scrapy框架下爬取51job网站信息，并存储到表格中

1. 通过命令创建项目
scrapy startproject JobSpider
2. 用pycharm打开项目
3. 通过命令创建爬虫
scrapy genspider job baidu.com
4. 配置settingsrobots_obey=False
Download_delay=0.5

Cookie_enable=False

DOWNLOADER_MIDDLEWARES = {
'JobSpider.middlewares.JobUserAgentMiddleware': 543,
'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': None
}

"""调用Pipeline中自己写的类"""
ITEM_PIPELINES = {
'JobSpider.pipelines.ToCsvPipeline': 300,
}

5. 自定义UserAgentMiddleWare
可以直接粘现成的# 自己添加的获取useragent类
class JobUserAgentMiddleware(object):
"""This middleware allows spiders to override the user_agent"""

def __init__(self, user_agent='Scrapy', name=''):
self.user_agent = UserAgent()

@classmethod
def from_crawler(cls, crawler):
# o = cls(crawler.settings['USER_AGENT'],'ZhangSAN)
o = cls()
# cls后的数据会自动赋值给构造函数的对应参数
# crawler.signals.connect(o.spider_opened, signal=signals.spider_opened)
return o

# 这个函数不能删，否则会报错
def spider_opened(self, spider):
# 等号右边代码的含义是 从spider中获得user_agent的属性
# 如果没有默认为self.user_agent的内容
# self.user_agent = getattr(spider, 'user_agent', self.user_agent)
pass

def process_request(self, request, spider):
if self.user_agent:
# b 转换为二进制，不能改
request.headers.setdefault(b'User-Agent', self.user_agent.random)
6. 开始解析数据
在Terminal终端里面，创建文件 scrapy genspider job baidu.com

1) 先大致规划一下需要几个函数
2) 函数1跳转到函数2使用 yield scrapy.Request(url,callback,meta,dont_filter)# -*- coding: utf-8 -*-
import scrapy
from ..items import JobspiderItem

# --老师--51job数据获取方法一
class JobSpider(scrapy.Spider):
name = 'job'
allowed_domains = ['51job.com']
start_urls = [
'http://search.51job.com/list/010000%252C020000%252C030200%252C040000,000000,0000,00,9,99,python,2,1.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare=',
'http://search.51job.com/list/010000%252C020000%252C030200%252C040000,000000,0000,00,9,99,php,2,1.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare=',
'http://search.51job.com/list/010000%252C020000%252C030200%252C040000,000000,0000,00,9,99,html,2,1.html?lang=c&stype=&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&providesalary=99&lonlat=0%2C0&radius=-1&ord_field=0&confirmdate=9&fromType=&dibiaoid=0&address=&line=&specialarea=00&from=&welfare=',
]

def parse(self, response):
yield scrapy.Request(
url=response.url,
callback=self.parse_job_info,
meta={},
dont_filter=True
)

def parse_next_page(self, response):
"""
解析下一页
:param response:
:return:
"""
next_page = response.xpath("//li[@class='bk'][2]/a/@href").extract_first('')
if next_page:
yield scrapy.Request(
url=next_page,
callback=self.parse_job_info,
meta={},
dont_filter=True
)
"""
递归:如果一个函数内部自己调用自己
这种形式就叫做递归
"""

def parse_job_info(self, response):
"""
解析工作信息
:param response:
:return:
"""
job_div_list = response.xpath("//div[@id='resultList']/div[@class='el']")
for job_div in job_div_list:
job_name = job_div.xpath("p/span/a/@title").extract_first('无工作名称').strip().replace(",", "/")
job_company_name = job_div.xpath("span[@class='t2']/a/@title").extract_first('无公司名称').strip()
job_place = job_div.xpath("span[@class='t3']/text()").extract_first('无地点名称').strip()
job_salary = job_div.xpath("span[@class='t4']/text()").extract_first('面议').strip()
job_time = job_div.xpath("span[@class='t5']/text()").extract_first('无时间信息').strip()
job_type = '51job' if '51job.com' in response.url else '其它'
print(job_type, job_name, job_company_name, job_place, job_salary, job_time)
"""
数据清洗:负责清除数据两端的空格,空行,特殊符号等
常用操作一般是strip
包括清除无效数据,例如数据格式不完整的数据
以及重复的数据
"""
item = JobspiderItem()
item['job_name'] = job_name
item['job_company_name'] = job_company_name
item['job_place'] = job_place
item['job_salary'] = job_salary
item['job_time'] = job_time
item['job_type'] = job_type
item['fan_kui_lv'] = "没有反馈率"
yield item
yield scrapy.Request(
url=response.url,
callback=self.parse_next_page,
dont_filter=True,
)

7. 将数据封装到items,记得yield item# -*- coding: utf-8 -*-

# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy

class JobspiderItem(scrapy.Item):
# define the fields for your item here like:
job_name = scrapy.Field()
job_company_name = scrapy.Field()
job_place = scrapy.Field()
job_salary = scrapy.Field()
job_time = scrapy.Field()
job_type = scrapy.Field()
fan_kui_lv = scrapy.Field()

8. 自定义pipelines将数据存储到数据库/文件中# -*- coding: utf-8 -*-

# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html """
Pipeline：俗称管道，用于接收爬虫返回的item数据
"""

class JobspiderPipeline(object):
def process_item(self, item, spider):
return item

"""
以下代码 在settings.py中调用
"""

class ToCsvPipeline(object):
def process_item(self, item, spider):
with open("job.csv", "a", encoding="gb18030") as f:
job_name = item['job_name']
job_company_name = item['job_company_name']
job_place = item['job_place']
job_salary = item['job_salary']
job_time = item['job_time']
job_type = item['job_type']
fan_kui_lv = item['fan_kui_lv']
job_info = [job_name, job_company_name, job_place, job_salary, job_time, job_type, fan_kui_lv, "\n"]
# 逗号，换格
f.write(",".join(job_info))
# 把item传递给下一个Pipeline做处理
return item

9.执行结果如下：



生成表格：