当用genfromtxt指定dtype时，2D数组变成1D-如何防止这种情况? [英] When dtype is specified with genfromtxt a 2D array becomes 1D - how to prevent this?

问题描述

如此处所示:

http://library.isr.ist.utl.pt/docs/numpy/user/basics.io.genfromtxt.html#choosing-the-data-type

在除第一种情况以外的所有情况下，输出都是具有结构化dtype的一维数组.此dtype具有与序列中的项一样多的字段.字段名称由names关键字定义."

问题是我该如何解决?我想将genfromtxt与带有列的数据文件一起使用，例如整数，字符串，整数.

The problem is how do I get around this? I want to use genfromtxt with a data file with columns that are, e.g. int, string, int.

如果我这样做:

dtype=(int, "|S5|", int)

然后整个形状从(x，y)变为仅(x，)，当我尝试使用蒙版时出现索引过多"错误.

Then the entire shape changes from (x, y) to merely (x, ) and I get 'too many indices' errors when I try to use masks.

当我使用dtype = None时，我会保留2D结构，但是如果该列的第一行看起来像是一个数字(它经常出现在我的数据集中)，这经常会出错.

When I use dtype=None I get to keep the 2D structure, but it often makes mistakes if the 1st row the column looks like it could be a number (this often occurs in my data set).

我如何最好地解决这个问题?

How am I best to get around this?

推荐答案

您不能拥有2D数组，这意味着要为每一行使用带有混合dtype的1D数组，这是不可能的.

You cannot have a 2D array, it would mean having 1D arrays with mixed dtype for each row, which is not possible.

拥有记录数组应该不是问题:

Having an array of records shouldn't be a problem:

In [1]: import numpy as np

In [2]: !cat test.txt
42 foo 41
40 bar 39

In [3]: data = np.genfromtxt('test.txt',
    ..:     dtype=np.dtype([('f1', int), ('f2', np.str_, 5), ('f3', int)]))

In [4]: data
Out[4]:
array([(42, 'foo', 41), (40, 'bar', 39)],
      dtype=[('f1', '<i8'), ('f2', '<U5'), ('f3', '<i8')])

In [5]: data['f3']
Out[5]: array([41, 39])

In [6]: data['f3'][1]
Out[6]: 39

如果您需要屏蔽的数组，请看这里:如何在Numpy中屏蔽记录数组的元素?

If you need a masked array, look here: How can I mask elements of a record array in Numpy?

要按第一列的值进行屏蔽:

To mask by 1st column value:

In [7]: data['f1'] == 40
Out[7]: array([False,  True], dtype=bool)

In [8]: data[data['f1'] == 40]
Out[8]:
array([(40, 'bar', 39)],
      dtype=[('f1', '<i8'), ('f2', '<U5'), ('f3', '<i8')])

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