怎么把numpy.recarray转换成numpy.array? [英] How to convert numpy.recarray to numpy.array?

问题描述

将numpy的recarray转换为普通数组的最佳方法是什么?

What's the best way to convert numpy's recarray to a normal array?

我可以先做.tolist()然后再做array()，但这似乎效率不高.

i could do a .tolist() first and then do an array() again, but that seems somewhat inefficient..

示例:

import numpy as np
a = np.recarray((2,), dtype=[('x', int), ('y', float), ('z', int)])

>>> a
  rec.array([(30408891, 9.2944097561804909e-296, 30261980),
   (44512448, 4.5273310988985789e-300, 29979040)], 
  dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])

>>> np.array(a.tolist())
   array([[  3.04088910e+007,   9.29440976e-296,   3.02619800e+007],
   [  4.45124480e+007,   4.52733110e-300,   2.99790400e+007]])

推荐答案

通过普通数组"，我认为您的意思是同类dtype的NumPy数组.给定一个rearray，例如:

By "normal array" I take it you mean a NumPy array of homogeneous dtype. Given a recarray, such as:

>>> a = np.array([(0, 1, 2),
              (3, 4, 5)],[('x', int), ('y', float), ('z', int)]).view(np.recarray)
rec.array([(0, 1.0, 2), (3, 4.0, 5)], 
      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])

我们必须首先使每个列具有相同的dtype.然后，我们可以通过使用相同的dtype查看数据，将其转换为普通数组":

we must first make each column have the same dtype. We can then convert it to a "normal array" by viewing the data by the same dtype:

>>> a.astype([('x', '<f8'), ('y', '<f8'), ('z', '<f8')]).view('<f8')
array([ 0.,  1.,  2.,  3.,  4.,  5.])

---

astype 返回一个新的numpy数组.因此，以上内容需要与a的大小成比例的额外内存. a的每一行需要4 + 8 + 4 = 16字节，而a.astype(...)则需要8 * 3 = 24字节.调用视图不需要新的内存，因为view只是更改了基础数据的解释方式.

---

astype returns a new numpy array. So the above requires additional memory in an amount proportional to the size of a. Each row of a requires 4+8+4=16 bytes, while a.astype(...) requires 8*3=24 bytes. Calling view requires no new memory, since view just changes how the underlying data is interpreted.

a.tolist()返回一个新的Python列表.每个Python数字都是一个对象，比其在numpy数组中的等效表示形式需要更多的字节.因此a.tolist()比a.astype(...)需要更多的内存.

a.tolist() returns a new Python list. Each Python number is an object which requires more bytes than its equivalent representation in a numpy array. So a.tolist() requires more memory than a.astype(...).

呼叫a.astype(...).view(...)也比np.array(a.tolist())快:

In [8]: a = np.array(zip(*[iter(xrange(300))]*3),[('x', int), ('y', float), ('z', int)]).view(np.recarray)

In [9]: %timeit a.astype([('x', '<f8'), ('y', '<f8'), ('z', '<f8')]).view('<f8')
10000 loops, best of 3: 165 us per loop

In [10]: %timeit np.array(a.tolist())
1000 loops, best of 3: 683 us per loop

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