Given the following pandas data frame with 60 elements.
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"])
Is there a formula to rescale this in such a way so that for each window bigger than 20 elements starting from index 0
to index i+1
, the data is rescaled down to 20 elements?
Here is a loop that is creating the windows with the data for rescaling, i just do not know any way of doing the rescaling itself for this problem at hand. Any suggestions on how this might be done?
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)
Basically after the rescaling the rescaledData
should have 40 arrays, each with a length of 20 prices.