Implementing dynamic arrays in Python: from beginner to proficient

Part1 Let’s talk about the essence of Python sequence types

In this blog, let’s talk about Python’s various “sequence” classes and the three built-in commonly used data structures-list class (list) , the essence of tuple class (tuple) and string class (str).

I don’t know if you have noticed it, but these classes have an obvious commonality. They can all be used to save multiple data elements. The most important function is: each class supports subscript (index) access to the data. Elements of a sequence, such as using the syntax Seq[i]​. In fact, each of the above classes is represented by a simple data structure such as an array.

But readers who are familiar with Python may know that these three data structures have some differences: for example, tuples and strings cannot be modified, but lists can be modified.

1. Array structure in computer memory

In computer architecture, we know that computer main memory is composed of bit information. These bits are usually classified into larger units, and these units are Depends on precise system architecture. A typical unit is a byte, which is equivalent to 8 bits.

Computer systems have a huge number of storage bytes, so how can we find which byte of our information exists? The answer is the storage address that everyone usually knows. Based on the storage address, any byte in main memory can be effectively accessed. In fact, each byte of storage is associated with a unique binary number that serves as its address. As shown in the figure below, each byte is assigned a storage address:

# Generally speaking, the programming language records the relationship between the address where the identifier and its associated value are stored. relation. For example, when we declare that the identifier x may be associated with a certain value in memory, the identifier y may be associated with some other value. A group of related variables can be stored one after another in a contiguous area of ​​computer memory. We call this approach an array.

Let's look at an example in Python. A text string HELLO is stored as a column of ordered characters. It is assumed that each Unicode character of the string requires two bytes of storage space. The bottom number is the index value of the string.

We can see that arrays can store multiple values ​​without the need to construct multiple variables with specific indexes to specify each item in it, and in almost all programming languages (such as C, Java, C#, C), but Python has more advantages. When Python builds a list, familiar readers may know that there is no need to predefine the size of the array or list. On the contrary, in Python, the list has a dynamic nature, and we can continuously add the data elements we want to the list. Next, let's look at the knowledge of Python lists (readers who are already familiar with it can quickly browse or skip it).

2. Python list

Operations of Python list

• The syntax format of creating a list:

[ele1, ele2, ele3 , ele4, ...]

• Syntax format for creating tuples:

(ele1, ele2, ele3, ele4, ...)

Tuples have higher memory space utilization than lists because tuples are fixed, so there is no need to create a dynamic array with remaining space.

We first create a list in Python's IDE, and then take a rough look at the built-in operations of the list. We first create a list named test_list, and then modify (insert or delete) elements, reverse or clear them. The list is as follows:

>>> test_list = []# 创建名为test_list的空列表
>>> test_list.append("Hello")
>>> test_list.append("World")
>>> test_list
['Hello', 'World']
>>> test_list = ["Hello", "Array", 2019, "easy learning", "DataStructure"]# 重新给test_list赋值
>>> len(test_list)# 求列表的长度
5
>>> test_list[2] = 1024# 修改列表元素
>>> test_list
['Hello', 'Array', 1024, 'easy learning', 'DataStructure']
>>>
>>> test_list.insert(1, "I love")# 向列表中指定位置中插入一个元素
>>> test_list
['Hello', 'I love', 'Array', 1024, 'easy learning', 'DataStructure']
>>> test_list.append(2020)# 向列表末尾增加一个元素
>>> test_list
['Hello', 'I love', 'Array', 1024, 'easy learning', 'DataStructure', 2020]
>>>
>>> test_list.pop(1)# 删除指定位置的元素
'I love'
>>> test_list.remove(2020)# 删除指定元素
>>> 
>>> test_list.index('Hello')# 查找某个元素的索引值
0
>>> test_list.index('hello')# 如果查找某个元素不在列表中，返回ValueError错误
Traceback (most recent call last):
File "<pyshell#11>", line 1, in <module>
test_list.index('hello')
ValueError: 'hello' is not in list
>>> 
>>> test_list.reverse()# 反转整个列表
>>> test_list
['DataStructure', 'easy learning', 2019, 'Array', 'Hello']
>>> test_list.clear()# 清空列表
>>> test_list
[]

When we look at the above code, we can see the related operations of the list - add, delete, modify and check. It is already very powerful. There are also some built-in methods that are not shown here and are left to the readers. Discover and experience it yourself.

The Basics Behind Memory Allocation of Python Lists

So let’s look at this extra through coding practice and the relationship between the actual size of the array held in memory and the given size space demonstration.

Go to Jupyter notebook to practice. Or use any editor or development environment of your choice. Copy the code written below.

# 导入sys模块能方便我们使用gestsizeof函数
import sys

# set n
n = 20
# set empty list
list = []
for i in range(n):
a = len(list)
# 调用getsizeof函数用于给出Python中存储对象的真实字节数
b = sys.getsizeof(list)
print('Length:{0:3d}; Size of bytes:{1:4d}'.format(a, b))
# Increase length by one
list.append(n)

Run the code and you can see the following output:

Now, as we increase the length of the list, the bytes also increase. Let's analyze it. When the element at position Length: 1

is filled in the list, the number of bytes jumps from 64 to 96, an increase of 32 bytes. Because this experiment was run on a 64-bit machine, this means that each memory address is 64 bits (i.e. 8 bytes). The additional 32 bytes are the size of the array allocated to store 4 object references. When adding the 2nd, 3rd or 4th element, there is no change in memory usage. The number of bytes 96 can provide references to 4 objects.

96 = 64 8 times 4

when Length:10

时，字节数从一开始的64跳到192，能存下16个对象的引用，

192 = 64 + 8 times 16

一直到Length: 17

后又开始跳转，所以理论上264个字节数应该可以存下25个对象

264 = 64 + 8 times 25

但因为我们在代码中设置n=20，然后程序就终止了。

我们可以看到Python内置的list类足够智能，知道当需要额外的空间来分配数据时，它会为它们提供额外的大小，那么这究竟是如何被实现的呢？

好吧，答案是动态数组。说到这里，不知道大家学Python列表的时候是不是这样想的——列表很简单嘛，就是list()类、用中括号[]括起来，然后指导书籍或文档上的各类方法append、insert、pop...在各种IDE一顿操作过后，是的我觉得我学会了。

但其实背后的原理真的很不简单，比如我举个例子：A[-1]这个操作怎么实现？列表切片功能怎么实现？如何自己写pop()默认删除列表最右边的元素(popleft删除最左边简单)？...这些功能用起来爽，但真的自己实现太难了（我也还在学习中，大佬们请轻喷！）如果我们能学习并理解，肯定可以加强我们对数组这一结构的理解。

3、动态数组

什么是动态数组

动态数组是内存的连续区域，其大小随着插入新数据而动态增长。在静态数组中，我们需要在分配时指定大小。在定义数组的时候，其实计算机已经帮我们分配好了内存来存储，实际上我们不能扩展数组，因为它的大小是固定的。比如：我们分配一个大小为10的数组，则不能插入超过10个项目。

但是动态数组会在需要的时候自动调整其大小。这一点有点像我们使用的Python列表，可以存储任意数量的项目，而无需在分配时指定大小。

所以实现一个动态数组的实现的关键是——如何扩展数组？当列表list1的大小已满时，而此时有新的元素要添加进列表，我们会执行一下步骤来克服其大小限制的缺点：

1. 分配具有更大容量的新数组list2
2. 设置list2[i] = list1[i] (i=0,1,2,...，n-1)，其中n是该项目的当前编号
3. 设置list1 = list2，也就是说，list2正在作为新的数组来引用我们的新列表。
4. 然后，只要将新的元素插入（添加）到我们的列表list1即可。

接下来要思考的问题是，新数组应该多大？通常我们得做法是：新数组的大小是已满的旧数组的2倍。我们将在Python中编程实现动态数组的概念，并创建一个简单的代码，很多功能不及Python强大。

实现动态数组的Python代码

在Python中，我们利用ctypes的内置库来创建自己的动态数组类，因为ctypes模块提供对原始数组的支持，为了更快的对数组进行学习，所以对ctypes的知识可以查看官方文档进行学习。关于Python的公有方法与私有方法，我们在方法名称前使用双下划线**__**使其保持隐藏状态，代码如下：

# -*- coding: utf-8 -*-
# @Time: 2019-11-01 17:10
# @Author: yuzhou_1su
# @ContactMe : https://blog.csdn.net/yuzhou_1shu
# @File: DynamicArray.py
# @Software: PyCharm

import ctypes


class DynamicArray:
"""A dynamic array class akin to a simplified Python list."""

def __init__(self):
"""Create an empty array."""
self.n = 0 # count actual elements
self.capacity = 1# default array capacity
self.A = self._make_array(self.capacity)# low-level array

def is_empty(self):
""" Return True if array is empty"""
return self.n == 0

def __len__(self):
"""Return numbers of elements stored in the array."""
return self.n

def __getitem__(self, i):
"""Return element at index i."""
if not 0 <= i < self.n:
# Check it i index is in bounds of array
raise ValueError('invalid index')
return self.A[i]

def append(self, obj):
"""Add object to end of the array."""
if self.n == self.capacity:
# Double capacity if not enough room
self._resize(2 * self.capacity)
self.A[self.n] = obj# Set self.n index to obj
self.n += 1

def _resize(self, c):
"""Resize internal array to capacity c."""
B = self._make_array(c) # New bigger array
for k in range(self.n):# Reference all existing values
B[k] = self.A[k]
self.A = B# Call A the new bigger array
self.capacity = c # Reset the capacity

@staticmethod
def _make_array(c):
"""Return new array with capacity c."""
return (c * ctypes.py_object)()

def insert(self, k, value):
"""Insert value at position k."""
if self.n == self.capacity:
self._resize(2 * self.capacity)
for j in range(self.n, k, -1):
self.A[j] = self.A[j-1]
self.A[k] = value
self.n += 1

def pop(self, index=0):
"""Remove item at index (default first)."""
if index >= self.n or index < 0:
raise ValueError('invalid index')
for i in range(index, self.n-1):
self.A[i] = self.A[i+1]
self.A[self.n - 1] = None
self.n -= 1

def remove(self, value):
"""Remove the first occurrence of a value in the array."""
for k in range(self.n):
if self.A[k] == value:
for j in range(k, self.n - 1):
self.A[j] = self.A[j+1]
self.A[self.n - 1] = None
self.n -= 1
return
raise ValueError('value not found')

def _print(self):
"""Print the array."""
for i in range(self.n):
print(self.A[i], end=' ')
print()

测试动态数组Python代码

上面我们已经实现了一个动态数组的类，相信都很激动，接下来让我们来测试一下，看能不能成功呢？在同一个文件下，写的测试代码如下：

def main():
# Instantiate
mylist = DynamicArray()

# Append new element
mylist.append(10)
mylist.append(9)
mylist.append(8)
# Insert new element in given position
mylist.insert(1, 1024)
mylist.insert(2, 2019)
# Check length
print('The array length is: ', mylist.__len__())
# Print the array
print('Print the array：')
mylist._print()
# Index
print('The element at index 1 is :', mylist[1])
# Remove element
print('Remove 2019 in array:')
mylist.remove(2019)
mylist._print()
# Pop element in given position
print('Pop pos 2 in array:')
# mylist.pop()
mylist.pop(2)
mylist._print()


if __name__ == '__main__':
main()

测试结果

激动人心的时刻揭晓，测试结果如下。请结合测试代码和数组的结构进行理解，如果由疏漏，欢迎大家指出。

The array length is:5
Print the array：
10 1024 2019 9 8 
The element at index 1 is : 1024
Remove 2019 in array:
10 1024 9 8 
Pop pos 2 in array:
10 1024 8 

Part2总结

通过以上的介绍，我们知道了数组存在静态和动态类型。而在本博客中，我们着重介绍了什么是动态数组，并通过Python代码进行实现。希望你能从此以复杂的方式学会数组。总结发言，其实越是简单的操作，背后实现原理可能很复杂。

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