dictionary[index][1]
           index = (index + 1) % table_size
       return None  # Key not found

Python Dictionary Example

Here's a simple example of using a Python dictionary:

# Creating a dictionary
my_dict = {
    "name": "Alice",
    "age": 30,
    "city": "New York"
}

# Accessing a value
print(my_dict["name"])  # Output: Alice

# Adding a new key-value pair
my_dict["profession"] = "Engineer"

# Deleting a key-value pair
del my_dict["age"]

# Iterating over key-value pairs
for key, value in my_dict.items():
    print(f"{key}: {value}")

# Checking if a key exists
if "city" in my_dict:
    print("City is in the dictionary")

# Getting a value with a default
print(my_dict.get("age", "Not specified"))  # Output: Not specified

Summary

• Hash Table: The backbone of the dictionary, providing efficient key-value mappings.
• Hash Function: Converts keys into hash values to determine their position in the table.
• Collision Handling: Uses open addressing with quadratic probing to resolve collisions.
• Dynamic Resizing: Adjusts the size of the hash table to maintain performance.
• Compact Dictionary (Python 3.6+): Optimized to maintain insertion order and improve memory efficiency.

Python's dictionaries are highly optimized for performance and memory efficiency, making them one of the most versatile and widely used data structures in the language. For more in-depth knowledge and practical examples on Python data structures and other programming concepts, consider exploring Grokking the Coding Interview on DesignGurus.io, which provides comprehensive courses on essential coding and interview techniques.