Python's underlying technology revealed: how to implement a hash table

Revealing the underlying technology of Python: How to implement a hash table

The hash table is a very common and important data structure in the computer field. It can efficiently store and Find a large number of key-value pairs. In Python, we can use hash tables using dictionaries, but few people understand its implementation details in depth. This article will reveal the underlying implementation technology of hash tables in Python and give specific code examples.

The core idea of ​​a hash table is to map keys into a fixed-size array through a hash function, rather than simply storing them in order. This can greatly speed up searches. Below we will introduce the implementation of hash table step by step.

1. Hash function
   The hash function is a very critical part of the hash table, which maps keys to index positions in the array. A good hash function should be able to map keys evenly to different positions in the array to reduce the probability of collisions. In Python, we can use the hash() function to generate a hash value, but because the value it generates is too long, we generally need to perform a modulo operation on it to adapt it to the size of the array.

The following is an example of a simple hash function:

def hash_func(key, size):
    return hash(key) % size

2. Implementation of hash table
   In Python, a hash table is created through a dictionary (dict ) object to achieve. The dictionary object uses a hash table internally to store key-value pairs. A simplest hash table can be implemented using arrays and linked lists.

First we define a hash table object, which contains an array and a linked list:

class HashTable:
    def __init__(self, size):
        self.size = size
        self.table = [[] for _ in range(size)]

Then we define the insertion and search methods:

    def insert(self, key, value):
        index = hash_func(key, self.size)
        for item in self.table[index]:
            if item[0] == key:
                item[1] = value
                return
        self.table[index].append([key, value])

    def get(self, key):
        index = hash_func(key, self.size)
        for item in self.table[index]:
            if item[0] == key:
                return item[1]
        raise KeyError(key)

In When inserting, we first obtain the index of the key through the hash function, and then find whether the key already exists in the linked list at the index position. If it exists, update the value; otherwise, insert a new key-value pair at the end of the linked list.

When searching, we also obtain the index of the key through the hash function, and then perform a linear search in the linked list at the index position. If the corresponding key-value pair is found, the value is returned; otherwise, a KeyError exception is thrown.

3. Using Hash Table
   Now we can use the hash table we implemented. The following is a simple example:

hash_table = HashTable(10)
hash_table.insert("name", "Tom")
hash_table.insert("age", 20)
hash_table.insert("gender", "male")

print(hash_table.get("name"))  # 输出：Tom
print(hash_table.get("age"))  # 输出：20
print(hash_table.get("gender"))  # 输出：male

4. Summary
   This article introduces the underlying implementation technology of hash tables in Python and gives specific code examples. A hash table is an efficient data structure that allows insertion and lookup operations in constant time. Mastering the implementation principles and related technologies of hash tables can help us better understand and use dictionary objects in Python.

I hope this article will help you understand the underlying implementation of hash tables. If you have any questions or suggestions, please feel free to communicate with us.

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