spiderofwords

本文最后更新于:3 个月前

scripy爬虫爬取单词(word.iciba.com)

scripy爬虫爬取单词X

首先创建一个有关数据库操作的py

dbhelper.py

import pymysql
from scrapy.utils.project import get_project_settings

// MYSQL_HOST等参数定义在setting.py中
// 也可以直接定义在这个文件中
class DBHelper():

    def __init__(self):
        self.settings = get_project_settings()
        self.host = self.settings['MYSQL_HOST']
        self.port = self.settings['MYSQL_PORT']
        self.user = self.settings['MYSQL_USER']
        self.passwd = self.settings['MYSQL_PASSWD']
        self.db = self.settings['MYSQL_DBNAME']

    // 建立数据库连接
    def connectMysql(self):
        conn = pymysql.connect(host=self.host,
                               port=self.port,
                               user=self.user,
                               passwd=self.passwd,
                               charset='utf8')
        return conn

	// 创建数据库
    def connectDatabase(self):
        conn = pymysql.connect(host=self.host,
                               port=self.port,
                               user=self.user,
                               passwd=self.passwd,
                               db=self.db,
                               charset='utf8')
        return conn

        # 创建要使用到的数据库
    def createDatabase(self):
        conn = self.connectMysql()
        sql = "create database if not exists " + self.db
        cur = conn.cursor()
        cur.execute(sql)  #
        cur.close()
        conn.close()

        # 创建数据表
    def createTable(self, sql):
        conn = self.connectDatabase()

        cur = conn.cursor()
        cur.execute(sql)
        cur.close()
        conn.close()

        # 向数据库中插入数据
    def insert(self, sql, *params):  #
        conn = self.connectDatabase()
        cur = conn.cursor();
        cur.execute(sql, params)
        conn.commit()
        cur.close()
        conn.close()
        # 数据库更新操作
    def update(self, sql, *params):
        conn = self.connectDatabase()
        cur = conn.cursor()
        cur.execute(sql, params)
        conn.commit()
        cur.close()
        conn.close()


    def delete(self, sql, *params):
        conn = self.connectDatabase()
        cur = conn.cursor()
        cur.execute(sql, params)
        conn.commit()
        cur.close()
        conn.close()

class TestDBHelper():
    def __init__(self):
        self.dbHelper = DBHelper()

    def testCreateDatebase(self):
        self.dbHelper.createDatabase()

    def testCreateTable(self):
        sql = "create table wordtable(id int primary key auto_increment,word varchar(50),soundmark varchar(100),url varchar(200),translation varchar(100))"
        self.dbHelper.createTable(sql)

    def testInsert(self):
        sql = "insert into wordtable(word,soundmark,url,translation) values(%s,%s,%s,%s)"
        params = ("test", "test", "test", "test")
        self.dbHelper.insert(sql, *params)

    def testUpdate(self):
        sql = "update testtable set word=%s,soundmark=%s,url=%s,translation=%s where id=%s"
        params = ("update", "update", "update", "update","1")
        self.dbHelper.update(sql, *params)

    def testDelete(self):
        sql = "delete from wordtable where id=%s"
        params = ("1")
        self.dbHelper.delete(sql, *params)

if __name__ == "__main__":
    testDBHelper = TestDBHelper()
    # testDBHelper.testCreateDatebase()
    # testDBHelper.testCreateTable()
    # testDBHelper.testInsert()

在编写爬虫之前先运行一次该程序,就可以在数据库中看到新创建的数据库

爬虫编写

setting.py

BOT_NAME = 'words'

SPIDER_MODULES = ['words.spiders']
NEWSPIDER_MODULE = 'words.spiders'


#Mysql数据库的配置信息
MYSQL_HOST = '127.0.0.1'
MYSQL_DBNAME = 'word'         #数据库名字,请修改
MYSQL_USER = 'root'             #数据库账号,请修改
MYSQL_PASSWD = 'root'         #数据库密码,请修改

MYSQL_PORT = 3306               #数据库端口,在dbhelper中使用


# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'word (+http://www.yourdomain.com)'
USER_AGENT = 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2049.0 Safari/537.36'

LOG_LEVEL = "WARNING"
ROBOTSTXT_OBEY = True

# Configure item pipelines
# See https://docs.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
    # 两个管道的优先级相同
    'words.pipelines.WebcrawlerScrapyPipeline': 300, #保存到mysql数据库
    'words.pipelines.JsonWithEncodingPipeline': 300, #保存到json文件中
}

items.py

import scrapy


class WordsItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    word = scrapy.Field()
    soundmark = scrapy.Field()
    url = scrapy.Field()
    translation = scrapy.Field()

web_word.py

爬虫文件

import scrapy
import re
import os
import urllib
import sys
import urllib.request
from scrapy.selector import Selector
from scrapy.http import HtmlResponse,Request
from words.items import WordsItem

class WebWordSpider(scrapy.Spider):
    name = 'web_word'
    allowed_domains = ['word.iciba.com/']

    # class 是四级必备单词,这里可以根据需要选择不同的单词表
    first_url = "http://word.iciba.com/?action=words&class=11&course={}"
    start_urls = []

    # 生成爬取需要的url地址池
    # 可以根据需要修改nums,控制爬取的单词数
    nums = 11  # 爬取前10页的单词,一共200个单词
    for page in range(1,nums):
        start_urls.append(first_url.format(page))
    print(start_urls)

    def parse(self, response):
        se = Selector(response)
        # 先判断页面中是否存在单词
        src = se.xpath("//div[@class='word_main']/ul/li")
        # 提取出url中的页数
        page = re.findall(r"course=\d+", response.url)[0]
        print("===" * 10 + "正在爬取第"+page[7:]+"页"+"===" * 10)
        if len(src) > 0:
            # 将单词的信息提取出来,word是一个数组,存放的是页面中的所有单词
            word = se.xpath("//li/div[@class='word_main_list_w']/span/@title" ).extract()  # 提取节点信息
            soundmark = se.xpath("//li/div[@class='word_main_list_y']/strong/text()" ).extract()
            url = se.xpath("//li/div[@class='word_main_list_y']/a/@id" ).extract()
            translation = se.xpath("//li/div[@class='word_main_list_s']/span/@title" ).extract()

            # 因为上一步提出的音标存在制表符,这里就用正则提取出正确的音标
            for i in range(0,len(word)):
                sm = re.findall(r"\[.*?\]",soundmark[i])
                soundmark[i] = sm[0]

            for i in range(0,len(word)):
                file_name = u"%s.mp3" % word[i]         # 用单词给mp3文件命名
                path = os.path.join("D:\Sunzh\word\cet4", file_name)  # mp3保存的路径
                urllib.request.urlretrieve(url[i], path)    # 下载该mp3文件
                item = WordsItem()
                item['word'] = word[i]
                item['soundmark'] = soundmark[i]
                item['url'] = url[i]
                item['translation'] = translation[i]
                # print(item)
                # 将item发给管道处理,在管道中写入数据库和josn文件
                yield item

这里是使用xpath获取的节点元素,很简单。

可以在浏览器中下载一个插件XPath Helper,辅助写出xpath路径,它可以将所匹配的元素高亮显示,保证程序中的xpath正确性。

image-20210502220632784

pipelines.py

import pymysql
import pymysql.cursors
import codecs
import json
from logging import log
from twisted.enterprise import adbapi

class JsonWithEncodingPipeline(object):
    '''保存到文件中对应的class
       1、在settings.py文件中配置
       2、在自己实现的爬虫类中yield item,会自动执行
    '''
    def __init__(self):
        self.file = codecs.open('word.json', 'w', encoding='utf-8')  # 保存为json文件

    def process_item(self, item, spider):
        line = json.dumps(dict(item)) + "\n"  # 转为json的
        self.file.write(line)                 # 写入文件中
        return item

    def spider_closed(self, spider):  # 爬虫结束时关闭文件
        self.file.close()

class WebcrawlerScrapyPipeline(object):
    '''
       保存到数据库中对应的class
       1、在settings.py文件中配置
       2、在自己实现的爬虫类中yield item,会自动执行
    '''

    def __init__(self, dbpool):
        self.dbpool = dbpool  # 定义一个数据库连接对象
        ''' 这里注释中采用写死在代码中的方式连接线程池,可以从settings配置文件中读取,更加灵活
            self.dbpool=adbapi.ConnectionPool('MySQLdb',
                                          host='127.0.0.1',
                                          db='crawlpicturesdb',
                                          user='root',
                                          passwd='123456',
                                          cursorclass=MySQLdb.cursors.DictCursor,
                                          charset='utf8',
                                          use_unicode=False)
        '''
    @classmethod
    def from_settings(cls, settings):
        '''
           1、@classmethod声明一个类方法,而对于平常我们见到的则叫做实例方法。
           2、类方法的第一个参数cls(class的缩写,指这个类本身),而实例方法的第一个参数是self,表示该类的一个实例
           3、可以通过类来调用,就像C.f(),相当于java中的静态方法
        '''
        dbparams = dict(
            host=settings['MYSQL_HOST'],  # 读取settings中的配置
            db=settings['MYSQL_DBNAME'],
            user=settings['MYSQL_USER'],
            passwd=settings['MYSQL_PASSWD'],
            charset='utf8',               # 编码要加上,否则可能出现中文乱码问题
            cursorclass=pymysql.cursors.DictCursor,
            use_unicode=False,
        )
        dbpool = adbapi.ConnectionPool('pymysql', **dbparams)  # **表示将字典扩展为关键字参数,相当于host=xxx,db=yyy....
        return cls(dbpool)                                     # 相当于dbpool付给了这个类,self中可以得到

    # pipeline默认调用
    def process_item(self, item, spider):
        query = self.dbpool.runInteraction(self._conditional_insert, item)  # 调用插入的方法
        query.addErrback(self._handle_error, item, spider)                  # 调用异常处理方法
        return item

    # 写入数据库中
    def _conditional_insert(self, tx, item):
        # print (item['word'])
        sql = "insert into wordtable(word,soundmark,url,translation) values(%s,%s,%s,%s)"
        params = (item["word"], item["soundmark"], item["url"], item["translation"])
        tx.execute(sql, params)

    # 错误处理方法
    def _handle_error(self, failue, item, spider):
        print('--------------database operation exception!!-----------------')
        print('-------------------------------------------------------------')
        print(failue)

pipelines中写了两个方法,分别是将单词的相关性信息写入到数据库,另一个是将单词信息保存到一个json文件中

运行结果

运行截图

image-20210502221134698

json文件截图

image-20210502221223747

下载的音频

image-20210502221344459

数据库内容

image-20210502221904968

爬取壁纸的爬虫

import scrapy
import pymysql
import re
import os
import urllib
import sys
import urllib.request

from word.items import WordItem
from scrapy.selector import Selector
from scrapy.http import HtmlResponse,Request


class SpiderPicSpider(scrapy.Spider):
    name = 'spider_pic'
    allowed_domains = ['desk.zol.com.cn'] # 爬虫搜索的域名范围
    first_url = "http://desk.zol.com.cn/meinv/1920x1080/{0}.html"
    start_urls = [first_url.format(1)] # 爬虫开始的url


    def parse(self, response):
        for i in range(1, 10):
            url = self.first_url.format(i)

            yield Request(
                url=url,
                callback=self.parse_one
            )

    def parse_one(self, response):
        se = Selector(response)  # 创建查询对象
        if (re.match("https://desk.zol.com.cn/meinv/\d+x\d+/\d+.html", response.url)):
            # 正则是判断改url是否正确
            src = se.xpath("//ul[@class='pic-list2  clearfix']/li")  # 使用xpath找到所有图片所在的节点
            for i in range(1, len(src)):
                imgURLs = se.xpath("//ul[@class='pic-list2  clearfix']/li[%d]/a/img/@src" % i).extract()  # 提取节点信息
                title = se.xpath("//ul[@class='pic-list2  clearfix']/li[%d]/a/img/@title" % i).extract()

                if imgURLs:
                    # 替换url中的尺寸信息 可以下载更大的图片
                    realURL = imgURLs[0].replace("t_s208x130c5", "t_s2560x1600c5")
                    file_name = u"%s.jpg" % title[0]

                    path = os.path.join("D:\Sunzh\pic\meinv", file_name)  # 拼接图片的路径
                    type = sys.getfilesystemencoding()
                    print(file_name.encode(type))
                    item = WordItem()
                    item['name'] = file_name
                    item['url'] = realURL
                    # print(item['name'],item['url'])

                    yield item
                    urllib.request.urlretrieve(realURL, path)  # 接收文件路径和需要保存的路径,会自动去文件路径下载并保存到我们指定的本地路径

image-20210502223115320

image-20210502223132992


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