但是在删除的值之后,值的重复上移使得这种技术对于长列表来说效率很低。这段代码的可视化执行在autbor.com/iteratebackwards1进行。你可以在图 8-3 中看到向前迭代和向后迭代的区别。 图8-3:向前(左)和向后(右)迭代时从列表中删除偶数 类似地,当您向后遍历列表时,您可以将项目添加到列表的末尾。在交互式 Sh...
The reason why this loop works is because Python considers a “string” as a sequence of characters instead of looking at the string as a whole. Using the for loop to iterate over a Python list or tuple ListsandTuplesare iterable objects. Let’s look at how we can loop over the elemen...
但是在删除的值之后,值的重复上移使得这种技术对于长列表来说效率很低。这段代码的可视化执行在autbor.com/iteratebackwards1进行。你可以在图 8-3 中看到向前迭代和向后迭代的区别。 图8-3:向前(左)和向后(右)迭代时从列表中删除偶数 类似地,当您向后遍历列表时,您可以将项目添加到列表的末尾。在交互式 Sh...
# The 3rd argument of range means we iterate backwards, reducing the count # of i by 1 for i in range(n, -1, -1): heapify(nums, n, i) # Move the root of the max heap to the end of for i in range(n - 1, 0, -1): nums[i], nums[0] = nums[0], nums[i] heapify(...
这段代码的可视化执行在autbor.com/iteratebackwards2进行。通过向后迭代,我们可以在列表中添加或删除条目。但是这可能很难做到正确,因为对这一基本技术的微小改变最终可能会引入错误。创建新列表比修改原始列表简单得多。正如 Python 核心开发者 Raymond Hettinger 所说: ...
The.splitlines()method splits a string at line boundaries, such as the newline characters (\n), carriage returns (\r), and some combinations like\r\n. It returns a list of lines that you can iterate over or manipulate further:
for i in range(5) is a loop that iterates over the numbers from 0 to 4, inclusive. The range parameters start, stop, and step define where the sequence begins, ends, and the interval between numbers. Ranges can go backward in Python by using a negative step value and reversed by usin...
The ordering is the important item to understand to get list comprehensions right. It is like a loop written backwards. To illustrate this, the following two examples produce the same result, one with a loop and the other one with a list comprehension:...
In Python 2, aBuf was a string, so c was a 1-character string. (That’s what you get when you iterate over a string — all the characters, one by one.) But now, aBuf is a byte array, so c is an int, not a 1-character string. In other words, there’s no need to call...
在金融投资组合中,其组成资产的回报取决于许多因素,如宏观和微观经济条件以及各种金融变量。随着因素数量的增加,建模投资组合行为所涉及的复杂性也在增加。鉴于计算资源是有限的,再加上时间限制,为新因素进行额外计算只会增加投资组合建模计算的瓶颈。一种用于降维的线性技术是主成分分析(PCA)。正如其名称所示,PCA 将...