Python爬虫_福布斯排行榜_数据可视化

目录

1. 保存的excel数据​

2. 福布斯前十排行的数据可视化效果​

3. 各个国家上榜人数所占比例的统计与可视化​

4. 爬取网页数据解析为一个list集合

5. 将爬取的list集合保存到本地excel文件

6. 读取excel文件数据进行分析绘制条形图和饼状图

7. 完整代码

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使用python requests库爬取福布斯排行榜数据存放到本地excel文件，并通过matplotlab将数据进行分析和可视化

原网页如下所示 https://www.phb123.com/renwu/fuhao/shishi_1.html

1. 保存的excel数据

2. 福布斯前十排行的数据可视化效果

3. 各个国家上榜人数所占比例的统计与可视化

4. 爬取网页数据解析为一个list集合

## 读取一页的数据
def loaddata(url):
   from bs4 import BeautifulSoup
   import requests
   headers = {
       'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/537.36 (KHTML, like Gecko) '
                    'Chrome/72.0.3626.121 Safari/537.36'
   }
   f = requests.get(url,headers=headers)   #Get该网页从而获取该html内容
   soup = BeautifulSoup(f.content, "lxml")  #用lxml解析器解析该网页的内容, 好像f.text也是返回的html
   # print(f.content.decode())        #尝试打印出网页内容,看是否获取成功
   ranktable = soup.find_all('table',class_="rank-table" )[0]   #获取排行榜表格
   trlist = ranktable.find_all('tr') #获取表格中所有tr标签
   trlist.pop(0) #去掉第一个元素
   persionlist = []
   for tr in trlist:
      persion = {}
      persion['num'] = tr.find_all('td')[0].string  #编号
      persion['name'] = tr.find_all('td')[1].p.string #名称
      persion['money'] = tr.find_all('td')[2].string #财产
      persion['company'] = tr.find_all('td')[3].string #企业
      persion['country'] = tr.find_all('td')[4].a.string #国家
      persionlist.append(persion)
   print("页面"+url+"爬取成功")
   return persionlist


## 读取所有福布斯排行榜数据
def loadalldata():
   alldata = []
   for i in range(1,16,1):
      url = "https://www.phb123.com/renwu/fuhao/shishi_"+str(i)+".html"
      data = loaddata(url)
      alldata = alldata + data
   return alldata

5. 将爬取的list集合保存到本地excel文件

## 将爬取的数据保存到文件
def savedata(path,persionlist):
   import xlwt
   workbook = xlwt.Workbook()
   worksheet = workbook.add_sheet('test')
   worksheet.write(0, 0, '排名')
   worksheet.write(0, 1, '姓名')
   worksheet.write(0, 2, '财富')
   worksheet.write(0, 3, '企业')
   worksheet.write(0, 4, '国家')
   for i in range(1,len(persionlist)+1,1):
      worksheet.write(i, 0, persionlist[i-1]['num'])
      worksheet.write(i, 1, persionlist[i-1]['name'])
      worksheet.write(i, 2, persionlist[i-1]['money'])
      worksheet.write(i, 3, persionlist[i-1]['company'])
      worksheet.write(i, 4, persionlist[i-1]['country'])
   workbook.save(path)
   print("数据保存成功："+path)

6. 读取excel文件数据进行分析绘制条形图和饼状图

## 取出排行榜前十的姓名和财富数据 以两个list返回
def loadtop10(path):
    import xlrd
    book = xlrd.open_workbook(path)
    sheet1 = book.sheets()[0]
    namelist = sheet1.col_values(1)
    moneylist = sheet1.col_values(2)
    namelist = namelist[1:11]
    moneylist = moneylist[1:11]

    moneylist2 = []
    for a in moneylist:
        a = int(a[0:-3])
        moneylist2.append(a)
    print("取出排行榜前十的姓名和财富数据")
    print(namelist)
    print(moneylist2)
    return namelist,moneylist2

## 统计排行榜中每个国家的上榜人数 以字典list返回
def countcountrynum(path):
   import xlrd
   book = xlrd.open_workbook(path)
   sheet1 = book.sheets()[0]
   countrylist = sheet1.col_values(4)[1:-1]
   print(countrylist)
   countryset = list(set(countrylist))
   dictlist = []
   for country in countryset:
      obj = {"name":country,"count":0}
      dictlist.append(obj)
   ## 统计出每个国家对应的数量
   for obj in dictlist:
      for a in countrylist:
         if obj['name'] == a:
            obj['count'] = obj['count'] + 1
   print(dictlist)
   ## 将dictlist排序 数量多的放前面 8 5 6 9 3 2 4
   for i in range(0,len(dictlist),1):
      for j in range(0,len(dictlist)-i-1,1):
          if dictlist[j]['count'] < dictlist[j+1]['count']:
             temp = dictlist[j]
             dictlist[j] = dictlist[j+1]
             dictlist[j+1] = temp
   dictlist2 = dictlist[0:5]
   set2 = []
   for a in dictlist2:
      set2.append(a['name'])
   othercount = 0;
   for a in dictlist:
      if a['name'] not in set2:
         othercount = othercount + 1
   dictlist2.append({"name":"其他","count":othercount})
   print('获取排行榜中每个国家的上榜人数')
   print(dictlist2)
   return dictlist2

## 绘制条形图和饼状图
def drow():
   import matplotlib.pyplot as plt
   plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体
   plt.figure('福布斯前十榜',figsize=(15,5))

   ## 读取福布斯排行榜前十的数据
   listx,listy = loadtop10('rank.xls')

   plt.title('福布斯前十榜', fontsize=16)
   plt.xlabel('人物', fontsize=14)
   plt.ylabel('金额/亿美元', fontsize=14)
   plt.tick_params(labelsize=10)
   plt.grid(linestyle=':', axis='y')
   a = plt.bar(listx, listy, color='dodgerblue', label='Apple', align='center')
   # 设置标签
   for i in a:
      h = i.get_height()
      plt.text(i.get_x() + i.get_width() / 2, h, '%d' % int(h), ha='center', va='bottom')
   ## -------------------------------------------------------------------------
   dictlist = countcountrynum("rank.xls")
   plt.figure('各国家上榜人数所占比例')
   labels = []
   sizes = []
   for a in dictlist:
      labels.append(a['name'])
      sizes.append(a['count'])
   explode = (0.1, 0, 0, 0, 0, 0)
   plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=False, startangle=150)
   plt.title("各国家上榜人数所占比例", fontsize=16)
   plt.axis('equal')  # 该行代码使饼图长宽相等

   plt.show()

7. 完整代码

## 读取一页的数据
def loaddata(url):
   from bs4 import BeautifulSoup
   import requests
   headers = {
       'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/537.36 (KHTML, like Gecko) '
                    'Chrome/72.0.3626.121 Safari/537.36'
   }
   f = requests.get(url,headers=headers)   #Get该网页从而获取该html内容
   soup = BeautifulSoup(f.content, "lxml")  #用lxml解析器解析该网页的内容, 好像f.text也是返回的html
   # print(f.content.decode())        #尝试打印出网页内容,看是否获取成功
   ranktable = soup.find_all('table',class_="rank-table" )[0]   #获取排行榜表格
   trlist = ranktable.find_all('tr') #获取表格中所有tr标签
   trlist.pop(0) #去掉第一个元素
   persionlist = []
   for tr in trlist:
      persion = {}
      persion['num'] = tr.find_all('td')[0].string  #编号
      persion['name'] = tr.find_all('td')[1].p.string #名称
      persion['money'] = tr.find_all('td')[2].string #财产
      persion['company'] = tr.find_all('td')[3].string #企业
      persion['country'] = tr.find_all('td')[4].a.string #国家
      persionlist.append(persion)
   print("页面"+url+"爬取成功")
   return persionlist


## 读取所有福布斯排行榜数据
def loadalldata():
   alldata = []
   for i in range(1,16,1):
      url = "https://www.phb123.com/renwu/fuhao/shishi_"+str(i)+".html"
      data = loaddata(url)
      alldata = alldata + data
   return alldata

## 将爬取的数据保存到文件
def savedata(path,persionlist):
   import xlwt
   workbook = xlwt.Workbook()
   worksheet = workbook.add_sheet('test')
   worksheet.write(0, 0, '排名')
   worksheet.write(0, 1, '姓名')
   worksheet.write(0, 2, '财富')
   worksheet.write(0, 3, '企业')
   worksheet.write(0, 4, '国家')
   for i in range(1,len(persionlist)+1,1):
      worksheet.write(i, 0, persionlist[i-1]['num'])
      worksheet.write(i, 1, persionlist[i-1]['name'])
      worksheet.write(i, 2, persionlist[i-1]['money'])
      worksheet.write(i, 3, persionlist[i-1]['company'])
      worksheet.write(i, 4, persionlist[i-1]['country'])
   workbook.save(path)
   print("数据保存成功："+path)

## 取出排行榜前十的姓名和财富数据 以两个list返回
def loadtop10(path):
    import xlrd
    book = xlrd.open_workbook(path)
    sheet1 = book.sheets()[0]
    namelist = sheet1.col_values(1)
    moneylist = sheet1.col_values(2)
    namelist = namelist[1:11]
    moneylist = moneylist[1:11]

    moneylist2 = []
    for a in moneylist:
        a = int(a[0:-3])
        moneylist2.append(a)
    print("取出排行榜前十的姓名和财富数据")
    print(namelist)
    print(moneylist2)
    return namelist,moneylist2

## 统计排行榜中每个国家的上榜人数 以字典list返回
def countcountrynum(path):
   import xlrd
   book = xlrd.open_workbook(path)
   sheet1 = book.sheets()[0]
   countrylist = sheet1.col_values(4)[1:-1]
   print(countrylist)
   countryset = list(set(countrylist))
   dictlist = []
   for country in countryset:
      obj = {"name":country,"count":0}
      dictlist.append(obj)
   ## 统计出每个国家对应的数量
   for obj in dictlist:
      for a in countrylist:
         if obj['name'] == a:
            obj['count'] = obj['count'] + 1
   print(dictlist)
   ## 将dictlist排序 数量多的放前面 8 5 6 9 3 2 4
   for i in range(0,len(dictlist),1):
      for j in range(0,len(dictlist)-i-1,1):
          if dictlist[j]['count'] < dictlist[j+1]['count']:
             temp = dictlist[j]
             dictlist[j] = dictlist[j+1]
             dictlist[j+1] = temp
   dictlist2 = dictlist[0:5]
   set2 = []
   for a in dictlist2:
      set2.append(a['name'])
   othercount = 0;
   for a in dictlist:
      if a['name'] not in set2:
         othercount = othercount + 1
   dictlist2.append({"name":"其他","count":othercount})
   print('获取排行榜中每个国家的上榜人数')
   print(dictlist2)
   return dictlist2

## 绘制条形图和饼状图
def drow():
   import matplotlib.pyplot as plt
   plt.rcParams['font.sans-serif'] = ['SimHei'] # 设置中文字体
   plt.figure('福布斯前十榜',figsize=(15,5))

   ## 读取福布斯排行榜前十的数据
   listx,listy = loadtop10('rank.xls')

   plt.title('福布斯前十榜', fontsize=16)
   plt.xlabel('人物', fontsize=14)
   plt.ylabel('金额/亿美元', fontsize=14)
   plt.tick_params(labelsize=10)
   plt.grid(linestyle=':', axis='y')
   a = plt.bar(listx, listy, color='dodgerblue', label='Apple', align='center')
   # 设置标签
   for i in a:
      h = i.get_height()
      plt.text(i.get_x() + i.get_width() / 2, h, '%d' % int(h), ha='center', va='bottom')
   ## -------------------------------------------------------------------------
   dictlist = countcountrynum("rank.xls")
   plt.figure('各国家上榜人数所占比例')
   labels = []
   sizes = []
   for a in dictlist:
      labels.append(a['name'])
      sizes.append(a['count'])
   explode = (0.1, 0, 0, 0, 0, 0)
   plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=False, startangle=150)
   plt.title("各国家上榜人数所占比例", fontsize=16)
   plt.axis('equal')  # 该行代码使饼图长宽相等

   plt.show()

if __name__ == '__main__':

   ## 爬取数据
   data = loadalldata()
   ## 保存数据
   savedata("rank.xls",data)    # py文件同级目录创建rank.xls文件
   ## 展示数据
   drow()

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