Home > other >  Everyone a great god for help python matplotlib time series mapping problem
Everyone a great god for help python matplotlib time series mapping problem

Time:04-23

Now I want to pass the python map a time-sharing futures trading market, because of the characteristics of the time-sharing chart is that yesterday night night at 21:00 p.m.to llp.m.est disk data as the beginning of the data, a day of trading time is discrete, the morning 9:00 - when the first half, 10:00 a.m. - 11:30 on the morning of the second half, and the afternoon 1:30-3:00 in the afternoon, so that the whole timeline is a discrete time data, y data is the market price,

Simple map price when the diagram below is a continuous curve,
but once time as the x axis data is a discrete price curve, shown in the following figure,

I want a result is price curve is continuous, but the following can happen time corresponds to the price, help you a great god, and see if there are any good method?

Source code is too large, contains data for everybody great god help debugging, on the 1st floor, thank you!

CodePudding user response:

Code data on we
 
# coding=utf-8
The from __future__ import division
The import matplotlib. Pyplot as PLT
The import pandas as pd

Duan="-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -" # of difference in the console line breaks

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'
nullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnullnull
  • Related