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[python爬虫]scrapy+django+mysql爬大众点评餐厅数据

简介: scrapy爬大众点评餐厅信息。利用scrapy的css选择器和xpath选择器解析网页,利用django的orm保存数据到mysql,项目github地址:https://github.com/jjzhu-ncu/Jpider
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环境

  • python 3.6(推荐使用anaconda)
  • django 1.11(pip install django)
  • scrapy 1.3.3 (pip install scrapy)
  • mysql 5.7.17
  • mac os 10.11.6
  • chrome 57.0.2987.133 (64-bit)

概述

利用scrapy的css选择器和xpath选择器解析网页,利用django的orm保存数据到mysql,项目github地址:https://github.com/zhujiajunup/Jpider

点评爬虫

先创建django项目和scrapy项目
项目结构如下所示:
_2017_04_26_12_04_21

_2017_04_26_12_04_59
在django app spiders包下的models.py 创建shop信息对应的model

class ShopInfo(models.Model):
    shop_id = models.CharField(max_length=20, primary_key=True)
    shop_name = models.CharField(max_length=200, default='')
    review_count = models.CharField(max_length=20, default='')
    avg_price = models.CharField(max_length=20, default='')
    taste = models.CharField(max_length=10, default='')
    env = models.CharField(max_length=10, default='')
    service = models.CharField(max_length=10, default='')
    address = models.CharField(max_length=200, default='')
    open_time = models.CharField(max_length=200, default='')
    rank_star = models.CharField(max_length=20, default='')
    place = models.CharField(max_length=20, default='')
    classify = models.CharField(max_length=20, default='')
    star_all = models.CharField(max_length=20, default='')
    star_5 = models.CharField(max_length=20, default='')
    star_4 = models.CharField(max_length=20, default='')
    star_3 = models.CharField(max_length=20, default='')
    star_2 = models.CharField(max_length=20, default='')
    star_1 = models.CharField(max_length=20, default='')
    feature = models.BooleanField(default=False)
    feature2 = models.CharField(max_length=200, default='')

在Jpider包下的setting.py配置mysql数据库相关信息


DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
        'NAME': 'spider',
        'USER': 'root',
        'HOST': '127.0.0.1',
        'PASSWORD': '1234',
        'PORT': 3306,
        'OPTIONS': {'charset':'utf8mb4'},
    }
}

执行如下命令初始化mysql数据库表

python manage.py makemigrations
python manage.py migrate

如果要使用django的orm来与mysql交互,需要在爬虫项目的items.py里配置一下,需要scrapy_djangoitem包,通过如下命令安装

pip install scrapy_djangoitem

并定义item

import scrapy
from spiders.models import ShopInfo, ReviewDedail, ShopId
from scrapy_djangoitem import DjangoItem

class DazongdianpingItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    pass

class ShopInfoItem(DjangoItem):
    django_model = ShopInfo

class ReviewDetailItem(DjangoItem):
    django_model = ReviewDedail

class ShopIdItem(DjangoItem):
    django_model = ShopId

还需要注意的是,在不启动django项目的时候要使用django的模块,需要手动启动,在scrapy的__init__.py里加入如下代码:

import sys
import os
import django

sys.path.append('../../../Jpider') # 具体路径
os.environ['DJANGO_SETTINGS_MODULE'] = 'Jpider.settings'
django.setup()

写爬虫之前,需要了解一下要爬的网站的url组成规则,打开www.dianping.com,打开chrom的调试模式(option+command+i),由于朋友需要美食类下的餐厅信息,

self.start_urls = [
            'http://www.dianping.com/search/category/2/10/g110', # 北京火锅
            'http://www.dianping.com/search/category/2/10/g107', # 北京台湾菜
            'http://www.dianping.com/search/category/2/10/g112', # 北京小吃快餐
            'http://www.dianping.com/search/category/2/10/g250', # 北京创意菜
            'http://www.dianping.com/search/category/2/10/g116', # 北京西餐
            'http://www.dianping.com/search/category/2/10/g113', # 北京日本菜
            'http://www.dianping.com/search/category/2/10/g103', # 北京粤菜
            'http://www.dianping.com/search/category/2/10/g115', # 北京东南亚菜
            'http://www.dianping.com/search/category/2/10/g102', # 北京川菜
            'http://www.dianping.com/search/category/1/10/g113', # 上海日本菜???
            'http://www.dianping.com/search/category/1/10/g110', # 上海火锅
            'http://www.dianping.com/search/category/1/10/g107', # 上海台湾菜
            'http://www.dianping.com/search/category/1/10/g103', # 上海粤菜
            'http://www.dianping.com/search/category/1/10/g102', # 上海川菜
            'http://www.dianping.com/search/category/1/10/g112', # 上海小吃快餐
            'http://www.dianping.com/search/category/1/10/g115', # 上海东南亚菜

            'http://www.dianping.com/search/category/1/10/g116',  # 上海西餐

        ]

那就上海火锅http://www.dianping.com/search/category/1/10/g110 为例
_2017_04_26_10_46_51
在调试模式下,可以发现,当前页的餐厅信息是在<div calss='shop-list ...'>的li标签下,而餐厅的url包含在a标签的href下
_2017_04_26_10_48_23
一个
所以就可以先取出li标签,再取出a下的href,处理函数如下:

   def parse_pg(self, response):
        print(response.url)
        shops = response.css('div.content div.shop-list li')
        for s in shops:
            shop_id_item = ShopIdItem()
            short_url = s.css('div.tit a::attr(href)').extract()[0].strip()
            shop_url = self.root_url+short_url
            shop_id = short_url.split('/')[2]

            shop_id_item['shop_id'] = shop_id

            shop_id_item.save()

            self.count += 1
            yield scrapy.Request(shop_url, callback=self.parse_detail)
        self.logger.error('total count %d' % self.count)

当然需要处理分页问题
_2017_04_26_10_55_04
同理,通过scrapy的css+xpath很容易定位

    def parse(self, response):
        yield scrapy.Request(response.url, callback=self.parse_pg)
        pages = int(response.css('div.page a::text').extract()[-2])
        for pg in range(1, pages+1):
            print(response.url + 'p' + str(pg))
            yield scrapy.Request(response.url + 'p' + str(pg), callback=self.parse_pg)

现在就可以提取餐厅的具体信息了

    def parse_detail(self, response):
        print(response.url)

        shop_id = response.url[response.url.rindex('/')+1:]


        basic_info = response.css('div.basic-info')

        closed_class = basic_info.css('p.shop-closed').extract()

        if closed_class != []:  # 未营业
            shop_info = ShopInfoItem()
            shop_info['shop_id'] = shop_id
            shop_name = basic_info.css('h1.shop-name::text').extract()[0].strip()
            shop_info['shop_name'] = shop_name
            shop_info.save()
            self.logger.error('%s 未营业' % response.url)
            return None
        try:
            rank_star = basic_info.css('div.brief-info span.mid-rank-stars::attr(title)').extract()[0].strip()
            shop_name = basic_info.css('h1.shop-name::text').extract()[0].strip()
            review_count = basic_info.css('div.brief-info').xpath('./span/text()').extract()[0].strip()
            avg_price = basic_info.css('div.brief-info').xpath('./span[@id="avgPriceTitle"]/text()').extract()[0].strip()
            comment_score = basic_info.css('div.brief-info').xpath('./span[@id="comment_score"]').css('span.item::text').extract()
            address = basic_info.css('div.address').xpath('./span[@itemprop="street-address"]/text()').extract()[0].strip()
            info_indent = basic_info.css('div.other p.info')

            print(basic_info.css('div.promosearch-wrapper').extract())
            tuan = basic_info.css('div.promosearch-wrapper p.expand-info').css('span.info-name::text').extract()

            print('-'*10+str(tuan)+'-'*10)

            breadcrumb = response.css('div.breadcrumb')
            bars = breadcrumb.css('a::text').extract()
            if len(bars) >= 3:

                place = bars[1].strip()
                classify = bars[2].strip()
            else:
                place = ''
                classify = ''


            open_time = ''
            for ind in info_indent:
                # print(ind.css('span.info-name::text').extract())
                if ind.css('span.info-name::text').extract()[0].strip().startswith('营业时间'):
                    open_time = ind.css('span.item::text').extract()[0].strip()
                    break

            # print(shop_id+'\t'+shop_name+'\t'+review_count+'\t'+avg_price+'\t'+str(comment_score)+'\t'+str(address)+'\t'+open_time)
            shop_info = ShopInfoItem()
            shop_info['shop_id'] = shop_id
            shop_info['shop_name'] = shop_name
            shop_info['review_count'] = review_count
            shop_info['avg_price'] = avg_price
            shop_info['address'] = address
            shop_info['open_time'] = open_time
            shop_info['taste'] = comment_score[0]
            shop_info['env'] = comment_score[1]
            shop_info['service'] = comment_score[2]
            shop_info['rank_star'] = rank_star
            shop_info['place'] = place
            shop_info['classify'] = classify
            shop_file = open(self.save_dir + 'shop/' + str(shop_id) + '.html', 'w')
            shop_file.write(response.body.decode('utf-8'))
            shop_info.save()
            yield scrapy.Request(response.url+'/review_more_newest', callback=self.parse_review)
        except Exception:
            self.logger.error(response.url+' exception')
            self.logger.error(traceback.format_exc())

启动scrapy

scrapy crawl dazongdianping

查看数据
_2017_04_26_12_44_46

导出数据到excel


import sys
import os
import django
import django.db.models
sys.path.append('../Jpider')
os.environ['DJANGO_SETTINGS_MODULE'] = 'Jpider.settings'
django.setup()

from spiders.models import ShopInfo, ReviewDedail, ShopId

import xlwt
category_dict = {'g110':'火锅', 'g107':'台湾菜', 'g112':'小吃快餐', 'g250': '创意菜',
                 'g116': '西餐', 'g113': '日本菜', 'g103': '粤菜', 'g115': '东南亚菜', 'g102': '川菜'}

rank_star_dict = {
    '五星商户': 5,
    '准五星商户':4.5,
    '四星商户': 4,
    '准四星商户': 3.5,
    '三星商户': 3,
    '准三星商户': 2.5,
    '二星商户': 2,
    '准二星商户': 1.5,
    '一星商户': 1,
    '准一星商户': 0.5,
    '该商户暂无星级': 0,
    '': '无'
}


workbook = xlwt.Workbook()
sheet = workbook.add_sheet('dazongdianping',cell_overwrite_ok=True)
title = ['餐厅id','城市', '餐厅名称', '餐厅地点', '餐厅地址', '餐厅类别', '人均价格', '是否参加营销活动', '营业时间', '点评数量',
         '总体评分', '口味评分', '环境评分', '服务评分', '五星', '四星', '三星', '二星', '一星', '第一条评论时间']
for i in range(len(title)):
    sheet.write(0, i, title[i] )

shops = ShopInfo.objects.all()

result_dic = {}

for j in range(1, len(shops)+1):
    shop = shops[j-1]
    info_list = []
    info_list.append(str(shop.shop_id)) # id
    print(shop.shop_id)
    try:
        url = ShopId.objects.get(pk=shop.shop_id).from_url
    except ShopId.DoesNotExist:
        continue
    if url is None:
        continue
    city_no = url.split('/')[-3]
    city = '北京' if city_no == '2' else '上海'
    info_list.append(city)
    category = category_dict[url.split('/')[-1][:4]]
    info_list.append(shop.shop_name)
    info_list.append(shop.place if shop.place is not None else '')
    info_list.append(shop.address if shop.address is not None else '')
    info_list.append(category)
    avg_price = shop.avg_price.split(':')[1]
    if len(avg_price) != 1:
        avg_price = avg_price[:-1]

    info_list.append(avg_price )
    features = shop.feature2.split(';')
    print(features)
    f_l = []
    for f in features:
        if f == 'huo':
            print('活动')
            f_l.append('活动')
        elif f == 'ka':
            print('会员卡')
            f_l.append('会员卡')
        else:
            f_l.append(f)
    info_list.append(';'.join(f_l))
    f_l.clear()
    info_list.append(shop.open_time.replace('\t', ' ').replace('\r','').replace('\n', ';') if shop.open_time is not None else '')
    info_list.append(shop.review_count[:-3])
    info_list.append(rank_star_dict[shop.rank_star])
    info_list.append(shop.taste.split(':')[1])
    info_list.append(shop.env.split(':')[1])
    info_list.append(shop.service.split(':')[1])

    review = ReviewDedail.objects.get(pk=shop.shop_id)
    info_list.append(review.star_5)
    info_list.append(review.star_4)
    info_list.append(review.star_3)
    info_list.append(review.star_2)
    info_list.append(review.star_1)
    info_list.append(review.first_review_time)
    for i in range(len(info_list)):
        if info_list[i] is None:
            info_list[i] = ' '
    li = result_dic.get(city+'_'+category, [])
    li.append(info_list.copy())
    result_dic[city+'_'+category] = li
    info_list.clear()

book = xlwt.Workbook()
for city_cate, infos in result_dic.items():
    sheet = book.add_sheet(city_cate)
    for i in range(len(title)):
        sheet.write(0, i, title[i])
    for i in range(1, len(infos)):
        for j in range(len(infos[i])):
            sheet.write(i, j, infos[i][j])
book.save('./all-data.xls')

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