numpy.loadtxt和制表符分隔的值:不理解数据类型 [英] numpy.loadtxt and tab separated values: data type not understood

问题描述

我在使用numpy.loadtxt导入制表符分隔值时遇到问题.

I'm having problems importing tab separated values using numpy.loadtxt.

我需要导入的行具有以下形式:

The rows I need to import have the following form:

01-Aug-2013 1143_051-100    r   702 135 32  7   

我只想读取第0、2、3、4、5、6列.这是我到目前为止的内容:

I only want to read columns 0,2,3,4,5,6. This is what I have so far:

numpy.loadtxt(test,dtype= (str,str,int,int,int,int), delimiter= "\t", usecols = (0,2,3,4,5,6))

这将返回data type not understood.我在这里想念什么?

This returns data type not understood. What am I missing here?

推荐答案

为实现快速索引编制，NumPy依赖于每个具有固定宽度的dtype.因此，如果指定字符串dtype，则还必须指定字符串中的字节数.所以

To achieve fast indexing, NumPy relies on each dtype having a fixed width. So if you specify a string dtype, you also have to specify the number of bytes in the string. So

dtype = '|S11,|S1,<i4,<i4,<i4,<i4'

可以处理您发布的数据.

would work for the data you posted.

但是，当字符串的宽度可变时，使用np.genfromtxt代替np.loadtxt更容易，因为您可以指定dtype=None并让np.genfromtxt对每一列的dtype进行有根据的猜测.

---

However, it is easier to use np.genfromtxt instead of np.loadtxt when the strings have variable width, since you can specify dtype=None and let np.genfromtxt make an educated guess about the dtype of each column.

In [15]: np.genfromtxt('data', delimiter='\t', dtype=None, usecols=(0,2,3,4,5,6))
Out[15]: 
array(('01-Aug-2013', 'r', 702, 135, 32, 7), 
      dtype=[('f0', 'S11'), ('f1', 'S1'), ('f2', '<i4'), ('f3', '<i4'), ('f4', '<i4'), ('f5', '<i4')])

或

In [16]: np.loadtxt('data', delimiter='\t', dtype='|S11,|S1,<i4,<i4,<i4,<i4', usecols=(0,2,3,4,5,6))
Out[16]: 
array(('01-Aug-2013', 'r', 702, 135, 32, 7), 
      dtype=[('f0', 'S11'), ('f1', 'S1'), ('f2', '<i4'), ('f3', '<i4'), ('f4', '<i4'), ('f5', '<i4')])

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