python dict到numpy结构化数组 [英] python dict to numpy structured array

问题描述

我有一个字典，需要将其转换为NumPy结构化数组.我正在使用arcpy函数 NumPyArraytoTable ，因此NumPy结构化数组是唯一可以使用的数据格式.

I have a dictionary that I need to convert to a NumPy structured array. I'm using the arcpy function NumPyArraytoTable, so a NumPy structured array is the only data format that will work.

基于此线程:从字典写入numpy数组，这线程:如何将Python字典对象转换为numpy数组

Based on this thread: Writing to numpy array from dictionary and this thread: How to convert Python dictionary object to numpy array

我已经尝试过了:

result = {0: 1.1181753789488595, 1: 0.5566080288678394, 2: 0.4718269778030734, 3: 0.48716683119447185, 4: 1.0, 5: 0.1395076201641266, 6: 0.20941558441558442}

names = ['id','data']
formats = ['f8','f8']
dtype = dict(names = names, formats=formats)
array=numpy.array([[key,val] for (key,val) in result.iteritems()],dtype)

但是我不断得到expected a readable buffer object

以下方法有效，但很愚蠢，显然不适用于真实数据.我知道有一种更优雅的方法，我只是想不通.

The method below works, but is stupid and obviously won't work for real data. I know there is a more graceful approach, I just can't figure it out.

totable = numpy.array([[key,val] for (key,val) in result.iteritems()])
array=numpy.array([(totable[0,0],totable[0,1]),(totable[1,0],totable[1,1])],dtype)

推荐答案

您可以使用np.array(list(result.items()), dtype=dtype):

import numpy as np
result = {0: 1.1181753789488595, 1: 0.5566080288678394, 2: 0.4718269778030734, 3: 0.48716683119447185, 4: 1.0, 5: 0.1395076201641266, 6: 0.20941558441558442}

names = ['id','data']
formats = ['f8','f8']
dtype = dict(names = names, formats=formats)
array = np.array(list(result.items()), dtype=dtype)

print(repr(array))

收益

array([(0.0, 1.1181753789488595), (1.0, 0.5566080288678394),
       (2.0, 0.4718269778030734), (3.0, 0.48716683119447185), (4.0, 1.0),
       (5.0, 0.1395076201641266), (6.0, 0.20941558441558442)], 
      dtype=[('id', '<f8'), ('data', '<f8')])

---

如果您不想创建元组的中间列表list(result.items())，则可以改用np.fromiter:

---

If you don't want to create the intermediate list of tuples, list(result.items()), then you could instead use np.fromiter:

在Python2中:

array = np.fromiter(result.iteritems(), dtype=dtype, count=len(result))

在Python3中:

array = np.fromiter(result.items(), dtype=dtype, count=len(result))

---

为什么使用列表[key,val]不起作用:

---

Why using the list [key,val] does not work:

顺便说一下，您的尝试

numpy.array([[key,val] for (key,val) in result.iteritems()],dtype)

非常接近工作.如果将列表[key, val]更改为元组(key, val)，则它将起作用.当然，

was very close to working. If you change the list [key, val] to the tuple (key, val), then it would have worked. Of course,

numpy.array([(key,val) for (key,val) in result.iteritems()], dtype)

与

numpy.array(result.items(), dtype)

在Python2中，或

in Python2, or

numpy.array(list(result.items()), dtype)

在Python3中.

np.array处理列表与元组的方法不同: Robert Kern解释说:

np.array treats lists differently than tuples: Robert Kern explains:

通常，元组被视为标量"记录，而列表则被视为 递归.此规则有助于numpy.array()找出哪个 序列是记录，是要重现的其他序列 之上;即哪些序列创建了另一个维度，哪些是 原子元素.

As a rule, tuples are considered "scalar" records and lists are recursed upon. This rule helps numpy.array() figure out which sequences are records and which are other sequences to be recursed upon; i.e. which sequences create another dimension and which are the atomic elements.

由于(0.0, 1.1181753789488595)被认为是这些原子元素之一，因此它应该是一个元组，而不是列表.

Since (0.0, 1.1181753789488595) is considered one of those atomic elements, it should be a tuple, not a list.

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