如何在python中使用scrapy爬虫库-创新互联
这期内容当中小编将会给大家带来有关如何在python中使用scrapy爬虫库，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。
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'?([\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'([\s\S]+?)<\/td>')
con = re.findall(ur'([\s\S]+?)
[\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
上述就是小编为大家分享的如何在python中使用scrapy爬虫库了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注创新互联成都网站设计公司行业资讯频道。
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