将2D numpy数组转换为结构化数组 [英] Converting a 2D numpy array to a structured array

问题描述

我正在尝试将二维数组转换为具有命名字段的结构化数组.我希望2D数组中的每一行成为结构化数组中的新记录.不幸的是，我尝试过的一切都没有达到我期望的方式.

I'm trying to convert a two-dimensional array into a structured array with named fields. I want each row in the 2D array to be a new record in the structured array. Unfortunately, nothing I've tried is working the way I expect.

我从:

>>> myarray = numpy.array([("Hello",2.5,3),("World",3.6,2)])
>>> print myarray
[['Hello' '2.5' '3']
 ['World' '3.6' '2']]

我想转换成这样的东西:

I want to convert to something that looks like this:

>>> newarray = numpy.array([("Hello",2.5,3),("World",3.6,2)], dtype=[("Col1","S8"),("Col2","f8"),("Col3","i8")])
>>> print newarray
[('Hello', 2.5, 3L) ('World', 3.6000000000000001, 2L)]

我尝试过的事情:

>>> newarray = myarray.astype([("Col1","S8"),("Col2","f8"),("Col3","i8")])
>>> print newarray
[[('Hello', 0.0, 0L) ('2.5', 0.0, 0L) ('3', 0.0, 0L)]
 [('World', 0.0, 0L) ('3.6', 0.0, 0L) ('2', 0.0, 0L)]]

>>> newarray = numpy.array(myarray, dtype=[("Col1","S8"),("Col2","f8"),("Col3","i8")])
>>> print newarray
[[('Hello', 0.0, 0L) ('2.5', 0.0, 0L) ('3', 0.0, 0L)]
 [('World', 0.0, 0L) ('3.6', 0.0, 0L) ('2', 0.0, 0L)]]

这两种方法都尝试将myarray中的每个条目转换为具有给定dtype的记录，因此将插入多余的零.我不知道如何将其转换为记录.

Both of these approaches attempt to convert each entry in myarray into a record with the given dtype, so the extra zeros are inserted. I can't figure out how to get it to convert each row into a record.

另一种尝试:

>>> newarray = myarray.copy()
>>> newarray.dtype = [("Col1","S8"),("Col2","f8"),("Col3","i8")]
>>> print newarray
[[('Hello', 1.7219343871178711e-317, 51L)]
 [('World', 1.7543139673493688e-317, 50L)]]

这一次不执行任何实际转换.内存中的现有数据只是重新解释为新的数据类型.

This time no actual conversion is performed. The existing data in memory is just re-interpreted as the new data type.

正在从文本文件中读取我刚开始的数组.数据类型是事先未知的，因此我无法在创建时设置dtype.我需要一个高性能，优雅的解决方案，该解决方案对于一般情况都适用，因为我将针对各种各样的应用程序进行很多次此类转换.

The array that I'm starting with is being read in from a text file. The data types are not known ahead of time, so I can't set the dtype at the time of creation. I need a high-performance and elegant solution that will work well for general cases since I will be doing this type of conversion many, many times for a large variety of applications.

谢谢！

推荐答案

您可以使用numpy.core.records.fromarrays 如下:

>>> import numpy as np
>>> myarray = np.array([("Hello",2.5,3),("World",3.6,2)])
>>> print myarray
[['Hello' '2.5' '3']
 ['World' '3.6' '2']]


>>> newrecarray = np.core.records.fromarrays(myarray.transpose(), 
                                             names='col1, col2, col3',
                                             formats = 'S8, f8, i8')

>>> print newrecarray
[('Hello', 2.5, 3) ('World', 3.5999999046325684, 2)]

我正在尝试做类似的事情.我发现，当numpy从现有2D数组(使用np.core.records.fromarrays)创建结构化数组时，它将二维数组中的每一列(而不是每一行)都视为一条记录.因此，您必须移调它. numpy的这种行为似乎不是很直观，但是也许有充分的理由.

I was trying to do something similar. I found that when numpy created a structured array from an existing 2D array (using np.core.records.fromarrays), it considered each column (instead of each row) in the 2-D array as a record. So you have to transpose it. This behavior of numpy does not seem very intuitive, but perhaps there is a good reason for it.

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