python爬虫与数据可视化书(python大数据可视化)

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发布于 2022-07-28 20:17:17

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发布于 2022-07-28 20:17:17

文章被收录于专栏：全栈程序员必看全栈程序员必看

大家好，又见面了，我是你们的朋友全栈君。

之前写过篇爬取前程无忧职位信息并保存到Excel的博客， 这里仔细的讲讲并且增加可视化内容

项目仓库：https://github.com/haohaizhi/51job_spiders

文章目录

1.数据挖掘

代码所需包

import urllib.request
import xlwt
import re
import urllib.parse
import time

进入前程无忧官网 我这里以搜索大数据职位信息

打开开发者模式 Request Headers 里面是我们用浏览器访问网站的信息，有了信息后就能模拟浏览器访问这也是为了防止网站封禁IP，不过前程无忧一般是不会封IP的。

模拟浏览器

header={
    'Host':'search.51job.com',
    'Upgrade-Insecure-Requests':'1',
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'
}

这些基本数据都可以爬取：为了实现交互型爬取，我写了一个能够实现输入想了解的职位就能爬取相关内容的函数

def getfront(page,item):       #page是页数，item是输入的字符串，见后文
     result = urllib.parse.quote(item)					#先把字符串转成十六进制编码
     ur1 = result+',2,'+ str(page)+'.html'
     ur2 = 'https://search.51job.com/list/000000,000000,0000,00,9,99,'
     res = ur2+ur1															#拼接网址
     a = urllib.request.urlopen(res)
     html = a.read().decode('gbk')          # 读取源代码并转为unicode
     return html

def getInformation(html):
    reg = re.compile(r'class="t1 ">.*? <a target="_blank" title="(.*?)" href="(.*?)".*? <span class="t2"><a target="_blank" title="(.*?)" href="(.*?)".*?<span class="t3">(.*?)</span>.*?<span class="t4">(.*?)</span>.*?<span class="t5">(.*?)</span>.*?',re.S)#匹配换行符
    items=re.findall(reg,html)
    return items

这里我除了爬取图上信息外，还把职位超链接后的网址，以及公司超链接的网址爬取下来了。这里先不讲，后面后面会说到，接下来就需要储存信息，这里使用Excel，虽然比较麻烦，不过胜在清晰直观

#新建表格空间
excel1 = xlwt.Workbook()
# 设置单元格格式
sheet1 = excel1.add_sheet('Job', cell_overwrite_ok=True)
sheet1.write(0, 0, '序号')
sheet1.write(0, 1, '职位')
sheet1.write(0, 2, '公司名称')
sheet1.write(0, 3, '公司地点')
sheet1.write(0, 4, '公司性质')
sheet1.write(0, 5, '薪资')
sheet1.write(0, 6, '学历要求')
sheet1.write(0, 7, '工作经验')
sheet1.write(0, 8, '公司规模')
sheet1.write(0, 9, '公司类型')
sheet1.write(0, 10,'公司福利')
sheet1.write(0, 11,'发布时间')

爬取代码如下，这里就能利用双层循环来实现换页爬取与换行输出我这里为了获得大量数据所以爬取了1000页，调试时可以只爬取几页

number = 1
item = input()
for j in range(1,1000):   #页数自己随便改
    try:
        print("正在爬取第"+str(j)+"页数据...")
        html = getfront(j,item)      #调用获取网页原码
        for i in getInformation(html):
            try:
                url1 = i[1]          #职位网址
                res1 = urllib.request.urlopen(url1).read().decode('gbk')
                company = re.findall(re.compile(r'<div class="com_tag">.*?<p class="at" title="(.*?)"><span class="i_flag">.*?<p class="at" title="(.*?)">.*?<p class="at" title="(.*?)">.*?',re.S),res1)
                job_need = re.findall(re.compile(r'<p class="msg ltype".*?>.*?&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;(.*?)&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;(.*?)&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;.*?</p>',re.S),res1)
                welfare = re.findall(re.compile(r'<span class="sp4">(.*?)</span>',re.S),res1)
                print(i[0],i[2],i[4],i[5],company[0][0],job_need[2][0],job_need[1][0],company[0][1],company[0][2],welfare,i[6])
                sheet1.write(number,0,number)
                sheet1.write(number,1,i[0])
                sheet1.write(number,2,i[2])
                sheet1.write(number,3,i[4])
                sheet1.write(number,4,company[0][0])
                sheet1.write(number,5,i[5])
                sheet1.write(number,6,job_need[1][0])
                sheet1.write(number,7,job_need[2][0])
                sheet1.write(number,8,company[0][1])
                sheet1.write(number,9,company[0][2])
                sheet1.write(number,10,("  ".join(str(i) for i in welfare)))
                sheet1.write(number,11,i[6])
                number+=1
                excel1.save("51job.xls")
                time.sleep(0.3) #休息间隔，避免爬取海量数据时被误判为攻击，IP遭到封禁
            except:
                pass
    except:
        pass

结果如下：

2.数据清洗

首先要打开文件

#coding:utf-8
import pandas as pd
import re
#除此之外还要安装xlrd包

data = pd.read_excel(r'51job.xls',sheet_name='Job')
result = pd.DataFrame(data)

清洗思路： 1、出现有空值（NAN）得信息，直接删除整行

a = result.dropna(axis=0,how='any')
pd.set_option('display.max_rows',None)     #输出全部行，不省略

2、职位出错（很多职位都是与大数据无关的职业）

b = u'数据'
number = 1
li = a['职位']
for i in range(0,len(li)):
    try:
        if b in li[i]:
            #print(number,li[i])
            number+=1
        else:
            a = a.drop(i,axis=0)
    except:
        pass

3、其他地方出现的信息错位，比如在学历里出现 ‘招多少人’

b2= u'人'
li2 = a['学历要求']
for i in range(0,len(li2)):
    try:
        if b2 in li2[i]:
            #print(number,li2[i])
            number+=1
            a = a.drop(i,axis=0)
    except:
        pass

4、转换薪资单位如上图就出现单位不一致的情况

b3 =u'万/年'
b4 =u'千/月'
li3 = a['薪资']
#注释部分的print都是为了调试用的
for i in range(0,len(li3)):
    try:
        if b3 in li3[i]:
            x = re.findall(r'\d*\.?\d+',li3[i])
            #print(x)
            min_ = format(float(x[0])/12,'.2f')              #转换成浮点型并保留两位小数
            max_ = format(float(x[1])/12,'.2f')
            li3[i][1] = min_+'-'+max_+u'万/月'
        if b4 in li3[i]:
            x = re.findall(r'\d*\.?\d+',li3[i])
            #print(x)
            #input()
            min_ = format(float(x[0])/10,'.2f')
            max_ = format(float(x[1])/10,'.2f')
            li3[i][1] = str(min_+'-'+max_+'万/月')
        print(i,li3[i])

    except:
        pass

保存到另一个Excel文件

a.to_excel('51job2.xlsx', sheet_name='Job', index=False)

这里只是简单的介绍了一些数据清理的思路，并不是说只要清理这些就行了有时候有的公司网页并不是前程无忧类型的，而是他们公司自己做的网页，这也很容易出错不过只要有了基本思路，这些都不难清理

3.数据可视化

数据可视化可以说是很重要的环节，如果只是爬取数据而不去可视化处理，那么可以说数据的价值根本没有发挥可视化处理能使数据更加直观，更有利于分析甚至可以说可视化是数据挖掘最重要的内容

同样的我们先看代码需要的包

# -*- coding: utf-8 -*-
import pandas as pd
import re
from pyecharts import Funnel,Pie,Geo
import matplotlib.pyplot as plt

若找不到或者安装失败，可用源码安装的方式

https://github.com/pyecharts/pyecharts

接下来就是正戏一样的先要打开文件

file = pd.read_excel(r'51job2.xls',sheet_name='Job')
f = pd.DataFrame(file)
pd.set_option('display.max_rows',None)

1、创建多个列表来单独存放【‘薪资’】【‘工作经验’】【‘学历要求’】【‘公司地点’】等信息

add = f['公司地点']
sly = f['薪资']
edu = f['学历要求']
exp = f['工作经验']
address =[]
salary = []
education = []
experience = []
for i in range(0,len(f)):
    try:
        a = add[i].split('-')
        address.append(a[0])
        #print(address[i])
        s = re.findall(r'\d*\.?\d+',sly[i])
        s1= float(s[0])
        s2 =float(s[1])
        salary.append([s1,s2])
        #print(salary[i])
        education.append(edu[i])
        #print(education[i])
        experience.append(exp[i])
        #print(experience[i])
    except:
       pass

2、matploblib库生成工作经验—薪资图与学历—薪资图

min_s=[]							#定义存放最低薪资的列表
max_s=[]							#定义存放最高薪资的列表
for i in range(0,len(experience)):
    min_s.append(salary[i][0])
    max_s.append(salary[i][0])

my_df = pd.DataFrame({'experience':experience, 'min_salay' : min_s, 'max_salay' : max_s})				#关联工作经验与薪资
data1 = my_df.groupby('experience').mean()['min_salay'].plot(kind='line')
plt.show()
my_df2 = pd.DataFrame({'education':education, 'min_salay' : min_s, 'max_salay' : max_s})				#关联学历与薪资
data2 = my_df2.groupby('education').mean()['min_salay'].plot(kind='line')
plt.show()

3、学历要求圆环图

def get_edu(list):
    education2 = {}
    for i in set(list):
        education2[i] = list.count(i)
    return education2
dir1 = get_edu(education)
# print(dir1)

attr= dir1.keys()
value = dir1.values()
pie = Pie("学历要求")
pie.add("", attr, value, center=[50, 50], is_random=False, radius=[30, 75], rosetype='radius',
        is_legend_show=False, is_label_show=True,legend_orient='vertical')
pie.render('学历要求玫瑰图.html')

4、大数据城市需求地理位置分布图

def get_address(list):
    address2 = {}
    for i in set(list):
        address2[i] = list.count(i)
    address2.pop('异地招聘')
    # 有些地名可能不合法或者地图包里没有可以自行删除，之前以下名称都会报错，现在好像更新了
    #address2.pop('山东')
    #address2.pop('怒江')
    #address2.pop('池州')
    return address2
dir2 = get_address(address)
#print(dir2)

geo = Geo("大数据人才需求分布图", title_color="#2E2E2E",
          title_text_size=24,title_top=20,title_pos="center", width=1300,height=600)
attr2 = dir2.keys()
value2 = dir2.values()
geo.add("",attr2, value2, type="effectScatter", is_random=True, visual_range=[0, 1000], maptype='china',symbol_size=8, effect_scale=5, is_visualmap=True)
geo.render('大数据城市需求分布图.html')

5、工作经验要求漏斗图

def get_experience(list):
    experience2 = {}
    for i in set(list):
         experience2[i] = list.count(i)
    return experience2
dir3 = get_experience(experience)
#print(dir3)

attr3= dir3.keys()
value3 = dir3.values()
funnel = Funnel("工作经验漏斗图",title_pos='center')
funnel.add("", attr3, value3,is_label_show=True,label_pos="inside", label_text_color="#fff",legend_orient='vertical',legend_pos='left')
funnel.render('工作经验要求漏斗图.html')

当然，pyecharts里面的图还有很多种，就靠大家去自己发掘了。

【反馈】

接到部分人反应的乱码情况，主要可能是因为网站规则变动。我去重新更新了一下代码，并且改进了一些地方，如果遇到爬取过程中途停下的情况，可能是网络问题或者陷入阻塞，可以重新运行一次代码

所有代码如下：

# -*- coding:utf-8 -*-
import urllib.request
import xlwt
import re
import urllib.parse
import time
header={ 
   
    'Host':'search.51job.com',
    'Upgrade-Insecure-Requests':'1',
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'
}
def getfront(page,item):       #page是页数，item是输入的字符串
     result = urllib.parse.quote(item)					#先把字符串转成十六进制编码
     ur1 = result+',2,'+ str(page)+'.html'
     ur2 = 'https://search.51job.com/list/000000,000000,0000,00,9,99,'
     res = ur2+ur1															#拼接网址
     a = urllib.request.urlopen(res)
     html = a.read().decode('gbk')          # 读取源代码并转为unicode
     return html
def getInformation(html):
    reg = re.compile(r'class="t1 ">.*? <a target="_blank" title="(.*?)" href="(.*?)".*? <span class="t2"><a target="_blank" title="(.*?)" href="(.*?)".*?<span class="t3">(.*?)</span>.*?<span class="t4">(.*?)</span>.*?<span class="t5">(.*?)</span>.*?',re.S)#匹配换行符
    items=re.findall(reg,html)
    return items
#新建表格空间
excel1 = xlwt.Workbook()
# 设置单元格格式
sheet1 = excel1.add_sheet('Job', cell_overwrite_ok=True)
sheet1.write(0, 0, '序号')
sheet1.write(0, 1, '职位')
sheet1.write(0, 2, '公司名称')
sheet1.write(0, 3, '公司地点')
sheet1.write(0, 4, '公司性质')
sheet1.write(0, 5, '薪资')
sheet1.write(0, 6, '学历要求')
sheet1.write(0, 7, '工作经验')
sheet1.write(0, 8, '公司规模')
sheet1.write(0, 9, '公司类型')
sheet1.write(0, 10,'公司福利')
sheet1.write(0, 11,'发布时间')
number = 1
item = input()
for j in range(1,10000):   #页数自己随便改
    try:
        print("正在爬取第"+str(j)+"页数据...")
        html = getfront(j,item)      #调用获取网页原码
        for i in getInformation(html):
            try:
                url1 = i[1]          #职位网址
                res1 = urllib.request.urlopen(url1).read().decode('gbk')
                company = re.findall(re.compile(r'<div class="com_tag">.*?<p class="at" title="(.*?)"><span class="i_flag">.*?<p class="at" title="(.*?)">.*?<p class="at" title="(.*?)">.*?',re.S),res1)
                job_need = re.findall(re.compile(r'<p class="msg ltype".*?>.*?&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;(.*?)&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;(.*?)&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;.*?</p>',re.S),res1)
                welfare = re.findall(re.compile(r'<span class="sp4">(.*?)</span>',re.S),res1)
                print(i[0],i[2],i[4],i[5],company[0][0],job_need[2][0],job_need[1][0],company[0][1],company[0][2],welfare,i[6])
                sheet1.write(number,0,number)
                sheet1.write(number,1,i[0])
                sheet1.write(number,2,i[2])
                sheet1.write(number,3,i[4])
                sheet1.write(number,4,company[0][0])
                sheet1.write(number,5,i[5])
                sheet1.write(number,6,job_need[2][0])
                sheet1.write(number,7,job_need[1][0])
                sheet1.write(number,8,company[0][1])
                sheet1.write(number,9,company[0][2])
                sheet1.write(number,10,(" ".join(str(i) for i in welfare)))
                sheet1.write(number,11,i[6])
                number+=1
                excel1.save("51job.xls")
                time.sleep(0.3) #休息间隔，避免爬取海量数据时被误判为攻击，IP遭到封禁
            except:
                pass
    except:
        pass

#coding:utf-8
import pandas as pd
import re

data = pd.read_excel(r'51job.xls',sheet_name='Job')
result = pd.DataFrame(data)

a = result.dropna(axis=0,how='any')
pd.set_option('display.max_rows',None)     #输出全部行，不省略

b = u'数据'
number = 1
li = a['职位']
for i in range(0,len(li)):
    try:
        if b in li[i]:
            #print(number,li[i])
            number+=1
        else:
            a = a.drop(i,axis=0)  #删除整行
    except:
        pass

b2 = '人'
li2 = a['学历要求']
for i in range(0,len(li2)):
    try:
        if b2 in li2[i]:
            # print(number,li2[i])
            number += 1
            a = a.drop(i, axis=0)
    except:
        pass

b3 =u'万/年'
b4 =u'千/月'
li3 = a['薪资']
#注释部分的print都是为了调试用的
for i in range(0,len(li3)):
    try:
        if b3 in li3[i]:
            x = re.findall(r'\d*\.?\d+',li3[i])
            #print(x)
            min_ = format(float(x[0])/12,'.2f')              #转换成浮点型并保留两位小数
            max_ = format(float(x[1])/12,'.2f')
            li3[i][1] = min_+'-'+max_+u'万/月'
        if b4 in li3[i]:
            x = re.findall(r'\d*\.?\d+',li3[i])
            #print(x)
            #input()
            min_ = format(float(x[0])/10,'.2f')
            max_ = format(float(x[1])/10,'.2f')
            li3[i][1] = str(min_+'-'+max_+'万/月')
        print(i,li3[i])

    except:
        pass
a.to_excel('51job2.xls', sheet_name='Job', index=False)
#############################################################################################
import pandas as pd
import re
from pyecharts import Funnel,Pie,Geo
import matplotlib.pyplot as plt

file = pd.read_excel(r'51job2.xls',sheet_name='Job')
f = pd.DataFrame(file)
pd.set_option('display.max_rows',None)

add = f['公司地点']
sly = f['薪资']
edu = f['学历要求']
exp = f['工作经验']
address =[]
salary = []
education = []
experience = []
for i in range(0,len(f)):
    try:
        a = add[i].split('-')
        address.append(a[0])
        #print(address[i])
        s = re.findall(r'\d*\.?\d+',sly[i])
        s1= float(s[0])
        s2 =float(s[1])
        salary.append([s1,s2])
        #print(salary[i])
        education.append(edu[i])
        #print(education[i])
        experience.append(exp[i])
        #print(experience[i])
    except:
       pass

min_s=[]							#定义存放最低薪资的列表
max_s=[]							#定义存放最高薪资的列表
for i in range(0,len(experience)):
    min_s.append(salary[i][0])
    max_s.append(salary[i][0])
#matplotlib模块如果显示不了中文字符串可以用以下代码。
plt.rcParams['font.sans-serif'] = ['KaiTi'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题

my_df = pd.DataFrame({ 
   'experience':experience, 'min_salay' : min_s, 'max_salay' : max_s})				#关联工作经验与薪资
data1 = my_df.groupby('experience').mean()['min_salay'].plot(kind='line')
plt.show()
my_df2 = pd.DataFrame({ 
   'education':education, 'min_salay' : min_s, 'max_salay' : max_s})				#关联学历与薪资
data2 = my_df2.groupby('education').mean()['min_salay'].plot(kind='line')
plt.show()

def get_edu(list):
    education2 = { 
   }
    for i in set(list):
        education2[i] = list.count(i)
    return education2
dir1 = get_edu(education)
# print(dir1)

attr= dir1.keys()
value = dir1.values()
pie = Pie("学历要求")
pie.add("", attr, value, center=[50, 50], is_random=False, radius=[30, 75], rosetype='radius',
        is_legend_show=False, is_label_show=True,legend_orient='vertical')
pie.render('学历要求玫瑰图.html')

def get_address(list):
    address2 = { 
   }
    for i in set(list):
        address2[i] = list.count(i)
    address2.pop('异地招聘')
    # 有些地名可能不合法或者地图包里没有可以自行删除，之前以下名称都会报错，现在好像更新了
    #address2.pop('山东')
    #address2.pop('怒江')
    #address2.pop('池州')
    return address2
dir2 = get_address(address)
#print(dir2)

geo = Geo("大数据人才需求分布图", title_color="#2E2E2E",
          title_text_size=24,title_top=20,title_pos="center", width=1300,height=600)
attr2 = dir2.keys()
value2 = dir2.values()
geo.add("",attr2, value2, type="effectScatter", is_random=True, visual_range=[0, 1000], maptype='china',symbol_size=8, effect_scale=5, is_visualmap=True)
geo.render('大数据城市需求分布图.html')

def get_experience(list):
    experience2 = { 
   }
    for i in set(list):
         experience2[i] = list.count(i)
    return experience2
dir3 = get_experience(experience)
#print(dir3)

attr3= dir3.keys()
value3 = dir3.values()
funnel = Funnel("工作经验漏斗图",title_pos='center')
funnel.add("", attr3, value3,is_label_show=True,label_pos="inside", label_text_color="#fff",legend_orient='vertical',legend_pos='left')
funnel.render('工作经验要求漏斗图.html')

HTML文件最好用谷歌浏览器打开，如果点开没反应可以在文件夹里找到该文件然后打开

最近比较多人说爬取数据没有动静，我去看了下，其实不是什么问题，就是网页源码有更改，之前python爬取到的信息是用HTML写的，而现在数据那里是JavaScript写的，这样的话正则肯定就不匹配了。我也花时间改了改。有些东西也去的去，加的加，不过不影响后面数据可视化。

# -*- coding:utf-8 -*-
import urllib.request
import xlwt
import re
import urllib.parse
import time
header={ 
   
    'Host':'search.51job.com',
    'Upgrade-Insecure-Requests':'1',
    'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'
}
def getfront(page,item):       #page是页数，item是输入的字符串
     result = urllib.parse.quote(item)					#先把字符串转成十六进制编码
     ur1 = result+',2,'+ str(page)+'.html'
     ur2 = 'https://search.51job.com/list/000000,000000,0000,00,9,99,'
     res = ur2+ur1    #拼接网址
     a = urllib.request.urlopen(res)
     html = a.read().decode('gbk')      # 读取源代码并转为unicode
     html = html.replace('\\','')       # 将用于转义的"\"替换为空
     html = html.replace('[', '')
     html = html.replace(']', '')
     #print(html)
     return html

def getInformation(html):
    reg = re.compile(r'\{"type":"engine_search_result","jt":"0".*?"job_href":"(.*?)","job_name":"(.*?)".*?"company_href":"(.*?)","company_name":"(.*?)","providesalary_text":"(.*?)".*?"updatedate":"(.*?)".*?,'
                     r'"companytype_text":"(.*?)".*?"jobwelf":"(.*?)".*?"attribute_text":"(.*?)","(.*?)","(.*?)","(.*?)","companysize_text":"(.*?)","companyind_text":"(.*?)","adid":""},',re.S)#匹配换行符
    items=re.findall(reg,html)
    print(items)
    return items

#新建表格空间
excel1 = xlwt.Workbook()
# 设置单元格格式
sheet1 = excel1.add_sheet('Job', cell_overwrite_ok=True)
sheet1.write(0, 0, '序号')
sheet1.write(0, 1, '职位')
sheet1.write(0, 2, '公司名称')
sheet1.write(0, 3, '公司地点')
sheet1.write(0, 4, '公司性质')
sheet1.write(0, 5, '薪资')
sheet1.write(0, 6, '学历要求')
sheet1.write(0, 7, '工作经验')
sheet1.write(0, 8, '公司规模')
#sheet1.write(0, 9, '公司类型')
sheet1.write(0, 9,'公司福利')
sheet1.write(0, 10,'发布时间')
number = 1
item = input()

for j in range(1,10):   #页数自己随便改
    try:
        print("正在爬取第"+str(j)+"页数据...")
        html = getfront(j,item)      #调用获取网页原码
        for i in getInformation(html):
            try:
                #url1 = i[1] #职位网址
                #res1 = urllib.request.urlopen(url1).read().decode('gbk')
                #company = re.findall(re.compile(r'<div class="com_tag">.*?<p class="at" title="(.*?)"><span class="i_flag">.*?<p class="at" title="(.*?)">.*?<p class="at" title="(.*?)">.*?',re.S),res1)
                #job_need = re.findall(re.compile(r'<p class="msg ltype".*?>.*?&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;(.*?)&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;(.*?)&nbsp;&nbsp;<span>|</span>&nbsp;&nbsp;.*?</p>',re.S),res1)
                #welfare = re.findall(re.compile(r'<span class="sp4">(.*?)</span>',re.S),res1)
                #print(i[0],i[2],i[4],i[5],company[0][0],job_need[2][0],job_need[1][0],company[0][1],company[0][2],welfare,i[6])
                sheet1.write(number,0,number)
                sheet1.write(number,1,i[1])
                sheet1.write(number,2,i[3])
                sheet1.write(number,3,i[8])
                sheet1.write(number,4,i[6])
                sheet1.write(number,5,i[4])
                sheet1.write(number,6,i[10])
                sheet1.write(number,7,i[9])
                sheet1.write(number,8,i[12])
                #sheet1.write(number,9,i[7])
                sheet1.write(number,9,i[7])
                sheet1.write(number,10,i[5])
                number+=1
                excel1.save("51job.xls")
                time.sleep(0.3) #休息间隔，避免爬取海量数据时被误判为攻击，IP遭到封禁
            except:
                pass
    except:
        pass