Question

我在Python 2.7中运行Numpy 1.6，并且有一些我从另一个模块获得的一维数组。我想把这些数组并打包成一个结构化数组，这样我就可以按名称索引原始的1D数组。我无法弄清楚如何将1D阵列放入2D阵列并使dtype访问正确的数据。我的MWE如下：

>>> import numpy as np
>>> 
>>> x = np.random.randint(10,size=3)
>>> y = np.random.randint(10,size=3)
>>> z = np.random.randint(10,size=3)
>>> x
array([9, 4, 7])
>>> y
array([5, 8, 0])
>>> z
array([2, 3, 6])
>>> 
>>> w = np.array([x,y,z])
>>> w.dtype=[('x','i4'),('y','i4'),('z','i4')]
>>> w
array([[(9, 4, 7)],
       [(5, 8, 0)],
       [(2, 3, 6)]], 
      dtype=[('x', '<i4'), ('y', '<i4'), ('z', '<i4')])
>>> w['x']
array([[9],
       [5],
       [2]])
>>> 
>>> u = np.vstack((x,y,z))
>>> u.dtype=[('x','i4'),('y','i4'),('z','i4')]
>>> u
array([[(9, 4, 7)],
       [(5, 8, 0)],
       [(2, 3, 6)]],    
      dtype=[('x', '<i4'), ('y', '<i4'), ('z', '<i4')]) 

>>> u['x']
array([[9],
       [5],
       [2]])

>>> v = np.column_stack((x,y,z))
>>> v
array([[(9, 4, 7), (5, 8, 0), (2, 3, 6)]], 
      dtype=[('x', '<i4'), ('y', '<i4'), ('z', '<i4')])

>>> v.dtype=[('x','i4'),('y','i4'),('z','i4')]
>>> v['x']
array([[9, 5, 2]])

正如您所看到的，虽然我的原始x数组包含[9,4,7]，但我无法尝试堆叠数组，然后按'x'索引返回原始x数组。有办法做到这一点，还是我错了？

Answer 1

一种方法是

wtype=np.dtype([('x',x.dtype),('y',y.dtype),('z',z.dtype)])
w=np.empty(len(x),dtype=wtype)
w['x']=x
w['y']=y
w['z']=z

请注意，randint返回的每个数字的大小取决于你的平台，所以在我的机器上有一个int64，而不是int32，即'i4'，它是'i8'。另一种方式更便携。

Answer 2

您想使用np.column_stack：

import numpy as np

x = np.random.randint(10,size=3)
y = np.random.randint(10,size=3)
z = np.random.randint(10,size=3)

w = np.column_stack((x, y, z))
w = w.ravel().view([('x', x.dtype), ('y', y.dtype), ('z', z.dtype)])

>>> w
array([(5, 1, 8), (8, 4, 9), (4, 2, 6)], 
      dtype=[('x', '<i4'), ('y', '<i4'), ('z', '<i4')])
>>> x
array([5, 8, 4])
>>> y
array([1, 4, 2])
>>> z
array([8, 9, 6])
>>> w['x']
array([5, 8, 4])
>>> w['y']
array([1, 4, 2])
>>> w['z']
array([8, 9, 6])

Answer 3

您可能希望查看numpy的记录数组以供此用途：

“Numpy提供了强大的功能来创建结构或记录数组。这些数组允许通过结构或结构字段来操作数据。”

以下是记录数组的文档： http://docs.scipy.org/doc/numpy/user/basics.rec.html

您可以使用变量名作为字段名称。

Answer 4

要以所选答案为基础，可以使此过程动态化：

首先循环遍历数组（可以是单列）
然后您遍历列以获取数据类型
您使用这些数据类型创建空数组
然后我们重复这些循环以填充数组

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解决问题

# First, let's build a structured array
rows = [
    ("A", 1),
    ("B", 2),
    ("C", 3),
]
dtype = [
    ("letter", str, 1),
    ("number", int, 1),
]
arr = np.array(rows, dtype=dtype)

# Then, let's create a standalone column, of the same length:
rows = [
    1.0,
    2.0,
    3.0,
]
dtype = [
    ("float", float, 1)
]
new_col = np.array(rows, dtype=dtype)

希望有帮助

Answer 5

使用字典

#!/usr/bin/env python

import numpy

w = {}
for key in ('x', 'y', 'z'):
    w[key] = np.random.randint(10, size=3)

print w

numpy将1D数组堆叠成结构化数组

5 个答案: