Question

给出以下包含60个元素的pandas数据框。

import pandas as pd
data = [60,62.75,73.28,75.77,70.28
    ,67.85,74.58,72.91,68.33,78.59
    ,75.58,78.93,74.61,85.3,84.63
    ,84.61,87.76,95.02,98.83,92.44
    ,84.8,89.51,90.25,93.82,86.64
    ,77.84,76.06,77.75,72.13,80.2
    ,79.05,76.11,80.28,76.38,73.3
    ,72.28,77,69.28,71.31,79.25
    ,75.11,73.16,78.91,84.78,85.17
    ,91.53,94.85,87.79,97.92,92.88
    ,91.92,88.32,81.49,88.67,91.46
    ,91.71,82.17,93.05,103.98,105]

data_pd = pd.DataFrame(data, columns=["price"])

是否有一个公式以这种方式重新缩放，以便对于从索引0到索引i+1的大于20个元素的每个窗口，数据重新缩放到20个元素？

这是一个创建带有重新缩放数据的窗口的循环，我只是不知道为手头的这个问题做任何重新缩放的方法。关于如何做到这一点的任何建议？

miniLenght = 20
rescaledData = []
for i in range(len(data_pd)):
    if(i >= miniLenght):
        dataForScaling = data_pd[0:i]
        scaledDataToMinLenght = dataForScaling #do the scaling here so that the length of the rescaled data is always equal to miniLenght
        rescaledData.append(scaledDataToMinLenght)

基本上在重新缩放后，rescaledData应该有40个数组，每个数组的长度为20个。

Answer 1

从阅读论文开始，看起来您正在将列表调整回20个索引，然后在20个索引处插入数据。

我们会像他们那样制作索引（range(0, len(large), step = len(large)/miniLenght)），然后使用numpys interp - 有一百万种插值方法。 np.interp使用线性插值，所以如果你要求例如索引1.5，你得到点1和2的平均值，依此类推。

所以，这里是你的代码的快速修改（nb，我们可以使用'rolling'完全对它进行矢量化）：

import numpy as np
miniLenght = 20
rescaledData = []

for i in range(len(data_pd)):
    if(i >= miniLenght):
        dataForScaling = data_pd['price'][0:i]
        #figure out how many 'steps' we have
        steps = len(dataForScaling)
        #make indices where the data needs to be sliced to get 20 points
        indices = np.arange(0,steps, step = steps/miniLenght)
        #use np.interp at those points, with the original values as given
        rescaledData.append(np.interp(indices, np.arange(steps), dataForScaling))

输出符合预期：

[array([ 60.  ,  62.75,  73.28,  75.77,  70.28,  67.85,  74.58,  72.91,
         68.33,  78.59,  75.58,  78.93,  74.61,  85.3 ,  84.63,  84.61,
         87.76,  95.02,  98.83,  92.44]),
 array([ 60.    ,  63.2765,  73.529 ,  74.9465,  69.794 ,  69.5325,
         74.079 ,  71.307 ,  72.434 ,  77.2355,  77.255 ,  76.554 ,
         81.024 ,  84.8645,  84.616 ,  86.9725,  93.568 ,  98.2585,
         93.079 ,  85.182 ]),.....

将价格表从较长的长度重新缩放到较小的长度

1 个答案: