python scrapy简单使用

最近因为项目需求,需要写个爬虫爬取一些题库。在这之前爬虫我都是用node或者php写的。一直听说python写爬虫有一手,便入手了python的爬虫框架scrapy.

下面简单的介绍一下scrapy的目录结构与使用:

首先我们得安装scrapy框架

pip install scrapy

接着使用scrapy命令创建一个爬虫项目:

scrapy startproject questions

相关文件简介:

  • scrapy.cfg: 项目的配置文件
  • questions/: 该项目的python模块。之后您将在此加入代码。
  • questions/items.py: 项目中的item文件.
  • questions/pipelines.py: 项目中的pipelines文件.
  • questions/settings.py: 项目的设置文件.
  • questions/spiders/: 放置spider代码的目录.
  • questions/spiders/xueersi.py: 实现爬虫的主体代码.

xueersi.py  爬虫主体

# -*- coding: utf-8 -*-
import scrapy
import time
import numpy
import re
from questions.items import QuestionsItem


class xueersiSpider(scrapy.Spider):
    name = "xueersi" # 爬虫名字
    allowed_domains = ["tiku.xueersi.com"] # 目标的域名
  # 爬取的目标地址 start_urls
= [ "http://tiku.xueersi.com/shiti/list_1_1_0_0_4_0_1", "http://tiku.xueersi.com/shiti/list_1_2_0_0_4_0_1", "http://tiku.xueersi.com/shiti/list_1_3_0_0_4_0_1", ] levels = ['偏易','中档','偏难'] subjects = ['英语','语文','数学']
   # 爬虫开始的时候,自动调用该方法,如果该方法不存在会自动调用parse方法
# def start_requests(self): # yield scrapy.Request('http://tiku.xueersi.com/shiti/list_1_2_0_0_4_0_39',callback=self.getquestion)
  # start_requests方法不存在时,parse方法自动被调用 def parse(self, response):
     # xpath的选择器语法不多介绍,可以直接查看官方文档 arr
= response.xpath("//ul[@class='pagination']/li/a/text()").extract() total_page = arr[3]
     # 获取分页
for index in range(int(total_page)): yield scrapy.Request(response.url.replace('_0_0_4_0_1',"_0_0_4_0_"+str(index)),callback=self.getquestion) # 发出新的请求,获取每个分页所有题目 # 获取题目 def getquestion(self,response): for res in response.xpath('//div[@class="main-wrap"]/ul[@class="items"]/li'): item = QuestionsItem() # 实例化Item类 # 获取问题 questions = res.xpath('./div[@class="content-area"]').re(r'<div class="content-area">?([\s\S]+?)<(table|\/td|div|br)') if len(questions): # 获取题目 question = questions[0].strip() item['source'] = question dr = re.compile(r'<[^>]+>',re.S) question = dr.sub('',question) content = res.extract() item['content'] = question # 获取课目 subject = re.findall(ur'http:\/\/tiku\.xueersi\.com\/shiti\/list_1_(\d+)',response.url) item['subject'] = self.subjects[int(subject[0])-1] # 获取难度等级 levels = res.xpath('//div[@class="info"]').re(ur'难度:([\s\S]+?)<') item['level'] = self.levels.index(levels[0])+1 # 获取选项 options = re.findall(ur'[A-D][\..]([\s\S]+?)<(\/td|\/p|br)',content) item['options'] = options if len(options): url = res.xpath('./div[@class="info"]/a/@href').extract()[0] request = scrapy.Request(url,callback=self.getanswer) request.meta['item'] = item # 缓存item数据,传递给下一个请求 yield request #for option in options: # 获取答案 def getanswer(self,response): res = response.xpath('//div[@class="part"]').re(ur'<td>([\s\S]+?)<\/td>') con = re.findall(ur'([\s\S]+?)<br>[\s\S]+?([A-D])',res[0]) # 获取含有解析的答案 if con: answer = con[0][1] analysis = con[0][0] # 获取解析 else: answer = res[0] analysis = '' if answer: item = response.meta['item'] # 获取item item['answer'] = answer.strip() item['analysis'] = analysis.strip() item['answer_url'] = response.url yield item # 返回item,输出管道(pipelines.py)会自动接收该数据

 

items.py 数据结构定义:

# -*- 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 QuestionsItem(scrapy.Item):
    content = scrapy.Field()
    subject = scrapy.Field()
    level = scrapy.Field()
    answer = scrapy.Field()
    options = scrapy.Field()
    analysis = scrapy.Field()
    source = scrapy.Field()
    answer_url = scrapy.Field()
    pass

pipelines.py 输出管道(本例子输出的数据写入本地数据库):

# -*- 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
import pymysql
import md5


class QuestionsPipeline(object):
    def __init__(self):  
        # 建立数据库连接  
        self.connect = pymysql.connect('localhost','root','','question',use_unicode=True,charset='utf8')  
        # 获取游标 
        self.cursor = self.connect.cursor()  
        print("connecting mysql success!")  
        self.answer = ['A','B','C','D']
    def process_item(self, item, spider):
        content = pymysql.escape_string(item['content'])
     # 获取题目hash值,使用该字段过滤重复的题目 m1
= md5.new() m1.update(content) hash = m1.hexdigest() selectstr = "select id from question where hash='%s'"%(hash) self.cursor.execute(selectstr) res = self.cursor.fetchone() # 过滤相同的题目 if not res:
       # 插入题目 sqlstr
= "insert into question(content,source,subject,level,answer,analysis,hash,answer_url) VALUES('%s','%s','%s','%s','%s','%s','%s','%s')"%(content,pymysql.escape_string(item['source']),item['subject'],item['level'],item['answer'],pymysql.escape_string(item['analysis']),hash,item['answer_url']) self.cursor.execute(sqlstr) qid = self.cursor.lastrowid
       # 插入选项
for index in range(len(item['options'])): option = item['options'][index] answer = self.answer.index(item['answer']) if answer==index: ans = '2' else: ans = '1' sqlstr = "insert into options(content,qid,answer) VALUES('%s','%s','%s')"%(pymysql.escape_string(option[0]),qid,ans) self.cursor.execute(sqlstr) self.connect.commit() #self.connect.close() return item

 

爬虫构建完毕后,在项目的根目录下运行

scrapy crawl xueersi  # scrapy crawl 爬虫的名称

 

posted @ 2018-04-11 14:16  Ricky_boke  阅读(2522)  评论(0编辑  收藏