如何在NumPy数组中的特定列中乘以标量？

Question

我需要对来自水文地质领域工作的大型数据集进行一些分析.我正在使用NumPy.我想知道我怎么做:

1. 例如,我的数组的第二列乘以数字(例如5.2).然后

2. 计算该列中数字的累积总和.

正如我所提到的,我只想处理特定列而不是整个数组.

Answer 1

 you can do this in two simple steps using NumPy:

>>> # multiply column 2 of the 2D array, A, by 5.2
>>> A[:,1] *= 5.2

>>> # assuming by 'cumulative sum' you meant the 'reduced' sum:
>>> A[:,1].sum()

>>> # if in fact you want the cumulative sum (ie, returns a new column)
>>> # then do this for the second step instead:
>>> NP.cumsum(A[:,1])

一些模拟数据:

>>> A = NP.random.rand(8, 5)
>>> A
  array([[ 0.893,  0.824,  0.438,  0.284,  0.892],
         [ 0.534,  0.11 ,  0.409,  0.555,  0.96 ],
         [ 0.671,  0.817,  0.636,  0.522,  0.867],
         [ 0.752,  0.688,  0.142,  0.793,  0.716],
         [ 0.276,  0.818,  0.904,  0.767,  0.443],
         [ 0.57 ,  0.159,  0.144,  0.439,  0.747],
         [ 0.705,  0.793,  0.575,  0.507,  0.956],
         [ 0.322,  0.713,  0.963,  0.037,  0.509]])

>>> A[:,1] *= 5.2

>>> A
  array([[ 0.893,  4.287,  0.438,  0.284,  0.892],
         [ 0.534,  0.571,  0.409,  0.555,  0.96 ],
         [ 0.671,  4.25 ,  0.636,  0.522,  0.867],
         [ 0.752,  3.576,  0.142,  0.793,  0.716],
         [ 0.276,  4.255,  0.904,  0.767,  0.443],
         [ 0.57 ,  0.827,  0.144,  0.439,  0.747],
         [ 0.705,  4.122,  0.575,  0.507,  0.956],
         [ 0.322,  3.71 ,  0.963,  0.037,  0.509]])

>>> A[:,1].sum()
  25.596156138451427

在NumPy中只需要一些简单的规则来修改元素选择(索引):

• 与Python一样,NumPy是基于0的,因此例如,下面的"1"指的是第二列

• 逗号分隔括号内的尺寸,因此[行,列],例如A [2,3]表示第3行第4列的项目("单元格")

• 冒号表示该维度上的所有元素,例如A [:,1]创建A列2的视图; A [3,:]指的是第四行

Answer 2

当然:

import numpy as np
# Let a be some 2d array; here we just use dummy data 
# to illustrate the method
a = np.ones((10,5))
# Multiply just the 2nd column by 5.2 in-place
a[:,1] *= 5.2

# Now get the cumulative sum of just that column
csum = np.cumsum(a[:,1])

如果您不想在现场执行此操作,则需要采用略有不同的策略:

b = 5.2*a[:,1]
csum = np.cumsum(b)