张育源的实验报告

Part 1 年报爬取与下载

  from selenium import webdriver
  from selenium.webdriver.common.by import By
  from selenium.webdriver.common.action_chains import ActionChains
  from selenium.webdriver.support import expected_conditions
  from selenium.webdriver.support.wait import WebDriverWait
  from selenium.webdriver.common.keys import Keys
  from selenium.webdriver.support.select import Select
  from selenium.webdriver.common.desired_capabilities import DesiredCapabilities
  import pandas as pd
  import os
  import re
  import time
  import json
  import requests

  df_sz = pd.DataFrame({'index': ['000589','000599','000619','000659','000859','000887','000973','002014','002108','002224','002243','002263','002324','002372','002381','002395','002420','002522','002585','002641','002676','002694','002735','002768','002790','002812','002825','002838','002886','002984','003011','003018'],
                        'name': ['贵州轮胎','青岛双星','海螺型材','珠海中富','国风塑业','中鼎股份','佛塑科技','永新股份','沧州明珠','三力士','力合科创','大东南','普利特','伟星新材','双箭股份','双象股份','毅昌股份','浙江众成','双星新材','永高股份','顺威股份','顾地科技','王子新材','国恩股份','瑞尔特','恩捷股份','纳尔股份','道恩股份','沃特股份','森麒麟','海象新材','金富科技']
                        })
  df_sh = pd.DataFrame({'index': ['600143','600182','600210','600458','600469','601058','601163','601500','601966','603033','603212','603221','603266','603408','603580','603615','603655','603657','603726','603806','603856','603991','603992','605255','605488','688026','688219','688299','688323','688386','688560','688669','688680'],
                        'name': ['金发科技','SST佳通','紫江企业','时代新材','风神股份','赛轮轮胎','三角轮胎','通用股份','玲珑轮胎','三维股份','赛伍技术','爱丽家居','天龙股份','建霖家居','艾艾精工','茶花股份','朗博科技','春光科技','朗迪集团','福斯特','东宏股份','至正股份','松霖科技','天普股份','福莱新材','洁特生物','会通股份','长阳科技','瑞华泰','泛亚微透','明冠新材','聚石化学','海优新材']
                        })

  name_sz = df_sz['name'].tolist()
  code_sh = df_sh['index'].tolist()
  driver = webdriver.Edge()

  def getszHTML(name):  #定义函数，获取深交所上市公司html网页
      driver.get("http://www.szse.cn/disclosure/listed/fixed/index.html")
      driver.maximize_window()
      driver.implicitly_wait(3)
      driver.find_element(By.ID, "input_code").click()
      driver.find_element(By.ID, "input_code").send_keys(name)
      driver.find_element(By.ID, "input_code").send_keys(Keys.DOWN)
      driver.find_element(By.ID, "input_code").send_keys(Keys.ENTER)
      driver.find_element(By.CSS_SELECTOR, "#select_gonggao .c-selectex-btn-text").click()
      driver.find_element(By.LINK_TEXT, "年度报告").click()
      driver.find_element(By.CSS_SELECTOR, ".input-left").click()
      driver.find_element(By.CSS_SELECTOR, "#c-datepicker-menu-1 .calendar-year span").click()
      driver.find_element(By.CSS_SELECTOR, ".active li:nth-child(113)").click()
      driver.find_element(By.CSS_SELECTOR, "#c-datepicker-menu-1 tr:nth-child(1) > .available:nth-child(3) > .tdcontainer").click()
      driver.find_element(By.CSS_SELECTOR, "#c-datepicker-menu-2 tr:nth-child(2) > .weekend:nth-child(1) > .tdcontainer").click()
      driver.find_element(By.ID, "query-btn").click()
      element = driver.find_element(By.ID, 'disclosure-table')

  def getshHTML(code):  #定义函数，获取上交所上市公司html网页
      driver.get("http://www.sse.com.cn/disclosure/listedinfo/regular/")
      driver.maximize_window()
      driver.implicitly_wait(3)
      driver.find_element(By.ID, "inputCode").click()
      driver.find_element(By.ID, "inputCode").send_keys(code)
      driver.find_element(By.CSS_SELECTOR, ".sse_outerItem:nth-child(4) .filter-option-inner-inner").click()
      driver.find_element(By.LINK_TEXT, "年报").click()

  def Save(filename,content): #保存文件
      name = open(filename+'.html','w',encoding='utf-8')
      name.write(content)
      name.close()



  i=1
  for code in code_sh:#爬取深交所上市公司html
      getshHTML(code)
      time.sleep(1)
      html = driver.find_element(By.CLASS_NAME, 'table-responsive')
      innerHTML = html.get_attribute('innerHTML')
      Save(code,innerHTML)
      print('上交所共有',len(code_sh),'家，已获取第',i,'/',len(code_sh))
      i=i+1

  i=1
  for name in name_sz:#爬取上交所上市公司html
      getszHTML(name)
      time.sleep(1)
      html = driver.find_element(By.ID, 'disclosure-table')
      innerHTML = html.get_attribute('innerHTML')
      Save(name,innerHTML)
      driver.refresh()
      time.sleep(1)
      print('深交所共有',len(name_sz),'家，已获取第',i,'/',len(name_sz))
      i=i+1

  driver.quit()
  print('获取完毕')


  class DisclosureTable():
      '''
      解析深交所定期报告页搜索表格
      '''
      def __init__(self, innerHTML):
          self.html = innerHTML
          self.prefix = 'https://disc.szse.cn/download'
          self.prefix_href = 'https://www.szse.cn/'
          #
          p_a = re.compile('(.*?)', re.DOTALL)
          p_span = re.compile('(.*?)', re.DOTALL)
          self.get_code = lambda txt: p_a.search(txt).group(1).strip()
          self.get_time = lambda txt: p_span.search(txt).group(1).strip()
          #
          self.txt_to_df()

      def txt_to_df(self):
          # html table text to DataFrame
          html = self.html
          p = re.compile('(.*?)', re.DOTALL)
          trs = p.findall(html)

          p2 = re.compile('(.*?)', re.DOTALL)
          tds = [p2.findall(tr) for tr in trs[1:]]

          df = pd.DataFrame({'证券代码': [td[0] for td in tds],
                              '简称': [td[1] for td in tds],
                              '公告标题': [td[2] for td in tds],
                              '公告时间': [td[3] for td in tds]})
          self.df_txt = df

      def get_link(self, txt):
          p_txt = '(.*?)'
          p = re.compile(p_txt, re.DOTALL)
          matchObj = p.search(txt)
          attachpath = matchObj.group(1).strip()
          href       = matchObj.group(2).strip()
          title      = matchObj.group(3).strip()
          return([attachpath, href, title])

      def get_data(self):
          get_code = self.get_code
          get_time = self.get_time
          get_link = self.get_link
          #
          df = self.df_txt
          codes = [get_code(td) for td in df['证券代码']]
          short_names = [get_code(td) for td in df['简称']]
          ahts = [get_link(td) for td in df['公告标题']]
          times = [get_time(td) for td in df['公告时间']]
          #
          prefix = self.prefix
          prefix_href = self.prefix
          df = pd.DataFrame({'证券代码': codes,
                              '简称': short_names,
                              '公告标题': [aht[2] for aht in ahts],
                              'attachpath': [prefix + aht[0] for aht in ahts],
                              '公告时间': times,
                              'href': [prefix_href + aht[1] for aht in ahts]
                              })
          self.df_data = df
          return(df)


  def getshDATA(code):  #解析上交所公司html
      f = open(code+'.html',encoding='utf-8')
      html = f.read()
      f.close()

      p1 = re.compile('(.*?)', re.DOTALL)
      tds = p1.findall(html)

      p_code_name = re.compile('(.*?).*?(.*?)', re.DOTALL)
      codes = [p_code_name.search(td).group(1) for td in tds]
      names = [p_code_name.search(td).group(2) for td in tds]

      p2 = re.compile('(.*?)(.*?)',
                      re.DOTALL)
      href   = [p2.search(td).group(1) for td in tds]
      titles = [p2.search(td).group(2) for td in tds]
      times  = [p2.search(td).group(3) for td in tds]

      prefix0 = 'http://www.sse.com.cn'

      df = pd.DataFrame({'证券代码': codes,
                          '简称': names[1],
                          '公告标题': [lf.strip() for lf in titles],
                          'href': [prefix0 + lf.strip() for lf in href],
                          '公告时间': [t.strip() for t in times]
                          })
      return(df)


  def Readhtml(filename): #读取
        with open(filename+'.html', encoding='utf-8') as f:
            html = f.read()
        return html

  def tidy(df):  #清除
        d = []
        for index, row in df.iterrows():
            dd = row[2]
            n = re.search("摘要|取消|英文", dd)
            if n != None:
                d.append(index)
        df1 = df.drop(d).reset_index(drop = True)
        return df1


  def filter_links(words,df,include=True):
      ls=[]
      for word in words:
          if include:
              ls.append([word in f for f in df.公告标题])
          else:
              ls.append([word not in f for f in df.公告标题])
      index = []
      for r in range(len(df)):
          flag = not include
          for c in range(len(words)):
              if include:
                  flag = flag or ls[c][r]
              else:
                  flag = flag and ls[c][r]
          index.append(flag)
      df2 = df[index]
      return(df2)

  def rename(df):
      for i in df["简称"]:
          i = i.replace("*","")
          i = i.replace(" ","")
          if i !="-":
              sn=i
      return sn

  def Loadpdf_sh(df): #用于下载文件
        d1 = {}
        df["公告时间"] = pd.to_datetime(df["公告时间"])
        na = rename(df)
        for index, row in df.iterrows():
            names = na + str(row[4].year-1)+"年年度报告"
            d1[names] = row[3]
        for key, value in d1.items():
            f = requests.get(value)
            with open (key + ".pdf", "wb") as ff:
                ff.write(f.content)

  def Loadpdf(df): #用于下载文件
        d1 = {}
        for index, row in df.iterrows():
            d1[row[2]] = row[3]
        for key, value in d1.items():
            f = requests.get(value)
            with open (key + ".pdf", "wb") as code:
                code.write(f.content)



  i = 0
  for index,row in df_sh.iterrows(): #提取上交所信息表
      i+=1
      code = row[0]
      name = row[1]
      df = getshDATA(code)
      df_all = filter_links(["摘要","营业","并购","承诺","取消","英文"],df,include= False)
      df_orig = filter_links(["（","("],df_all,include = False)
      df_updt = filter_links(["（","("],df_all,include = True)
      df_updt = filter_links(["取消"],df_updt,include = False)
      df_all.to_csv(name+'.csv',encoding='utf-8-sig')
      os.makedirs(name,exist_ok=True)
      os.chdir(name)
      Loadpdf_sh(df_all)
      print(code+'年报已保存完毕。共',len(code_sh),'所公司，当前第',i,'所。')
      os.chdir('../')


  i = 0
  for index,row in df_sz.iterrows():  #提取深交所信息表
      i+=1
      name = row[1].replace('*','')
      html = Readhtml(name)
      dt = DisclosureTable(html)
      df = dt.get_data()
      df1 = tidy(df)
      df1.to_csv(name+'.csv',encoding='utf-8-sig')
      os.makedirs(name,exist_ok=True)
      os.chdir(name)
      Loadpdf(df1)
      print(name+'年报已保存完毕。共',len(name_sz),'所公司，当前第',i,'所。')
      os.chdir('../')

结果

Part 2 数据处理与筛选

import pandas as pd
import fitz
import re

df_company = pd.DataFrame({'index': ['000589','000599','000659','000859','000887','002108','002224','002263','002324','002381','002395','002420','002522','002585','002641','002676','002694','002735','002768','002790','002825','002838','002886','002984','003011','003018'],
                           'name': ['贵州轮胎','青岛双星','珠海中富','国风塑业','中鼎股份','沧州明珠','三力士','大东南','普利特','双箭股份','双象股份','毅昌股份','浙江众成','双星新材','永高股份','顺威股份','顾地科技','王子新材','国恩股份','瑞尔特','纳尔股份','道恩股份','沃特股份','森麒麟','海象新材','金富科技']
                            })

company = df_company['name'].tolist()



t=0
for member in company:
    t+=1
    member = member.replace('*','')
    df = pd.read_csv(member+'.csv',converters={'证券代码':str})
    df = df.sort_index(ascending=False)
    final = pd.DataFrame(index=range(2012,2022),columns=['营业收入（元）','基本每股收益（元/股）'])
    final.index.name='年份'
    code = str(df.iloc[0,1])
    name = df.iloc[-1,2].replace(' ','')


    for i in range(len(df)):
        title=df.iloc[i,3]
        doc = fitz.open('./%s/%s.pdf'%(member,title))
        text=''
        for j in range(20):
            page = doc[j]
            text += page.get_text()
        p_year=re.compile('.*?(\d{4}) .*?年度报告.*?')
        year = int(p_year.findall(text)[0])
        #设置需要匹配的四种数据的pattern
        p_rev = re.compile('(?<=\n)营业总?收入（?\w?）?\s?\n?([\d+,.]*)\s\n?')
        p_eps = re.compile('(?<=\n)基本每股收益（元/?／?\n?股）\s?\n?([-\d+,.]*)\s?\n?')
        p_site = re.compile('(?<=\n)\w*办公地址：?\s?\n?(.*?)\s?(?=\n)',re.DOTALL)
        p_web =re.compile('(?<=\n)公司\w*网址：?\s?\n?([a-zA-Z./:]*)\s?(?=\n)',re.DOTALL)

        revenue=float(p_rev.search(text).group(1).replace(',',''))
        eps=p_eps.search(text).group(1)
        final.loc[year,'营业收入（元）']=revenue
        final.loc[year,'基本每股收益（元/股）']=eps

    final.to_csv('%s数据.csv' %member,encoding='utf-8-sig')


    site=p_site.search(text).group(1)
    web=p_web.search(text).group(1)


    with open('%s数据.csv'%member,'a',encoding='utf-8-sig') as f:
        result='股票简称,%s\n股票代码,%s\n办公地址,%s\n公司网址,%s'%(name,code,site,web)
        f.write(result)
    print(name+'数据已保存完毕'+'(',t,'/',len(company),')')

    add = hori_rev
    add.loc["总营收"] = add.apply(lambda x:x.sum()) # 数据筛选

结果

Part 3 图像绘制与对照

  import pandas as pd
  import matplotlib.pyplot as plt

  df_company = pd.DataFrame({'index': ['000589','000599','000859','000887','002263','002324','002381','002395','002585','002984'],
                        'name': ['贵州轮胎','青岛双星','国风塑业','中鼎股份','大东南','普利特','双箭股份','双象股份','双星新材','森麒麟']
                        })
  company = df_company['name'].tolist()



  dflist=[]
  for name in company:
      com = name.replace('*','')
      data=pd.read_csv(com+'数据.csv')
      dflist.append(data)

  comps = len(dflist)
  for i in range(comps):
      dflist[i]=dflist[i].set_index('年份')

  def rev(data):
      df=pd.DataFrame(columns=['近十年营业收入总额（元）'])
      for i in range(comps):
          df.loc[dflist[i].loc['股票简称','营业收入（元）'],'近十年营业收入总额（元）']=dflist[i].iloc[:10,0].astype(float).sum()
      return df
  rank=rev(dflist).sort_values('近十年营业收入总额（元）',ascending=False)



  names=['贵州轮胎','青岛双星','国风塑业','中鼎股份','大东南','普利特','双箭股份','双象股份','双星新材','森麒麟']



  indexes=[]
  for idx in names:
      indexes.append(company.index(idx))


  datalist=[]
  datalist1=[]
  for i in indexes: #在dflist里选出所需公司的营业收入数据
          datalist.append(pd.DataFrame(dflist[i].iloc[:10,0]))
  for df in datalist:
      df.index=df.index.astype(int)
      df['营业收入（元）']=df['营业收入（元）'].astype(float)/100000000
  for i in indexes: #在dflist里选出所需公司的每股收益数据
          datalist1.append(pd.DataFrame(dflist[i].iloc[:10,1]))
  for df in datalist1:
      df.index=df.index.astype(int)
      df['基本每股收益（元/股）']=df['基本每股收益（元/股）'].astype(float)



  hori_rev=pd.concat(datalist,axis=1)
  hori_eps=pd.concat(datalist1,axis=1)
  hori_rev.columns=rank.index
  hori_eps.columns=rank.index


  # 绘制对比图


  plt.rcParams['font.sans-serif']=['SimHei']
  plt.figure(figsize=(16,30))
  x = datalist[0].index

  y_1 = hori_rev.iloc[:,0]
  y_2 = hori_rev.iloc[:,1]
  y_3 = hori_rev.iloc[:,2]
  y_4 = hori_rev.iloc[:,3]
  y_5 = hori_rev.iloc[:,4]
  y_6 = hori_rev.iloc[:,5]
  y_7 = hori_rev.iloc[:,6]
  y_8 = hori_rev.iloc[:,7]
  y_9 = hori_rev.iloc[:,8]
  y_10 = hori_rev.iloc[:,9]
  plt.xlim(2011,2022,1)
  #plt.ylim()
  plt.xticks(range(2011,2022),fontsize=18)
  plt.yticks(fontsize=18)

  plt.plot(x, y_1, color='r', marker='^',markersize=10, linestyle='-', label=hori_rev.columns[0],linewidth = 2.5,alpha=0.8)
  plt.plot(x, y_2, color='orange', marker='^', markersize=10,linestyle='-', label=hori_rev.columns[1],linewidth = 2.5,alpha=0.8)
  plt.plot(x, y_3, color='yellow', marker='^', markersize=10,linestyle='-', label=hori_rev.columns[2],linewidth = 2.5,alpha=0.8)
  plt.plot(x, y_4, color='greenyellow', marker=9, markersize=9,linestyle='-', label=hori_rev.columns[3],linewidth = 2,alpha=0.8)
  plt.plot(x, y_5, color='paleturquoise', marker=9,markersize=9, linestyle='-', label=hori_rev.columns[4],linewidth = 2,alpha=0.8)
  plt.plot(x, y_6, color='lightskyblue', marker=9, markersize=9,linestyle='-', label=hori_rev.columns[5],linewidth =2,alpha=0.8)
  plt.plot(x, y_7, color='lightpink', marker='D', markersize=7,linestyle='-', label=hori_rev.columns[6],linewidth =1.5,alpha=0.8)
  plt.plot(x, y_8, color='orchid', marker='D', markersize=7,linestyle='-', label=hori_rev.columns[7],linewidth =1.5,alpha=0.8)
  plt.plot(x, y_9, color='k', marker='s', markersize=7,linestyle='-', label=hori_rev.columns[8],linewidth =1.5,alpha=0.8)
  plt.plot(x, y_10, color='grey', marker='s', markersize=7,linestyle='-', label=hori_rev.columns[9],linewidth =1.5,alpha=0.8)


  plt.legend(loc = "upper left",prop={'family':'simsun', 'size': 20})  # 显示图例
  plt.grid(True)
  title="营业收入变化趋势图（2012-2021）"
  plt.title(title,fontsize=25)


  plt.ylabel("营业收入（亿元）",fontsize=22)  # 设置Y轴标签
  plt.xlabel("年份",fontsize=22,loc='left')  # 设置X轴标签

  plt.savefig("sum") # 保存图片
  plt.show()


  plt.rcParams['font.sans-serif']=['SimHei']
  plt.rcParams['axes.unicode_minus']=False
  plt.figure(figsize=(18,24))
  x = datalist[0].index
  #y = range(len(names_y))

  y_1 = hori_eps.iloc[:,0]
  y_2 = hori_eps.iloc[:,1]
  y_3 = hori_eps.iloc[:,2]
  y_4 = hori_eps.iloc[:,3]
  y_5 = hori_eps.iloc[:,4]
  y_6 = hori_eps.iloc[:,5]
  y_7 = hori_eps.iloc[:,6]
  y_8 = hori_eps.iloc[:,7]
  y_9 = hori_eps.iloc[:,8]
  y_10 = hori_eps.iloc[:,9]
  plt.xlim(2011,2022,1)
  #plt.ylim()
  plt.xticks(range(2012,2022),fontsize=18)
  plt.yticks(fontsize=18)

  plt.plot(x, y_1, color='r', marker='^',markersize=9, linestyle='-', label=hori_eps.columns[0],linewidth = 2,alpha=0.8)
  plt.plot(x, y_2, color='orange', marker='^', markersize=9,linestyle='-', label=hori_eps.columns[1],linewidth = 2,alpha=0.8)
  plt.plot(x, y_3, color='yellow', marker='^', markersize=9,linestyle='-', label=hori_eps.columns[2],linewidth = 2,alpha=0.8)
  plt.plot(x, y_4, color='greenyellow', marker=9, markersize=9,linestyle='-', label=hori_eps.columns[3],linewidth = 2,alpha=0.8)
  plt.plot(x, y_5, color='paleturquoise', marker=9,markersize=9, linestyle='-', label=hori_eps.columns[4],linewidth = 2,alpha=0.8)
  plt.plot(x, y_6, color='lightskyblue', marker=9, markersize=9,linestyle='-', label=hori_eps.columns[5],linewidth =2,alpha=0.8)
  plt.plot(x, y_7, color='lightpink', marker='D', markersize=9,linestyle='-', label=hori_eps.columns[6],linewidth =2,alpha=0.8)
  plt.plot(x, y_8, color='orchid', marker='D', markersize=9,linestyle='-', label=hori_eps.columns[7],linewidth =2,alpha=0.8)
  plt.plot(x, y_9, color='k', marker='s', markersize=7,linestyle='-', label=hori_rev.columns[8],linewidth =1.5,alpha=0.8)
  plt.plot(x, y_10, color='grey', marker='s', markersize=7,linestyle='-', label=hori_rev.columns[9],linewidth =1.5,alpha=0.8)


  plt.legend(loc = "upper left",prop={'family':'simsun', 'size': 20},framealpha=0.8)  # 显示图例
  plt.grid(True)
  title="基本每股收益变化趋势图（2011-2021）"
  plt.title(title,fontsize=25)
  plt.ylabel("基本每股收益（元/股）",fontsize=22)  # 设置Y轴标签
  plt.xlabel("年份",fontsize=22)  # 设置X轴标签

  plt.savefig("per") # 保存图片
  plt.show()



  hori_revup=hori_rev.head(5)
  hori_revdown=hori_rev.tail(5)

  hori_epsup=hori_eps.head(5)
  hori_epsdown=hori_eps.tail(5)

  plt.rcParams['font.sans-serif']=['SimHei']
  plt.rcParams['axes.unicode_minus']=False

  ax1=hori_revup.plot(kind='bar',color=['r','orange','yellow','greenyellow',
                                        'paleturquoise','lightskyblue','lightpink','orchid','k','grey']
                      ,figsize=(24,8),fontsize=18,alpha=0.7,grid=True)
  ax1.legend(loc='best',prop={'family':'simsun', 'size': 14},framealpha=0.5)
  ax1.set_xlabel('年份',loc='left',fontsize=18)
  ax1.set_ylabel('营业收入（亿元）',fontsize=18)
  ax1.set_title('行业内横向对比营业收入（2012-2016）',fontsize=20)
  ax1.figure.savefig('cmp1')


  ax2=hori_revdown.plot(kind='bar',color=['r','orange','yellow','greenyellow',
                                        'paleturquoise','lightskyblue','lightpink','orchid','k','grey'],
                        figsize=(16 ,8),fontsize=18,alpha=0.7,grid=True)
  ax2.legend(loc='best',prop={'family':'simsun', 'size': 14},framealpha=0.5)
  ax2.set_xlabel('年份',loc='left',fontsize=18)
  ax2.set_ylabel('营业收入（亿元）',fontsize=18)
  ax2.set_title('行业内横向对比营业收入（2017-2021）',fontsize=20)
  ax2.figure.savefig('cmp2')




  ax1=hori_epsup.plot(kind='bar',color=['r','orange','yellow','greenyellow',
                                        'paleturquoise','lightskyblue','lightpink','orchid','k','grey']
                      ,figsize=(18,10),fontsize=18,grid=True,alpha=0.7)
  ax1.legend(loc='best',prop={'family':'simsun', 'size': 14},framealpha=0.7)
  ax1.set_xlabel('年份',loc='right',fontsize=18)
  ax1.set_ylabel('基本每股收益（元/股）',fontsize=18)
  ax1.set_title('行业内横向对比基本每股收益（2012-2016）',fontsize=20)
  ax1.figure.savefig('cmp3')


  ax2=hori_epsdown.plot(kind='bar',color=['r','orange','yellow','greenyellow',
                                        'paleturquoise','lightskyblue','lightpink','orchid','k','grey'],
                        figsize=(18,10),fontsize=18,grid=True,alpha=0.7)
  ax2.set_xlabel('年份',loc='right',fontsize=18)
  ax2.set_ylabel('基本每股收益（元/股）',fontsize=18)
  ax2.set_title('行业内横向对比基本每股收益（2017-2021）',fontsize=20)
  ax2.figure.savefig('cmp4')

结果

行业分析

实验感想