各位大神求助python matplotlib 时间序列绘图问题
现在我想通过Python绘制一个期货交易分时图，因为分时图的特点是昨天的夜间交易数据从晚上21:00到23:00开始，全天的交易时间是不连续的。上午的前半部分是9:00-10:15，上午的后半部分是10:30-11:30，下午的时段是1:30-3:00，所以整个时间线是一个不连续的时间数据，Y轴数据是市场的价格。当简单地绘制价格图时，它是一条连续曲线，如下所示。
当时间作为X轴数据加入时，它是一条不连续的价格曲线，如下图所示。
我想要的是价格曲线是连续的，但是它对应于价格的时间，所以让我们看看是否有一个好的方法来做到这一点。
源代码太大，包含数据，方便您帮忙调试，谢谢！#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()
这应该是楼主想要的效果吧？细节可以调整。df=pd.DataFrame({'date':nump_array_date,'price':nump_array_price})
df_night=df
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())
else:
x_lb.append("")
plt.plot(x_tk,df_final.price)
plt.xticks(x_tk,(x_lb),rotation=80)
plt.show()
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