第三次作业

作业①

单、多线程爬取照片

要求：指定一个网站，爬取这个网站中的所有的所有图片，例如中国气象网（http://www.weather.com.cn）。分别使用单线程和多线程的方式爬取。
输出信息：将下载的Url信息在控制台输出，并将下载的图片存储在images子文件中，并给出截图。

单线程代码：

from bs4 import BeautifulSoup
from bs4 import UnicodeDammit
import urllib.request
import time

def imageSpider(start_url):
    global thread
    try:
        urls = []
        req = urllib.request.Request(start_url, headers=headers)
        data = urllib.request.urlopen(req)
        data = data.read()
        dammit = UnicodeDammit(data, ["utf-8", "gbk"])
        data = dammit.unicode_markup
        soup = BeautifulSoup(data, "lxml")
        images1 = soup.select("img")
        for image in images1:
            try:
                src = image["src"]
                url = urllib.request.urljoin(start_url, src)
                if url not in urls:
                    urls.append(url)
                    download(url)
            except Exception as err:
                print(err)
    except Exception as err:
        print(err)

def download(url):
    try:
        if url[-4] == ".":
            name = url.split("/")[-1]
            req = urllib.request.Request(url, headers=headers)
            data = urllib.request.urlopen(req, timeout=100)
            data = data.read()
            fobj = open('D:/new file/images1/' + name, "wb")    #创建打开文件夹
            fobj.write(data)
            fobj.close()
            print("downloaded " + name)
    except Exception as err:
        print(err)

start_url = "http://www.weather.com.cn/weather1d/101280501.shtml"
headers = {
    "User-Agent": "Mozilla/5.0 (Windows; U; Windows NT 6.0 x64; en-US;rv:1.9pre) Gecko/2008072421 Minefield/3.0.2pre"}
thread = []

time_start = time.time()   #开始计时
imageSpider(start_url)
print("the End")
time_end = time.time()   #计时结束
print(time_end - time_start)    #输出耗时

运行结果部分截图：

多线程代码：

from bs4 import BeautifulSoup
from bs4 import UnicodeDammit
import urllib.request
import threading
import time


def imageSpider(start_url):
    global thread
    try:
        urls = []
        req = urllib.request.Request(start_url, headers=headers)
        data = urllib.request.urlopen(req)
        data = data.read()
        dammit = UnicodeDammit(data, ["utf-8", "gbk"])
        data = dammit.unicode_markup
        soup = BeautifulSoup(data, "lxml")
        images1 = soup.select("img")
        for image in images1:
            try:
                src = image["src"]
                url = urllib.request.urljoin(start_url, src)
                if url not in urls:
                    urls.append(url)
                    T = threading.Thread(target=download, args=(url,))
                    T.setDaemon(False)
                    T.start()
                    thread.append(T)
            except Exception as err:
                print(err)
    except Exception as err:
        print(err)


def download(url):
    try:
        if url[-4] == ".":
            name = url.split("/")[-1]
            req = urllib.request.Request(url, headers=headers)
            data = urllib.request.urlopen(req, timeout=100)
            data = data.read()
            fobj = open('D:/new file/images/' + name, "wb")
            fobj.write(data)
            fobj.close()
            print("downloaded " + name)
    except Exception as err:
        print(err)


start_url = "http://www.weather.com.cn/weather1d/101280501.shtml"
headers = {
    "User-Agent": "Mozilla/5.0 (Windows; U; Windows NT 6.0 x64; en-US;rv:1.9pre) Gecko/2008072421 Minefield/3.0.2pre"}
thread = []

time_start = time.time()
imageSpider(start_url)
for t in thread:
    t.join()
print("the End")
time_end = time.time()
print(time_end - time_start)

运行结果部分截图：

心得体会

实践了一下多线程的代码，也解决了遇到的问题。（对于thread.Thread()的参数args是一个数组变量参数，如果只传递一个值，就只需要url, 如果需要传递多个参数，那么还可以继续传递下去其他的参数，其中的逗号不能少，元组中只包含一个元素时，需要在元素后面添加逗号。）

作业②

scrapy爬取照片

要求：使用scrapy框架复现作业①。
输出信息：同作业①

weather.py

import scrapy
from ..items import WeatherJpgItem
from scrapy.selector import Selector

class WeatherSpider(scrapy.Spider):
    name = 'weather'
    start_urls = ['http://www.weather.com.cn/']

    def parse(self, response):
        selector=Selector(text=response.text)
        srclist=selector.xpath("//img/@src").extract()
        for src in srclist:
            item=WeatherJpgItem()
            item['src']=src
            yield item

items.py

import scrapy

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

settings.py

BOT_NAME = 'weather_jpg'

SPIDER_MODULES = ['weather_jpg.spiders']
NEWSPIDER_MODULE = 'weather_jpg.spiders'
ROBOTSTXT_OBEY = False
LOG_LEVEL='ERROR'
ITEM_PIPELINES = {
    'weather_jpg.pipelines.imgpipelines':1,
}
IMAGES_STORE = 'D:/new file/images'  #图片保存地址

pipilenes.py

from scrapy.pipelines.images import ImagesPipeline
import scrapy
class imgpipelines(ImagesPipeline):   #自定义一个管道类

    def get_media_requests(self, item, info):
        #根据图片地址进行图片数据的请求
        yield scrapy.Request(item['src'])

    #指定存储路径
    def file_path(self, request, response=None, info=None):
        imgname=request.url.split("/")[-1]
        return imgname

    def item_completed(self, results, item, info):
        return item    #返回给下一个即将被执行的管道类

运行结果部分截图：

心得体会

初步体验scrapy框架,对于图片爬取有点了解。

作业③

scrapy爬取股票

要求：使用scrapy框架爬取股票相关信息
候选网站：东方财富网：https://www.eastmoney.com/
新浪股票：http://finance.sina.com.cn/stock/
输出信息：

stocks.py

import scrapy
import re
import json
from demo.items import StocksItem
import math


class StocksSpider(scrapy.Spider):
    name = 'stocks'
    start_urls = [
        'http://31.push2.eastmoney.com/api/qt/clist/get?cb=jQuery112409705185363781139_1602849464971&pn=1&pz=20&po=1&np=1&ut=bd1d9ddb04089700cf9c27f6f7426281&fltt=2&invt=2&fid=f3&fs=m:0+t:6,m:0+t:13,m:0+t:80,m:1+t:2,m:1+t:23&fields=f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152&_=1602849464977']

    def parse(self, response):
        try:
            data = response.body.decode()
            datas = re.findall("{.*?}", data[re.search("\[", data).start():])  # 获取每支股票信息,一个{...}对应一支
            for n in range(len(datas)):
                stock = json.loads(datas[n])  # 文本解析成json格式
                item = StocksItem()  # 获取相应的变量
                item['code'] = stock['f12']
                item['name'] = stock['f14']
                item['latest_price'] = str(stock['f2'])
                item['range'] = str(stock['f3'])
                item['amount'] = str(stock['f4'])
                item['trading'] = str(stock['f5'])
                yield item
            all_page = math.ceil(eval(re.findall('"total":(\d+)', response.body.decode())[0]) / 20)  # 获取页数
            page = re.findall("pn=(\d+)", response.url)[0]  # 当前页数
            if int(page) < all_page:  # 判断页数
                url = response.url.replace("pn=" + page, "pn=" + str(int(page) + 1))  # 跳转下一页
                yield scrapy.Request(url=url, callback=self.parse)  # 函数回调
        except Exception as err:
            print(err)

itams.py

import scrapy


class StocksItem(scrapy.Item):
    # define the fields for your item here like:
    # name = scrapy.Field()
    code=scrapy.Field()               #对象结构定义
    name=scrapy.Field()
    latest_price=scrapy.Field()
    range=scrapy.Field()
    amount=scrapy.Field()
    trading=scrapy.Field()

pipielines.py

class StocksPipeline:
    count = 0
    fp = None

    # 开启爬虫时执行，只执行一次
    def open_spider(self, spider):
        print("打开文件，开始写入数据")
        self.fp = open("D:/new file/stocks.txt", 'w')
        self.fp.write(
            "    序号    	    代码    	　　　　             名称　　　　	                 最新价    	  涨跌幅     涨跌额    	  成交量")
        self.fp.write("\n")

        # 保存数据
    def process_item(self, item, spider):
        StocksPipeline.count += 1
        tplt = "{0:^10}\t{1:^10}\t{2:{7}^10}\t{3:^10}\t{4:^10}\t{5:^10}\t{6:^10}"
        try:
            self.fp.write(
                tplt.format(StocksPipeline.count, item["code"], item["name"], item['latest_price'], item['range'],
                            item['amount'], item['trading'], chr(12288)))
            self.fp.write("\n")
            return item
        except Exception as err:
            print(err)

        # 关闭爬虫时执行，只执行一次（如果爬虫中间发生异常导致崩溃，close_spider可能也不会执行）
        def close_spider(self, spider):
            print("写入结束，关闭文件")
            self.fp.close()

        return item

settings.py

BOT_NAME = 'demo'

SPIDER_MODULES = ['demo.spiders']
NEWSPIDER_MODULE = 'demo.spiders'
ROBOTSTXT_OBEY =False
ITEM_PIPELINES = {
    'demo.pipelines.StocksPipeline': 300,
}

运行结果部分截图：

心得体会

了解到到scrapy在爬取设定的url之前，它会先向服务器根目录请求一个txt文件，这个文件规定了爬取范围，scrapy会遵守这个范围协议，查看自己是否符合权限，如果爬取目标目录在目标网站的robots.txt文本中列为disallow，则scrapy会放弃爬取动作。通常被列为disallow的目录都是敏感目录，比如需要用户登录之后才能访问的目录等。这个有点君子协定的意思，但是有时候我们的好奇心驱使我们需要做小人，于是只能禁用这个配置，违反这个协定，更改方法：ROBOTSTXT_OBEY = False