Python中的内存管理之python list内存使用详解_Python

前言

使用 Python 的时候，我们知道 list 是一个长度可变对的数组，可以通过 insert，append 和 extend 轻易的拓展其中的元素个数。也可以使用运算符如： [1] + [2] 生成新的数组[1, 2]

extend()、"+"、"+="的区别

"+"将两个 list 相加，会返回到一个新的 list 对象
append 在原 list 上进行修改，没有返回值

从以下代码可以看到，调用 b = b + [3, 4] 之后，通过id(b) 查看 b 变成了一个新对象。

In [5]: b = [1, 2]
In [6]: id(b)
Out[6]: 1628740249224

In [7]: b = b + [3, 4]
In [8]: id(b)
Out[8]: 1628740456520

使用extend() 完成相同的步骤，可以看到对象c 的id保持和原来的一致

In [9]: c = [1, 2]
In [10]: id(c)
Out[10]: 1628740392584

In [11]: c.extend([3, 4])
In [12]: id(c)
Out[12]: 1628740392584

使用 "+=" 连接列表，看到效果和 extend() 是相同的。

In [1]: a = [1, 2]
In [2]: id(a)
Out[2]: 1628740021448

In [3]: a += [3, 4]
In [4]: id(a)
Out[4]: 1628740021448

结论：减少内存的拷贝，修改一个列表的数据时，应避免使用 list1= list1+ list2 这样的语法。

List的内存使用

一个示例：

In [1]: import sys

In [2]: lst1 = [1]
In [3]: lst2 = []
In [4]: lst2.append(1)

In [5]: lst1 == lst2
Out[5]: True

In [6]: sys.getsizeof(lst1)
Out[6]: 72
In [7]: sys.getsizeof(lst2)
Out[7]: 96

可以看到，lst1 == lst2，但是当使用 sys.getsizeof 获取对象的内存大小时，两者却是不同的。

如下图所示， list_a 长度为4，当执行 append(4) 时，底层的数据长度其实申请了4个元素的空间，当再次执行 append(5) 的时候，不需要再次申请内存。

Python中的内存管理之python list内存使用详解

因为执行 append() 操作时，Python将一次拓展N个元素的内存，因为一个 append 操作很可能是很多 append 操作的开始，通过额外分配内存来减少可能的内存分配和内存copy的次数。

In [1]: import sys

In [2]: l = []
...: print(f'list initial size {sys.getsizeof(l)}')
...: for i in range(80):
...: cur_size = sys.getsizeof(l)
...: l.append(i)
...: new_size = sys.getsizeof(l)
...: print(f'list len {i+1}:\t current_size {new_size}\t new_allocated 8 * {(new_size-cur_size)/8}')
...:
list initial size 64
list len 1: current_size 96 new_allocated 8 * 4.0
list len 2: current_size 96 new_allocated 8 * 0.0
list len 3: current_size 96 new_allocated 8 * 0.0
list len 4: current_size 96 new_allocated 8 * 0.0
list len 5: current_size 128 new_allocated 8 * 4.0
list len 6: current_size 128 new_allocated 8 * 0.0
list len 7: current_size 128 new_allocated 8 * 0.0
list len 8: current_size 128 new_allocated 8 * 0.0
list len 9: current_size 192 new_allocated 8 * 8.0
list len 10: current_size 192 new_allocated 8 * 0.0
list len 11: current_size 192 new_allocated 8 * 0.0
list len 12: current_size 192 new_allocated 8 * 0.0
list len 13: current_size 192 new_allocated 8 * 0.0
list len 14: current_size 192 new_allocated 8 * 0.0
list len 15: current_size 192 new_allocated 8 * 0.0
list len 16: current_size 192 new_allocated 8 * 0.0
list len 17: current_size 264 new_allocated 8 * 9.0
list len 18: current_size 264 new_allocated 8 * 0.0
list len 19: current_size 264 new_allocated 8 * 0.0
list len 20: current_size 264 new_allocated 8 * 0.0
list len 21: current_size 264 new_allocated 8 * 0.0
list len 22: current_size 264 new_allocated 8 * 0.0
list len 23: current_size 264 new_allocated 8 * 0.0
list len 24: current_size 264 new_allocated 8 * 0.0
list len 25: current_size 264 new_allocated 8 * 0.0
list len 26: current_size 344 new_allocated 8 * 10.0
list len 27: current_size 344 new_allocated 8 * 0.0
list len 28: current_size 344 new_allocated 8 * 0.0
list len 29: current_size 344 new_allocated 8 * 0.0
list len 30: current_size 344 new_allocated 8 * 0.0
list len 31: current_size 344 new_allocated 8 * 0.0
list len 32: current_size 344 new_allocated 8 * 0.0
list len 33: current_size 344 new_allocated 8 * 0.0
list len 34: current_size 344 new_allocated 8 * 0.0
list len 35: current_size 344 new_allocated 8 * 0.0
list len 36: current_size 432 new_allocated 8 * 11.0
list len 37: current_size 432 new_allocated 8 * 0.0
list len 38: current_size 432 new_allocated 8 * 0.0
list len 39: current_size 432 new_allocated 8 * 0.0
list len 40: current_size 432 new_allocated 8 * 0.0
list len 41: current_size 432 new_allocated 8 * 0.0
list len 42: current_size 432 new_allocated 8 * 0.0
list len 43: current_size 432 new_allocated 8 * 0.0
list len 44: current_size 432 new_allocated 8 * 0.0
list len 45: current_size 432 new_allocated 8 * 0.0
list len 46: current_size 432 new_allocated 8 * 0.0
list len 47: current_size 528 new_allocated 8 * 12.0
list len 48: current_size 528 new_allocated 8 * 0.0
list len 49: current_size 528 new_allocated 8 * 0.0
list len 50: current_size 528 new_allocated 8 * 0.0
list len 51: current_size 528 new_allocated 8 * 0.0
list len 52: current_size 528 new_allocated 8 * 0.0
list len 53: current_size 528 new_allocated 8 * 0.0
list len 54: current_size 528 new_allocated 8 * 0.0
list len 55: current_size 528 new_allocated 8 * 0.0
list len 56: current_size 528 new_allocated 8 * 0.0
list len 57: current_size 528 new_allocated 8 * 0.0
list len 58: current_size 528 new_allocated 8 * 0.0
list len 59: current_size 640 new_allocated 8 * 14.0
list len 60: current_size 640 new_allocated 8 * 0.0
list len 61: current_size 640 new_allocated 8 * 0.0
list len 62: current_size 640 new_allocated 8 * 0.0
list len 63: current_size 640 new_allocated 8 * 0.0
list len 64: current_size 640 new_allocated 8 * 0.0
list len 65: current_size 640 new_allocated 8 * 0.0
list len 66: current_size 640 new_allocated 8 * 0.0
list len 67: current_size 640 new_allocated 8 * 0.0
list len 68: current_size 640 new_allocated 8 * 0.0
list len 69: current_size 640 new_allocated 8 * 0.0
list len 70: current_size 640 new_allocated 8 * 0.0
list len 71: current_size 640 new_allocated 8 * 0.0
list len 72: current_size 640 new_allocated 8 * 0.0
list len 73: current_size 768 new_allocated 8 * 16.0
list len 74: current_size 768 new_allocated 8 * 0.0
list len 75: current_size 768 new_allocated 8 * 0.0
list len 76: current_size 768 new_allocated 8 * 0.0
list len 77: current_size 768 new_allocated 8 * 0.0
list len 78: current_size 768 new_allocated 8 * 0.0
list len 79: current_size 768 new_allocated 8 * 0.0
list len 80: current_size 768 new_allocated 8 * 0.0

通过观察可以发现，列表从0 增加到 80长度的过程中，新申请的内存长度为 [4, 4, 8, 9, 10, 11, 12, 13, 14, 16] 。反之，当执行 remove 或者 pop 减少列表中的数据时，列表也会自动缩容。

扩容条件，新长度大于底层数组长度；
缩容条件，新长度小于底层数组长度的一半；

结论：避免使用类似 append 语法初始化列表，优先使用列表表达式

# Bad ❌
list_a = []
for i in range(50):
  list_a.append(i)

# Good ✔️
list_b = [i for i in range(50)]

结论：

① 避免使用 "+" 修改数组

② 尽量避免多次使用 append 函数

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原文链接：https://blog.csdn.net/be5yond/article/details/120013569

Python中的内存管理之python list内存使用详解

前言

extend()、"+"、"+="的区别

List的内存使用

结论：

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