用NumPy从文件中读取非统一数据 [英] Reading non-uniform data from file into array with NumPy

问题描述

假设我有一个如下所示的文本文件：

33 3

46 12

23 10 23 11 23 12 23 13 23 14 23 15 23 16 24 10 24 11 24 12 24 13 24 14 24 15 24 16 25 14 25 15 25 16 26 16 27 16 28 16 29 16

33 17 33 18 33 19 34 17 34 18 34 19 35 17 35 18 35 19 36 19

41 32 41 33 42 32 42 33

我想把每一行读到一个单独的整数数组中，如（伪代码）所示：

 
 currentArray = firstLine 
执行currentArray 
  

在第一次迭代中，currentArray将会是

lockquote

array（[33，3]）

，在第二次迭代中，currentArray将是

lockquote

数组直到最后一次迭代，当currentArray将是

array（[41,32,41,33,42,32,42,33]）

<基本上，我想有numpy函数的函数loadtxt：

currentArray = loadtxt（'scienceVertices.txt'，usecols =（））

除了usecols之外，还可以指定行，例如

currentArray = loadtxt（'scienceVertices.txt'，userows =（line））

解决方案

这里是一行：

  arrays = [np.array（map （int，line.split（）））for open in line（'scienceVertices.txt'）] 
  

数组是numpy数组的列表。

Suppose I have a text file that looks like this:

33 3
46 12
23 10 23 11 23 12 23 13 23 14 23 15 23 16 24 10 24 11 24 12 24 13 24 14 24 15 24 16 25 14 25 15 25 16 26 16 27 16 28 16 29 16
33 17 33 18 33 19 34 17 34 18 34 19 35 17 35 18 35 19 36 19
41 32 41 33 42 32 42 33

I would like to read each line into a separate array of integers, as in (pseudo code):

for line in textfile:  
    currentArray = firstLine  
    do stuff with currentArray

where in the first iteration, currentArray would be

array([33, 3])

and in the second iteration, currentArray would be

array([46, 12])

until the last iteration, when currentArray would be

array([41, 32, 41, 33, 42, 32, 42, 33])

Basically, I would like to have the functionality of the numpy function loadtxt:

currentArray = loadtxt('scienceVertices.txt', usecols=() )

Except instead of usecols, being able to specify the row, e.g.,

currentArray = loadtxt('scienceVertices.txt', userows=(line) )

解决方案

Here's a one-liner:

arrays = [np.array(map(int, line.split())) for line in open('scienceVertices.txt')]

arrays is a list of numpy arrays.

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