Python Scrapy爬虫框架入门与题库抓取实战
本文详细介绍Python爬虫框架Scrapy的基础使用,涵盖环境安装、项目创建及目录结构解析。通过一个具体的题库抓取案例,演示如何编写Spider、定义Items、配置Pipeline以及处理数据库存储,帮助开发者快速掌握Scrapy的核心流程与代码实现。
Scrapy环境搭建与项目初始化
近期因项目需求,需要开发一个爬虫程序来采集题库数据。在此之前,我主要使用Node.js或PHP进行爬虫开发。鉴于Python在爬虫领域的广泛好评,我决定尝试使用Python的Scrapy框架来完成这项任务。
接下来,简要介绍Scrapy的项目目录结构及其基本使用方法。
首先,需要完成Scrapy框架的安装。
pip install scrapy
随后,利用Scrapy命令行工具创建一个爬虫项目。
scrapy startproject questions
以下是对生成文件中关键部分的简要说明:
scrapy.cfg:这是项目的配置文件,用于管理不同部署环境的配置。
questions/:这是项目的Python模块目录,后续开发的代码将放置于此。
questions/items.py:用于定义项目中抓取的数据结构(Item)。
questions/pipelines.py:用于定义数据处理管道,对抓取到的数据进行后续处理。
questions/settings.py:项目的设置文件,包含爬虫的各种配置参数。
questions/spiders/:存放Spider代码的目录,Spider是定义如何爬取特定网站的核心组件。
questions/spiders/xueersi.py:实现具体爬虫逻辑的主体代码文件。
爬虫核心代码实现
xueersi.py 爬虫主体代码实现如下:
# -*- 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
数据模型定义
items.py 数据结构定义如下:
# -*- 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
数据处理管道配置
pipelines.py 输出管道配置(本例中将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
运行与扩展
爬虫构建完成后,在项目根目录下执行运行命令。
scrapy crawl xueersi # scrapy crawl 爬虫的名称
如需了解更多关于Python爬虫库Scrapy的使用方法,请参考相关链接。
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