1. #coding:utf8
  2. import tushare as ts
  3. import pandas as pd
  4. import numpy as np
  5. import pymysql,datetime
  6. import matplotlib.pyplot as plt
  7. import logging
  8. import sys ,requests,re
  9.  
  10. def init_env():
  11.     # token='23b817c8b6e2b772f37ad6f5628ad348a0aefed07ed9b07ecc75976d'
  12.     # db=pymysql.connect(host='127.0.0.1',db='stock',user='root',passwd='root',charset='utf8')
  13.     # cursor=db.cursor() 
  14.  
  15.     # pro=ts.pro_api(token)
  16.     aa=1
  17.     return aa
  18. # #初始化db，tushare token
  19. # db,cursor,pro=init_env()
  20.  
  21. #coding=utf-8 
  22.  
  23. duan ="--------------------------"  #在控制台断行区别的     
  24.  
  25. if __name__ == "__main__":  
  26.  
  27.     nump_array_date  = ['20170808210000' ,'20170808210100' ,'20170808210200' ,'20170808210300'
  28.  ,'20170808210400' ,'20170808210500' ,'20170808210600' ,'20170808210700'
  29.  ,'20170808210800' ,'20170808210900' ,'20170808211000' ,'20170808211100'
  30.  ,'20170808211200' ,'20170808211300' ,'20170808211400' ,'20170808211500'
  31.  ,'20170808211600' ,'20170808211700' ,'20170808211800' ,'20170808211900'
  32.  ,'20170808212000' ,'20170808212100' ,'20170808212200' ,'20170808212300'
  33.  ,'20170808212400' ,'20170808212500' ,'20170808212600' ,'20170808212700'
  34.  ,'20170808212800' ,'20170808212900' ,'20170808213000' ,'20170808213100'
  35.  ,'20170808213200' ,'20170808213300' ,'20170808213400' ,'20170808213500'
  36.  ,'20170808213600' ,'20170808213700' ,'20170808213800' ,'20170808213900'
  37.  ,'20170808214000' ,'20170808214100' ,'20170808214200' ,'20170808214300'
  38.  ,'20170808214400' ,'20170808214500' ,'20170808214600' ,'20170808214700'
  39.  ,'20170808214800' ,'20170808214900' ,'20170808215000' ,'20170808215100'
  40.  ,'20170808215200' ,'20170808215300' ,'20170808215400' ,'20170808215500'
  41.  ,'20170808215600' ,'20170808215700' ,'20170808215800' ,'20170808215900'
  42.  ,'20170808220000' ,'20170808220100' ,'20170808220200' ,'20170808220300'
  43.  ,'20170808220400' ,'20170808220500' ,'20170808220600' ,'20170808220700'
  44.  ,'20170808220800' ,'20170808220900' ,'20170808221000' ,'20170808221100'
  45.  ,'20170808221200' ,'20170808221300' ,'20170808221400' ,'20170808221500'
  46.  ,'20170808221600' ,'20170808221700' ,'20170808221800' ,'20170808221900'
  47.  ,'20170808222000' ,'20170808222100' ,'20170808222200' ,'20170808222300'
  48.  ,'20170808222400' ,'20170808222500' ,'20170808222600' ,'20170808222700'
  49.  ,'20170808222800' ,'20170808222900' ,'20170808223000' ,'20170808223100'
  50.  ,'20170808223200' ,'20170808223300' ,'20170808223400' ,'20170808223500'
  51.  ,'20170808223600' ,'20170808223700' ,'20170808223800' ,'20170808223900'
  52.  ,'20170808224000' ,'20170808224100' ,'20170808224200' ,'20170808224300'
  53.  ,'20170808224400' ,'20170808224500' ,'20170808224600' ,'20170808224700'
  54.  ,'20170808224800' ,'20170808224900' ,'20170808225000' ,'20170808225100'
  55.  ,'20170808225200' ,'20170808225300' ,'20170808225400' ,'20170808225500'
  56.  ,'20170808225600' ,'20170808225700' ,'20170808225800' ,'20170808225900'
  57.  ,'20170808230000' ,'20170809090000' ,'20170809090100' ,'20170809090200'
  58.  ,'20170809090300' ,'20170809090400' ,'20170809090500' ,'20170809090600'
  59.  ,'20170809090700' ,'20170809090800' ,'20170809090900' ,'20170809091000'
  60.  ,'20170809091100' ,'20170809091200' ,'20170809091300' ,'20170809091400'
  61.  ,'20170809091500' ,'20170809091600' ,'20170809091700' ,'20170809091800'
  62.  ,'20170809091900' ,'20170809092000' ,'20170809092100' ,'20170809092200'
  63.  ,'20170809092300' ,'20170809092400' ,'20170809092500' ,'20170809092600'
  64.  ,'20170809092700' ,'20170809092800' ,'20170809092900' ,'20170809093000'
  65.  ,'20170809093100' ,'20170809093200' ,'20170809093300' ,'20170809093400'
  66.  ,'20170809093500' ,'20170809093600' ,'20170809093700' ,'20170809093800'
  67.  ,'20170809093900' ,'20170809094000' ,'20170809094100' ,'20170809094200'
  68.  ,'20170809094300' ,'20170809094400' ,'20170809094500' ,'20170809094600'
  69.  ,'20170809094700' ,'20170809094800' ,'20170809094900' ,'20170809095000'
  70.  ,'20170809095100' ,'20170809095200' ,'20170809095300' ,'20170809095400'
  71.  ,'20170809095500' ,'20170809095600' ,'20170809095700' ,'20170809095800'
  72.  ,'20170809095900' ,'20170809100000' ,'20170809100100' ,'20170809100200'
  73.  ,'20170809100300' ,'20170809100400' ,'20170809100500' ,'20170809100600'
  74.  ,'20170809100700' ,'20170809100800' ,'20170809100900' ,'20170809101000'
  75.  ,'20170809101100' ,'20170809101200' ,'20170809101300' ,'20170809101400'
  76.  ,'20170809103000' ,'20170809103100' ,'20170809103200' ,'20170809103300'
  77.  ,'20170809103400' ,'20170809103500' ,'20170809103600' ,'20170809103700'
  78.  ,'20170809103800' ,'20170809103900' ,'20170809104000' ,'20170809104100'
  79.  ,'20170809104200' ,'20170809104300' ,'20170809104400' ,'20170809104500'
  80.  ,'20170809104600' ,'20170809104700' ,'20170809104800' ,'20170809104900'
  81.  ,'20170809105000' ,'20170809105100' ,'20170809105200' ,'20170809105300'
  82.  ,'20170809105400' ,'20170809105500' ,'20170809105600' ,'20170809105700'
  83.  ,'20170809105800' ,'20170809105900' ,'20170809110000' ,'20170809110100'
  84.  ,'20170809110200' ,'20170809110300' ,'20170809110400' ,'20170809110500'
  85.  ,'20170809110600' ,'20170809110700' ,'20170809110800' ,'20170809110900'
  86.  ,'20170809111000' ,'20170809111100' ,'20170809111200' ,'20170809111300'
  87.  ,'20170809111400' ,'20170809111500' ,'20170809111600' ,'20170809111700'
  88.  ,'20170809111800' ,'20170809111900' ,'20170809112000' ,'20170809112100'
  89.  ,'20170809112200' ,'20170809112300' ,'20170809112400' ,'20170809112500'
  90.  ,'20170809112600' ,'20170809112700' ,'20170809112800' ,'20170809112900'
  91.  ,'20170809133000' ,'20170809133100' ,'20170809133200' ,'20170809133300'
  92.  ,'20170809133400' ,'20170809133500' ,'20170809133600' ,'20170809133700'
  93.  ,'20170809133800' ,'20170809133900' ,'20170809134000' ,'20170809134100'
  94.  ,'20170809134200' ,'20170809134300' ,'20170809134400' ,'20170809134500'
  95.  ,'20170809134600' ,'20170809134700' ,'20170809134800' ,'20170809134900'
  96.  ,'20170809135000' ,'20170809135100' ,'20170809135200' ,'20170809135300'
  97.  ,'20170809135400' ,'20170809135500' ,'20170809135600' ,'20170809135700'
  98.  ,'20170809135800' ,'20170809135900' ,'20170809140000' ,'20170809140100'
  99.  ,'20170809140200' ,'20170809140300' ,'20170809140400' ,'20170809140500'
  100.  ,'20170809140600' ,'20170809140700' ,'20170809140800' ,'20170809140900'
  101.  ,'20170809141000' ,'20170809141100' ,'20170809141200' ,'20170809141300'
  102.  ,'20170809141400' ,'20170809141500' ,'20170809141600' ,'20170809141700'
  103.  ,'20170809141800' ,'20170809141900' ,'20170809142000' ,'20170809142100'
  104.  ,'20170809142200' ,'20170809142300' ,'20170809142400' ,'20170809142500'
  105.  ,'20170809142600' ,'20170809142700' ,'20170809142800' ,'20170809142900'
  106.  ,'20170809143000' ,'20170809143100' ,'20170809143200' ,'20170809143300'
  107.  ,'20170809143400' ,'20170809143500' ,'20170809143600' ,'20170809143700'
  108.  ,'20170809143800' ,'20170809143900' ,'20170809144000' ,'20170809144100'
  109.  ,'20170809144200' ,'20170809144300' ,'20170809144400' ,'20170809144500'
  110.  ,'20170809144600' ,'20170809144700' ,'20170809144800' ,'20170809144900'
  111.  ,'20170809145000' ,'20170809145100' ,'20170809145200' ,'20170809145300'
  112.  ,'20170809145400' ,'20170809145500' ,'20170809145600' ,'20170809145700'
  113.  ,'20170809145800' ,'20170809145900']
  114.  
  115.     nump_array_date= pd.to_datetime(nump_array_date) # convert str to date
  116.  
  117.     nump_array_price  = [3900.0,  3903.0,  3891.0,  3888.0,  3893.0,  3895.0,  3899.0,  3906.0,  3914.0,  3911.0,
  118.   3912.0,  3910.0,  3914.0,  3920.0,  3920.0,  3915.0,  3915.0,  3915.0,  3911.0,  3903.0,
  119.   3901.0,  3899.0,  3894.0,  3901.0,  3901.0,  3897.0,  3891.0,  3882.0,  3878.0,  3881.0,
  120.   3878.0,  3885.0,  3886.0,  3889.0,  3887.0,  3887.0,  3886.0,  3885.0,  3886.0,  3887.0,
  121.   3894.0,  3888.0,  3890.0,  3887.0,  3888.0,  3883.0,  3880.0,  3885.0,  3887.0,  3882.0,
  122.   3882.0,  3887.0,  3886.0,  3885.0,  3890.0,  3891.0,  3887.0,  3890.0,  3886.0,  3891.0,
  123.   3888.0,  3891.0,  3881.0,  3878.0,  3877.0,  3875.0,  3871.0,  3872.0,  3879.0,  3876.0,
  124.   3879.0,  3885.0,  3884.0,  3883.0,  3879.0,  3877.0,  3880.0,  3878.0,  3882.0,  3885.0,
  125.   3883.0,  3884.0,  3883.0,  3881.0,  3882.0,  3889.0,  3896.0,  3891.0,  3897.0,  3905.0,
  126.   3901.0,  3902.0,  3899.0,  3897.0,  3896.0,  3899.0,  3902.0,  3902.0,  3905.0,  3913.0,
  127.   3910.0,  3909.0,  3902.0,  3901.0,  3902.0,  3897.0,  3903.0,  3902.0,  3901.0,  3900.0,
  128.   3903.0,  3906.0,  3906.0,  3909.0,  3904.0,  3902.0,  3902.0,  3902.0,  3904.0,  3909.0,
  129.   3909.0,  3941.0,  3934.0,  3947.0,  3938.0,  3939.0,  3938.0,  3932.0,  3930.0,  3929.0,
  130.   3924.0,  3930.0,  3930.0,  3926.0,  3929.0,  3918.0,  3914.0,  3912.0,  3908.0,  3912.0,
  131.   3913.0,  3910.0,  3915.0,  3916.0,  3913.0,  3915.0,  3918.0,  3913.0,  3908.0,  3912.0,
  132.   3911.0,  3916.0,  3913.0,  3915.0,  3918.0,  3917.0,  3916.0,  3920.0,  3920.0,  3917.0,
  133.   3916.0,  3912.0,  3913.0,  3909.0,  3911.0,  3910.0,  3907.0,  3908.0,  3901.0,  3907.0,
  134.   3908.0,  3909.0,  3910.0,  3909.0,  3911.0,  3912.0,  3914.0,  3915.0,  3913.0,  3919.0,
  135.   3917.0,  3915.0,  3918.0,  3919.0,  3918.0,  3926.0,  3925.0,  3925.0,  3927.0,  3923.0,
  136.   3926.0,  3926.0,  3920.0,  3921.0,  3919.0,  3919.0,  3917.0,  3921.0,  3924.0,  3922.0,
  137.   3921.0,  3923.0,  3922.0,  3922.0,  3927.0,  3928.0,  3928.0,  3929.0,  3926.0,  3927.0,
  138.   3928.0,  3926.0,  3922.0,  3912.0,  3911.0,  3908.0,  3912.0,  3910.0,  3913.0,  3905.0,
  139.   3910.0,  3904.0,  3893.0,  3896.0,  3898.0,  3896.0,  3903.0,  3905.0,  3905.0,  3907.0,
  140.   3906.0,  3909.0,  3910.0,  3910.0,  3913.0,  3911.0,  3911.0,  3914.0,  3913.0,  3908.0,
  141.   3913.0,  3910.0,  3910.0,  3911.0,  3914.0,  3918.0,  3917.0,  3917.0,  3919.0,  3919.0,
  142.   3917.0,  3922.0,  3926.0,  3924.0,  3927.0,  3925.0,  3940.0,  3940.0,  3943.0,  3949.0,
  143.   3953.0,  3951.0,  3951.0,  3953.0,  3950.0,  3957.0,  3964.0,  3964.0,  3960.0,  3958.0,
  144.   3963.0,  3963.0,  3956.0,  3959.0,  3959.0,  3957.0,  3961.0,  3960.0,  3960.0,  3963.0,
  145.   3972.0,  3971.0,  3974.0,  3982.0,  3980.0,  3981.0,  3969.0,  3970.0,  3970.0,  3972.0,
  146.   3968.0,  3968.0,  3970.0,  3974.0,  3973.0,  3974.0,  3971.0,  3972.0,  3978.0,  3982.0,
  147.   3975.0,  3971.0,  3970.0,  3972.0,  3971.0,  3970.0,  3973.0,  3973.0,  3976.0,  3976.0,
  148.   3975.0,  3981.0,  3980.0,  3979.0,  3979.0,  3984.0,  3980.0,  3977.0,  3983.0,  3984.0,
  149.   3984.0,  3980.0,  3979.0,  3979.0,  3978.0,  3978.0,  3978.0,  3979.0,  3983.0,  3978.0,
  150.   3978.0,  3981.0,  3984.0,  3990.0,  3993.0,  3997.0,  4001.0,  4000.0,  3997.0,  4003.0,
  151.   4003.0,  4000.0,  4002.0,  3991.0,  3994.0,  4006.0,]
  152.  
  153.     df=pd.DataFrame({'date':nump_array_date,'price':nump_array_price})
  154.     df_night=df[df.date<'2017-08-09 09:00:00']
  155.     df_0900=df[(df.date>='2017-08-09 09:00:00')&(df.date<='2017-08-09 10:15:00')]
  156.     df_1030=df[(df.date>='2017-08-09 10:30:00')&(df.date<='2017-08-09 11:30:00')]
  157.     df_1330=df[(df.date>='2017-08-09 13:30:00')&(df.date<='2017-08-09 15:00:00')]
  158.     df_all=df_0900.append(df_1030)
  159.     df_final=df_all.append(df_1330)
  160.  
  161.     x_tk=[]
  162.     x_lb=[]
  163.  
  164.     for i in range(0,len(df_final.date.tolist())):
  165.         x_tk.append(i)
  166.         if i % 7==0:
  167.             x_lb.append(df_final.date.tolist()[i])
  168.         else:
  169.             x_lb.append("")
  170.  
  171.     plt.plot(x_tk,df_final.price)
  172.     plt.xticks(x_tk,(x_lb),rotation=80)
  173.     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=
return aa
# #初始化db，tushare token
# db,cursor,pro=init_env()
#coding=utf-8
 
duan ="--------------------------" #在控制台断行区别的
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(,len(df_final.date.tolist())):
x_tk.append(i)
if i % ==:
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=)
plt.show()

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