将一个numpy数组拆分为两个numpy阵列

Question

我有一个类似这样的numpy数组：

A=[(datetime.datetime(2016, 6, 8, 12, 37, 27, 826000), 3.0)
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 827000), nan)
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 832000), nan)
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 833000), nan)
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000), 3.0)
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000), 35.0)]

我想把它分成两个numpy数组：

B=[(datetime.datetime(2016, 6, 8, 12, 37, 27, 826000),
  (datetime.datetime(2016, 6, 8, 12, 37, 27, 827000),
  (datetime.datetime(2016, 6, 8, 12, 37, 27, 832000),
  (datetime.datetime(2016, 6, 8, 12, 37, 27, 833000),
  (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000), 
  (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000)]
C=[3.0,nan,nan,nan,3.0,35.0]

为了给你更多的细节，这个numpy数组最初是一个dictionary，我已经将其转换为numpy数组，你可以找到下面的代码：

def convertarray(dictionary):
    names=['id','data']
    formats=['datetime64[ms]','f8']
    dtype=dict(names=names, formats=formats)
    result=np.array(dictionary.items(),dtype)
    return result

Answer 1

如果你只是有一个带有dtype=object的普通数组，我认为你最好的办法是通过在几个列表中迭代旧数组来构建新数组：

将numpy导入为np来自numpy进口nan导入日期时间

A=np.array([(datetime.datetime(2016, 6, 8, 12, 37, 27, 826000), 3.0),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 827000), nan),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 832000), nan),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 833000), nan),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000), 3.0),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000), 35.0)])
print(A.dtype)
times = np.array([x[0] for x in A])
values = np.array([x[1] for x in A])
print(times)
print(values)

话虽如此，使用记录数组可能会稍微干净一些：

import numpy as np
from numpy import nan
import datetime
A=np.array([(datetime.datetime(2016, 6, 8, 12, 37, 27, 826000), 3.0),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 827000), nan),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 832000), nan),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 833000), nan),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000), 3.0),
   (datetime.datetime(2016, 6, 8, 12, 37, 27, 837000), 35.0)],
   dtype=[('time', object), ('value', float)])
print(A.dtype)
print(A['time'])
print(A['value'])

Answer 2

您可能想要对数据进行切片。为该维度插入:将选择该维度的所有元素。

B = A[:, 0]
C = A[:, 1]