Python实训---第四天

Python实训---第四天

爬虫

1. 新建html文件
2. 读取
3. 使用xpath语法进行提取

#调用Lxml
from lxml import  html

#读取本地html文件
with open('./index.html','r',encoding='utf-8') as f:
    html_data = f.read()
   
    # 解析html文件，获得selector对象,解析树的根结点
    selector = html.fromstring(html_data)

    # selector中调用xpath方法，提取h1标签中的内容
    h1=selector.xpath('/html/body/h1/text()')
    print(h1)

request获取响应

#导入
import requests
url='https://www.baidu.com'
response=requests.get(url)
print(response)
#获取str类型的响应
#response常用
print(response.text)
#获取bytes类型的响应,下载图片用到
print(response.content)
#获取响应头，
print(response.headers)
#获取状态码:200 404 500
print(response.status_code)
#获取编码
print(response.encoding)

当当网爬虫

import requests
from lxml import html
import pandas as pd
from matplotlib import pyplot as plt
plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def spider_dangdang(isbn):
    booklist=[]
    #目标站点地址
    url='http://search.dangdang.com/?key={}&act=input'.format(isbn)
    #print(url)
    #获取站点str类型的响应
    headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}

    resp=requests.get(url,headers=headers)
    html_data=resp.text
    #将html页面写入本地
    # with open('./dangdang.html','w',encoding='utf-8') as f:
    #     f.write(html_data)

    #提取目标站点的信息
    selector=html.fromstring(html_data)
    ul_list=selector.xpath('//div[@id="search_nature_rg"]/ul/li')
    print('您好，共有{}家店铺售卖此图书'.format(len(ul_list)))
    #遍历ul_list
    for li in ul_list:
        #图书名称
        title=li.xpath('./a/@title')[0].strip()
        print(title)
        # 图书购买链接
        link = li.xpath('a/@href')[0]
        print(link)
        #图书价格
        price=li.xpath('./p[@class="price"]/span[@class="search_now_price"]/text()')[0]
        price=float(price.replace('¥',''))
        print(price)

        #图书卖家名称
        store=li.xpath('./p[@class="search_shangjia"]/a/text()')
        if len(store)==0:
            store='当当自营'
        else:
            store=store[0]
        #store ='当当自营' if len(store)==0 else store[0]
        print(store)

        #添加每一个商家信息
        booklist.append({
            'title':title,
            'price':price,
            'link':link,
            'store':store
        })
        #按照价格进行排序
    booklist.sort(key=lambda x:x['price'],reverse=True)
        #遍历booklist
    for book in booklist:
        print(book)
        #展示价格最低的前10家  柱状图
        #店铺名称
    top10_store=[booklist[i] for i in range(10)]
        # x=[]
        # for store in top10_store:
        #     x.append(store['store'])
    x=[x['store'] for x in top10_store]
    print(x)
        #图书的价格
    y=[x['price'] for x in top10_store]
    print(y)
    plt.barh(x,y)
    plt.show()
        #存储为CSV文件
    df=pd.DataFrame(booklist)
    df.to_csv('dangdang.csv')
spider_dangdang('9787115428028')

豆瓣网爬虫

#练习：https://movie.douban.com/cinema/later/chongqing/
#电影名，上映日期，类型，上映国家，想看人数
#根据想看人数进行排序
#绘制即将上映电影国家的占比图
#绘制top5最想看的电影

#请求远程端站点
import requests
from lxml import  html
import pandas as pd
from matplotlib import pyplot as plt

plt.rcParams["font.sans-serif"] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

counts={}
# 目标站点地址
def spider_douban():
    movie_list=[]
    url = 'https://movie.douban.com/cinema/later/chongqing/'
    headers = {"User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36"}
    resp = requests.get(url, headers=headers)
    html_data = resp.text

#  将html页面写入本地
#     with open('dangdang.html', 'w', encoding='utf-8') as f:
#         f.write(html_data)
    #提取目标站信息
    selector = html.fromstring(html_data)
    ul_list = selector.xpath('//div[@id="showing-soon"]/div/div')
    print('您好，共有{}部电影即将在重庆上映'.format(len(ul_list)))

    # 遍历ul_list
    for li in ul_list:
        # 电影名称
        title = li.xpath('./h3/a/text()')[0].strip()
        print(title)
        # 上映日期
        date = li.xpath('./ul/li/text()')[0]
        print(date)
        # 类型
        type = li.xpath('./ul/li/text()')[1]
        print(type)

        # 上映国家
        country = li.xpath('./ul/li/text()')[2]
        print(country)
        # 想看人数
        num = li.xpath('./ul/li/span/text()')[0]
        print(num)
        num = int(num.replace('人想看', ''))

        #添加电影信息
        movie_list.append({
            'title':title,
            'date': date,
            'type':type,
            'country':country,
            'num':num
        })

    #按照人数进行排序
    movie_list.sort(key=lambda x:x['num'],reverse=True)

    #遍历booklist
    for movie in movie_list:
        print(movie)

    #画饼图，把国家提取出来
    city=[]
    # 提取国家信息
    for country in movie_list:
        city.append((country['country']))

    # 将国家信息汇总
    for country in city:
        if len(country) <= 1:
            continue
        else:
            counts[country] = counts.get(country, 0) + 1
    items = list(counts.items())
    print(items)

    movie_name=[]
    people=[]
    for i in range(4):
        role, count = items[i]
        print(role, count)
        movie_name.append(role)
        people.append(count)


     #绘制即将上映电影国家的占比图，饼图

    explode = [0.1, 0, 0, 0]
    plt.pie(people, explode=explode,labels=movie_name, shadow=True, autopct='%1.1f%%')
    plt.axis('equal')  # 保证饼状图是正圆，否则会有点扁
    plt.show()


    # 展示最想看的前5家，柱状图
    # 电影名称
    top5_movie = [movie_list[i] for i in range(5)]
    print(top5_movie)
    x = [x['title'] for x in top5_movie]
    print(x)
    # 想看人数
    y = [x['num'] for x in top5_movie]
    print(y)

    plt.bar(x,y)
    #plt.barh(x,y)
    plt.show()
    存储成csv文件
    df = pd.DataFrame(movie_list)
    df.to_csv('douban.csv')

spider_douban()