list comprehension替换2D矩阵中的循环 [英] list comprehension replace for loop in 2D matrix

问题描述

我尝试使用list comprehension来替换for循环。

I try to use list comprehension to replace the for loop.

原始文件是

2 3 4 5 6 3
1 2 2 4 5 5
1 2 2 2 2 4

for循环

line_number = 0
for line in file:
    line_data = line.split()
    Cordi[line_number, :5] = line_data 
    line_number += 1

输出

[[2 3 4 5 6 3]
 [1 2 2 4 5 5]
 [1 2 2 2 2 4]]

如果使用列表理解，我可以想到的是（我必须将数据类型更改为int，因此可以在程序的后续部分中绘制）

if use list comprehension instead, for what I can think of is (I have to change the data type to int, so it can be plotted in later part of the program)

Cordi1= [int(x) for x in line.split() for line in data]

但输出

[1, 1, 1]

但 line.split（）表示数据中的行实际上是一个列表，如果我尝试

but line.split() for line in data is actually a list, and if I try

Cordi1 = [int(x) for x in name of the list]

它有效，为什么会发生这种情况？

it works, why this happens?

推荐答案

您已经交换了循环的顺序;它们的排序方式应该是从左到右嵌套的方式：

You have the order of your loops swapped; they should be ordered in the same way they would be nested, from left to right:

[int(x) for line in data for x in line.split()]

这循环超过数据首先，然后对于每个行迭代，迭代 line.split（）以生成 X 。然后，您可以从这些生成一个 flat 整数列表。

This loops over data first, then for each line iteration, iterates over line.split() to produce x. You then produce one flat list of integers from these.

但是，由于您正在尝试构建列表列表，因此需要嵌套另一个列表理解：

However, since you are trying to build a list of lists, you need to nest a list comprehension inside another:

Cordi1 = [[int(i) for i in line.split()] for line in data]

演示：

>>> data = '''\
... 2 3 4 5 6 3
... 1 2 2 4 5 5
... 1 2 2 2 2 4
... '''.splitlines()
>>> [int(x) for line in data for x in line.split()]
[2, 3, 4, 5, 6, 3, 1, 2, 2, 4, 5, 5, 1, 2, 2, 2, 2, 4]
>>> [[int(i) for i in line.split()] for line in data]
[[2, 3, 4, 5, 6, 3], [1, 2, 2, 4, 5, 5], [1, 2, 2, 2, 2, 4]]

如果你想要一个从这个多维numpy数组，您可以将上面的数据直接转换为数组或从数据创建数组然后重塑：

If you wanted a multidimensional numpy array from this, you can either convert the above directly to an array or create an array from the data then reshape:

>>> import numpy as np
>>> np.array([[int(i) for i in line.split()] for line in data])
array([[2, 3, 4, 5, 6, 3],
       [1, 2, 2, 4, 5, 5],
       [1, 2, 2, 2, 2, 4]])
>>> np.array([int(i) for line in data for i in line.split()]).reshape((3, 6))
array([[2, 3, 4, 5, 6, 3],
       [1, 2, 2, 4, 5, 5],
       [1, 2, 2, 2, 2, 4]])

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