我在矩阵 x
中有大量数据,我需要分析一些子矩阵。
我正在使用以下代码来选择子矩阵:
>>> import numpy as np
>>> x = np.random.normal(0,1,(20,2))
>>> x
array([[-1.03266826, 0.04646684],
[ 0.05898304, 0.31834926],
[-0.1916809 , -0.97929025],
[-0.48837085, -0.62295003],
[-0.50731017, 0.50305894],
[ 0.06457385, -0.10670002],
[-0.72573604, 1.10026385],
[-0.90893845, 0.99827162],
[ 0.20714399, -0.56965615],
[ 0.8041371 , 0.21910274],
[-0.65882317, 0.2657183 ],
[-1.1214074 , -0.39886425],
[ 0.0784783 , -0.21630006],
[-0.91802557, -0.20178683],
[ 0.88268539, -0.66470235],
[-0.03652459, 1.49798484],
[ 1.76329838, -0.26554555],
[-0.97546845, -2.41823586],
[ 0.32335103, -1.35091711],
[-0.12981597, 0.27591674]])
>>> index = x[:,1] > 0
>>> index
array([ True, True, False, False, True, False, True, True, False,
True, True, False, False, False, False, True, False, False,
False, True], dtype=bool)
>>> x1 = x[index, :] #x1 is a copy of the submatrix
>>> x1
array([[-1.03266826, 0.04646684],
[ 0.05898304, 0.31834926],
[-0.50731017, 0.50305894],
[-0.72573604, 1.10026385],
[-0.90893845, 0.99827162],
[ 0.8041371 , 0.21910274],
[-0.65882317, 0.2657183 ],
[-0.03652459, 1.49798484],
[-0.12981597, 0.27591674]])
>>> x1[0,0] = 1000
>>> x1
array([[ 1.00000000e+03, 4.64668400e-02],
[ 5.89830401e-02, 3.18349259e-01],
[ -5.07310170e-01, 5.03058935e-01],
[ -7.25736045e-01, 1.10026385e+00],
[ -9.08938455e-01, 9.98271624e-01],
[ 8.04137104e-01, 2.19102741e-01],
[ -6.58823174e-01, 2.65718300e-01],
[ -3.65245877e-02, 1.49798484e+00],
[ -1.29815968e-01, 2.75916735e-01]])
>>> x
array([[-1.03266826, 0.04646684],
[ 0.05898304, 0.31834926],
[-0.1916809 , -0.97929025],
[-0.48837085, -0.62295003],
[-0.50731017, 0.50305894],
[ 0.06457385, -0.10670002],
[-0.72573604, 1.10026385],
[-0.90893845, 0.99827162],
[ 0.20714399, -0.56965615],
[ 0.8041371 , 0.21910274],
[-0.65882317, 0.2657183 ],
[-1.1214074 , -0.39886425],
[ 0.0784783 , -0.21630006],
[-0.91802557, -0.20178683],
[ 0.88268539, -0.66470235],
[-0.03652459, 1.49798484],
[ 1.76329838, -0.26554555],
[-0.97546845, -2.41823586],
[ 0.32335103, -1.35091711],
[-0.12981597, 0.27591674]])
>>>
但我希望 x1 只是一个指针或类似的东西。每次我需要子矩阵时复制数据对我来说太昂贵了。 我该怎么做?
编辑: 显然 numpy 数组没有任何解决方案。从这个角度来看,pandas 数据框架更好吗?
最佳答案
x
数组的信息汇总在 .__array_interface__
属性中
In [433]: x.__array_interface__
Out[433]:
{'descr': [('', '<f8')],
'strides': None,
'data': (171396104, False),
'typestr': '<f8',
'version': 3,
'shape': (20, 2)}
它有数组shape
、strides
(此处默认)和指向数据缓冲区的指针。 view
可以指向相同的数据缓冲区(可能更远),并有自己的 shape
和 strides
。
但是用你的 bool 值索引不能用那几个数字来概括。它要么必须一直携带 index
数组,要么从 x
数据缓冲区复制选定的项目。 numpy
选择复制。您可以选择何时应用 index
,是现在还是在调用堆栈的更下方。
关于python - numpy 切片数组而不复制它,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/30238092/