问题

以下是翻译好的部分：

"The time complexity of a for loop with n as the input is O(n) from what I've understood till now but what about the code inside the loop?"

"for循环的时间复杂度，以n作为输入，根据我现在的理解是O(n)，但循环内的代码呢？"

    arr.remove(var)

"arr is a list with n elements and var can be a string or a number."

"arr是一个包含n个元素的列表，而var可以是字符串或数字。"

"How do I know if I should multiply or add time complexities? Is the time complexity of the above code O(n**2) or O(n)?"

"我如何知道是要相乘还是相加时间复杂度？上面的代码的时间复杂度是O(n**2)还是O(n)？"

    arr.remove(var)
    arr.remove(var1)

"What would the time complexity be now? What should I add or multiply?"

"现在的时间复杂度是多少？我应该相加还是相乘？"

"I tried learning about time complexity but couldn't understand how to deal with code having more than one time complexity."

"我尝试学习时间复杂度，但无法理解如何处理具有多个时间复杂度的代码。"

while var in arr:
    arr.remove(var)
    

for i in range(n):
    arr.remove(var)
    arr.remove(var1)

答案1

得分: 2

在循环内部内容中，你需要了解时间复杂度。

```python
for i in arr:  # O(n) 
    print(sum(arr) - i)  # O(n)

在这种情况下，.pop(0) 嵌套在循环内部，所以你需要将复杂度乘以循环的复杂度：O(n) * O(n) > O(n*n) > O(n²)。

for i in arr:  # O(n) 
    print(sum(arr) - i)  # O(n)
    print(sum(arr) - i)  # O(n)

在这种情况下，它是

O(n) * (O(n) + O(n))
O(n) * O(n+n)
O(n) * O(2n)
O(n) * O(n)
O(n*n)
O(n²)

查看更多关于时间复杂度计算中何时添加和何时相乘的信息，请参考 When to add and when to multiply to find time complexity。

对于 while 循环，它不会改变任何内容：将内容与 while 的复杂度相乘。

<details>
<summary>英文:</summary>

You need to know the time complexity of the content inside the loop.

```python
for i in arr:  # O(n) 
    print(sum(arr) - i)  # O(n)

In this case, the .pop(0) is nested in the forloop, so you need to multiply the complexity to the forloop complexity: O(n) * O(n) > O(n*n) > O(n²).

for i in arr:  # O(n) 
    print(sum(arr) - i)  # O(n)
    print(sum(arr) - i)  # O(n)

In this case, it's

O(n) * (O(n) + O(n))
O(n) * O(n+n)
O(n) * O(2n)
O(n) * O(n)
O(n*n)
O(n²)

See When to add and when to multiply to find time complexity for more information about that.

For a while loop, it doesn't change anything: multiply content with the complexity of the while.