曾容康的实验报告

代码一

 from selenium import webdriver
 from selenium.webdriver.common.by import By
 from selenium.webdriver.common.keys import Keys
 import time
 from parse_disclosure_table import DisclosureTable

 import re
 import requests
 import pandas as pd
 import fitz
 import csv
 import matplotlib.pyplot as plt
 import matplotlib.font_manager as fm



 browser = webdriver.Edge()
 browser.get('https://www.szse.cn/disclosure/listed/fixed/index.html')

 #筛选报告类型
 element = browser.find_element(By.CSS_SELECTOR,"#select_gonggao .glyphicon").click()
 element = browser.find_element(By.LINK_TEXT,"年度报告").click()

 #筛选日期
 element = browser.find_element(By.CSS_SELECTOR, ".input-left").click()
 element = browser.find_element(By.CSS_SELECTOR, "#c-datepicker-menu-1 .calendar-year span").click()
 element = browser.find_element(By.CSS_SELECTOR, ".active li:nth-child(113)").click()
 element = browser.find_element(By.LINK_TEXT, "6月").click()
 element = browser.find_element(By.CSS_SELECTOR, ".active > .dropdown-menu li:nth-child(1)").click()
 element = browser.find_element(By.CSS_SELECTOR, "#c-datepicker-menu-1 tr:nth-child(2) > .weekend:nth-child(1) > .tdcontainer").click()
 element = browser.find_element(By.CSS_SELECTOR, ".today > .tdcontainer").click()
 element = browser.find_element(By.ID, "query-btn").click()

 #将行业分类结果PDF文件下载到电脑上
 href = 'http://www.csrc.gov.cn/csrc/c100103/c1558619/1558619/files/1638277734844_11692.pdf'
 r = requests.get(href,allow_redirects=True)
 f = open('2021年3季度上市公司行业分类结果.pdf','wb')
 f.write(r.content)
 f.close()
 r.close()

 #获取行业分类结果PDF文件，并选出在31类行业中所有的上市公司
 doc = fitz.open('2021年3季度上市公司行业分类结果.pdf')
 page = doc[32]
 toc_txt = page.get_text()

 r = re.compile('(?<=\n黑色金属冶炼和压延加工业\n)(.*?)(?=\n有色金属冶炼和压延加工业)',re.DOTALL)
 txt = r.findall(toc_txt)

 r = re.compile('(\d{6})\s*(\w+)\s*')
 text = r.findall(txt[0])
 firm = text

 #控制浏览器使其自动选取PDF文件中所选择的公司
for i in range(len(firm)):
    name = firm[i][1]
    code = firm[i][0]
    f = open('inner_HTML_%s.html' %name,'w',encoding='utf-8')
    element = browser.find_element(By.ID, "input_code").click()
    element = browser.find_element(By.ID,'input_code').send_keys('%s' %code)
    time.sleep(0.5)
    element = browser.find_element(By.ID, "input_code").send_keys(Keys.ENTER)
    #element = browser.find_element(By.ID,'input_code')
    #element.send_keys('股票名称' + Keys.RETURN)
    element = browser.find_element(By.ID,'disclosure-table')
    time.sleep(0.5)
    innerHTML = element.get_attribute('innerHTML')
    f.write(innerHTML)
    time.sleep(0.5)
    f.close()
    element = browser.find_element(By.CSS_SELECTOR, ".selected-item:nth-child(2) > .icon-remove").click()
    time.sleep(0.5)

browser.quit()

#将获取的公司年报地址存入csv文件中
for i in range(len(firm)):
    name = firm[i][1]
    f = open('inner_HTML_%s.html' %name,encoding='utf-8')
    t = f.read()
    f.close()

    dt = DisclosureTable(t)
    df = dt.get_data()
    df.to_csv('data_%s.csv' %name)

#去除掉csv文件中的摘要文件链接
lst = {}
df5 = pd.DataFrame(columns = ['股票简称','attachpath'])
df4 = pd.DataFrame(columns = ['股票简称'])
for i in range(len(firm)):
    name = firm[i][1]
    df1 = pd.DataFrame(columns = ['股票简称','attachpath'])
    with open('data_%s.csv' %name,'r',newline='',encoding='utf-8') as csvfile:
        csvreader = csv.reader(csvfile)
        reader = next(csvreader)
        for row in csvreader:
            r = re.compile('.*摘要.*',re.DOTALL)
            f = r.findall(row[3])
            if f == []:
                lst1 = {}
                lst['股票简称'] = name
                lst['attachpath'] = row[4]
                lst1['股票简称'] = name
                df1 = df1.append(lst,ignore_index=True)
                df4 = df4.append(lst1,ignore_index=True)
                df5 = df5.append(lst,ignore_index=True)
            df4 = df4.drop_duplicates()
            #df1.drop(df1.index[df1['股票简称']=='简称'],inplace =True)
            #df1.drop(df1.index[df1['attachpath']=='attachpath'],inplace =True)
            #df4.drop(df4.index[df4['股票简称']=='简称'],inplace =True)
    #下载获取的pdf文件
    for k in range(len(df1[df1['股票简称']=='{}'.format(name)])):
        r = requests.get(df1['attachpath'][k],allow_redirects=True)
        f = open('{0}_{1}.pdf'.format(df1['股票简称'][k],k),'wb')
        f.write(r.content)
        f.close()
        r.close()

#提取PDF文件中“股票简称”,“股票代码”,“办公地址”,“公司网址”等信息
df2 = pd.DataFrame(columns=['股票简称','股票代码','办公地址','公司网址'])

for x in range(len(df4)):
    name = df4['股票简称'][x]
    doc = fitz.open('{0}_0.pdf'.format(name))

    lst = ['股票简称','股票代码','办公地址','公司[国际互联网]*网址']
    pages = {}
    lst_text = {}
    for i in lst:
        try:
            p = re.compile(i,re.DOTALL)
            page_number = doc.page_count#获取文件页数
            #对每一页进行遍历，匹配lst中的每一个元素
            for page in range(page_number):
                txt = doc[page].get_text()
                match = p.findall(txt)
                #若匹配到的macth不为空，则提取此时的页码
                if len(match) != 0:
                    pages[i] = page
                    for k,v in pages.items():
                        text = doc[v].get_text()
                        r1 = re.compile('股票简称\s+(.+?)\n',re.DOTALL)
                        p1 = r1.findall(text)
                        lst_text['股票简称'] = p1[0]
                        r2 = re.compile('股票代码\s+(\d+)\s+',re.DOTALL)
                        p2 = r2.findall(text)
                        lst_text['股票代码'] = p2[0]
                        r3 = re.compile('办公地址\s+(.+?)\n',re.DOTALL)
                        p3 = r3.findall(text)
                        lst_text['办公地址'] = p3[0]
                        r4 = re.compile('公司[国际互联网]*网址\s+(.*?.+?)\s+',re.DOTALL)
                        p4 = r4.findall(text)
                        lst_text['公司网址'] = p4[0]

        except Exception:
            print('错误')
    df2 = df2.append(lst_text,ignore_index=True)

结果一

代码二

  #提取“主要会计数据和财务指标”中的“营业收入（元）”

    r1 = re.compile('\s营业[总]*收入（元）\s*(-?[\d,.]+)\s*',re.DOTALL)
    r2 = re.compile('\n(20[\d]{2}\s年)年度报告',re.DOTALL)
    r3 = re.compile('\s基本每股收益（元/股）\s*(-?[\d,.]+)\s*',re.DOTALL)

    for n in range(len(df4)):
        x = df4['股票简称'][n]
        data = pd.DataFrame()
        for i in range(len(df5[df5['股票简称']=='{}'.format(x)])):
            #遍历每一个PDF文件
            doc = fitz.open('{0}_{1}.pdf'.format(x,i))
            #读取报告年份
            f2 = doc[0].get_text()
            year = r2.findall(f2)
            page_num = doc.page_count
            for page in range(page_num):
                #匹配营业收入
                f1 = doc[page].get_text()
                match1 = r1.findall(f1)
                if match1 != []:
                    profit = match1[0]
                    data1 = pd.DataFrame(profit,index=[x],columns=year)
            data = pd.concat([data1,data],join='outer',axis=1)
            data.to_csv('{}——营业收入.csv'.format(x),encoding='utf-8')

    #提取“基本每股收益（元/股）”

    for n in range(len(df4)):
        x = df4['股票简称'][n]
        data = pd.DataFrame()

        for i in range(len(df5[df5['股票简称']=='{}'.format(x)])):
            #遍历每一个PDF文件
            doc = fitz.open('{0}_{1}.pdf'.format(x,i))
            #读取报告年份
            f2 = doc[0].get_text()
            year = r2.findall(f2)
            page_num = doc.page_count
            #name = df4.loc[n]
            for page in range(page_num):
                #匹配营业收入
                f1 = doc[page].get_text()
                match1 = r3.findall(f1)
                if match1 != []:
                    profit = match1[0]
                    data1 = pd.DataFrame(profit,index=[x],columns=year)
            data = pd.concat([data1,data],join='outer',axis=1)
            data.to_csv('{}——每股收益.csv'.format(x),encoding='utf-8')


    #绘制折线图


    #读取营业收入数据

    dt0 = pd.read_csv('鞍钢股份——营业收入.csv',encoding='utf-8')
    dt0 = dt0.rename(columns={'Unnamed: 0':'Data'})
    dt0 = dt0.set_index('Data')
    #删除重复列
    del dt0['2020 年.1']
    #将数据转换为浮点型
    dt0.loc['鞍钢股份'] = dt0.loc['鞍钢股份'].str.replace(',','').astype(float)

    dt1 = pd.read_csv('宝钢股份——营业收入.csv',encoding='utf-8')
    dt1 = dt1.rename(columns={'Unnamed: 0':'Data'})
    dt1 = dt1.set_index('Data')
    dt1.loc['宝钢股份'] = dt1.loc['宝钢股份'].str.replace(',','').astype(float)

    dt2 = pd.read_csv('河钢股份——营业收入.csv',encoding='utf-8')
    dt2 = dt2.rename(columns={'Unnamed: 0':'Data'})
    dt2 = dt2.set_index('Data')
    dt2.loc['河钢股份'] = dt2.loc['河钢股份'].str.replace(',','').astype(float)

    dt3 = pd.read_csv('华菱钢铁——营业收入.csv',encoding='utf-8')
    dt3 = dt3.rename(columns={'Unnamed: 0':'Data'})
    dt3 = dt3.set_index('Data')
    dt3.loc['华菱钢铁'] = dt3.loc['华菱钢铁'].str.replace(',','').astype(float)

    dt4 = pd.read_csv('柳钢股份——营业收入.csv',encoding='utf-8')
    dt4 = dt4.rename(columns={'Unnamed: 0':'Data'})
    dt4 = dt4.set_index('Data')
    dt4.loc['柳钢股份'] = dt4.loc['柳钢股份'].str.replace(',','').astype(float)

    dt5 = pd.read_csv('马钢股份——营业收入.csv',encoding='utf-8')
    dt5 = dt5.rename(columns={'Unnamed: 0':'Data'})
    dt5 = dt5.set_index('Data')
    dt5.loc['马钢股份'] = dt5.loc['马钢股份'].str.replace(',','').astype(float)

    dt6 = pd.read_csv('山东钢铁——营业收入.csv',encoding='utf-8')
    dt6 = dt6.rename(columns={'Unnamed: 0':'Data'})
    dt6 = dt6.set_index('Data')
    dt6.loc['山东钢铁'] = dt6.loc['山东钢铁'].str.replace(',','').astype(float)

    dt7 = pd.read_csv('首钢股份——营业收入.csv',encoding='utf-8')
    dt7 = dt7.rename(columns={'Unnamed: 0':'Data'})
    dt7 = dt7.set_index('Data')
    dt7.loc['首钢股份'] = dt7.loc['首钢股份'].str.replace(',','').astype(float)

    dt8 = pd.read_csv('太钢不锈——营业收入.csv',encoding='utf-8')
    dt8 = dt8.rename(columns={'Unnamed: 0':'Data'})
    dt8 = dt8.set_index('Data')
    #删除重复列
    del dt8['2021 年.1']
    dt8.loc['太钢不锈'] = dt8.loc['太钢不锈'].str.replace(',','').astype(float)

    dt9 = pd.read_csv('中信特钢——营业收入.csv',encoding='utf-8')
    dt9 = dt9.rename(columns={'Unnamed: 0':'Data'})
    dt9 = dt9.set_index('Data')
    dt9.loc['中信特钢'] = dt9.loc['中信特钢'].str.replace(',','').astype(float)

    #绘制营业收入折线图
    #显示中文字体
    plt.rcParams['font.sans-serif'] = ['SimHei']
    #显示负号
    plt.rcParams['axes.unicode_minus'] = False

    # fname为你下载的字体库路径，
    # 注意 SourceHanSansSC-Bold.otf
    # 字体的路径
    fname = "C:\Windows\Fonts\STXINGKA.TTF"
    zhfont1 = fm.FontProperties(fname=fname)
    #设置显示图片清晰度
    plt.rcParams['figure.dpi'] = 100


    #绘制营业收入折线图
    fig = plt.figure(figsize=(15,20))
    plt.subplots_adjust(wspace=0.3,hspace=0.6)
    fig.suptitle('营业收入',fontsize=40)

    ax0 = fig.add_subplot(5,2,1)
    ax0.plot(dt0.loc['鞍钢股份'])
    ax0.set_xlabel('年 份',fontsize=14)
    ax0.set_ylabel('营业收入',fontsize=14)
    ax0.set_title('鞍钢股份',fontsize=14)

    ax1 = fig.add_subplot(5,2,2)
    ax1.plot(dt1.loc['宝钢股份'])
    ax1.set_xlabel('年 份',fontsize=14)
    ax1.set_ylabel('营业收入',fontsize=14)
    ax1.set_title('宝钢股份',fontsize=14)

    ax2 = fig.add_subplot(5,2,3)
    ax2.plot(dt2.loc['河钢股份'])
    ax2.set_xlabel('年 份',fontsize=14)
    ax2.set_ylabel('营业收入',fontsize=14)
    ax2.set_title('河钢股份',fontsize=14)

    ax3 = fig.add_subplot(5,2,4)
    ax3.plot(dt3.loc['华菱钢铁'])
    ax3.set_xlabel('年 份',fontsize=14)
    ax3.set_ylabel('营业收入',fontsize=14)
    ax3.set_title('华菱钢铁',fontsize=14)

    ax4 = fig.add_subplot(5,2,5)
    ax4.plot(dt4.loc['柳钢股份'])
    ax4.set_xlabel('年 份',fontsize=14)
    ax4.set_ylabel('营业收入',fontsize=14)
    ax4.set_title('柳钢股份',fontsize=14)

    ax5 = fig.add_subplot(5,2,6)
    ax5.plot(dt5.loc['马钢股份'])
    ax5.set_xlabel('年 份',fontsize=14)
    ax5.set_ylabel('营业收入',fontsize=14)
    ax5.set_title('马钢股份',fontsize=14)

    ax6 = fig.add_subplot(5,2,7)
    ax6.plot(dt6.loc['山东钢铁'])
    ax6.set_xlabel('年 份',fontsize=14)
    ax6.set_ylabel('营业收入',fontsize=14)
    ax6.set_title('山东钢铁',fontsize=14)

    ax7 = fig.add_subplot(5,2,8)
    ax7.plot(dt7.loc['首钢股份'])
    ax7.set_xlabel('年 份',fontsize=14)
    ax7.set_ylabel('营业收入',fontsize=14)
    ax7.set_title('首钢股份',fontsize=14)

    ax8 = fig.add_subplot(5,2,9)
    ax8.plot(dt8.loc['太钢不锈'])
    ax8.set_xlabel('年 份',fontsize=14)
    ax8.set_ylabel('营业收入',fontsize=14)
    ax8.set_title('太钢不锈',fontsize=14)

    ax9 = fig.add_subplot(5,2,10)
    ax9.plot(dt9.loc['中信特钢'])
    ax9.set_xlabel('年 份',fontsize=14)
    ax9.set_ylabel('营业收入',fontsize=14)
    ax9.set_title('中信特钢',fontsize=14)

    plt.show()

结果二

营业收入折线图

代码三

  #读取基本每股收益数据
  dt_0 = pd.read_csv('鞍钢股份——每股收益.csv',encoding='utf-8')
  dt_0 = dt_0.rename(columns={'Unnamed: 0':'Data'})
  dt_0 = dt_0.set_index('Data')
  #删除重复列
  del dt_0['2020 年.1']
  dt_0.loc['鞍钢股份'] = dt_0.loc['鞍钢股份'].astype(float)

  dt_1 = pd.read_csv('宝钢股份——每股收益.csv',encoding='utf-8')
  dt_1 = dt_1.rename(columns={'Unnamed: 0':'Data'})
  dt_1 = dt_1.set_index('Data')
  dt_1.loc['宝钢股份'] = dt_1.loc['宝钢股份'].astype(float)

  dt_2 = pd.read_csv('河钢股份——每股收益.csv',encoding='utf-8')
  dt_2 = dt_2.rename(columns={'Unnamed: 0':'Data'})
  dt_2 = dt_2.set_index('Data')
  dt_2.loc['河钢股份'] = dt_2.loc['河钢股份'].astype(float)

  dt_3 = pd.read_csv('华菱钢铁——每股收益.csv',encoding='utf-8')
  dt_3 = dt_3.rename(columns={'Unnamed: 0':'Data'})
  dt_3 = dt_3.set_index('Data')
  dt_3.loc['华菱钢铁'] = dt_3.loc['华菱钢铁'].astype(float)

  dt_4 = pd.read_csv('柳钢股份——每股收益.csv',encoding='utf-8')
  dt_4 = dt_4.rename(columns={'Unnamed: 0':'Data'})
  dt_4 = dt_4.set_index('Data')
  del dt_4['2014 年.1']
  del dt_4['2014 年.2']
  del dt_4['2014 年.3']
  del dt_4['2021 年.1']
  dt_4.loc['柳钢股份'] = dt_4.loc['柳钢股份'].astype(float)

  dt_5 = pd.read_csv('马钢股份——每股收益.csv',encoding='utf-8')
  dt_5 = dt_5.rename(columns={'Unnamed: 0':'Data'})
  dt_5 = dt_5.set_index('Data')
  dt_5.loc['马钢股份'] = dt_5.loc['马钢股份'].astype(float)

  dt_6 = pd.read_csv('山东钢铁——每股收益.csv',encoding='utf-8')
  dt_6 = dt_6.rename(columns={'Unnamed: 0':'Data'})
  dt_6 = dt_6.set_index('Data')
  dt_6.loc['山东钢铁'] = dt_6.loc['山东钢铁'].astype(float)

  dt_7 = pd.read_csv('首钢股份——每股收益.csv',encoding='utf-8')
  dt_7 = dt_7.rename(columns={'Unnamed: 0':'Data'})
  dt_7 = dt_7.set_index('Data')
  dt_7.loc['首钢股份'] = dt_7.loc['首钢股份'].astype(float)

  dt_8 = pd.read_csv('太钢不锈——每股收益.csv',encoding='utf-8')
  dt_8 = dt_8.rename(columns={'Unnamed: 0':'Data'})
  dt_8 = dt_8.set_index('Data')
  del dt_8['2021 年.1']
  dt_8.loc['太钢不锈'] = dt_8.loc['太钢不锈'].astype(float)

  dt_9 = pd.read_csv('中信特钢——每股收益.csv',encoding='utf-8')
  dt_9 = dt_9.rename(columns={'Unnamed: 0':'Data'})
  dt_9 = dt_9.set_index('Data')
  dt_9.loc['中信特钢'] = dt_9.loc['中信特钢'].astype(float)

  #绘制基本每股收益折线图
  fig = plt.figure(figsize=(15,20))
  plt.subplots_adjust(wspace=0.3,hspace=0.6)
  fig.suptitle('基本每股收益',fontsize=30)

  ax0 = fig.add_subplot(5,2,1)
  ax0.plot(dt_0.loc['鞍钢股份'])
  ax0.set_xlabel('年 份',fontsize=14)
  ax0.set_ylabel('基本每股收益',fontsize=14)
  ax0.set_title('鞍钢股份',fontsize=14)

  ax1 = fig.add_subplot(5,2,2)
  ax1.plot(dt_1.loc['宝钢股份'])
  ax1.set_xlabel('年 份',fontsize=14)
  ax1.set_ylabel('基本每股收益',fontsize=14)
  ax1.set_title('宝钢股份',fontsize=14)

  ax2 = fig.add_subplot(5,2,3)
  ax2.plot(dt_2.loc['河钢股份'])
  ax2.set_xlabel('年 份',fontsize=14)
  ax2.set_ylabel('基本每股收益',fontsize=14)
  ax2.set_title('河钢股份',fontsize=14)

  ax3 = fig.add_subplot(5,2,4)
  ax3.plot(dt_3.loc['华菱钢铁'])
  ax3.set_xlabel('年 份',fontsize=14)
  ax3.set_ylabel('基本每股收益',fontsize=14)
  ax3.set_title('华菱钢铁',fontsize=14)

  ax4 = fig.add_subplot(5,2,5)
  ax4.plot(dt_4.loc['柳钢股份'])
  ax4.set_xlabel('年 份',fontsize=14)
  ax4.set_ylabel('基本每股收益',fontsize=14)
  ax4.set_title('柳钢股份',fontsize=14)

  ax5 = fig.add_subplot(5,2,6)
  ax5.plot(dt_5.loc['马钢股份'])
  ax5.set_xlabel('年 份',fontsize=14)
  ax5.set_ylabel('基本每股收益',fontsize=14)
  ax5.set_title('马钢股份',fontsize=14)

  ax6 = fig.add_subplot(5,2,7)
  ax6.plot(dt_6.loc['山东钢铁'])
  ax6.set_xlabel('年 份',fontsize=14)
  ax6.set_ylabel('基本每股收益',fontsize=14)
  ax6.set_title('山东钢铁',fontsize=14)

  ax7 = fig.add_subplot(5,2,8)
  ax7.plot(dt_7.loc['首钢股份'])
  ax7.set_xlabel('年 份',fontsize=14)
  ax7.set_ylabel('基本每股收益',fontsize=14)
  ax7.set_title('首钢股份',fontsize=14)

  ax8 = fig.add_subplot(5,2,9)
  ax8.plot(dt_8.loc['太钢不锈'])
  ax8.set_xlabel('年 份',fontsize=14)
  ax8.set_ylabel('基本每股收益',fontsize=14)
  ax8.set_title('太钢不锈',fontsize=14)

  ax9 = fig.add_subplot(5,2,10)
  ax9.plot(dt_9.loc['中信特钢'])
  ax9.set_xlabel('年 份',fontsize=14)
  ax9.set_ylabel('基本每股收益',fontsize=14)
  ax9.set_title('中信特钢',fontsize=14)

  plt.show()

结果三

基本每股收益折线图

代码四

#绘制两个横向对比图
  import numpy as np
  dt = pd.concat([dt0,dt1,dt2,dt3,dt4,dt5,dt6,dt7,dt8,dt9])
  index_row = dt.index
  index_columns = dt.columns
  #利用pd.values.T对dataframe进行转置
  dt = pd.DataFrame(dt.values.T,columns=index_row,index=index_columns)
  index = np.arange(len(dt))
  plt.bar(index,dt['中信特钢'],width=0.05)
  plt.bar(index+0.08,dt['河钢股份'],width=0.08)
  plt.bar(index+0.16,dt['宝钢股份'],width=0.08)
  plt.bar(index+0.24,dt['华菱钢铁'],width=0.08)
  plt.bar(index+0.32,dt['鞍钢股份'],width=0.08)
  plt.bar(index+0.4,dt['首钢股份'],width=0.08)
  plt.bar(index+0.48,dt['马钢股份'],width=0.08)
  plt.bar(index+0.56,dt['山东钢铁'],width=0.08)
  plt.bar(index+0.64,dt['太钢不锈'],width=0.08)
  plt.bar(index+0.72,dt['同花顺'],width=0.08)
  plt.legend(['中信特钢','河钢股份','宝钢股份','华菱钢铁','鞍钢股份','首钢股份',
              '马钢股份','山东钢铁','太钢不锈','柳钢股份'])
  plt.xticks(index+0.3,dt.index)


  dt_ = pd.concat([dt_0,dt_1,dt_2,dt_3,dt_4,dt_5,dt_6,dt_7,dt_8,dt_9])
  index_row = dt_.index
  index_columns = dt_.columns
  dt_ = pd.DataFrame(dt_.values.T,columns=index_row,index=index_columns)
  index = np.arange(len(dt_))
  plt.bar(index,dt_['中信特钢'],width=0.08)
  plt.bar(index+0.08,dt_['河钢股份'],width=0.08)
  plt.bar(index+0.16,dt_['宝钢股份'],width=0.08)
  plt.bar(index+0.24,dt_['民生控股'],width=0.08)
  plt.bar(index+0.32,dt_['华菱钢铁'],width=0.08)
  plt.bar(index+0.40,dt_['鞍钢股份'],width=0.08)
  plt.bar(index+0.48,dt_['首钢股份'],width=0.08)
  plt.bar(index+0.56,dt_['山东钢铁'],width=0.08)
  plt.bar(index+0.64,dt_['太钢不锈'],width=0.08)
  plt.bar(index+0.72,dt_['同花顺'],width=0.08)
  plt.legend(['中信特钢','河钢股份','宝钢股份','华菱钢铁','鞍钢股份','首钢股份',
              '马钢股份','山东钢铁','太钢不锈','柳钢股份'])
  plt.xticks(index+0.3,dt_.index)

结果四

行业内横向对比营业收入

行业内横向对比基本每股收益

解释

可以看出宝钢股份的营业收入遥遥领先，处于行业龙头地位，其他营业收入处于前十的公司营业收入差距不大，形成了“一超多强”的格局，行业整体营业收入有稳中有升的趋势。从每股收益来看，收益也是有上升的趋势，但是在2015年由于某种原因，每股收益大多数为负数，并且在2019年行业的每股收益几乎都有下降的趋势，但是整体在一元每股左右，波动幅度不大，基本每股收益与营业收入的波动性往往同向变化，整体稳中向好。

心得