如何在NumPy数组中的特定列上乘以标量? [英] How to multiply a scalar throughout a specific column within a NumPy array?

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问题描述

我需要对水文地质学现场工作的大型数据集进行一些分析.我正在使用NumPy.我想知道我该怎么做:

I need to do some analysis on a large dataset from a hydrolgeology field work. I am using NumPy. I want to know how I can:

  1. 乘以我的数组的第二列以数字表示(例如5.2).然后

  1. multiply e.g. the 2nd column of my array by a number (e.g. 5.2). And then

计算该列中数字的累加总和.

calculate the cumulative sum of the numbers in that column.

正如我提到的,我只想处理特定的列而不是整个数组.

As I mentioned I only want to work on a specific column and not the whole array.

推荐答案

 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])


带有一些模拟数据:


with some mocked data:

>>> 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


仅需要一些简单的规则即可在NumPy中进行元素选择(索引):


just a few simple rules are required to grok element selection (indexing) in NumPy:

  • NumPy和Python一样,都是基于0的,因此例如下面的"1"是指第二列

  • NumPy, like Python, is 0-based, so eg, the "1" below refers to the second column

用逗号分隔括号内的尺寸,因此[行,列],例如A [2,3]表示第三行第四列的​​项目(单元格")

commas separate the dimensions inside the brackets, so [rows, columns], eg, A[2,3] means the item ("cell") at row three, column four

冒号表示该维度上所有元素的全部,例如A [:,1]创建A的第2列的视图; A [3 ,:]指向第四行

a colon means all of the elements along that dimension, eg, A[:,1] creates a view of A's column 2; A[3,:] refers to the fourth row

这篇关于如何在NumPy数组中的特定列上乘以标量?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

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