csdn的一次回答問題

#coding:utf8 
import tushare as ts 
import pandas as pd 
import numpy as np 
import pymysql,datetime
import matplotlib.pyplot as plt
import logging
import sys ,requests,re


def init_env():
    # token='23b817c8b6e2b772f37ad6f5628ad348a0aefed07ed9b07ecc75976d'
    # db=pymysql.connect(host='127.0.0.1',db='stock',user='root',passwd='root',charset='utf8')
    # cursor=db.cursor() 
    
    # pro=ts.pro_api(token)
    aa=1
    return aa
# #初始化db，tushare token
# db,cursor,pro=init_env()

#coding=utf-8 
 

duan ="--------------------------"  #在控制檯斷行區別的     

if __name__ == "__main__":  
       
   
    
    nump_array_date  = ['20170808210000' ,'20170808210100' ,'20170808210200' ,'20170808210300'
 ,'20170808210400' ,'20170808210500' ,'20170808210600' ,'20170808210700'
 ,'20170808210800' ,'20170808210900' ,'20170808211000' ,'20170808211100'
 ,'20170808211200' ,'20170808211300' ,'20170808211400' ,'20170808211500'
 ,'20170808211600' ,'20170808211700' ,'20170808211800' ,'20170808211900'
 ,'20170808212000' ,'20170808212100' ,'20170808212200' ,'20170808212300'
 ,'20170808212400' ,'20170808212500' ,'20170808212600' ,'20170808212700'
 ,'20170808212800' ,'20170808212900' ,'20170808213000' ,'20170808213100'
 ,'20170808213200' ,'20170808213300' ,'20170808213400' ,'20170808213500'
 ,'20170808213600' ,'20170808213700' ,'20170808213800' ,'20170808213900'
 ,'20170808214000' ,'20170808214100' ,'20170808214200' ,'20170808214300'
 ,'20170808214400' ,'20170808214500' ,'20170808214600' ,'20170808214700'
 ,'20170808214800' ,'20170808214900' ,'20170808215000' ,'20170808215100'
 ,'20170808215200' ,'20170808215300' ,'20170808215400' ,'20170808215500'
 ,'20170808215600' ,'20170808215700' ,'20170808215800' ,'20170808215900'
 ,'20170808220000' ,'20170808220100' ,'20170808220200' ,'20170808220300'
 ,'20170808220400' ,'20170808220500' ,'20170808220600' ,'20170808220700'
 ,'20170808220800' ,'20170808220900' ,'20170808221000' ,'20170808221100'
 ,'20170808221200' ,'20170808221300' ,'20170808221400' ,'20170808221500'
 ,'20170808221600' ,'20170808221700' ,'20170808221800' ,'20170808221900'
 ,'20170808222000' ,'20170808222100' ,'20170808222200' ,'20170808222300'
 ,'20170808222400' ,'20170808222500' ,'20170808222600' ,'20170808222700'
 ,'20170808222800' ,'20170808222900' ,'20170808223000' ,'20170808223100'
 ,'20170808223200' ,'20170808223300' ,'20170808223400' ,'20170808223500'
 ,'20170808223600' ,'20170808223700' ,'20170808223800' ,'20170808223900'
 ,'20170808224000' ,'20170808224100' ,'20170808224200' ,'20170808224300'
 ,'20170808224400' ,'20170808224500' ,'20170808224600' ,'20170808224700'
 ,'20170808224800' ,'20170808224900' ,'20170808225000' ,'20170808225100'
 ,'20170808225200' ,'20170808225300' ,'20170808225400' ,'20170808225500'
 ,'20170808225600' ,'20170808225700' ,'20170808225800' ,'20170808225900'
 ,'20170808230000' ,'20170809090000' ,'20170809090100' ,'20170809090200'
 ,'20170809090300' ,'20170809090400' ,'20170809090500' ,'20170809090600'
 ,'20170809090700' ,'20170809090800' ,'20170809090900' ,'20170809091000'
 ,'20170809091100' ,'20170809091200' ,'20170809091300' ,'20170809091400'
 ,'20170809091500' ,'20170809091600' ,'20170809091700' ,'20170809091800'
 ,'20170809091900' ,'20170809092000' ,'20170809092100' ,'20170809092200'
 ,'20170809092300' ,'20170809092400' ,'20170809092500' ,'20170809092600'
 ,'20170809092700' ,'20170809092800' ,'20170809092900' ,'20170809093000'
 ,'20170809093100' ,'20170809093200' ,'20170809093300' ,'20170809093400'
 ,'20170809093500' ,'20170809093600' ,'20170809093700' ,'20170809093800'
 ,'20170809093900' ,'20170809094000' ,'20170809094100' ,'20170809094200'
 ,'20170809094300' ,'20170809094400' ,'20170809094500' ,'20170809094600'
 ,'20170809094700' ,'20170809094800' ,'20170809094900' ,'20170809095000'
 ,'20170809095100' ,'20170809095200' ,'20170809095300' ,'20170809095400'
 ,'20170809095500' ,'20170809095600' ,'20170809095700' ,'20170809095800'
 ,'20170809095900' ,'20170809100000' ,'20170809100100' ,'20170809100200'
 ,'20170809100300' ,'20170809100400' ,'20170809100500' ,'20170809100600'
 ,'20170809100700' ,'20170809100800' ,'20170809100900' ,'20170809101000'
 ,'20170809101100' ,'20170809101200' ,'20170809101300' ,'20170809101400'
 ,'20170809103000' ,'20170809103100' ,'20170809103200' ,'20170809103300'
 ,'20170809103400' ,'20170809103500' ,'20170809103600' ,'20170809103700'
 ,'20170809103800' ,'20170809103900' ,'20170809104000' ,'20170809104100'
 ,'20170809104200' ,'20170809104300' ,'20170809104400' ,'20170809104500'
 ,'20170809104600' ,'20170809104700' ,'20170809104800' ,'20170809104900'
 ,'20170809105000' ,'20170809105100' ,'20170809105200' ,'20170809105300'
 ,'20170809105400' ,'20170809105500' ,'20170809105600' ,'20170809105700'
 ,'20170809105800' ,'20170809105900' ,'20170809110000' ,'20170809110100'
 ,'20170809110200' ,'20170809110300' ,'20170809110400' ,'20170809110500'
 ,'20170809110600' ,'20170809110700' ,'20170809110800' ,'20170809110900'
 ,'20170809111000' ,'20170809111100' ,'20170809111200' ,'20170809111300'
 ,'20170809111400' ,'20170809111500' ,'20170809111600' ,'20170809111700'
 ,'20170809111800' ,'20170809111900' ,'20170809112000' ,'20170809112100'
 ,'20170809112200' ,'20170809112300' ,'20170809112400' ,'20170809112500'
 ,'20170809112600' ,'20170809112700' ,'20170809112800' ,'20170809112900'
 ,'20170809133000' ,'20170809133100' ,'20170809133200' ,'20170809133300'
 ,'20170809133400' ,'20170809133500' ,'20170809133600' ,'20170809133700'
 ,'20170809133800' ,'20170809133900' ,'20170809134000' ,'20170809134100'
 ,'20170809134200' ,'20170809134300' ,'20170809134400' ,'20170809134500'
 ,'20170809134600' ,'20170809134700' ,'20170809134800' ,'20170809134900'
 ,'20170809135000' ,'20170809135100' ,'20170809135200' ,'20170809135300'
 ,'20170809135400' ,'20170809135500' ,'20170809135600' ,'20170809135700'
 ,'20170809135800' ,'20170809135900' ,'20170809140000' ,'20170809140100'
 ,'20170809140200' ,'20170809140300' ,'20170809140400' ,'20170809140500'
 ,'20170809140600' ,'20170809140700' ,'20170809140800' ,'20170809140900'
 ,'20170809141000' ,'20170809141100' ,'20170809141200' ,'20170809141300'
 ,'20170809141400' ,'20170809141500' ,'20170809141600' ,'20170809141700'
 ,'20170809141800' ,'20170809141900' ,'20170809142000' ,'20170809142100'
 ,'20170809142200' ,'20170809142300' ,'20170809142400' ,'20170809142500'
 ,'20170809142600' ,'20170809142700' ,'20170809142800' ,'20170809142900'
 ,'20170809143000' ,'20170809143100' ,'20170809143200' ,'20170809143300'
 ,'20170809143400' ,'20170809143500' ,'20170809143600' ,'20170809143700'
 ,'20170809143800' ,'20170809143900' ,'20170809144000' ,'20170809144100'
 ,'20170809144200' ,'20170809144300' ,'20170809144400' ,'20170809144500'
 ,'20170809144600' ,'20170809144700' ,'20170809144800' ,'20170809144900'
 ,'20170809145000' ,'20170809145100' ,'20170809145200' ,'20170809145300'
 ,'20170809145400' ,'20170809145500' ,'20170809145600' ,'20170809145700'
 ,'20170809145800' ,'20170809145900']
    
    nump_array_date= pd.to_datetime(nump_array_date) # convert str to date
    
    nump_array_price  = [3900.0,  3903.0,  3891.0,  3888.0,  3893.0,  3895.0,  3899.0,  3906.0,  3914.0,  3911.0,
  3912.0,  3910.0,  3914.0,  3920.0,  3920.0,  3915.0,  3915.0,  3915.0,  3911.0,  3903.0,
  3901.0,  3899.0,  3894.0,  3901.0,  3901.0,  3897.0,  3891.0,  3882.0,  3878.0,  3881.0,
  3878.0,  3885.0,  3886.0,  3889.0,  3887.0,  3887.0,  3886.0,  3885.0,  3886.0,  3887.0,
  3894.0,  3888.0,  3890.0,  3887.0,  3888.0,  3883.0,  3880.0,  3885.0,  3887.0,  3882.0,
  3882.0,  3887.0,  3886.0,  3885.0,  3890.0,  3891.0,  3887.0,  3890.0,  3886.0,  3891.0,
  3888.0,  3891.0,  3881.0,  3878.0,  3877.0,  3875.0,  3871.0,  3872.0,  3879.0,  3876.0,
  3879.0,  3885.0,  3884.0,  3883.0,  3879.0,  3877.0,  3880.0,  3878.0,  3882.0,  3885.0,
  3883.0,  3884.0,  3883.0,  3881.0,  3882.0,  3889.0,  3896.0,  3891.0,  3897.0,  3905.0,
  3901.0,  3902.0,  3899.0,  3897.0,  3896.0,  3899.0,  3902.0,  3902.0,  3905.0,  3913.0,
  3910.0,  3909.0,  3902.0,  3901.0,  3902.0,  3897.0,  3903.0,  3902.0,  3901.0,  3900.0,
  3903.0,  3906.0,  3906.0,  3909.0,  3904.0,  3902.0,  3902.0,  3902.0,  3904.0,  3909.0,
  3909.0,  3941.0,  3934.0,  3947.0,  3938.0,  3939.0,  3938.0,  3932.0,  3930.0,  3929.0,
  3924.0,  3930.0,  3930.0,  3926.0,  3929.0,  3918.0,  3914.0,  3912.0,  3908.0,  3912.0,
  3913.0,  3910.0,  3915.0,  3916.0,  3913.0,  3915.0,  3918.0,  3913.0,  3908.0,  3912.0,
  3911.0,  3916.0,  3913.0,  3915.0,  3918.0,  3917.0,  3916.0,  3920.0,  3920.0,  3917.0,
  3916.0,  3912.0,  3913.0,  3909.0,  3911.0,  3910.0,  3907.0,  3908.0,  3901.0,  3907.0,
  3908.0,  3909.0,  3910.0,  3909.0,  3911.0,  3912.0,  3914.0,  3915.0,  3913.0,  3919.0,
  3917.0,  3915.0,  3918.0,  3919.0,  3918.0,  3926.0,  3925.0,  3925.0,  3927.0,  3923.0,
  3926.0,  3926.0,  3920.0,  3921.0,  3919.0,  3919.0,  3917.0,  3921.0,  3924.0,  3922.0,
  3921.0,  3923.0,  3922.0,  3922.0,  3927.0,  3928.0,  3928.0,  3929.0,  3926.0,  3927.0,
  3928.0,  3926.0,  3922.0,  3912.0,  3911.0,  3908.0,  3912.0,  3910.0,  3913.0,  3905.0,
  3910.0,  3904.0,  3893.0,  3896.0,  3898.0,  3896.0,  3903.0,  3905.0,  3905.0,  3907.0,
  3906.0,  3909.0,  3910.0,  3910.0,  3913.0,  3911.0,  3911.0,  3914.0,  3913.0,  3908.0,
  3913.0,  3910.0,  3910.0,  3911.0,  3914.0,  3918.0,  3917.0,  3917.0,  3919.0,  3919.0,
  3917.0,  3922.0,  3926.0,  3924.0,  3927.0,  3925.0,  3940.0,  3940.0,  3943.0,  3949.0,
  3953.0,  3951.0,  3951.0,  3953.0,  3950.0,  3957.0,  3964.0,  3964.0,  3960.0,  3958.0,
  3963.0,  3963.0,  3956.0,  3959.0,  3959.0,  3957.0,  3961.0,  3960.0,  3960.0,  3963.0,
  3972.0,  3971.0,  3974.0,  3982.0,  3980.0,  3981.0,  3969.0,  3970.0,  3970.0,  3972.0,
  3968.0,  3968.0,  3970.0,  3974.0,  3973.0,  3974.0,  3971.0,  3972.0,  3978.0,  3982.0,
  3975.0,  3971.0,  3970.0,  3972.0,  3971.0,  3970.0,  3973.0,  3973.0,  3976.0,  3976.0,
  3975.0,  3981.0,  3980.0,  3979.0,  3979.0,  3984.0,  3980.0,  3977.0,  3983.0,  3984.0,
  3984.0,  3980.0,  3979.0,  3979.0,  3978.0,  3978.0,  3978.0,  3979.0,  3983.0,  3978.0,
  3978.0,  3981.0,  3984.0,  3990.0,  3993.0,  3997.0,  4001.0,  4000.0,  3997.0,  4003.0,
  4003.0,  4000.0,  4002.0,  3991.0,  3994.0,  4006.0,]

    df=pd.DataFrame({'date':nump_array_date,'price':nump_array_price})
    df_night=df[df.date<'2017-08-09 09:00:00']
    df_0900=df[(df.date>='2017-08-09 09:00:00')&(df.date<='2017-08-09 10:15:00')]
    df_1030=df[(df.date>='2017-08-09 10:30:00')&(df.date<='2017-08-09 11:30:00')]
    df_1330=df[(df.date>='2017-08-09 13:30:00')&(df.date<='2017-08-09 15:00:00')]
    df_all=df_0900.append(df_1030)
    df_final=df_all.append(df_1330)
    
    x_tk=[]
    x_lb=[]

    for i in range(0,len(df_final.date.tolist())):
        x_tk.append(i)
        if i % 7==0:
            x_lb.append(df_final.date.tolist()[i])
        else:
            x_lb.append("")
    
    plt.plot(x_tk,df_final.price)
    plt.xticks(x_tk,(x_lb),rotation=80)
    plt.show()

　　

#coding:utf8

import tushare as ts

import pandas as pd

import numpy as np

import pymysql,datetime

import matplotlib.pyplot as plt

import logging

import sys ,requests,re

def init_env():

# token='23b817c8b6e2b772f37ad6f5628ad348a0aefed07ed9b07ecc75976d'

# db=pymysql.connect(host='127.0.0.1',db='stock',user='root',passwd='root',charset='utf8')

# cursor=db.cursor()

 

# pro=ts.pro_api(token)

aa= 1

return aa

# #初始化db，tushare token

# db,cursor,pro=init_env()

#coding=utf-8

 

duan = "--------------------------" #在控制檯斷行區別的

if __name__ == "__main__":

 

 

 

nump_array_date = [ '20170808210000' , '20170808210100' , '20170808210200' , '20170808210300'

, '20170808210400' , '20170808210500' , '20170808210600' , '20170808210700'

, '20170808210800' , '20170808210900' , '20170808211000' , '20170808211100'

, '20170808211200' , '20170808211300' , '20170808211400' , '20170808211500'

, '20170808211600' , '20170808211700' , '20170808211800' , '20170808211900'

, '20170808212000' , '20170808212100' , '20170808212200' , '20170808212300'

, '20170808212400' , '20170808212500' , '20170808212600' , '20170808212700'

, '20170808212800' , '20170808212900' , '20170808213000' , '20170808213100'

, '20170808213200' , '20170808213300' , '20170808213400' , '20170808213500'

, '20170808213600' , '20170808213700' , '20170808213800' , '20170808213900'

, '20170808214000' , '20170808214100' , '20170808214200' , '20170808214300'

, '20170808214400' , '20170808214500' , '20170808214600' , '20170808214700'

, '20170808214800' , '20170808214900' , '20170808215000' , '20170808215100'

, '20170808215200' , '20170808215300' , '20170808215400' , '20170808215500'

, '20170808215600' , '20170808215700' , '20170808215800' , '20170808215900'

, '20170808220000' , '20170808220100' , '20170808220200' , '20170808220300'

, '20170808220400' , '20170808220500' , '20170808220600' , '20170808220700'

, '20170808220800' , '20170808220900' , '20170808221000' , '20170808221100'

, '20170808221200' , '20170808221300' , '20170808221400' , '20170808221500'

, '20170808221600' , '20170808221700' , '20170808221800' , '20170808221900'

, '20170808222000' , '20170808222100' , '20170808222200' , '20170808222300'

, '20170808222400' , '20170808222500' , '20170808222600' , '20170808222700'

, '20170808222800' , '20170808222900' , '20170808223000' , '20170808223100'

, '20170808223200' , '20170808223300' , '20170808223400' , '20170808223500'

, '20170808223600' , '20170808223700' , '20170808223800' , '20170808223900'

, '20170808224000' , '20170808224100' , '20170808224200' , '20170808224300'

, '20170808224400' , '20170808224500' , '20170808224600' , '20170808224700'

, '20170808224800' , '20170808224900' , '20170808225000' , '20170808225100'

, '20170808225200' , '20170808225300' , '20170808225400' , '20170808225500'

, '20170808225600' , '20170808225700' , '20170808225800' , '20170808225900'

, '20170808230000' , '20170809090000' , '20170809090100' , '20170809090200'

, '20170809090300' , '20170809090400' , '20170809090500' , '20170809090600'

, '20170809090700' , '20170809090800' , '20170809090900' , '20170809091000'

, '20170809091100' , '20170809091200' , '20170809091300' , '20170809091400'

, '20170809091500' , '20170809091600' , '20170809091700' , '20170809091800'

, '20170809091900' , '20170809092000' , '20170809092100' , '20170809092200'

, '20170809092300' , '20170809092400' , '20170809092500' , '20170809092600'

, '20170809092700' , '20170809092800' , '20170809092900' , '20170809093000'

, '20170809093100' , '20170809093200' , '20170809093300' , '20170809093400'

, '20170809093500' , '20170809093600' , '20170809093700' , '20170809093800'

, '20170809093900' , '20170809094000' , '20170809094100' , '20170809094200'

, '20170809094300' , '20170809094400' , '20170809094500' , '20170809094600'

, '20170809094700' , '20170809094800' , '20170809094900' , '20170809095000'

, '20170809095100' , '20170809095200' , '20170809095300' , '20170809095400'

, '20170809095500' , '20170809095600' , '20170809095700' , '20170809095800'

, '20170809095900' , '20170809100000' , '20170809100100' , '20170809100200'

, '20170809100300' , '20170809100400' , '20170809100500' , '20170809100600'

, '20170809100700' , '20170809100800' , '20170809100900' , '20170809101000'

, '20170809101100' , '20170809101200' , '20170809101300' , '20170809101400'

, '20170809103000' , '20170809103100' , '20170809103200' , '20170809103300'

, '20170809103400' , '20170809103500' , '20170809103600' , '20170809103700'

, '20170809103800' , '20170809103900' , '20170809104000' , '20170809104100'

, '20170809104200' , '20170809104300' , '20170809104400' , '20170809104500'

, '20170809104600' , '20170809104700' , '20170809104800' , '20170809104900'

, '20170809105000' , '20170809105100' , '20170809105200' , '20170809105300'

, '20170809105400' , '20170809105500' , '20170809105600' , '20170809105700'

, '20170809105800' , '20170809105900' , '20170809110000' , '20170809110100'

, '20170809110200' , '20170809110300' , '20170809110400' , '20170809110500'

, '20170809110600' , '20170809110700' , '20170809110800' , '20170809110900'

, '20170809111000' , '20170809111100' , '20170809111200' , '20170809111300'

, '20170809111400' , '20170809111500' , '20170809111600' , '20170809111700'

, '20170809111800' , '20170809111900' , '20170809112000' , '20170809112100'

, '20170809112200' , '20170809112300' , '20170809112400' , '20170809112500'

, '20170809112600' , '20170809112700' , '20170809112800' , '20170809112900'

, '20170809133000' , '20170809133100' , '20170809133200' , '20170809133300'

, '20170809133400' , '20170809133500' , '20170809133600' , '20170809133700'

, '20170809133800' , '20170809133900' , '20170809134000' , '20170809134100'

, '20170809134200' , '20170809134300' , '20170809134400' , '20170809134500'

, '20170809134600' , '20170809134700' , '20170809134800' , '20170809134900'

, '20170809135000' , '20170809135100' , '20170809135200' , '20170809135300'

, '20170809135400' , '20170809135500' , '20170809135600' , '20170809135700'

, '20170809135800' , '20170809135900' , '20170809140000' , '20170809140100'

, '20170809140200' , '20170809140300' , '20170809140400' , '20170809140500'

, '20170809140600' , '20170809140700' , '20170809140800' , '20170809140900'

, '20170809141000' , '20170809141100' , '20170809141200' , '20170809141300'

, '20170809141400' , '20170809141500' , '20170809141600' , '20170809141700'

, '20170809141800' , '20170809141900' , '20170809142000' , '20170809142100'

, '20170809142200' , '20170809142300' , '20170809142400' , '20170809142500'

, '20170809142600' , '20170809142700' , '20170809142800' , '20170809142900'

, '20170809143000' , '20170809143100' , '20170809143200' , '20170809143300'

, '20170809143400' , '20170809143500' , '20170809143600' , '20170809143700'

, '20170809143800' , '20170809143900' , '20170809144000' , '20170809144100'

, '20170809144200' , '20170809144300' , '20170809144400' , '20170809144500'

, '20170809144600' , '20170809144700' , '20170809144800' , '20170809144900'

, '20170809145000' , '20170809145100' , '20170809145200' , '20170809145300'

, '20170809145400' , '20170809145500' , '20170809145600' , '20170809145700'

, '20170809145800' , '20170809145900']

 

nump_array_date= pd.to_datetime(nump_array_date) # convert str to date

 

nump_array_price = [ 3900.0, 3903.0, 3891.0, 3888.0, 3893.0, 3895.0, 3899.0, 3906.0, 3914.0, 3911.0,

3912.0, 3910.0, 3914.0, 3920.0, 3920.0, 3915.0, 3915.0, 3915.0, 3911.0, 3903.0,

3901.0, 3899.0, 3894.0, 3901.0, 3901.0, 3897.0, 3891.0, 3882.0, 3878.0, 3881.0,

3878.0, 3885.0, 3886.0, 3889.0, 3887.0, 3887.0, 3886.0, 3885.0, 3886.0, 3887.0,

3894.0, 3888.0, 3890.0, 3887.0, 3888.0, 3883.0, 3880.0, 3885.0, 3887.0, 3882.0,

3882.0, 3887.0, 3886.0, 3885.0, 3890.0, 3891.0, 3887.0, 3890.0, 3886.0, 3891.0,

3888.0, 3891.0, 3881.0, 3878.0, 3877.0, 3875.0, 3871.0, 3872.0, 3879.0, 3876.0,

3879.0, 3885.0, 3884.0, 3883.0, 3879.0, 3877.0, 3880.0, 3878.0, 3882.0, 3885.0,

3883.0, 3884.0, 3883.0, 3881.0, 3882.0, 3889.0, 3896.0, 3891.0, 3897.0, 3905.0,

3901.0, 3902.0, 3899.0, 3897.0, 3896.0, 3899.0, 3902.0, 3902.0, 3905.0, 3913.0,

3910.0, 3909.0, 3902.0, 3901.0, 3902.0, 3897.0, 3903.0, 3902.0, 3901.0, 3900.0,

3903.0, 3906.0, 3906.0, 3909.0, 3904.0, 3902.0, 3902.0, 3902.0, 3904.0, 3909.0,

3909.0, 3941.0, 3934.0, 3947.0, 3938.0, 3939.0, 3938.0, 3932.0, 3930.0, 3929.0,

3924.0, 3930.0, 3930.0, 3926.0, 3929.0, 3918.0, 3914.0, 3912.0, 3908.0, 3912.0,

3913.0, 3910.0, 3915.0, 3916.0, 3913.0, 3915.0, 3918.0, 3913.0, 3908.0, 3912.0,

3911.0, 3916.0, 3913.0, 3915.0, 3918.0, 3917.0, 3916.0, 3920.0, 3920.0, 3917.0,

3916.0, 3912.0, 3913.0, 3909.0, 3911.0, 3910.0, 3907.0, 3908.0, 3901.0, 3907.0,

3908.0, 3909.0, 3910.0, 3909.0, 3911.0, 3912.0, 3914.0, 3915.0, 3913.0, 3919.0,

3917.0, 3915.0, 3918.0, 3919.0, 3918.0, 3926.0, 3925.0, 3925.0, 3927.0, 3923.0,

3926.0, 3926.0, 3920.0, 3921.0, 3919.0, 3919.0, 3917.0, 3921.0, 3924.0, 3922.0,

3921.0, 3923.0, 3922.0, 3922.0, 3927.0, 3928.0, 3928.0, 3929.0, 3926.0, 3927.0,

3928.0, 3926.0, 3922.0, 3912.0, 3911.0, 3908.0, 3912.0, 3910.0, 3913.0, 3905.0,

3910.0, 3904.0, 3893.0, 3896.0, 3898.0, 3896.0, 3903.0, 3905.0, 3905.0, 3907.0,

3906.0, 3909.0, 3910.0, 3910.0, 3913.0, 3911.0, 3911.0, 3914.0, 3913.0, 3908.0,

3913.0, 3910.0, 3910.0, 3911.0, 3914.0, 3918.0, 3917.0, 3917.0, 3919.0, 3919.0,

3917.0, 3922.0, 3926.0, 3924.0, 3927.0, 3925.0, 3940.0, 3940.0, 3943.0, 3949.0,

3953.0, 3951.0, 3951.0, 3953.0, 3950.0, 3957.0, 3964.0, 3964.0, 3960.0, 3958.0,

3963.0, 3963.0, 3956.0, 3959.0, 3959.0, 3957.0, 3961.0, 3960.0, 3960.0, 3963.0,

3972.0, 3971.0, 3974.0, 3982.0, 3980.0, 3981.0, 3969.0, 3970.0, 3970.0, 3972.0,

3968.0, 3968.0, 3970.0, 3974.0, 3973.0, 3974.0, 3971.0, 3972.0, 3978.0, 3982.0,

3975.0, 3971.0, 3970.0, 3972.0, 3971.0, 3970.0, 3973.0, 3973.0, 3976.0, 3976.0,

3975.0, 3981.0, 3980.0, 3979.0, 3979.0, 3984.0, 3980.0, 3977.0, 3983.0, 3984.0,

3984.0, 3980.0, 3979.0, 3979.0, 3978.0, 3978.0, 3978.0, 3979.0, 3983.0, 3978.0,

3978.0, 3981.0, 3984.0, 3990.0, 3993.0, 3997.0, 4001.0, 4000.0, 3997.0, 4003.0,

4003.0, 4000.0, 4002.0, 3991.0, 3994.0, 4006.0,]

df=pd.DataFrame({ 'date':nump_array_date, 'price':nump_array_price})

df_night=df[df.date< '2017-08-09 09:00:00']

df_0900=df[(df.date>= '2017-08-09 09:00:00')&(df.date<= '2017-08-09 10:15:00')]

df_1030=df[(df.date>= '2017-08-09 10:30:00')&(df.date<= '2017-08-09 11:30:00')]

df_1330=df[(df.date>= '2017-08-09 13:30:00')&(df.date<= '2017-08-09 15:00:00')]

df_all=df_0900.append(df_1030)

df_final=df_all.append(df_1330)

 

x_tk=[]

x_lb=[]

for i in range( 0, len(df_final.date.tolist())):

x_tk.append(i)

if i % 7== 0:

x_lb.append(df_final.date.tolist()[i])

else:

x_lb.append( "")

 

plt.plot(x_tk,df_final.price)

plt.xticks(x_tk,(x_lb), rotation= 80)

plt.show()