各位大神求助python matplotlib 时间序列绘图问题

qinchun 2017-08-14 10:59:47

我现在想通过python绘制一个期货交易行情的分时图，因为分时图的特点是，昨天的夜盘数据晚上21:00 -23:00 作为数据的开始，一天内的交易时间是不连续的，上午9:00 - 10:15 上半场，上午10:30-11:30 上午的下半场，还有下午1:30-3:00 下午场，因此整个时间轴是一个不连续的时间数据， y轴数据就是行情的价格。

单纯绘制价格图的时候是一个连续的曲线如下图，

但是一旦把时间作为x轴数据加入的时候就是一个不连续的价格曲线，如下图。

我想要的一个效果是价格曲线是连续的，但是下面又能够对应上该价格的发生时间，求助诸位大神，看看有没有什么好的方法？

源代码太大，含有数据，方便各位大神帮忙调试，放在1楼了，感谢诸位了！

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qinchun 2021-05-18

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您好，问题里面图二的效果就是二楼的源代码不变即可。

Sunny_Xu_ 2021-04-22

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请问楼主，如何得到你问题里图二的效果，我想要这种效果

小布 2018-12-16

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额，看错题目了，代码更正如下：

    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()

小布 2018-12-16

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小布 2018-12-16

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这应该是楼主想要的效果吧？具体细节可以再调整。



    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')]



    

    # plt.plot(df_night.date,df_night.price)

    plt.plot(df_0900.date,df_0900.price)

    plt.plot(df_1030.date,df_1030.price)

    plt.plot(df_1330.date,df_1330.price)



    plt.xticks(rotation=80)

    plt.show()

chenyunf95 2018-12-14

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https://blog.csdn.net/tpoy0099/article/details/49511907

qinchun 2017-08-14

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代码有数据放在本楼


#coding=utf-8 
from __future__ import division
import matplotlib.pyplot as plt
import pandas as pd

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,]

    

    fig, ax = plt.subplots() 
   
#     plt.plot(nump_array_date,nump_array_price)
    plt.plot(nump_array_price)
    plt.show()