temp数据预处理--以24h为周期的序列

1.按照周期来截取数据

从数据库加载下来的是以5min取一次mean()的列，因此24h应取了24*60/5=288次数据

首先把这8352个数据（最后一个以倒数第二个填充）改成288*30的形式

txt=open('my_data.csv','r')
txt1=open('new_data.csv','w')
temp=[]
for line in txt.readlines():
    line=line.strip('\n')
    temp.append(line)

j=1
n=288
while n:
    s=[]
    for i in range(len(temp)):
        if (i+1)%288 == j:
            s.append(temp[i])
        else:
            pass
    for k in s:
        txt1.write(k)
        txt1.write(',')
    j=j+1
    n=n-1
    if j==288:
        s=[]
        txt1.write('\n')
        for i in range(len(temp)):
            if (i+1)%288 ==0:
                s.append(temp[i])
            else:
                pass
        for k in s:
            txt1.write(k)
            txt1.write(',')
    else:
        pass
    txt1.write('\n')

txt.close()
txt1.close()

2.缺失值和异常处理

https://blog.csdn.net/wangxingfan316/article/details/79363420

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import Imputer
import scipy.fftpack as fftpact

def meaninstead(list):
    #遍历数据进行处理
    con_clou = len(list)
    for i in range(0,cont_clou-1):
        for j in range(0,29):
            if(list[i][j]>1000):
                list[i][j]=(list[i-1][j]+list[i+1][j])
            elif(list[i][j]<=0):
                list[i][j]=(list[i-1][j]+list[i+1][j])
    return list

dataset = pd.read_csv('new_data.csv')
list = np.array(dataset.values.tolist())
list = meaninstead(list)
list.to_csv('clean.csv')

3.降噪

https://www.douban.com/note/698037655/?type=like

傅里叶变换降噪失真度的计算

https://max.book118.com/html/2018/1126/8077042115001134.shtm

5.分箱

import numpy as np
import pandas as pd
dataset = pd.read_csv('june_total.csv')
list1= np.array(dataset.values.tolist())
temp = list1[1,0:]
string =str(pd.qcut(temp,3)[1])
s = string.strip("(").strip("]").split(",")
left = float(s[0])
right=float(s[1])

num=0
count=0
for i in temp:
    if left<i and right>i:
        num+=i
        count+=1
    else:
        continue
print(num/count)