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()

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')

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)