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This article mainly introduces Python how to draw a visualization map, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

Preface

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# time series trend chart import csvimport matplotlibimport numpy as npimport pandas as pdfrom datetime import datetimefrom matplotlibimport pyplot as plt# reads CSV file data file1='D:\\ data. Csv'for file2 in file1: print ('read file:% s'% file2) try: with open (file1) as f: # Open this file And store the result file object in f reader=csv.reader (f) # create a reader reader header_row=next (reader) # return the next line of date,r,VaR= in the file [], [] # declare the storage date List of most values for row in reader: date1=datetime.strptime (row [0]) '% Y _ date1% m _ r.append% d') # convert date data to datetime object date.append (date1) # Storage date r1=float (row [3]) # convert string to numeric r.append (R1) # maximum storage temperature VaR1=float (row [2]) VaR .append (VaR1) # minimum storage temperature except Exception as e: print ('read error' File2,':', e) zhfont1 = matplotlib.font_manager.FontProperties\ (fname= "D:\\ simhei.ttf") # define font matplotlib.rcParams ['axes.unicode_minus'] = False # solve the problem of minus sign'-'display as square # draw image fig=plt.figure (dpi=128,figsize= (8line 5)) plt.plot (date,r,label='r',linewidth=2,c='blue',alpha=1) # Transparency of the color specified by argument alpha 0 means completely transparent 1 (default) completely opaque plt.plot (date,VaR,'--',label='95%VaR',c='m',linewidth=2,alpha=1) plt.fill_between (date,r,VaR,facecolor='blue',alpha=0.1) # fill the chart area with color plt.title ("differential rate of return and its risk time series trend chart, fontproperties=zhfont1,fontsize=12) # chart coordinates set plt.xlabel (" time ", fontproperties=zhfont1,fontsize=10) plt.ylabel (" numerical ", fontproperties=zhfont1 Fontsize=10) plt.tick_params (axis='both',which='major',labelsize=10) plt.legend () # legend shows fig.autofmt_xdate () # draw oblique date label plt.grid (True) # add grid True initials plt.show () # display image Thank you for reading this article carefully. I hope the article "how to draw a visual map for Python" shared by the editor will be helpful to everyone. At the same time, I also hope that you will support us and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

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