Some cases Matplotlib treatment of common Chinese

Some cases Matplotlib treatment of common Chinese

Where some of the art often involve processing a timing chart, the time necessary to display Chinese on x axis in an appropriate manner.
Although this problem is very small, but because of professional reasons, often display a variety of time on the x axis, for example, only show month - day - when this unconventional x-axis label, and requested that part of the picture the proportion accounted for not too big, otherwise affect the appearance. And the network processing time on the x-axis is relatively small, local write record count.
This is a record matplotlib treatment blog, but only by a certain nostalgia and only Bin ride a three-year master's career, and finally thank you very much exciting work and daily harassment by a provided material for this blog (probably again He is said to be less hypocritical ha ha ha ha dry).

key point

• Import parameters can be displayed in Chinese

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

• by formatting the x-axis and rotated to show the appearance (OCD did you will)

  ax1.xaxis.set_major_formatter(mdate.DateFormatter('%H:%M'))
  plt.xticks(rotation=30)
  

  Input data, and the results of the code

  • Only part of the data input, too affect the appearance, readers need to make up the brain Ha ha ha

  4:15,7,1,1
  4:30,53,21,46
  4:45,673,433,588
  5:00,2449,1711,2209
  5:15,5555,4522,5094
  5:30,8854,8710,9431
  5:45,12886,14687,13918
  6:00,16459,23762,20121
  6:15,20542,37169,26034
  6:30,24708,59783,34398
  6:45,28093,88462,43049
  7:00,30505,134718,51161
  7:15,32046,180216,59468
  7:30,35503,215490,64192
  7:45,39600,239502,70168
  8:00,43334,228761,73596
  8:15,45938,202122,72283
  8:30,45756,167016,68193
  8:45,46982,138231,64956
  

• Code

  
  import pandas as pd
  import numpy as np
  from datetime import datetime
  import matplotlib.dates as mdate
  import matplotlib.pyplot as plt 
  plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
  plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
  
  def convert(strAim):
      t = strAim.split(":")
      h, m, s = int(t[0]),float(t[1])/60.0,float(t[2])/3600.0
      return h+m+s
  
  def draw(addressIn,addressOut):
      df =pd.read_csv(addressIn,header = None)
      #print(df.head())
      x = []
      y1 = []
      y2 = []
      y3 = []
      for i in range(len(df)):
          tx = datetime.strptime('2017-8-1 '+df.iloc[i][0], '%Y-%m-%d %H:%M')
          x.append(tx)
          y1.append(df.iloc[i][1])
          y2.append(df.iloc[i][2])
          y3.append(df.iloc[i][3])
      fig1 = plt.figure(figsize=(12,6))#figsize=(16,8)
      ax1 = fig1.add_subplot(1,1,1)
      ax1.plot(x,y1,color='green',label='假期')#折线图
      ax1.plot(x,y2,color='black',label='工作日')
      ax1.plot(x,y3,color='red',ls='--',label='非工作日')
      ax1.xaxis.set_major_formatter(mdate.DateFormatter('%H:%M'))
      #plt.xticks(pd.timedelta_range(start='05:00:00.000000', periods=38, freq='30T'),rotation=90)
      #plt.xticks(x)
      plt.xticks(rotation=30)
      #plt.ylim(0,6000000)
      #ax1.set_title('全天')
      ax1.set_xlabel('时间')
      ax1.set_ylabel('日客流量(人次)')
      plt.legend(loc='upper right')
      plt.subplots_adjust(left=0.2, bottom=0.2, right=0.8, top=0.8)
      plt.savefig('pic%s.png'%(addressOut),dpi=100)
  if __name__ == '__main__':
      draw('differDay.csv','differDay')
  
  
  

• The results shown