python-爬虫实操

1、爬取豆瓣正热映电影

　xpath应用

import requests
from scrapy.selector import Selector
headers = {
    "User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
    "Referer":"https://movie.douban.com/"
}
url = "https://movie.douban.com/cinema/nowplaying/guangzhou/"

response = requests.get(url=url,headers = headers)
# print(response.text)
selector = Selector(text=response.text)
ul = selector.xpath("//div[@id='nowplaying']//ul[@class='lists']")
li_list = ul.xpath("./li")
movies = []
for ul in li_list:
    title = ul.xpath("@data-title").extract_first('')   # 标题
    data_score = ul.xpath("@data-score").extract_first('')   # 影片评分
    data_release = ul.xpath("@data-release").extract_first('')   # 播出年份
    data_duration = ul.xpath("@data-duration").extract_first('')  # 影片时长
    data_director = ul.xpath("@data-director").extract_first('')   # 影片导演
    data_actors = ul.xpath("@data-actors").extract_first('')    # 影片演员
    data_img = ul.xpath(".//img/@src").extract_first('')    # 影片宣传图片

    movie = {
        "title":title,
        "data_score":data_score,
        "data_release":data_release,
        "data_duration":data_duration,
        "data_director":data_director,
        "data_actors":data_actors,
        "data_img":data_img
    }
    movies.append(movie)

爬取豆瓣电影信息

2、爬取电影天堂-->最新电影

　https://www.dytt8.net/html/gndy/dyzz/list_23_1.html

样本：

import requests
from scrapy.selector import Selector

BASE_DOMAIN = "https://www.dytt8.net"
headers = {
    "User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
    "Referer":"https://www.dytt8.net/html/gndy/dyzz/list_23_1.html"
}
# url = "https://www.dytt8.net/html/gndy/dyzz/list_23_1.html"   # 电影天堂-最新电影url
def get_detail_url(url):
    # 获取每一页最新电影下，每个电影的url链接
    response = requests.get(url=url,headers = headers)
    text = response.text # 响应数据如果是乱码，可以尝试先encode-utf8，再decode-utf8
    # text = response.content.decode('gbk')  # 电影天堂源代码使用的是gb2312编码，需要解码成str(Unicode)
    selector = Selector(text=text)
    url_list = selector.xpath("//table[@class='tbspan']//a/@href")
    detail_url = map(lambda url:BASE_DOMAIN + url ,url_list)
    return detail_url

def spider():
    for i in range(1,8):   # 爬取前七页
        url = 'https://www.dytt8.net/html/gndy/dyzz/list_23_{}.html'.format(i)
        detail_urls = get_detail_url(url)  # 获取每一页每条电影数据对应的url链接，是个list
        for detail_url in detail_urls:  # 取出每个电影url，爬取该电影详情页的详细信息
            detail_info= detail_infos()
            


def detail_infos():
    # 爬取每条电影数据的详细信息
    pass


if __name__ == '__main__':
    spider()

View Code

3、爬取中国天气网各个城市的气温情况，并进行可视化处理

init初始化：

    def __init__(self):
        self.headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
        }
        self.url = "http://www.weather.com.cn/textFC/hb.shtml"   # 中国天气网 -->华北地区
        self.url_domain = "http://www.weather.com.cn"

        # self.province_forecast = {}  # 用于保存中国天气网所有省下面每个城市的最低气温信息
        self.province_forecast = []  # 用于保存中国天气网所有省下面每个城市的最低气温信息
        self.city_temp = {}   # 临时字典

area_url函数：获取各个地区对应的url

    def area_url(self):
        # 获取各个地区的url，及当天日期(只采集当天的天气情况)
        response = requests.get(url=self.url,headers = self.headers)
        response = response.content.decode('utf8')
        selector = Selector(text=response)
        area_urls = selector.css(".lq_contentboxTab2 li span a::attr(href)").extract()  # 每个地区的url
        area_urls_list = map(lambda url: self.url_domain + url,area_urls[:-1])
        date_now = selector.css(".selected::text").extract_first('')  # 当天日期
        match_re = re.match(".*?(\d+).*?(\d+).*",date_now)
        if match_re:
            date_now_list = [match_re.group(1),match_re.group(2)]
            date_now = "-".join(date_now_list)
        return date_now,area_urls_list

get_detail_forecast函数：获取每个城市名称及最低气温

    def get_detail_forecast(self):
        # 获取省-->城市的详细数据
        self.date_now , area_urls = self.area_url()  # 拿到当天日期、每个地区的url
        for area_url in area_urls:
            response = requests.get(url=area_url,headers=self.headers)
            text = response.content.decode("utf8")
            selector = Selector(text=text)
            div = selector.css(".hanml > div:not(style)")[0]   # 拿到当天div下的table数据，其他div/table是隐藏的，不需要用到
            tables = div.css("table")   # 拿到当天某地区下的所有省-城市的天气预报信息
            for table in tables:  # 当前页面-->每个省的数据
                trs = table.css("tr") [2:]  # 拿到该省下面-->所有城市的天气预报
                # province_name = trs[0].css("td.rowsPan a::text").extract_first('')   # 省名称
                for tr in trs:  # 获取该省每个城市的最低气温数据
                    try:
                        td1 = tr.css("td.rowsPan")
                    except:
                        td1 = None
                    if td1:  # 如果存在，则表示是table中的第一行，第一行数据比其他行都多一列的数据(即省名称)，需要区别操作
                        tds = tr.css("td")[1:]
                    else:
                        tds = tr.css("td")
                    city =tds[0].css("a::text").extract_first("")  # 城市名称
                    min_temperature =tds[-2].css("::text").extract_first("")   # 最低气温
                    self.city_temp["city"] = city
                    self.city_temp["min_temperature"] = int(min_temperature)
                    self.province_forecast.append(self.city_temp)
                    self.city_temp = {}  # 清空临时字典

        self.data_visualization()

data_visualization函数：数据可视化处理，将获取到的所有城市根据温度进行排序，对前20条数据进行可视化处理
需安装：

pip install pyecharts   # 可视化包
pip install pyecharts_snapshot

    def data_visualization(self):
        # 数据可视化处理
        self.province_forecast.sort(key=lambda data: data['min_temperature'])
        data_list = self.province_forecast[0:20]  # 仅对前20条数据进行排行显示
        cities = list(map(lambda city:city['city'],data_list))
        temps = list(map(lambda temp:temp['min_temperature'],data_list))
        chart = Bar("中国天气最低气温排行榜--前20名")   # 可视化 -柱状图
        chart.add('',cities,temps)  # cities：横坐标 -->城市 ； temps：纵坐标 -->最低温度
        chart.render("temperature.html")  # 渲染成HTML页面

完整代码：

import requests
from scrapy.selector import Selector
import re
from pyecharts import Bar


class tq_spider(object):
    def __init__(self):
        self.headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
        }
        self.url = "http://www.weather.com.cn/textFC/hb.shtml"   # 中国天气网 -->华北地区
        self.url_domain = "http://www.weather.com.cn"

        # self.province_forecast = {}  # 用于保存中国天气网所有省下面每个城市的最低气温信息
        self.province_forecast = []  # 用于保存中国天气网所有省下面每个城市的最低气温信息
        self.city_temp = {}   # 临时字典

    def area_url(self):
        # 获取各个地区的url，及当天日期(只采集当天的天气情况)
        response = requests.get(url=self.url,headers = self.headers)
        response = response.content.decode('utf8')
        selector = Selector(text=response)
        area_urls = selector.css(".lq_contentboxTab2 li span a::attr(href)").extract()  # 每个地区的url
        area_urls_list = map(lambda url: self.url_domain + url,area_urls[:-1])
        date_now = selector.css(".selected::text").extract_first('')  # 当天日期
        match_re = re.match(".*?(\d+).*?(\d+).*",date_now)
        if match_re:
            date_now_list = [match_re.group(1),match_re.group(2)]
            date_now = "-".join(date_now_list)
        return date_now,area_urls_list

    def get_detail_forecast(self):
        # 获取省-->城市的详细数据
        self.date_now , area_urls = self.area_url()  # 拿到当天日期、每个地区的url
        for area_url in area_urls:
            response = requests.get(url=area_url,headers=self.headers)
            text = response.content.decode("utf8")
            selector = Selector(text=text)
            div = selector.css(".hanml > div:not(style)")[0]   # 拿到当天div下的table数据，其他div/table是隐藏的，不需要用到
            tables = div.css("table")   # 拿到当天某地区下的所有省-城市的天气预报信息
            for table in tables:  # 当前页面-->每个省的数据
                trs = table.css("tr") [2:]  # 拿到该省下面-->所有城市的天气预报
                # province_name = trs[0].css("td.rowsPan a::text").extract_first('')   # 省名称
                for tr in trs:  # 获取该省每个城市的最低气温数据
                    try:
                        td1 = tr.css("td.rowsPan")
                    except:
                        td1 = None
                    if td1:  # 如果存在，则表示是table中的第一行，第一行数据比其他行都多一列的数据(即省名称)，需要区别操作
                        tds = tr.css("td")[1:]
                    else:
                        tds = tr.css("td")
                    city =tds[0].css("a::text").extract_first("")  # 城市名称
                    min_temperature =tds[-2].css("::text").extract_first("")   # 最低气温
                    self.city_temp["city"] = city
                    self.city_temp["min_temperature"] = int(min_temperature)
                    self.province_forecast.append(self.city_temp)
                    self.city_temp = {}  # 清空临时字典

        self.data_visualization()
        # self.province_forecast.sort(key = lambda data:data['min_temperature'])
        # print("province_forecast:",self.province_forecast)

    def data_visualization(self):
        # 数据可视化处理
        self.province_forecast.sort(key=lambda data: data['min_temperature'])
        data_list = self.province_forecast[0:20]  # 仅对前20条数据进行排行显示
        cities = list(map(lambda city:city['city'],data_list))
        temps = list(map(lambda temp:temp['min_temperature'],data_list))
        chart = Bar("中国天气最低气温排行榜--前20名")   # 可视化 -柱状图
        chart.add('',cities,temps)  # cities：横坐标 -->城市 ； temps：纵坐标 -->最低温度
        chart.render("temperature.html")  # 渲染成HTML页面


if __name__ == '__main__':
    sp = tq_spider()
    sp.get_detail_forecast()

View Code

4、多线程爬虫应用

1）多线程-->condition版生产者-消费者模型

# Condition版生产者-消费者模型示例

import threading
import random
import time

gCondition = threading.Condition()
count = 10  # 生产者只可以生产十次
all_money = 1000  # 基础金钱1000元


class producers(threading.Thread):
    # 生产者模式
    def run(self):
        global count
        global all_money
        while True:
            gCondition.acquire()  # 处理数据之前先上锁
            if count > 0:  # 为true表示生产还没到十次，可以继续生产
                money = random.randint(100 , 1000)  # 随机生产100-1000元
                all_money += money  # 总金额数
                count -=1  # 允许生产次数-1
                print('生产者%s生产了%d元，目前剩余%d元'%(threading.current_thread(),money,all_money))
            else:  # 已生产十次
                gCondition.release()  # 解锁
                break  # 生产解锁，跳出循环
            gCondition.notify_all()   # 通知所有等待的消费者，生产已经完成，可以接着消费了
            gCondition.release()  # 解锁
            time.sleep(0.5)



class comsumer(threading.Thread):
    # 消费者
    def run(self):
        global all_money
        global count
        while True:
            gCondition.acquire()  # 处理数据前，先上锁
            money = random.randint(100.1000)  # 随机消费100-1000元
            while money > all_money:  # 消费的钱如果大于剩余的钱，则一直在这循环，直到剩余的钱大于消费的钱
                if count == 0:
                    gCondition.release()  # 生产者生产次数已达上限，释放锁
                    return   # 结束函数
                print("消费者%s需要消费%d元，目前剩余金钱%d元，不足消费"%(threading.current_thread(),money,all_money))
                gCondition.wait()  # 进入等待（阻塞线程）
            all_money -= money  # 剩余金额大于消费金额时
            print("消费者%s需要消费%d元，目前剩余金钱%d元" % (threading.current_thread(), money, all_money))
            gCondition.release()  # 解锁
            time.sleep(0.5)

if __name__ == '__main__':
    for i in range(3):
        th = comsumer(name='线程%d'%i)
        th.start()

    for i in range(5):
        th = producers(name="线程%d"%i)
        th.start()

View Code

运行结果：

G:\Envs\spider_pro\Scripts\python.exe G:/Python/spider_pro/multi_thread/multi_thread.py
消费者<comsumer(线程0, started 16992)>需要消费630元，剩余金钱370元
消费者<comsumer(线程1, started 15748)>需要消费403元，目前剩余金钱370元，不足消费
消费者<comsumer(线程2, started 16564)>需要消费479元，目前剩余金钱370元，不足消费
生产者<producers(线程0, started 14904)>生产了994元，目前剩余1364元
消费者<comsumer(线程1, started 15748)>需要消费403元，剩余金钱961元
消费者<comsumer(线程2, started 16564)>需要消费479元，剩余金钱482元
生产者<producers(线程1, started 17332)>生产了669元，目前剩余1151元
生产者<producers(线程2, started 17088)>生产了837元，目前剩余1988元
生产者<producers(线程3, started 17240)>生产了314元，目前剩余2302元
生产者<producers(线程4, started 16840)>生产了873元，目前剩余3175元
消费者<comsumer(线程0, started 16992)>需要消费223元，剩余金钱2952元
生产者<producers(线程0, started 14904)>生产了550元，目前剩余3502元
消费者<comsumer(线程1, started 15748)>需要消费151元，剩余金钱3351元
消费者<comsumer(线程2, started 16564)>需要消费127元，剩余金钱3224元
生产者<producers(线程4, started 16840)>生产了107元，目前剩余3331元
生产者<producers(线程3, started 17240)>生产了348元，目前剩余3679元
生产者<producers(线程2, started 17088)>生产了380元，目前剩余4059元
生产者<producers(线程1, started 17332)>生产了725元，目前剩余4784元
消费者<comsumer(线程0, started 16992)>需要消费940元，剩余金钱3844元
消费者<comsumer(线程1, started 15748)>需要消费769元，剩余金钱3075元
消费者<comsumer(线程2, started 16564)>需要消费492元，剩余金钱2583元
消费者<comsumer(线程0, started 16992)>需要消费229元，剩余金钱2354元
消费者<comsumer(线程1, started 15748)>需要消费160元，剩余金钱2194元
消费者<comsumer(线程2, started 16564)>需要消费445元，剩余金钱1749元
消费者<comsumer(线程0, started 16992)>需要消费341元，剩余金钱1408元
消费者<comsumer(线程1, started 15748)>需要消费238元，剩余金钱1170元
消费者<comsumer(线程2, started 16564)>需要消费289元，剩余金钱881元
消费者<comsumer(线程0, started 16992)>需要消费835元，剩余金钱46元

View Code

2）爬取斗图网斗图图片

http://www.doutula.com/photo/list/

2.1）同步式爬取数据

import requests
import re
import os
from scrapy.selector import Selector
from urllib import request


class doutu_spider(object):
    def __init__(self):
        self.headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
        }
        # self.url = "http://www.doutula.com/photo/list/?page=1"  # 斗图网


    def get_pages(self):
        # 拿到页码数
        url = "http://www.doutula.com/photo/list/?page=1"
        response = requests.get(url=url,headers=self.headers)
        text = response.text
        selector = Selector(text=text)
        page_lis = selector.css(".pagination li")[-2]
        last_page = page_lis.css('a::text').extract_first("")  # 最后一页
        last_page = int(last_page)
        return last_page

    def get_img_spider(self):
        # 爬取所有页面的斗图图片，保存到本地
        # pages = self.get_pages()
        # for i in range(1,pages): # 下载所有页码的斗图
        for i in range(1,3): # 下载1-2页的斗图图片
            url = 'http://www.doutula.com/photo/list/?page={}'.format(i)
            response = requests.get(url=url,headers=self.headers)
            text = response.text
            selector = Selector(text=text)
            img_list = selector.css(".page-content a img:not(.gif)")
            for img in img_list:
                # img1 = img.css("::attr(src)").extract_first('')
                img_url = img.css("::attr(data-original)").extract_first('')
                alt = img.css("::attr(alt)").extract_first('')
                alt = re.sub(r"[\?？\.。,!！]","",alt)  # 去掉标题符合
                suffix = os.path.splitext(img_url)[1]  # 获取img链接的后缀
                filename = alt + suffix   # 文件名
                request.urlretrieve(img_url,'images/'+filename)  # 使用urllib.request自带保存图片
        # pass



if __name__ == '__main__':
    dt_spider = doutu_spider()
    # dt_spider.get_pages()
    dt_spider.get_img_spider()

View Code

2.2）使用多线程实现异步式爬取数据

生产者：

爬取所有图片url，压入队列中，再交由消费者进行下载

class producers(threading.Thread):
    # 生产者，只负责爬取图片url，再交由消费者下载图片
    def __init__(self,page_queue,img_url_queue,*args,**kwargs):
        super(producers,self).__init__(*args,**kwargs)
        self.page_queue = page_queue
        self.img_url_queue = img_url_queue
        self.headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
                }
        self.put_pages_to_queue()  # 爬取页码数，并put进page_queue队列中

    def put_pages_to_queue(self):
        # 拿到页码数,将要爬取的页面数都存入page_queue中
        url = "http://www.doutula.com/photo/list/?page=1"
        response = requests.get(url=url,headers=self.headers)
        text = response.text
        selector = Selector(text=text)
        page_lis = selector.css(".pagination li")[-2]
        last_page = page_lis.css('a::text').extract_first("")  # 最后一页
        last_page = int(last_page)
        for i in range(1,3):  # range(1,last_page) ，测试时只爬取两页
            url = "http://www.doutula.com/photo/list/?page={}".format(i)
            self.page_queue.put(url)

    def run(self):   # 主函数
        while True:
            if self.page_queue.empty(): # 如果页码为空，表示数据爬取完，跳出循环
                break
            url = self.page_queue.get()  # 获取每一页的url
            self.get_img_spider(url)   # 爬取该url对应页面下的所有图片链接，并put进img_url_queue队列中，让消费者下载图片

    def get_img_spider(self,url):
        # 爬取当前页面的斗图图片，put到img_url_queue队列中，再由消费者下载
        response = requests.get(url=url,headers=self.headers)
        text = response.text
        selector = Selector(text=text)
        img_list = selector.css(".page-content a img:not(.gif)")
        for img in img_list:
            # img1 = img.css("::attr(src)").extract_first('')
            img_url = img.css("::attr(data-original)").extract_first('')
            alt = img.css("::attr(alt)").extract_first('')
            alt = re.sub(r"[\?？\.。,!！\*]","",alt)  # 去掉标题符合
            suffix = os.path.splitext(img_url)[1]  # 获取img链接的后缀
            filename = alt + suffix   # 文件名
            self.img_url_queue.put((img_url,filename),) # put进img_url_queue中
            # request.urlretrieve(img_url,'images/'+filename)  # 使用urllib.request自带保存图片

生产者

消费者：

将生产者压入队列中的所有url链接进行下载(图片下载)

class comsumers(threading.Thread):
    # 消费者，负责下载生产者提供的图片url链接
    def __init__(self,page_queue,img_url_queue,*args,**kwargs):
        super(comsumers,self).__init__(*args,**kwargs)
        self.page_queue = page_queue
        self.img_url_queue = img_url_queue

    def run(self):   # 主函数
        while True:
            if self.img_url_queue.empty() and self.page_queue.empty():  # 都为空，表示图片链接全都get出来了
                break
            img_url,filename = self.img_url_queue.get()
            print(filename+"  下载成功！！！")
            request.urlretrieve(img_url, 'images/' + filename)  # 使用urllib.request自带保存图片

消费者

完整代码：

import requests
import re
import os
from scrapy.selector import Selector
from urllib import request
import threading
from queue import Queue

class producers(threading.Thread):
    # 生产者，只负责爬取图片url，再交由消费者下载图片
    def __init__(self,page_queue,img_url_queue,*args,**kwargs):
        super(producers,self).__init__(*args,**kwargs)
        self.page_queue = page_queue
        self.img_url_queue = img_url_queue
        self.headers = {
            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
                }
        self.put_pages_to_queue()  # 爬取页码数，并put进page_queue队列中

    def put_pages_to_queue(self):
        # 拿到页码数,将要爬取的页面数都存入page_queue中
        url = "http://www.doutula.com/photo/list/?page=1"
        response = requests.get(url=url,headers=self.headers)
        text = response.text
        selector = Selector(text=text)
        page_lis = selector.css(".pagination li")[-2]
        last_page = page_lis.css('a::text').extract_first("")  # 最后一页
        last_page = int(last_page)
        for i in range(1,3):  # range(1,last_page) ，测试时只爬取两页
            url = "http://www.doutula.com/photo/list/?page={}".format(i)
            self.page_queue.put(url)

    def run(self):   # 主函数
        while True:
            if self.page_queue.empty(): # 如果页码为空，表示数据爬取完，跳出循环
                break
            url = self.page_queue.get()  # 获取每一页的url
            self.get_img_spider(url)   # 爬取该url对应页面下的所有图片链接，并put进img_url_queue队列中，让消费者下载图片

    def get_img_spider(self,url):
        # 爬取当前页面的斗图图片，put到img_url_queue队列中，再由消费者下载
        response = requests.get(url=url,headers=self.headers)
        text = response.text
        selector = Selector(text=text)
        img_list = selector.css(".page-content a img:not(.gif)")
        for img in img_list:
            # img1 = img.css("::attr(src)").extract_first('')
            img_url = img.css("::attr(data-original)").extract_first('')
            alt = img.css("::attr(alt)").extract_first('')
            alt = re.sub(r"[\?？\.。,!！\*]","",alt)  # 去掉标题符合
            suffix = os.path.splitext(img_url)[1]  # 获取img链接的后缀
            filename = alt + suffix   # 文件名
            self.img_url_queue.put((img_url,filename),) # put进img_url_queue中
            # request.urlretrieve(img_url,'images/'+filename)  # 使用urllib.request自带保存图片


class comsumers(threading.Thread):
    # 消费者，负责下载生产者提供的图片url链接
    def __init__(self,page_queue,img_url_queue,*args,**kwargs):
        super(comsumers,self).__init__(*args,**kwargs)
        self.page_queue = page_queue
        self.img_url_queue = img_url_queue

    def run(self):   # 主函数
        while True:
            if self.img_url_queue.empty() and self.page_queue.empty():  # 都为空，表示图片链接全都get出来了
                break
            img_url,filename = self.img_url_queue.get()
            print(filename+"  下载成功！！！")
            request.urlretrieve(img_url, 'images/' + filename)  # 使用urllib.request自带保存图片


if __name__ == '__main__':
    page_queue = Queue(5)  # 页数队列
    img_url_queue = Queue(250)  # 图片数队列

    for i in range(2):  # 生产者
        t = producers(page_queue,img_url_queue)
        t.start()

    for i in range(4):  #消费者
        t = comsumers(page_queue,img_url_queue)
        t.start()

多线程爬取斗图网

5、动态网页爬取

1）ajax请求页面爬取

使用浏览器打开今日头条，搜索关键词‘街拍’，分析ajax请求，下面是提取到的ajax的主要信息

https://www.toutiao.com/search_content/?offset=80&format=json&keyword=%E8%A1%97%E6%8B%8D&autoload=true&count=20&cur_tab=1&from=search_tab

#？前面是ajax url链接，后面是携带参数
# offset：偏移量，经过测试，每次偏移20，依次叠加(20、40、60、80)
# format：不变的参数，表示json格式
# keyword：搜索的关键词，被加密处理
# count：显示数据数量
# 其他不管，都是不变的参数，照搬就可以了

将响应数据复制到json解析网站：https://www.json.cn/，

数据如下：

发现目标数据都在data列表当中，打开一个看具体数据：

　可看到，视频url存于share_url字段中，图片url存于large_img_url字段中

我们将这两个数据爬取下来，并以title来命名，下面就是代码的实现了：

import requests
from urllib import request
import re



def get_jsondata(offset):
    params = {
        'offset':offset,
        'format':'json',
        'keyword':'街拍',
        'autoload':'true',
        'count':'20',
        'cur_tab':'1',
        'from':'search_tab',
    }
    url = 'https://www.toutiao.com/search_content/'
    try:
        response = requests.get(url=url,params=params)
        if response.status_code == 200:
            r = response.json()
            return response.json()
    except:
        return None

def data_save(jsondata):
    if jsondata:
        try:
            datas = jsondata.get('data')
            for item in datas:
                title = item.get('title')
                # detail_imgs = item.get('share_url')
                img = item.get('large_image_url')
                titel = re.sub(r"[\?？\.。,!！\*]", "", title)  # 去掉标题符合
                filename = title + '.jpg'  # 图片标题
                request.urlretrieve(img,'images/'+ filename)  # 使用urllib.request保存图片

                pass

        except:
            print("未获取到data数据")


if __name__ == '__main__':
    offset_list = [20 * x for x in range(1,6)]
    for offset in offset_list:
        json_data = get_jsondata(offset)
        data_save(json_data)

爬取头条信息ajax动态网页

2）爬取拉钩网职位信息 -爬取ajax动态网页

首先，打开拉钩网，搜索关键词：python

在搜索返回页面，随便复制一个招聘信息的某些字段，查看‘源代码’，ctrl + f，查看网页的字段信息是否在源代码中：

在源代码中，找不到我们要的信息，说明该相关信息是通过js手段(ajax操作)初始化到页面。由此可见，搜索关键词返回的招聘信息时通过ajax异步实现数据显示的。点开‘审查元素’，转到‘network’，重新刷新页面，找与ajax相关的url操作：

找到如下链接（可能性最高）：

复制response的返回数据，到json解析网站中进行解析：

解析出来的数据，从中可以找到我们的目标信息，经过对比，确定是我们找的目标url。

分析reque.url，可看出该请求时post请求，

请求信息分析出来了，下面就该是代码实现职位信息爬取了。

测试：爬取每一页职位招聘数据，如果总提示‘操作过于频繁’，则需考虑采用随机更换ip、随机ip代理的方式来获取数据

import requests
import time
# from scrapy.selector import Selector

headers = {    # 拉钩网反爬虫设置比较多，headers尽量将必要的请求头信息添加上
    "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
    'Origin':'https://www.lagou.com',
    'Referer':'https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput=',
    'X-Requested-With':'XMLHttpRequest',
    'Cookie':'_ga=GA1.2.1625638612.1534692265; user_trace_token=20180819232428-f68a3fba-a3c3-11e8-a9f8-5254005c3644; LGUID=20180819232428-f68a44da-a3c3-11e8-a9f8-5254005c3644; index_location_city=%E5%B9%BF%E5%B7%9E; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%221661640a8471fd-01cbefbaf4543e-454c092b-1044480-1661640a848282%22%2C%22%24device_id%22%3A%221661640a8471fd-01cbefbaf4543e-454c092b-1044480-1661640a848282%22%2C%22props%22%3A%7B%22%24latest_utm_source%22%3A%22m_cf_cpc_360_pc%22%7D%7D; LG_LOGIN_USER_ID=764f1bc5eac9fd076d2ac9e0770c9ac7a751ae95c349ee5fa84dba94e086bb1d; showExpriedIndex=1; showExpriedCompanyHome=1; showExpriedMyPublish=1; hasDeliver=52; WEBTJ-ID=20181123152123-1673f71d4f83eb-03fafed6615d63-454c092b-1044480-1673f71d4f93e8; Hm_lvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1542593242,1542769087,1542786145,1542957684; LGSID=20181123152129-6594dfd6-eef0-11e8-8afa-5254005c3644; _gid=GA1.2.2129727196.1542957684; _putrc=BF038CDE5DE90B9E123F89F2B170EADC; JSESSIONID=ABAAABAAADEAAFIAF7430F8D73A4AF84848F60209F8B643; login=true; unick=%E9%99%88%E7%82%AF%E5%8D%97; gate_login_token=7b76aaa623c3af207fc90b3b4f8b004bdbd5d3013042444d960e8b29d53cdebf; TG-TRACK-CODE=search_code; _gat=1; Hm_lpvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1542959848; LGRID=20181123155733-6f29ee83-eef5-11e8-b6a1-525400f775ce; SEARCH_ID=dfcfbf85dcb2421d90f507e0c74d60f9'
}
url = 'https://www.lagou.com/jobs/positionAjax.json?city=%E5%B9%BF%E5%B7%9E&needAddtionalResult=false'   # ajax的请求url
data = {
    'first':'false',
    'pn':'1',
    'kd':'python',
}
def get_json_data():
    for i in range(1,12):
        data['pn'] = i
        response = requests.post(url=url,headers=headers,data=data)
        print(response.json()) # 返回的是json数据，通过json()可将json数据转为字典类型
        time.sleep(1)

if __name__ == '__main__':
    get_json_data()

获取每一页招聘信息的json数据

完整代码：

import requests
import time
from scrapy.selector import Selector
from w3lib.html import remove_tags   # 去除HTML标签
# 进入拉钩网，搜索python关键词，爬取python招聘相关的所有信息(假设爬取8页，每页有15条相关招聘信息)，拉勾网招聘信息是通过ajax动态加载，爬取不到完整的每条招聘信息的url，
# 需要我们手动构造每条招聘信息的url，再进入该url中，爬取详细信息
headers = {    # 拉钩网反爬虫设置比较多，headers尽量将必要的请求头信息添加上
    "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
    'Origin':'https://www.lagou.com',
    'Referer':'https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput=',
    'X-Requested-With':'XMLHttpRequest',
    'Cookie':'_ga=GA1.2.1625638612.1534692265; user_trace_token=20180819232428-f68a3fba-a3c3-11e8-a9f8-5254005c3644; LGUID=20180819232428-f68a44da-a3c3-11e8-a9f8-5254005c3644; index_location_city=%E5%B9%BF%E5%B7%9E; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%221661640a8471fd-01cbefbaf4543e-454c092b-1044480-1661640a848282%22%2C%22%24device_id%22%3A%221661640a8471fd-01cbefbaf4543e-454c092b-1044480-1661640a848282%22%2C%22props%22%3A%7B%22%24latest_utm_source%22%3A%22m_cf_cpc_360_pc%22%7D%7D; LG_LOGIN_USER_ID=764f1bc5eac9fd076d2ac9e0770c9ac7a751ae95c349ee5fa84dba94e086bb1d; showExpriedIndex=1; showExpriedCompanyHome=1; showExpriedMyPublish=1; hasDeliver=52; WEBTJ-ID=20181123152123-1673f71d4f83eb-03fafed6615d63-454c092b-1044480-1673f71d4f93e8; Hm_lvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1542593242,1542769087,1542786145,1542957684; LGSID=20181123152129-6594dfd6-eef0-11e8-8afa-5254005c3644; _gid=GA1.2.2129727196.1542957684; _putrc=BF038CDE5DE90B9E123F89F2B170EADC; JSESSIONID=ABAAABAAADEAAFIAF7430F8D73A4AF84848F60209F8B643; login=true; unick=%E9%99%88%E7%82%AF%E5%8D%97; gate_login_token=7b76aaa623c3af207fc90b3b4f8b004bdbd5d3013042444d960e8b29d53cdebf; TG-TRACK-CODE=search_code; _gat=1; Hm_lpvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1542959848; LGRID=20181123155733-6f29ee83-eef5-11e8-b6a1-525400f775ce; SEARCH_ID=dfcfbf85dcb2421d90f507e0c74d60f9'
}
url = 'https://www.lagou.com/jobs/positionAjax.json?city=%E5%B9%BF%E5%B7%9E&needAddtionalResult=false'   # ajax请求url
data = {
    'first':'false',
    'pn':'1',
    'kd':'python',
}
def get_json_data():
    for i in range(1,9):
        data['pn'] = i
        try:
            response = requests.post(url=url,headers=headers,data=data)
            # print(response.json()) # 返回的是json数据，通过json()可将json数据转为字典类型
            data_dict = response.json()
            positions = data_dict['content']['positionResult']['result']
            for position in positions:
                position_id = position['positionId']   # 招聘职位某id
                position_url = 'https://www.lagou.com/jobs/{}.html'.format(position_id)  # 构造每条招聘信息的完整url
                get_detail_info(position_url)   # 爬取每条招聘信息的详细数据
                pass

            time.sleep(1)

        except Exception as e:
            print("数据异常：",e)

def get_detail_info(url):
    response = requests.get(url=url ,headers=headers)
    selector = Selector(text=response.text)
    job_name = selector.css(".job-name::attr(title)").extract_first('')
    job_desc = selector.css(".job_bt div").extract()[0]
    job_desc = remove_tags(job_desc).replace('\n','').strip()   # 去除标签、空格、回车符合等
    pass


if __name__ == '__main__':
    get_json_data()

爬取拉钩网招聘信息

实际进行一次性爬取时，由于拉勾网的反爬设置，爬取失败的可能性比较大：

这时我们就该采取随机user-agent或多ip随机代理的方式去爬取数据了：

①、随机user-agent的方式：

　首先，需要安装：fake-useragent

pip install fake-useragent   # fake-useragent维护了很多user-agent版本，可以帮我们实现随机user-agent的替换

　随机user-agent代码：

from fake_useragent import UserAgent

    def get_user_agent(self):
        # 获取随机的user-agent
        ua = UserAgent()
        random_user_agent = ua.random  # 获取随机user-agent
        return random_user_agent

②、随机ip代理的方式

import random

    def get_random_ip(self):
        # 随机获取ip代理
        proxy = [
            {
                # 'http': 'http://61.135.217.7:80',
                'https': 'https://219.234.5.128:3128',
            },
            {
                # 'http': 'http://118.114.77.47:8080',
                'https': '101.236.57.99:8866',
            },
        ]
        random_proxy = random.choice(proxy)
        return random_proxy

完整代码：

import requests
import time
from scrapy.selector import Selector
from w3lib.html import remove_tags   # 去除HTML标签
from fake_useragent import UserAgent
import random
# 进入拉钩网，搜索python关键词，爬取python招聘相关的所有信息(假设爬取8页，每页有15条相关招聘信息)，拉勾网招聘信息是通过ajax动态加载，爬取不到完整的每条招聘信息的url，
# 需要我们手动构造每条招聘信息的url，再进入该url中，爬取详细信息

class lagou_spider(object):
    def __init__(self):
        self.headers = {    # 拉钩网反爬虫设置比较多，headers尽量将必要的请求头信息添加上
            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36",
            'Origin':'https://www.lagou.com',
            'Referer':'https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput=',
            'X-Requested-With':'XMLHttpRequest',
            'Cookie':'_ga=GA1.2.1625638612.1534692265; user_trace_token=20180819232428-f68a3fba-a3c3-11e8-a9f8-5254005c3644; LGUID=20180819232428-f68a44da-a3c3-11e8-a9f8-5254005c3644; index_location_city=%E5%B9%BF%E5%B7%9E; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%221661640a8471fd-01cbefbaf4543e-454c092b-1044480-1661640a848282%22%2C%22%24device_id%22%3A%221661640a8471fd-01cbefbaf4543e-454c092b-1044480-1661640a848282%22%2C%22props%22%3A%7B%22%24latest_utm_source%22%3A%22m_cf_cpc_360_pc%22%7D%7D; LG_LOGIN_USER_ID=764f1bc5eac9fd076d2ac9e0770c9ac7a751ae95c349ee5fa84dba94e086bb1d; showExpriedIndex=1; showExpriedCompanyHome=1; showExpriedMyPublish=1; hasDeliver=52; WEBTJ-ID=20181123152123-1673f71d4f83eb-03fafed6615d63-454c092b-1044480-1673f71d4f93e8; Hm_lvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1542593242,1542769087,1542786145,1542957684; LGSID=20181123152129-6594dfd6-eef0-11e8-8afa-5254005c3644; _gid=GA1.2.2129727196.1542957684; _putrc=BF038CDE5DE90B9E123F89F2B170EADC; JSESSIONID=ABAAABAAADEAAFIAF7430F8D73A4AF84848F60209F8B643; login=true; unick=%E9%99%88%E7%82%AF%E5%8D%97; gate_login_token=7b76aaa623c3af207fc90b3b4f8b004bdbd5d3013042444d960e8b29d53cdebf; TG-TRACK-CODE=search_code; _gat=1; Hm_lpvt_4233e74dff0ae5bd0a3d81c6ccf756e6=1542959848; LGRID=20181123155733-6f29ee83-eef5-11e8-b6a1-525400f775ce; SEARCH_ID=dfcfbf85dcb2421d90f507e0c74d60f9'
        }
        self.url = 'https://www.lagou.com/jobs/positionAjax.json?city=%E5%B9%BF%E5%B7%9E&needAddtionalResult=false'   # ajax请求url
        self.data = {
            'first':'false',
            'pn':'1',
            'kd':'python',
        }
    def get_json_data(self):
        for i in range(1,9):
            # random_user_agent = self.get_user_agent()
            # self.headers['User-Agent'] = random_user_agent
            self.data['pn'] = i
            try:
                response = requests.post(url=self.url,headers=self.headers,data=self.data)
                print(response.json())
                # print(response.json()) # 返回的是json数据，通过json()可将json数据转为字典类型
                data_dict = response.json()
                positions = data_dict['content']['positionResult']['result']
                for position in positions:
                    position_id = position['positionId']   # 招聘职位某id
                    position_url = 'https://www.lagou.com/jobs/{}.html'.format(position_id)  # 构造每条招聘信息的完整url
                    self.get_detail_info(position_url)   # 爬取每条招聘信息的详细数据
                    # pass
                    time.sleep(0.5)

            except Exception as e:
                print("数据异常：",e)

    def get_detail_info(self,url):
        random_user_agent = self.get_user_agent()  # 随机user-agent
        self.headers['User-Agent'] = random_user_agent
        proxy = self.get_random_ip()   # 随机ip代理
        response = requests.get(url=url ,headers=self.headers,proxies=proxy)  # 爬取前先获取随机user-agent、随机ip代理
        selector = Selector(text=response.text)
        job_name = selector.css(".job-name::attr(title)").extract_first('')
        job_desc = selector.css(".job_bt div").extract()[0]
        job_desc = remove_tags(job_desc).replace('\n','').strip()   # 去除标签、空格、回车符合等
        print("爬取‘{}’成功！".format(job_name))
        # pass

    def get_user_agent(self):
        # 获取随机的user-agent
        ua = UserAgent()
        random_user_agent = ua.random  # 获取随机user-agent
        return random_user_agent

    def get_random_ip(self):
        # 随机获取ip代理
        proxy = [
            {
                # 'http': 'http://61.135.217.7:80',
                'https': 'https://219.234.5.128:3128',
            },
            {
                # 'http': 'http://118.114.77.47:8080',
                'https': '101.236.57.99:8866',
            },
        ]
        random_proxy = random.choice(proxy)
        return random_proxy

if __name__ == '__main__':
    lagou_sp = lagou_spider()
    lagou_sp.get_json_data()

爬取拉钩网职位信息

3）使用selenium实现拉钩数据爬取

首先，安装selenium：

pip install selenium

先实现当前页 -->获取职位列表中每条招聘信息的url -->进入每条招聘信息指定url页面，实现详细信息的爬取

代码实现：

from scrapy.selector import Selector
# import re
import time
from selenium import webdriver
from w3lib.html import remove_tags


class LagouSpider(object):
    def __init__(self):
        self.driver = webdriver.Chrome()
        self.url = 'https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput='
        self.positions = []

    def run(self):
        self.driver.get(self.url)
        source = self.driver.page_source   # response
        self.parse_list_page(source)   # 爬取职位列表的每条数据的url

    def parse_list_page(self,source):
        # 爬取职位信息列表的urls
        html = Selector(text = source)
        links = html.css("#s_position_list .position_link::attr(href)").extract()  # 爬取拉钩职位列表中，每条招聘信息的url，是个list
        for link in links:
            self.request_detail_page(link)   # 将url传入，访问并爬取每条招聘信息的详细内容
            time.sleep(0.5)   # 每爬完一条招聘详细信息，休息0.5秒


    def request_detail_page(self,url):
        self.driver.get(url)   # 在当前页面依次打开每条职位信息的详细信息页面
        source = self.driver.page_source
        self.parse_detail_page(source)  # 真正爬取每条招聘信息的详细信息


    def parse_detail_page(self,source):
        # 爬取每条招聘信息的详细信息
        selector = Selector(text=source)
        job_name = selector.css(".job-name::attr(title)").extract_first('')
        job_desc = selector.css(".job_bt div").extract()[0]
        job_desc = ''.join(remove_tags(job_desc).replace('\n', '').strip()) # 去除标签、空格、回车符合等
        position = {
            'job_name':job_name,
            'job_desc':job_desc
        }
        print(position)
        self.positions.append(position)

爬取第一页对应所有招聘信息的详细数据

运行结果：

具体实现：

进入拉钩网搜索python后，爬取当前url列表页数据（第一页）--> 每条招聘信息的url
爬取每条招聘信息url对应的详情页面数据，每次都新开一个浏览器窗口爬取，爬取完成后关闭浏览器，将控制权交回列表页窗口
列表页窗口点击下一页，继续跑1、2步骤，直到爬取都最后不能再点击进入一页，则表示所有数据均爬取完毕，结束程序

完整代码：

from scrapy.selector import Selector
import time
from selenium import webdriver
from w3lib.html import remove_tags

from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By


class LagouSpider(object):
    def __init__(self):
        self.driver = webdriver.Chrome()
        self.url = 'https://www.lagou.com/jobs/list_python?labelWords=&fromSearch=true&suginput='
        self.positions = []

    def run(self):
        self.driver.get(self.url)
        while True:  # 当前页面爬取完成后，点击下一页爬取下一页数据，如此循环，直到最后一页，不能再click时退出循环
            WebDriverWait(driver=self.driver,timeout=5).until(EC.presence_of_element_located((By.CLASS_NAME,"pager_next ")))  # 显示等待，直到'下一页'按钮加载完毕，或过时才执行下面操作
            source = self.driver.page_source   # response
            self.parse_list_page(source)   # 爬取当前页 职位列表的每条数据的url
            # time.sleep(2)
            try:
                next_btn = self.driver.find_element_by_class_name('pager_next ')  # 下一页按钮
                if 'pager_next_disabled' in next_btn.get_attribute("class"):  # 如果是最后一页
                    self.driver.quit()   # 退出浏览器
                    break  # 跳出循环
                else:
                    next_btn.click()  # 点击下一页
            except:
                print(source)

    def parse_list_page(self,source):
        # 爬取职位信息列表的urls
        html = Selector(text = source)
        links = html.css("#s_position_list .position_link::attr(href)").extract()  # 爬取拉钩职位列表中，每条招聘信息的url，是个list
        for link in links:
            self.request_detail_page(link)   # 将url传入，访问并爬取每条招聘信息的详细内容
            time.sleep(0.5)   # 每爬完一条招聘详细信息，休息0.5秒


    def request_detail_page(self,url):
        # self.driver.get(url)   # 在当前页面依次打开每条职位信息的详细信息页面
        self.driver.execute_script("window.open('%s')"%url)  # 使用新窗口打开招聘信息详情页面
        self.driver.switch_to.window(self.driver.window_handles[1])  # 将控制权转到新窗口
        WebDriverWait(driver=self.driver, timeout=5).until(
            EC.presence_of_element_located((By.CLASS_NAME, "job-name")))  # 显示等待
        source = self.driver.page_source
        self.parse_detail_page(source)  # 真正爬取每条招聘信息的详细信息

        self.driver.close()   # 每个招聘信息详情信息爬取完成后，都关闭该窗口
        self.driver.switch_to.window(self.driver.window_handles[0])  # 切换回原(第一个)窗口


    def parse_detail_page(self,source):
        # 爬取每条招聘信息的详细信息
        selector = Selector(text=source)
        job_name = selector.css(".job-name::attr(title)").extract_first('')
        job_desc = selector.css(".job_bt div").extract()[0]
        job_desc = ''.join(remove_tags(job_desc).replace('\n', '').strip()) # 去除标签、空格、回车符合等
        position = {
            'job_name':job_name,
            'job_desc':job_desc
        }
        print(position)
        self.positions.append(position)





if __name__ == '__main__':
    lg = LagouSpider()
    lg.run()

selenium实现动态网页(拉钩网)数据爬取

爬取结果：

*** 验证码部分

1）tesseract的使用

在python代码中操作tesseract，需要安装pytesseract库：

pip install pytesseract

因为需要读取图片，因此需要安装pillow：

pip install PIL

如果运行中报错，可能是pywin32引起的，安装该包就可以了

使用pytesseract识别图片上的文字并转为文本文字示例代码：

import pytesseract
from PIL import Image

# 指定pytesseract的执行文件：tesseract.exe （tesseract需下载）
pytesseract.pytesseract.tesseract_cmd = r"D:\pro\TesseractOCR\tesseract.exe"

# 打开当前路径下的img图片
image = Image.open('img.png')
# 识别图片，并转为文本
text = pytesseract.image_to_string(image)   # 默认识别英文

# text = pytesseract.image_to_string(image，lang='chi_sim')   # lang参数，指定识别的类型。chi_sim：表示识别的是中文
print(text)

在使用pytesseract库之前，需要先到GitHub中下载 Tesseract-OCR库：

　https://github.com/tesseract-orc/

下载后才能在使用pytesseract库是引用Tesseract-ocr 的执行文件

6、实现12306网上抢票功能

使用selenium + Chromedriver实现

实现思路：

# 使用selenium + Chromedriver 来实现12306抢票功能

1.进行抢票前，先让用户输入一些相关信息，用于后续判断信息是否输入正确，以及是否输入来决定是否进入下一步
2.操作selenium ，让浏览器打开12306登录界面，手动输入账号密码进行登录
3.登录成功后会跳转到index页面(个人中心)，通过判断当前页面是否是index页面，判断是否登录成功
4.登录成功，操作浏览器跳转到抢票的界面
5.手动输入出发点、目的地、、出发日期，检查上述三个信息是否输入，以及输入内容是否与第1步用户输入的一致，
  确定输入无误；检测查询按钮是否可点击，确认可点击，执行点击事件，进行车次查询。
6.查找我们需要的车次，查看对应的席位（二等座）是否还有余票（有、数字），如果有，则执行'预订'按钮的点击
  事件，进行购票 ； 如果没检测到上述信息(有、数字)，则一直循环检测。
7.预订到我们想要的车次时，界面会跳出一个确认的对话框，找到对话框中的"确认按钮"，执行点击事件
8.跳出弹窗--> 乘客信息确认。点击确认按钮，完成抢票功能
8.手动付款

完整代码：

from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By

class Qiangpiao(object):
    def __init__(self):
        self.driver = webdriver.Chrome()
        self.login_url = 'https://kyfw.12306.cn/otn/resources/login.html'
        self.initmy_url = 'https://kyfw.12306.cn/otn/view/index.html'   # 登录成功后的index页面
        self.search_url = 'https://kyfw.12306.cn/otn/leftTicket/init'   # 查票url
        self.passenger_url = 'https://kyfw.12306.cn/otn/confirmPassenger/initDc'   # 提交订单的url

    def _login(self):
        # 打开登录页面，手动登录
        self.driver.get(self.login_url)
        self.driver.maximize_window()  # 窗口最大化
        # 显示等待
        WebDriverWait(self.driver,300).until(EC.url_to_be(self.initmy_url))   #300秒内，检查到当前url是登录后的index url，表示登录成功
        print("登录成功！")


    def _order_ticket(self):
        # 1、跳转到查票的窗口
        self.driver.get(self.search_url)
        # 2、等待出发地是否输入正确
        WebDriverWait(self.driver,300).until(
            EC.text_to_be_present_in_element_value((By.ID,"fromStationText"),self.from_station)
        )
        # 等待目的地是否输入正确
        WebDriverWait(self.driver,300).until(
            EC.text_to_be_present_in_element_value((By.ID,"toStationText"),self.to_station)
        )
        # 等待出发日期是否输入正确
        WebDriverWait(self.driver, 300).until(
            EC.text_to_be_present_in_element_value((By.ID, "train_date"), self.depart_time)
        )
        # 3.等待查询按钮是否能点击
        WebDriverWait(self.driver, 300).until(
            EC.element_to_be_clickable((By.ID, "query_ticket"))
        )
        # 4.如果输入都正确，能正常点击，那么就点击查询按钮进行查询操作
        searchBtn = self.driver.find_element_by_id("query_ticket")
        searchBtn.click()
        # 5.点击查询按钮后，等待查询的信息是否已显示出来
        WebDriverWait(self.driver,300).until(
            EC.presence_of_element_located((By.XPATH,".//tbody[@id='queryLeftTable']/tr"))
        )
        # 6.找到所有没有datatran属性的tr标签，这些标签存储了车次信息，是我们需要
        tr_list = self.driver.find_elements_by_xpath(".//tbody[@id='queryLeftTable']/tr[not(@datatran)]")
        # 7.遍历tr_list标签
        for tr in tr_list:
            train_number = tr.find_element_by_class_name("number").text  # 车次信息
            if train_number in self.trains:   # 如果查到的车次在用户输入的目标车次中，表示是我们要的
                left_ticket = tr.find_element_by_xpath(".//td[4]").text   # 拿到二等座对应的文本
                if left_ticket == "有" or left_ticket.isdigit:    # 如果二等座文本='有' 或 是个数字，表明是有二等座车票的
                    print(train_number+"有票，正预订中...")
                    orderBtn = tr.find_element_by_class_name("btn72")   # 预订按钮
                    orderBtn.click()  # 点击预订按钮

                    # 等待是否来到确认乘客的页面(提交订单页面)
                    WebDriverWait(self.driver,300).until(
                        EC.url_to_be(self.passenger_url)
                    )
                    # 在订单页面中，找到乘坐人信息，将其与用户输入的乘客信息进行对比，存在则打勾(点击)
                    li_list = self.driver.find_elements_by_xpath("//ul[@id='normal_passenger_id']//li")
                    for li in li_list:
                        passenger_info = li.find_element_by_xpath(".//label").text
                        if passenger_info in self.passengers: # 找到的乘客姓名，在用户输入的乘客列表中
                            li.find_element_by_xpath(".//input").click()  # 找到复选框，点击选中该乘客
                    certain_btn = self.driver.find_element_by_id("submitOrder_id")
                    certain_btn.click()  # 点击提交订单按钮
                    # 订单提交后会跳出信息确认弹窗，点击确认按钮，即成功抢票
                    confirm_btn = self.driver.find_element_by_id("qr_submit_id").click()   # 点击确认按钮，完成抢票功能
                    print("抢票成功！")






    def run(self):
        self.wait_input()
        self._login()
        self._order_ticket()


    def wait_input(self):
        self.from_station = input("出发地：")
        self.to_station = input("目的地：")
        self.depart_time = input("出发时间(格式：2018-03-11)：")
        self.passengers = input("乘客姓名(如有多个乘客，用英文逗号隔开)：").split(",")  # 注意：乘客必须是在12306上有添加过的
        self.trains = input("车次(如有多个车次，用英文逗号隔开)：").split(",")

if __name__ == '__main__':
    QP_spider = Qiangpiao()
    QP_spider.run()

12306网上抢票功能

Scrapy--爬虫相关

一、使用Scrapy模拟登录豆瓣网

1）新建虚拟环境

2）新建爬虫项目：scrapy startproject spider_name

3）进入爬虫项目中，新建爬虫文件：scrapy genspider url_name

4）使用pycharm打开该爬虫项目，安装scrapy等库

正题：

1、爬取豆瓣网数据-->网页分析，首先打开豆瓣网，点击登录，进入登录页面：https://accounts.douban.com/login

页面如下：

当输入信息，点击登录时，在form_data下多了如下数据：

也就是说，我们要登录时，使用scrapy.Request发送post请求，同时将上述数据带上。其中captcha-solution需要我们获取验证码图片，识别图片中验证码；captcha-id 可以在网页中获取，其他都可以手动输入。

分析完页面，就可以开始我们的代码编写了。首先，先将settings做如下改动：

ROBOTSTXT_OBEY = False

DEFAULT_REQUEST_HEADERS = {
  'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
  'Accept-Language': 'en',
  'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'
}

然后开始我们的主代码编写：

　验证码输入会比较麻烦，我们先实现获取验证码图片并保存到本地，再通过PIL打开验证码图片，人为识别并输入的方式

　执行爬虫文件命令：scrapy crawl douban

　执行流程：进入start_request访问豆瓣登录页面--> 执行回调函数parse，获取post请求登录豆瓣时必需的参数 --> 以post形式访问豆瓣登录页面，进行登录操作--> 调用回调函数captcha_identify ，登录之后的操作可以在此进行。比如修改个人信息、批量给文章点赞等待。

代码如下：

# -*- coding: utf-8 -*-
import scrapy
from urllib import request
from scrapy_spider.settings import captcha_img_dir
from PIL import Image

class DoubanSpider(scrapy.Spider):
    name = 'douban'
    allowed_domains = ['douban.com']
    start_urls = ['https://accounts.douban.com/login']
    login_url = 'https://accounts.douban.com/login'  # 用于发送post请求

    def parse(self, response):
        print("------------------hello-------------------------")
        # parse函数是访问登录页面后的回调函数，此时刚访问了login页面，执行回调函数中
        formdata = {   # 用于发送post请求，登录豆瓣网 ，以下是必需的data数据
            'source': 'None',
            'redir': 'https://www.douban.com',
            'form_email': '13642628657@163.com',
            'form_password': 'nan10695520',
            # 'captcha - solution': 'chest',
            # 'captcha - id': 's0iIcEcrjuMRXKXWCgYib5n6:en',
            'remember': 'on',
            'login': '登录'
        }
        captcha_img_src = response.css("#captcha_image::attr(src)").get('')   # 获取登录页面的验证码图片
        if captcha_img_src:
            captcha = self.captcha_identify(captcha_img_src)  # 识别验证码图片并获取验证码文本
            formdata['captcha-solution'] = captcha  # 将验证码放进formdata中
            captcha_id = response.xpath("//input[@name='captcha-id']/@value").get()
            formdata['captcha-id'] = captcha_id
        yield scrapy.FormRequest(url=self.login_url,formdata=formdata,callback=self.parse_after_login)  #执行post请求，进行登录操作



    def captcha_identify(self,captcha_img):
        # 保存验证码，并识别验证码文本
        img_name = captcha_img_dir + 'captcha.png'
        request.urlretrieve(captcha_img,img_name)  # 将验证码图片保存到captcha_img/captcha.png
        captcha_image = Image.open(img_name)  # 打开验证码图片
        captcha_image.show()  # 展示图片
        captcha = input("请输入验证码：")   # 手动输入验证码
        return captcha

    def parse_after_login(self,response):
        if response.url == 'https://www.douban.com/':  # 登录成功会跳转到这页面，如果一致则表示登录成功
            print("登录成功")
        else:
            print("登录失败")

View Code

使用scrapy模拟登录操作，需要考虑两点：

　1.登录操作简单，比如只需要输入账号密码，不需要我们从网页上获取到其他数据，这种可以直接在scrapy代码操作中，直接重写start_request函数，使用FormRequest直接模拟登录操作

　2.登录操作相对复杂，比如除账号密码外，还需要验证码等，如上述模拟登录豆瓣网。此时需要将scrapy代码中的start_url改成登录页面的login_url，先以get方式访问login_url，再在对应的回调函数中通过xpath、css选择器获取到验证码

或其他数据，一起保存到data字典中，再通过FromRequest模拟登录操作。也就是说这种不需要重写start_request函数，但需要在parse(start_request函数执行后默认调用parse函数)函数进行post请求，即FromRequest模拟登录操作。

　关于验证码识别，使用python自带库识别，对于一些不规则图片，识别成功率不高，我们需要借助别的工具进行高识别操作，如阿里云的图片验证码识别，万维易源网的图片验证码识别，当然这些事需要收费的，但不贵。

万维易源网图片验证码识别，可参考：https://www.cnblogs.com/Eric15/articles/9840478.html

二、使用scrapy --> 爬取汽车之家-宝马5系列图片

url：https://car.autohome.com.cn/pic/series/65.html#pvareaid=3454438

爬取上述各个分类组中的图片信息，保存到本地。

1、使用自定义保存图片的方式编写代码（同步下载）：

bmw5.py：

# -*- coding: utf-8 -*-
import scrapy
from scrapy_spider.items import BmwItem


class Bmw5Spider(scrapy.Spider):
    name = 'bmw5'
    allowed_domains = ['car.autohome.com.cn']
    # start_urls = ['http://car.autohome.com.cn/']
    start_urls = ['https://car.autohome.com.cn/pic/series/65.html#pvareaid=3454438']

    def parse(self, response):
        uiboxs = response.css("div.uibox")[1:]
        for uibox in uiboxs:
            category_title = uibox.css("div.uibox-title a::text").get('')  # 分类的标题
            urls = uibox.css("ul > li > a img::attr(src)").getall()   # 同一个分类下所有的汽车图片url
            urls = list(map(lambda url:response.urljoin(url),urls))  # response.urljoin：爬取到的图片url没有前域名，通过此函数可以补全url

            item = BmwItem(category_title = category_title,urls = urls)
            yield item

View Code

item.py：

class BmwItem(scrapy.Item):
    category_title = scrapy.Field()
    urls = scrapy.Field()

pipelines.py：

from scrapy_spider.settings import images_dir
import os
from urllib import request

class BmwPipeline(object):
    def __init__(self):
        self.img_path = images_dir

    def process_item(self, item, spider):
        category_title = item['category_title']
        urls = item['urls']
        category_path = os.path.join(self.img_path, category_title)
        if not os.path.exists(category_path): # 如果分类文件夹不存在则创建
            os.mkdir(category_path)

        for url in urls:
            img_name = url.split("_")[-1]  # 图片命名
            img_file = os.path.join(category_path,img_name)  # 图片存放路径
            request.urlretrieve(url,img_file)   # 保存
        return item

main.py：执行文件

from scrapy.cmdline import execute

execute(['scrapy','crawl','bmw5'])

爬取结果：

2、使用scrapy自带文件/图片保存方式编写代码（异步下载）：

文件下载(步骤)：

图片下载(步骤)：

使用scrapy自带文件/图片下载功能，按上述步骤处理就可以了，图片默认存放路径是在full文件夹下的。但本项目中，我们是将每个分类的所有图片都存放到对应的分类文件夹中，而不是全部存放于full文件夹中，因此我们需要重写scrapy自带的图片下载功能。具体实现：

bmw5.py（几乎没变化）：

# -*- coding: utf-8 -*-
import scrapy
from scrapy_spider.items import BmwItem


class Bmw5Spider(scrapy.Spider):
    name = 'bmw5'
    allowed_domains = ['car.autohome.com.cn']
    # start_urls = ['http://car.autohome.com.cn/']
    start_urls = ['https://car.autohome.com.cn/pic/series/65.html#pvareaid=3454438']

    def parse(self, response):
        uiboxs = response.css("div.uibox")[1:]
        for uibox in uiboxs:
            category_title = uibox.css("div.uibox-title a::text").get('')  # 分类的标题
            urls = uibox.css("ul > li > a img::attr(src)").getall()   # 同一个分类下所有的汽车图片url
            urls = list(map(lambda url:response.urljoin(url),urls))  # response.urljoin：爬取到的图片url没有前域名，通过此函数可以补全url

            item = BmwItem(category_title = category_title,image_urls = urls)
            yield item

View Code

item.py：image_urls、images需固定式命名

class BmwItem(scrapy.Item):
    # 使用scrapy自带图片下载功能
    category_title = scrapy.Field()
    image_urls = scrapy.Field()  # 下载图片固定式命名
    images = scrapy.Field()   # 下载图片固定式命名

View Code

pipelines.py：重写ImagesPipeline类

import os
from scrapy.pipelines.images import ImagesPipeline
from scrapy_spider import settings

class BmwPipeline(ImagesPipeline):
    # bnm5.py中，每次yield item都会进来处理一次(异步操作)
    # 使用scrapy自带图片下载功能,重写ImagesPipeline
    def get_media_requests(self, item, info):
        # 重写父类get_media_requests方法，这个方法是在发送下载请求之前调用的
        # 其实这个方法本身就是去发送下载请求的
        request_objs = super(BmwPipeline,self).get_media_requests(item,info)  # request_objs：是所有的request对象
        for request_obj in request_objs:
            request_obj.item = item  # 将item数据封装进当前request对象中，方便file_path函数调用
        return request_objs

    def file_path(self, request, response=None, info=None):
        # 重写父类file_path方法，这个方法是在图片将要被存储时调用，
        # 重写该方法，用来自定义我们存储图片的保存路径
        path = super(BmwPipeline,self).file_path(request,response,info)  # path：图片保存路径 -->full/name.png
        category_title = request.item['category_title']   # item数据是通过get_media_requests封装的item获取到的
        images_store = settings.IMAGES_STORE  # images文件夹路径
        category_path = os.path.join(images_store, category_title)  # 每个分类的文件夹
        if not os.path.exists(category_path): # 如果分类文件夹不存在则创建
            os.mkdir(category_path)

        image_name = path.replace("full/",'')  # 将图片默认保存路径修改掉，只保留图片名称
        image_path = os.path.join(category_path,image_name)
        return image_path   # 返回图片保存路径

settings.py：

ITEM_PIPELINES = {
   # 'scrapy_spider.pipelines.ScrapySpiderPipeline': 300,
   # 'scrapy_spider.pipelines.BmwPipeline': 10,
   'scrapy_spider.pipelines.BmwPipeline': 10,
   # 'scrapy.pipelines.images.ImagesPipeline': 10,
}


images_dir = os.path.join(scrapy_spider_dir,'images')   # images文件夹

IMAGES_STORE = images_dir   # 用于scrapy自带图片下载

执行得到的结果与第一种方式一样。

*** 下载器中间件

** boss直聘网--python招聘数据爬取

爬取页面：

1）搜索python职位，将搜索页面每一页的页码url都爬取下来：

2）将每一页的每个python招聘信息的url进行爬取，并调用回调函数对其对应详情页面进行分析，数据爬取及保存：

注意：爬取数据时，可通过scrapy shell url_name ，进行脚本爬取测试，这样可以避免每次测试代码是否正确都要爬取网页

1、代码实现：

1）实现招聘页面数据爬取：

from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule
from items import BossZhiPinItem


class ZhipinSpider(CrawlSpider):
    name = 'zhipin'
    allowed_domains = ['zhipin.com']
    start_urls = ['https://www.zhipin.com/c101280100/?query=python&page=1']

    rules = (
        Rule(LinkExtractor(allow=r'.+\?query=python&page=\d+'), follow=True), # 跟进，爬取每一页的数据
        Rule(LinkExtractor(allow=r'.+job_detail/\w+~.html'), callback='parse_job', follow=False),
    )

    def parse_job(self, response):
        publish_info = response.xpath("//div[@class='job-author']/span/text()").get('')
        publish_date,publish_time = publish_info.replace("发布于","").split()  # 发布日期、时间
        title = response.xpath("//h1/text()").get("")   # 标题
        job_info = response.css(".job-primary p::text").getall()
        job_city,job_experience,job_edu = job_info[0:3]   # 工作地点、工作经验、学历要求
        job_wages = response.css('h1 + span::text').get("")
        job_wages = job_wages.replace('\n','').strip()   # 工资
        company_name = response.css(".detail-content div.job-sec div.name::text").get("")  # 公司名称

        item = BossZhiPinItem(
            title = title,
            publish_date = publish_date,
            publish_time = publish_time,
            job_city = job_city,
            job_experience = job_experience,
            job_edu = job_edu,
            job_wages = job_wages,
            company_name = company_name
        )
        yield item

zhipin.py

2）item数据类型：

import scrapy

class BossZhiPinItem(scrapy.Item):
    publish_date = scrapy.Field()
    publish_time = scrapy.Field()
    title = scrapy.Field()
    job_city = scrapy.Field()
    job_experience = scrapy.Field()
    job_edu = scrapy.Field()
    job_wages = scrapy.Field()
    company_name = scrapy.Field()

item.py

3）pipelines数据保存--> 保存到json文件：

from scrapy.exporters import JsonLinesItemExporter


class BossPipeline(object):
    def __init__(self):
        self.fp = open("jobs.json",'wb')
        self.exporter = JsonLinesItemExporter(self.fp,ensure_ascii=False)

    def process_item(self,item,spider):
        self.exporter.export_item(item)
        return item

    def close_spider(self,spider):
        self.fp.close()

pipelines.py

4）settings.py配置：

# 1.
ROBOTSTXT_OBEY = False

# 2.添加User_Agent
DEFAULT_REQUEST_HEADERS = {
  'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
  'Accept-Language': 'en',
  'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'  # 新添加
}

# 3.pipelines配置
ITEM_PIPELINES = {
   'boss.pipelines.BossPipeline': 100,
}

settings.py

5）执行文件：

from scrapy.cmdline import execute

execute(['scrapy','crawl','zhipin'])

main.py

2、进阶

1）实现User_Agent随机化

随机user-agent实现，可参考链接：https://www.cnblogs.com/Eric15/articles/9937614.html(突破反爬虫限制)

　使用现有的被维护的user-agent库：fake-useragent ，需安装：pip install fake-useragent

fake-useragent测试：

from fake_useragent import UserAgent

ua = UserAgent()  # 实例化UserAgent

print(ua.random)  # 随机选择user-agent

# 输出结果：
Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36

test.py

正式使用：

settings配置：

# 1.DOWNLOADER_MIDDLEWARES 中代码实现
DOWNLOADER_MIDDLEWARES = {
   # 'boss.middlewares.BossDownloaderMiddleware': 543,
   'boss.middlewares.RandomUserAgentMiddlware': 100,   # 实现随机user-agent
   'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': None  # 此行，避免使用了默认的user-agent middleware
}

# 随机user-agent方式，random表示全部随机，Chrome表示谷歌user-agent随机
RANDOM_UA_TYPE = "random"  # 随机user-agent

settings.py

middlewares.py中实现随机user-agent：

from fake_useragent import UserAgent   # 引入fake-useragent的UserAgent


class RandomUserAgentMiddlware(object):
    #新创类，实现随机更换user-agent
    def __init__(self, crawler):
        super(RandomUserAgentMiddlware, self).__init__()
        self.ua = UserAgent()  # 实例化UserAgent
        self.ua_type = crawler.settings.get("RANDOM_UA_TYPE", "random")  # 从setting中获取useragent的类型(Firefox、Chrome、IE或random)

    @classmethod
    def from_crawler(cls, crawler):
        return cls(crawler)

    def process_request(self, request, spider):
        def get_ua():
            return getattr(self.ua, self.ua_type)  # 根据setting中获取的useragent类型，映射真正方法

        request.headers.setdefault('User-Agent', get_ua())  # 添加到headers中

middlewares.py

运行效果：

　每次爬取url都会随机更换user-agent

2）实现随机代理ip

方式一（强烈推荐）：

新建model_proxy.py文件，实现代码：

from datetime import datetime,timedelta

class ProxyModel(object):
    def __init__(self,data):
          # 'data':{'ip':'158.169.154.124','port':'3212','expire_time':'2018-10-20 22:10:10'}
        self.ip = data['ip']   # ip
        self.port = data ['port']   # 端口
        self.expire_str_time = data ['expire_time']  # 过期时间,字符串格式
        self.blacked = False   # 用于判断ip代理是否被拉黑，True表示被拉黑
        self.expire_time()  # 生成代理ip

    def expire_time(self):
        # 生成代理ip：http/https://127.0.0.1:8080
        date_str, time_str = self.expire_str_time.split(" ")  # 将日期字符串分割为日期、时间
        year, month, day = date_str.split("-")
        hour, minute, second = time_str.split(":")
        self.ex_time = datetime(year=int(year),month=int(month),day=int(day),hour=int(hour),minute=int(minute),second=int(second))  # 过期时间，日期格式
        self.proxy = "https://{}:{}".format(self.ip,self.port)

    @property
    def is_expiring(self):
        # 判断代理ip是否过期
        now = datetime.now()
        if (self.ex_time - now) < timedelta(seconds=5):   # 如果距离过期时间小于5秒
            return True
        else:
            return False

model_proxy.py

在middlewares.py中新建类： IPProxyDownloadMiddleware

实现代码：

from twisted.internet.defer import DeferredLock
import requests
import json
from model_proxy import ProxyModel

class IPProxyDownloadMiddleware(object):
    PPOXY_URL = "http://webapi.http.zhimacangku.com/getip?num=1&type=2&pro=&city=0"  # 获取代理ip信息的api接口，收费版

    def __init__(self):
        super(IPProxyDownloadMiddleware, self).__init__()
        self.current_proxy = None   # 设置当前代理为None
        self.lock = DeferredLock()  # 实例化twisted自带的锁

    def process_request(self,request,spider):
        # 爬取数据前，设置ip代理
        if 'proxy' not in request.meta or self.current_proxy.is_expiring:
            # 如果未设置ip代理，或ip代理即将过期
            self.update_proxy()   # 获取或更新ip代理

        request.meta['proxy'] = self.current_proxy.proxy  # 将代理ip放到headers下的proxy中


    def process_response(self,request,response,spider):
        # 返回的response不是期望中的数据，需进行ip代理相关操作
        if response.status != 200 or "captcha" in response.url:
            # 如果爬取到的页面返回的状态码不是200，说明该页面数据爬取不成功
            # 或者爬取到的页面是200成功页面，但其实是验证码验证的页面，也归为数据爬取不成功
            # 出现上面情况，说明该ip可能被拉黑(或被识别为爬虫)，所以需要更换ip代理
            if not self.current_proxy.blacked: # ip被拉黑，设定拉黑标识符，为true表示被拉黑
                self.current_proxy.blacked = True  # 确定为拉黑，update_proxy()操作才会更新ip代理
            print("%s这个代理被加入黑名单了"%self.current_proxy.ip)
            self.update_proxy()
            # 如果来到这里，说明该ip被识别为爬虫或被拉黑名单，此时并没有获取到页面数据
            # 因此需要返回request到调度器，再重新爬取该页面(此时已经更换代理ip)
            return request

        return response   # 没有发生上面的情况，说明正常爬取到页面，返回response

    def update_proxy(self):
        self.lock.acquire()   # 执行当前函数时属于异步操作，为了避免多个url请求进入此函数中，给它加入锁，即当前函数被处理时，其他请求再不能调用
        if not self.current_proxy or self.current_proxy.is_expiring or self.current_proxy.blacked:
            # 如果当前未设置代理ip，或当前代理ip即将过期，或当前代理ip被拉黑，才重新获取新ip代理
            response = requests.get(self.PPOXY_URL)  # 获取ip代理api接口的数据
            text = response.text  #
            print('重新获取了一个代理：',text)
            # text: {'code':0,'success':true,'msg':'0','data':[{'ip':'158.169.154.124','port':'3212','expire_time':'2018-10-20 22:10:10'}]}
            result = json.loads(text)
            if len(result['data']) > 0:   # data有数据，表明获取成功
                data = result['data'][0]
                proxy_model = ProxyModel(data)
                self.current_proxy = proxy_model  # current_proxy存有ip、过期时间等
        self.lock.release()  # 释放锁

IPProxyDownloadMiddleware

settings.py中配置：

DOWNLOADER_MIDDLEWARES = {

   'boss.middlewares.RandomUserAgentMiddlware': 100,   # 实现随机user-agent
   'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': None  # 此行，避免使用了默认的user-agent middleware

    'boss.middlewares.IPProxyDownloadMiddleware': 110,   # 新增，实现随机ip代理

}

settings.py

方式二：

　数据库存有从代理ip的主网站中爬取下来的ip信息数据表，我们通过获取数据库中ip信息，来实现ip代理（此过程是同步机制）：

　　流程：每次从数据库获取一个ip → 判断该ip是否有效，有效则返回给爬虫使用，无效则删除该ip信息，重新获取新的ip，如此循环

　数据库 → ip库(两千多条数据)：

获取ip实现代码：

import MySQLdb
import requests

conn = MySQLdb.connect(host="127.0.0.1", user="root", passwd="0315", db="article_spider", charset="utf8")
cursor = conn.cursor()

class GetIP(object):

    def delete_ip(self,ip):
        # 从数据库删除无效的ip
        delete_sql = """
        delete from proxy_ip where ip='{}'
        """.format(ip)
        cursor.execute(delete_sql)
        print(ip)
        conn.commit()
        return True

    def judge_ip(self,ip,port):
        # 使用代理模式访问百度，测试ip是否可用
        http_url = "https://www.baidu.com"
        proxy_url = "https://{0}:{1}".format(ip,port)  # 代理ip设置
        try:
            proxy_dict = {
                "https":proxy_url,
            }
            response = requests.get(http_url,proxies = proxy_dict)  # proxies要去传入的是个dict类型，键值对类型："http"："http://www.baidu.com"等
        except Exception as e:
            print("Invalid ip and port")
            self.delete_ip(ip)
            return False
        else:
            code = response.status_code
            if code >=200 and code <300:
                print("Effective ip")
                return True
            else:
                print("Invalid ip and port")
                self.delete_ip(ip)
                return False

    def get_random_ip(self):
        # 随机获取mysql中某条数据的ip及端口
        random_sql = """
        select ip, port from proxy_ip where proxy_type='https'
        order by RAND()
        limit 1
        """
        result = cursor.execute(random_sql)
        for ip_info in cursor.fetchall():
            ip = ip_info[0]
            port = ip_info[1]

            judge_re = self.judge_ip(ip,port)
            if judge_re:            # 测试通过，表示该端口及ip可用，直接return即可
                return "http://{0}:{1}".format(ip,port)
            else:
                print("无效ip")
                return self.get_random_ip()  # 测试失败，ip无效，重新获取随机ip

if __name__ == '__main__':
    get_ip = GetIP()
    ip = get_ip.get_random_ip()
    print("ip:",ip)

View Code

在项目中使用：

简单demo：因为ip数据是采集下来免费的，最简单的就是直接在process_request中使用

from ArticleSpider.tools.crawl_xici_ip import GetIP  # 引人tools/crawl_xici_ip.py中自定义的脚本
    def process_request(self, request, spider):
        def get_ua():
            return getattr(self.ua, self.ua_type)  # 根据setting中获取的useragent类型，映射真正方法
        request.headers.setdefault('User-Agent', get_ua())  # 添加到headers中
        request.meta["proxy"] = self.get_ip.get_random_ip()  # 使用ip代理池，实现随机ip代理

View Code

实际在项目中使用需要采取跟上述第一种随机ip代理的使用方式一样，这是为项目自身性能及合理性等考虑。

项目运行，爬取到的json数据：

** 简书网整站爬虫应用

1、数据爬取：

　从简书首页入手，制定规则：符合文章对应的url，都需要爬取 → 进入每篇文章(url)的详情中爬取数据 → 爬取到的数据，如发布时间数据有带个*号，需要去除；文章id，需要通过正则表达式获取

→ 本文通过ItemLoader机制来实现 → 在简书的爬虫文件中，只负责将数据爬取下来，关于数据的清洗、处理都交给item来处理，具体代码实现如下：

　具体实现：从js.py中爬取到数据，给到item.py的对应item中，item拿到数据，通过执行：input_processor = MapCompose(remove_asterisk) ，将数据交给对应的函数处理，之后再传回来，最后通过：default_output_processor = TakeFirst()，将每条数据的第一个数据提取出来。

1）js.py：爬虫主程序

# import scrapy
from scrapy.linkextractors import LinkExtractor
from scrapy.spiders import CrawlSpider, Rule
from jianshu.items import JianShuItemLoader,JianshuItem
# import re


class JsSpider(CrawlSpider):
    name = 'js'
    allowed_domains = ['jianshu.com']
    start_urls = ['https://www.jianshu.com/']

    rules = (
        Rule(LinkExtractor(allow=r'.*/p/[0-9a-z]{12}.*'), callback='parse_detail', follow=True),
    )

    def parse_detail(self, response):
        # title = response.xpath("//div[@class='article']/h1[@class='title']/text()").get("")
        # title = response.css(".article h1::text").get("")
        # avatar = response.xpath("//a[@class='avatar']/img/@src").get("")
        # author = response.xpath("//span[@class='name']/a/text()").get("")
        # pub_time = response.xpath("//div[@class='meta']/span[@class='publish-time']/text()").get("").replace("*","")
        # url = response.url
        # re_url = re.match(".*/p/([0-9a-z]{12})\??.*",url)
        # if re_url:
        #     article_id = re_url.group(1)
        # content = response.css(".show-content").get()

        item_loader = JianShuItemLoader(item=JianshuItem(),response=response)  # 使用ItemLoader机制，自定义ItemLoader类
        item_loader.add_css("title",".article h1::text")   # 标题
        item_loader.add_xpath("avatar","//a[@class='avatar']/img/@src")   # 头像的url
        item_loader.add_xpath("author","//span[@class='name']/a/text()")   # 作者
        item_loader.add_xpath("pub_time","//div[@class='meta']/span[@class='publish-time']/text()")   # 发布时间
        item_loader.add_value("url",response.url)   # request url链接
        item_loader.add_value("article_id",response.url)  # 文章id

        article_item = item_loader.load_item()  # 最后，一定要调用这个方法来对规则进行解析生成item对象

        yield article_item

js.py

2）item.py

import scrapy
from scrapy.loader import ItemLoader
from scrapy.loader.processors import MapCompose, TakeFirst, Join
import re


class JianShuItemLoader(ItemLoader):
    # 自定义拉钩ItemLoader
    default_output_processor = TakeFirst()  # 默认提取第一个数据


def remove_asterisk(value):
    # 去除*号
    return value.replace("*","")


def get_url_id(value):
    # 获取url上的id
    re_url = re.match(".*/p/([0-9a-z]{12})\??.*", value)
    if re_url:
        article_id = re_url.group(1)
    else:
        article_id = "error_url_id"
    return article_id



class JianshuItem(scrapy.Item):
    title = scrapy.Field()
    avatar = scrapy.Field()
    author = scrapy.Field()
    pub_time = scrapy.Field(
        input_processor = MapCompose(remove_asterisk)
    )
    url = scrapy.Field()
    article_id = scrapy.Field(
        input_processor = MapCompose(get_url_id)
    )
    content = scrapy.Field()

item.py

2、将数据存入mysql中

　数据存入mysql中，在我另一篇博文中有详细介绍，可参考：https://www.cnblogs.com/Eric15/articles/9937614.html

实现贴图：

实际使用，请参考上述我提供的另一篇博文，在mysql存储操作上比较整洁高效。

在进行简书整站数据爬取时，发现简书上每篇文章中都有部分数据是通过ajax加载展示的，比如下面截图中阅读数量、评论、喜欢、专题收集等，这些是通过ajax加载显示的，我们在使用scrapy爬取时无法爬取到。

此时，我们可以进行另一种方法：集成selenium到scrapy当中。如果你懂爬虫的话应该也会知道，每爬取一个url，无论发送request还是获取response都会经历下载中间件，我们在下载中间件中做点手脚，即截取response响应数据，让下载中间件返回我们指定的数据，而不是返回网页原先的response数据。

　实现原理：在下载中间件中，我们使用selenium访问url指定网页，将selenium访问后的response数据返回给spider调用解析，此时的response响应数据是经过selenium模拟浏览器所生成的数据，也就是说ajax等动态生成的数据也都是存在的，我们再通过spider的parse_detile等函数解析，便能获取所有的数据。但有一点缺陷，就是selenium执行部分时同步操作，需要打开浏览器、sleep等待等，在这部分会比较耗时。

实现代码：

from selenium import webdriver
from scrapy.http import HtmlResponse
import time

class SeleniumDownloadMiddleware(object):
    def __init__(self):
        self.driver = webdriver.Chrome()
    #通过selenium - chrome请求动态网页
    def process_request(self, request, spider):
        # 截获response响应数据
        # browser = webdriver.Chrome(executable_path="C:/chromedriver.exe")
        self.driver.get(request.url)
        time.sleep(1)
        try:  # 获取专题收录信息，如果没有会报错，需要try
            while True: # 专题收录，点击‘展示'更多’，直到没有更多，跳出循环
                showMore = self.driver.find_element_by_class_name('show-more')
                showMore.click()
                time.sleep(0.3)
                if not showMore:
                    break
        except:
            pass

        source = self.driver.page_source   # response数据，
        # 返回HtmlResponse给到spider分析，可以结束当条爬虫对下载中间件中其他类的执行，
        response = HtmlResponse(url=self.driver.current_url, body=source, encoding="utf-8", request=request)
        return response

    def process_response(self):
    # 每条数据爬取完成后调用
        pass

scrapy继集成selenium

参考博文链接：https://www.cnblogs.com/Eric15/articles/9961246.html

** 使用requests实现淘宝网商品信息爬取

实现：输入关键字搜索，爬取搜索关键词页面数据，进行数据持久化处理(保存到数据库或文件等)

打开淘宝，搜索python关键词，右键打开源码分析，发现搜索页面中的数据基本都是动态加载出来。在源码中查询数据，数据都存于<script>中：

我们使用requests库来模拟淘宝数据请求：

import requests

headers = {
    "cookie":"miid=9053363428195873751; l=Aunpx9THxIGX125nyLMMqSUgeZtCed3s; cna=ZG8KEADNMnICAXQWOuLdZHNa; t=1cb6790d77c8096d480c9ce5a3f89a8f; tg=0; enc=%2BrqQXxrsw0U9av2EDCsjFkuCYqoGqRLOWIHazUt5bouZ39CV70ZxlwAkOUdFasya7CBKazg9C7VcsZeV4ZaZdw%3D%3D; UM_distinctid=1642ba5252df3-062f70fb829da6-6b1b1279-ff000-1642ba5252e330; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1; thw=cn; hng=CN%7Czh-CN%7CCNY%7C156; _uab_collina=153951050952550241593612; _umdata=535523100CBE37C378FE937E2D4C337D51C3F4A10BD43036CBABF2F47CA16A32E9537C0D08377CE5CD43AD3E795C914C1D0B5F4E0AD62C7671F006234671942E; cookie2=14969d96aef000324d26d6bde647f3dd; _tb_token_=e6e35ef43e1b3; alitrackid=www.taobao.com; swfstore=133687; JSESSIONID=B8D1A10E659E01B8EAF9AB8FE2E7C7E0; lastalitrackid=login.taobao.com; whl=-1%260%260%261543820856592; v=0; unb=787123649; sg=690; _l_g_=Ug%3D%3D; skt=075a1eb5376f0bb9; cookie1=Vyh4R%2BvzaAhoQrGi%2FGoF6oQff8n1obVXKUwfwrnK%2BgM%3D; csg=bd372668; uc3=vt3=F8dByR1SqeqhF4LIcrw%3D&id2=VAmtG4JUGtBn&nk2=F5RAQI07NhhsoQs%3D&lg2=V32FPkk%2Fw0dUvg%3D%3D; existShop=MTU0MzgyMDkxOQ%3D%3D; tracknick=tb522552_66; lgc=tb522552_66; _cc_=Vq8l%2BKCLiw%3D%3D; dnk=tb522552_66; _nk_=tb522552_66; cookie17=VAmtG4JUGtBn; mt=ci=12_1; uc1=cookie16=W5iHLLyFPlMGbLDwA%2BdvAGZqLg%3D%3D&cookie21=WqG3DMC9Edo1TBf%2BfpfnBg%3D%3D&cookie15=VFC%2FuZ9ayeYq2g%3D%3D&existShop=false&pas=0&cookie14=UoTYNcNUR1FpiA%3D%3D&tag=8&lng=zh_CN; isg=BAYG4FtkphWb5XmvH3ECz67wV_xIz0uvswTAhvAv3CkE86QNUPeaMezBzy8_3EI5",
    "referer":"https://www.taobao.com/?spm=a230r.1.1581860521.1.72c91dd8qIhSS7",
    "User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"
}
r = requests.get("https://s.taobao.com/search?q=python&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20181203&ie=utf8",headers=headers)
print(r.text)

运行后发现截图中的所有代码都在response.text（内容太多，暂不贴）当中，于是我们就可以通过正则表达式处理，来获取我们需要的scrip代码：

import requests
import re

headers = {
    "cookie":"miid=9053363428195873751; l=Aunpx9THxIGX125nyLMMqSUgeZtCed3s; cna=ZG8KEADNMnICAXQWOuLdZHNa; t=1cb6790d77c8096d480c9ce5a3f89a8f; tg=0; enc=%2BrqQXxrsw0U9av2EDCsjFkuCYqoGqRLOWIHazUt5bouZ39CV70ZxlwAkOUdFasya7CBKazg9C7VcsZeV4ZaZdw%3D%3D; UM_distinctid=1642ba5252df3-062f70fb829da6-6b1b1279-ff000-1642ba5252e330; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1; thw=cn; hng=CN%7Czh-CN%7CCNY%7C156; _uab_collina=153951050952550241593612; _umdata=535523100CBE37C378FE937E2D4C337D51C3F4A10BD43036CBABF2F47CA16A32E9537C0D08377CE5CD43AD3E795C914C1D0B5F4E0AD62C7671F006234671942E; cookie2=14969d96aef000324d26d6bde647f3dd; _tb_token_=e6e35ef43e1b3; alitrackid=www.taobao.com; swfstore=133687; JSESSIONID=B8D1A10E659E01B8EAF9AB8FE2E7C7E0; lastalitrackid=login.taobao.com; whl=-1%260%260%261543820856592; v=0; unb=787123649; sg=690; _l_g_=Ug%3D%3D; skt=075a1eb5376f0bb9; cookie1=Vyh4R%2BvzaAhoQrGi%2FGoF6oQff8n1obVXKUwfwrnK%2BgM%3D; csg=bd372668; uc3=vt3=F8dByR1SqeqhF4LIcrw%3D&id2=VAmtG4JUGtBn&nk2=F5RAQI07NhhsoQs%3D&lg2=V32FPkk%2Fw0dUvg%3D%3D; existShop=MTU0MzgyMDkxOQ%3D%3D; tracknick=tb522552_66; lgc=tb522552_66; _cc_=Vq8l%2BKCLiw%3D%3D; dnk=tb522552_66; _nk_=tb522552_66; cookie17=VAmtG4JUGtBn; mt=ci=12_1; uc1=cookie16=W5iHLLyFPlMGbLDwA%2BdvAGZqLg%3D%3D&cookie21=WqG3DMC9Edo1TBf%2BfpfnBg%3D%3D&cookie15=VFC%2FuZ9ayeYq2g%3D%3D&existShop=false&pas=0&cookie14=UoTYNcNUR1FpiA%3D%3D&tag=8&lng=zh_CN; isg=BAYG4FtkphWb5XmvH3ECz67wV_xIz0uvswTAhvAv3CkE86QNUPeaMezBzy8_3EI5",
    "referer":"https://www.taobao.com/?spm=a230r.1.1581860521.1.72c91dd8qIhSS7",
    "User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"
}
response = requests.get("https://s.taobao.com/search?q=python&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20181203&ie=utf8",headers=headers)
print(response.text)

data_info = re.search(r'g_page_config = (.*?)g_srp_loadCss',response.text,re.S)
print(data_info.group(1))

运行结果：

{"pageName":"mainsrp","mods":{"shopcombotip":{"status":"hide"},"phonenav":{"status":"hide"},"debugbar":{"status":"hide"},"shopcombo":{"status":"hide"},"itemlist":{"status":"show","data":{"postFeeText":"运费","trace":"msrp_auction","auctions":[{"p4p":1,"p4pSameHeight":true,"nid":"579053564662","category":"","pid":"","title":"新书现货 Python基础编程 Python视频教程 Python编程从零基础到项目实战 <span class=H>python</span>爬虫 <span class=H>python</span>从入门到精通 Python编程从入门到实践","raw_title":"Python基础教程  Python编程从入门到实践","pic_url":"//g-search1.alicdn.com/img/bao/uploaded/i4/imgextra/i1/122353380/O1CN01udOP8Y1aq5B8XhIa2_!!0-saturn_solar.jpg","detail_url":"https://click.simba.taobao.com/cc_im?p=python&s=1508725690&k=537&e=N9rgPtWJJg17whLYVXvxqzasv5mlJdmeavbQrbRxbrJ7w6GWgm7QYFj%2BywDL%2B9mnv7v1ILHIka%2F1SUtFBbQajHY9b6dFa6iI6lmQ%2FbPwXDa%2BS6Ns4vgxiHE4D8HqXGWA4QDlrK8PYD1P728hcklBEyy8nov4mcZIIXk28oPYHvKoUxS5Jfa53n3fqHVxv7yJnAxqL1d8FKkJ2w8DJUdizD1h%2BqjRF20ctm0%2B1XXjbePjomPO445j03UwwiaWJqZ7UgnJlg3v9vwlOqV47l6y9QrO95DgW00jkPERa3OTMyOA2%2Bqrz0zNMZd5ZCzmT7mscancrZfManSPPvzMsX22VXSgR8TrdqS0T3YO7RaY8rAbXfMc%2BLVBkEOJF%2FJYXMNHAVgUnWpPyMh38qAgYtPNFoKTIQRyvagHZwF0jtsI1yl9GUGnE9gfRv5nHe21bkzgfC9a5fALG25keKxUEtylQX8x4TpTASYu4enVWb0xbv3rg7yf%2FdQ2CD6Z3xn2wmuT7PxWSS9KuO41B7f7y0SzJw%3D%3D","view_price":"49.80","view_fee":"0.00","item_loc":"上海","view_sales":"788人付款","comment_count":"","user_id":"3161150069","nick":"华心图书专营店","shopcard":{"levelClasses":[],"isTmall":true,"delivery":[0,1,224],"description":[0,0,0],"service":[0,1,728],"encryptedUserId":"UvGHLvFHbvmNLOQTT"},"icon":[{"title":"掌柜热卖宝贝","dom_class":"icon-service-remai","position":"1","show_type":"0","icon_category":"baobei","outer_text":"0","html":"","icon_key":"icon-service-remai","trace":"srpservice","traceIdx":0,"innerText":"掌柜热卖宝贝","url":"//re.taobao.com/search?keyword=python&refpid=420432_1006&frcatid=&"},{"title":"尚天猫，就购了","dom_class":"icon-service-tianmao","position":"1","show_type":"0","icon_category":"baobei","outer_text":"0","html":"","icon_key":"icon-service-tianmao","trace":"srpservice","traceIdx":1,"innerText":"天猫宝贝"}],"isHideIM":true,"isHideNick":false,"comment_url":"https://click.simba.taobao.com/cc_im?p=python&s=1508725690&k=537&e=N9rgPtWJJg17whLYVXvxqzasv5mlJdmeavbQrbRxbrJ7w6GWgm7QYFj%2BywDL%2B9mnv7v1ILHIka%2F1SUtFBbQajHY9b6dFa6iI6lmQ%2FbPwXDa%2BS6Ns4vgxiHE4D8HqXGWA4QDlrK8PYD1P728hcklBEyy8nov4mcZIIXk28oPYHvKoUxS5Jfa53n3fqHVxv7yJnAxqL1d8FKkJ2w8DJUdizD1h%2BqjRF20ctm0%2B1XXjbePjomPO445j03UwwiaWJqZ7UgnJlg3v9vwlOqV47l6y9QrO95DgW00jkPERa3OTMyOA2%2Bqrz0zNMZd5ZCzmT7mscancrZfManSPPvzMsX22VXSgR8TrdqS0T3YO7RaY8rAbXfMc%2BLVBkEOJF%2FJYXMNHAVgUnWpPyMh38qAgYtPNFoKTIQRyvagHZwF0jtsI1yl9GUGnE9gfRv5nHe21bkzgfC9a5fALG25keKxUEtylQX8x4TpTASYu4enVWb0xbv3rg7yf%2FdQ2CD6Z3xn2wmuT7PxWSS9KuO41B7f7y0SzJw%3D%3D&on_comment=1","shopLink":"https://click.simba.taobao.com/cc_im?p=python&s=1508725690&k=493&e=66RMAV3eBnh7whLYVXvxqzasv5mlJdmeavbQrbRxbrJ7w6GWgm7QYFj%2BywDL%2B9mnv7v1ILHIka%2FCWrNALXaFZ3Y9b6dFa6iI6lmQ%2FbPwXDa%2BS6Ns4vgxiHE4D8HqXGWA4QDlrK8PYD1P728hcklBEyy8nov4mcZIIXk28oPYHvKoUxS5Jfa53n3fqHVxv7yJnAxqL1d8FKkJ2w8DJUdizD1h%2BqjRF20ctm0%2B1XXjbePjomPO445j03UwwiaWJqZ7UgnJlg3v9vwlOqV47l6y9QrO95DgW00jkPERa3OTMyOA2%2Bqrz0zNMZd5ZCzmT7mscancrZfManSPPvzMsX22VXSgR8TrdqS0T3YO7RaY8rAbXfMc%2BLVBkEOJF%2FJYXMNHWfR9OjkA0MoUCaYu0Uc5bxdYmTLsWL%2FZeOc%2BO3js2uxB9nwCqX%2BZwS7iiB%2Be20wJTMSHBjV4UVaE3nUErWYsMR5bad6LEmP%2ByXk5pK4yzjI%3D"},{"i2iTags":{"samestyle":{"url":""},"similar":{"url":"/search?type=similar&app=i2i&rec_type=1&uniqpid=&nid=535882394166"}},"p4pTags":[],"nid":"535882394166","category":"50512007","pid":"","title":"<span 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View Code

注意：

re.S解析：上述中，如果不加re.S，获取数据会失败。原因如下↓

　　正则表达式中，“.”的作用是匹配除“\n”以外的任何字符，也就是说，它是在一行中进行匹配。这里的“行”是以“\n”进行区分的。字符串有每行的末尾有一个“\n”，不过它不可见。如果不使用re.S参数，则只在每一行内进行匹配，如果一行没有，就换下一行重新开始，不会跨行。而使用re.S参数以后，正则表达式会将这个字符串作为一个整体，将“\n”当做一个普通的字符加入到这个字符串中，在整体中进行匹配。

拿到需要的数据，再到json在线对数据进行解析：https://www.json.cn/

解析到的数据：

从截图中可以发现列表中所有数据都存放在data下auctions中的，共有48条数据，每条数据如右边截图显示。

数据分析完毕，接下来可以完善我们的代码了 → 实现数据持久化，将数据写入到csv文件中：

import requests
import re
import json

headers = {
    "cookie":"miid=9053363428195873751; l=Aunpx9THxIGX125nyLMMqSUgeZtCed3s; cna=ZG8KEADNMnICAXQWOuLdZHNa; t=1cb6790d77c8096d480c9ce5a3f89a8f; tg=0; enc=%2BrqQXxrsw0U9av2EDCsjFkuCYqoGqRLOWIHazUt5bouZ39CV70ZxlwAkOUdFasya7CBKazg9C7VcsZeV4ZaZdw%3D%3D; UM_distinctid=1642ba5252df3-062f70fb829da6-6b1b1279-ff000-1642ba5252e330; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1; thw=cn; hng=CN%7Czh-CN%7CCNY%7C156; _uab_collina=153951050952550241593612; _umdata=535523100CBE37C378FE937E2D4C337D51C3F4A10BD43036CBABF2F47CA16A32E9537C0D08377CE5CD43AD3E795C914C1D0B5F4E0AD62C7671F006234671942E; cookie2=14969d96aef000324d26d6bde647f3dd; _tb_token_=e6e35ef43e1b3; alitrackid=www.taobao.com; swfstore=133687; JSESSIONID=B8D1A10E659E01B8EAF9AB8FE2E7C7E0; lastalitrackid=login.taobao.com; whl=-1%260%260%261543820856592; v=0; unb=787123649; sg=690; _l_g_=Ug%3D%3D; skt=075a1eb5376f0bb9; cookie1=Vyh4R%2BvzaAhoQrGi%2FGoF6oQff8n1obVXKUwfwrnK%2BgM%3D; csg=bd372668; uc3=vt3=F8dByR1SqeqhF4LIcrw%3D&id2=VAmtG4JUGtBn&nk2=F5RAQI07NhhsoQs%3D&lg2=V32FPkk%2Fw0dUvg%3D%3D; existShop=MTU0MzgyMDkxOQ%3D%3D; tracknick=tb522552_66; lgc=tb522552_66; _cc_=Vq8l%2BKCLiw%3D%3D; dnk=tb522552_66; _nk_=tb522552_66; cookie17=VAmtG4JUGtBn; mt=ci=12_1; uc1=cookie16=W5iHLLyFPlMGbLDwA%2BdvAGZqLg%3D%3D&cookie21=WqG3DMC9Edo1TBf%2BfpfnBg%3D%3D&cookie15=VFC%2FuZ9ayeYq2g%3D%3D&existShop=false&pas=0&cookie14=UoTYNcNUR1FpiA%3D%3D&tag=8&lng=zh_CN; isg=BAYG4FtkphWb5XmvH3ECz67wV_xIz0uvswTAhvAv3CkE86QNUPeaMezBzy8_3EI5",
    "referer":"https://www.taobao.com/?spm=a230r.1.1581860521.1.72c91dd8qIhSS7",
    "User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"
}
response = requests.get("https://s.taobao.com/search?q=python&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20181203&ie=utf8",headers=headers)
# print(response.text)

data_info = re.search(r'g_page_config = (.*?)g_srp_loadCss',response.text,re.S)
data_info = data_info.group(1)
data_info = data_info.strip(" \n;")
data_dict = json.loads(data_info)  # 字典格式
data = data_dict['mods']['itemlist']['data']['auctions']   # 数据所在位置

f = open("python搜索结果.csv",'w',encoding="utf-8")  # 数据持久化处理-->将数据写入csv文件中
f.write("标题,价格,购买人数,评论,是否包邮,是否天猫,地区,店名,url\n")

for item in data:
    temp = {
        'title':item['title'],
        'view_price':item['view_price'],
        'view_sales':item['view_sales'],
        'comment_count':int(item['comment_count']) if item['comment_count'] else 0,
        'view_fee':'否' if float(item['view_fee']) else '是',
        'isTmall':'是' if item['shopcard']['isTmall'] else '否',
        'area':item['item_loc'],
        'shop_name':item['nick'],
        'detail_url':item['detail_url']
    }
    f.write('{title},{view_price},{view_sales},{comment_count},{view_fee},{isTmall},{area},{shop_name},{detail_url}\n'.format(**temp))
f.close()

完整代码

运行上述程序，在csv文件中查看数据是否写入成功：

数据写入成功，共48条数据

现在，我们稍微改动下上面代码，试程序能实现：客户手动输入搜索关键词，根据客户输入的关键词查找数据，并写入到csv文件中

import requests
import re
import json

headers = {
    "cookie":"miid=9053363428195873751; l=Aunpx9THxIGX125nyLMMqSUgeZtCed3s; cna=ZG8KEADNMnICAXQWOuLdZHNa; t=1cb6790d77c8096d480c9ce5a3f89a8f; tg=0; enc=%2BrqQXxrsw0U9av2EDCsjFkuCYqoGqRLOWIHazUt5bouZ39CV70ZxlwAkOUdFasya7CBKazg9C7VcsZeV4ZaZdw%3D%3D; UM_distinctid=1642ba5252df3-062f70fb829da6-6b1b1279-ff000-1642ba5252e330; x=e%3D1%26p%3D*%26s%3D0%26c%3D0%26f%3D0%26g%3D0%26t%3D0%26__ll%3D-1; thw=cn; hng=CN%7Czh-CN%7CCNY%7C156; _uab_collina=153951050952550241593612; _umdata=535523100CBE37C378FE937E2D4C337D51C3F4A10BD43036CBABF2F47CA16A32E9537C0D08377CE5CD43AD3E795C914C1D0B5F4E0AD62C7671F006234671942E; cookie2=14969d96aef000324d26d6bde647f3dd; _tb_token_=e6e35ef43e1b3; alitrackid=www.taobao.com; swfstore=133687; JSESSIONID=B8D1A10E659E01B8EAF9AB8FE2E7C7E0; lastalitrackid=login.taobao.com; whl=-1%260%260%261543820856592; v=0; unb=787123649; sg=690; _l_g_=Ug%3D%3D; skt=075a1eb5376f0bb9; cookie1=Vyh4R%2BvzaAhoQrGi%2FGoF6oQff8n1obVXKUwfwrnK%2BgM%3D; csg=bd372668; uc3=vt3=F8dByR1SqeqhF4LIcrw%3D&id2=VAmtG4JUGtBn&nk2=F5RAQI07NhhsoQs%3D&lg2=V32FPkk%2Fw0dUvg%3D%3D; existShop=MTU0MzgyMDkxOQ%3D%3D; tracknick=tb522552_66; lgc=tb522552_66; _cc_=Vq8l%2BKCLiw%3D%3D; dnk=tb522552_66; _nk_=tb522552_66; cookie17=VAmtG4JUGtBn; mt=ci=12_1; uc1=cookie16=W5iHLLyFPlMGbLDwA%2BdvAGZqLg%3D%3D&cookie21=WqG3DMC9Edo1TBf%2BfpfnBg%3D%3D&cookie15=VFC%2FuZ9ayeYq2g%3D%3D&existShop=false&pas=0&cookie14=UoTYNcNUR1FpiA%3D%3D&tag=8&lng=zh_CN; isg=BAYG4FtkphWb5XmvH3ECz67wV_xIz0uvswTAhvAv3CkE86QNUPeaMezBzy8_3EI5",
    "referer":"https://www.taobao.com/?spm=a230r.1.1581860521.1.72c91dd8qIhSS7",
    "User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36"
}
search_k = input("请输入您要搜索的商品关键词：") # 用户输入
response = requests.get("https://s.taobao.com/search?q={}&imgfile=&js=1&stats_click=search_radio_all%3A1&initiative_id=staobaoz_20181203&ie=utf8".format(search_k),headers=headers)
# print(response.text)

data_info = re.search(r'g_page_config = (.*?)g_srp_loadCss',response.text,re.S)
data_info = data_info.group(1)
data_info = data_info.strip(" \n;")
data_dict = json.loads(data_info)  # 字典格式
data = data_dict['mods']['itemlist']['data']['auctions']   # 数据所在位置

f = open("{}搜索结果.csv".format(search_k),'w',encoding="utf-8")  # 数据持久化处理-->将数据写入csv文件中
f.write("标题,价格,购买人数,评论,是否包邮,是否天猫,地区,店名,url\n")

for item in data:
    temp = {
        'title':item['title'],
        'view_price':item['view_price'],
        'view_sales':item['view_sales'],
        'comment_count':int(item['comment_count']) if item['comment_count'] else 0,
        'view_fee':'否' if float(item['view_fee']) else '是',
        'isTmall':'是' if item['shopcard']['isTmall'] else '否',
        'area':item['item_loc'],
        'shop_name':item['nick'],
        'detail_url':item['detail_url']
    }
    f.write('{title},{view_price},{view_sales},{comment_count},{view_fee},{isTmall},{area},{shop_name},{detail_url}\n'.format(**temp))
f.close()

根据关键字，自动搜索商品信息并保存到csv文件

运行：

此时，多了一个java搜索结果.csv的文件：

至此，爬取淘宝商品数据的功能实现便告一段落了，当然，这只是基础的实现，再进一步可以将每一页的商品列表数据都爬取下来，以及每个商品详情页的详细数据。关于这些便不再累述，有兴趣的猿友可以试试。

posted on 2018-11-21 16:57 Eric_nan 阅读(2259) 评论(0) 收藏举报

1、爬取豆瓣正热映电影

2、爬取电影天堂-->最新电影

3、爬取中国天气网各个城市的气温情况，并进行可视化处理

4、多线程爬虫应用

1）多线程-->condition版生产者-消费者模型

2）爬取斗图网斗图图片

5、动态网页爬取

1）ajax请求页面爬取

2）爬取拉钩网职位信息 -爬取ajax动态网页

3）使用selenium实现拉钩数据爬取

具体实现：

*** 验证码部分

1）tesseract的使用

Scrapy--爬虫相关

一、使用Scrapy模拟登录豆瓣网

二、使用scrapy --> 爬取汽车之家-宝马5系列图片

1、使用自定义保存图片的方式编写代码（同步下载）：

2、使用scrapy自带文件/图片保存方式编写代码（异步下载）：

bmw5.py（几乎没变化）：

item.py：image_urls、images需固定式命名

pipelines.py：重写ImagesPipeline类

settings.py：

*** 下载器中间件

** boss直聘网--python招聘数据爬取

爬取页面：

1）搜索python职位，将搜索页面每一页的页码url都爬取下来：

2）将每一页的每个python招聘信息的url进行爬取，并调用回调函数对其对应详情页面进行分析，数据爬取及保存：

1、代码实现：

1）实现招聘页面数据爬取：

2）item数据类型：

3）pipelines数据保存--> 保存到json文件：

4）settings.py配置：

5）执行文件：

2、进阶

1）实现User_Agent随机化

正式使用：

settings配置：

middlewares.py中实现随机user-agent：

2）实现随机代理ip

方式一（强烈推荐）：

新建model_proxy.py文件，实现代码：

在middlewares.py中新建类： IPProxyDownloadMiddleware

实现代码：

settings.py中配置：

** 简书网整站爬虫应用

1、数据爬取：

1）js.py：爬虫主程序

2）item.py

2、将数据存入mysql中

** 使用requests实现淘宝网商品信息爬取

我们使用requests库来模拟淘宝数据请求：

数据分析完毕，接下来可以完善我们的代码了 → 实现数据持久化，将数据写入到csv文件中 ：

现在，我们稍微改动下上面代码，试程序能实现：客户手动输入搜索关键词，根据客户输入的关键词查找数据，并写入到csv文件中

数据分析完毕，接下来可以完善我们的代码了 → 实现数据持久化，将数据写入到csv文件中：