使np.loadtxt与多个可能的分隔符一起使用 [英] Making np.loadtxt work with multiple possible delimiters

问题描述

我有一个程序可以读取数据文件，用户可以选择要使用的列.我希望它在输入文件中更通用；有时，列看起来可能像这样:

I have a program that reads in data files and the user selects which column they want to use. I want it to be more universal with input files; sometimes, columns can look like this:

10:34:24.58  8.284  6.121

有时它们看起来可能像这样:

And sometimes they can look like this:

10 34 24.58  8.284  6.121

我希望程序在两种情况下都将其识别为5列，而不是第一列为5列，第二列为3列.基本上，我希望它能够将white space识别为定界符，并且将:识别为定界符.

I want the program to recognize this as 5 columns in BOTH cases, instead of 5 columns for the first and 3 for the second. Basically, I want it to recognize white space as a delimiter and : as a delimiter as well.

有没有简单的方法可以做到这一点?我知道numpy需要一个定界符命令，但据我所知它只能使用一个.

Is there a simple way to do this? I know numpy takes a delimiter command, but as far as I'm aware it can only use one.

推荐答案

感谢上面的回答，但是我相信我已经找到了一种解决方法，该方法可以实现简单的两行解决方案，而无需修改程序的其余部分.

Thanks to the answer above, but I believe I've found a workaround that allows for a simple two-line solution, without modifying the rest of my program.

初始的loadtxt行如下所示:

The initial loadtxt line looked like this:

import numpy as np
...
data = np.loadtxt(filename,skiprows=header,dtype=str)

通过使用StringIO，我们可以简单地读取文件并将':'的每个实例替换为" "，而无需修改任何其他代码.

By utilizing StringIO, we can simply read the file and replace every instance of ':' with " ", without having to modify any of the other code.

import numpy as np
import StringIO
...
s = open(filename).read().replace(':',' ')
data = np.loadtxt(StringIO.StringIO(s),skiprows=header,dtype=str)

我希望这对其他人有帮助！

I hope this helps someone else!

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