使用numpy.loadtxt加载同时包含float和string的文本文件 [英] Loading text file containing both float and string using numpy.loadtxt

问题描述

我有一个文本文件data.txt，其中包含:

I have a text file, data.txt, which contains:

5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica

如何使用numpy.loadtxt()加载此数据，以便在加载[['5.1' '3.5' '1.4' '0.2' 'Iris-setosa'] ['4.9' '3.0' '1.4' '0.2' 'Iris-setosa'] ...]这样的数据后得到NumPy数组?

How do I load this data using numpy.loadtxt() so that I get a NumPy array after loading such as [['5.1' '3.5' '1.4' '0.2' 'Iris-setosa'] ['4.9' '3.0' '1.4' '0.2' 'Iris-setosa'] ...]?

我尝试过

np.loadtxt(open("data.txt"), 'r',
           dtype={
               'names': (
                   'sepal length', 'sepal width', 'petal length',
                   'petal width', 'label'),
               'formats': (
                   np.float, np.float, np.float, np.float, np.str)},
           delimiter= ',', skiprows=0)

推荐答案

如果您使用 np.genfromtxt ，您可以指定dtype=None，这将告诉genfromtxt明智地猜测每列的dtype.最方便的是，它使您不必为字符串列指定所需的字节数. (通过指定例如np.str来忽略字节数是行不通的.)

If you use np.genfromtxt, you could specify dtype=None, which will tell genfromtxt to intelligently guess the dtype of each column. Most conveniently, it relieves you of the burder of specifying the number of bytes required for the string column. (Omitting the number of bytes, by specifying e.g. np.str, does not work.)

In [58]: np.genfromtxt('data.txt', delimiter=',', dtype=None, names=('sepal length', 'sepal width', 'petal length', 'petal width', 'label'))
Out[58]: 
array([(5.1, 3.5, 1.4, 0.2, 'Iris-setosa'),
       (4.9, 3.0, 1.4, 0.2, 'Iris-setosa'),
       (5.8, 2.7, 4.1, 1.0, 'Iris-versicolor'),
       (6.2, 2.2, 4.5, 1.5, 'Iris-versicolor'),
       (6.4, 3.1, 5.5, 1.8, 'Iris-virginica'),
       (6.0, 3.0, 4.8, 1.8, 'Iris-virginica')], 
      dtype=[('sepal_length', '<f8'), ('sepal_width', '<f8'), ('petal_length', '<f8'), ('petal_width', '<f8'), ('label', 'S15')])

---

如果您确实想使用np.loadtxt，然后以最小的修改来修复代码，则可以使用:

---

If you do want to use np.loadtxt, then to fix your code with minimal changes, you could use:

np.loadtxt("data.txt",
   dtype={'names': ('sepal length', 'sepal width', 'petal length', 'petal width', 'label'),
          'formats': (np.float, np.float, np.float, np.float, '|S15')},
   delimiter=',', skiprows=0)

主要区别只是将np.str更改为|S15(一个15字节的字符串).

The main difference is simply changing np.str to |S15 (a 15-byte string).

还请注意 open("data.txt"), 'r'应该是open("data.txt", 'r').但是，由于np.loadtxt可以接受文件名，因此您根本不需要使用open.

Also note that open("data.txt"), 'r' should be open("data.txt", 'r'). But since np.loadtxt can accept a filename, you don't really need to use open at all.

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