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python学习第四天

python学习第四天

作者: 梅若吖 | 来源:发表于2019-07-31 18:55 被阅读0次

    1.爬虫

    1. 大数据 , 提取本地hmtl中的数据
    2. 步骤
      ①新建html文件
      ②读取
      ③使用lxml中的xpath语法进行提取
    from lxml import html
    # 读取html文件
    with open('./index.html', 'r', encoding='utf-8') as f:
        html_data = f.read()
        # selector中调用xpath方法
        selector = html.fromstring(html_data)
        # 要获取标签中的内容,末尾要添加text()
        h1 = selector.xpath('/html/body/h1/text()')
        print(h1[0])
        # //可以从任意位置出发
        # //标签1[@属性=属性值]/标签2[@属性=属性值].../text()
        a = selector.xpath('//div[@id="container"]/a/text()')
        print(a[0])
        # 获取p标签内容
        p = selector.xpath('//div[@id="container"]/p/text()')
        print(p[0])
        # 获取属性 @属性名
        link = selector.xpath('//div[@id="container"]/a/@href')
        print(link[0])
    
    image.png

    2.关于requests

    # 导入
    import requests
    url = 'https://www.baidu.com'
    # url = 'https://www.taobao.com'
    # url = 'https://www.jd.com'
    response = requests.get(url)
    print(response)
    # 获取str类型的响应
    print(response.text)
    # 获取bytes类型的响应
    print(response.content)
    # 获取响应头
    print(response.headers)
    # 获取状态码
    print(response.status_code)
    # 编码方式
    print(response.encoding)
    # 没有添加请求头的知乎网网站
    # resp = requests.get('https://www.zhihu.com/')
    # print(resp.status_code)
    # 使用字典定义请求头
    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('https://www.zhihu.com/', headers = headers)
    print(resp.status_code)
    
    image.png

    3.爬当当网

    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):
        book_list = []
        # 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)
            # 添加每一个商家的图书信息
            book_list.append({
                'title': title,
                'price': price,
                'link': link,
                'store': store
            })
        # 按照价格进行排序
        book_list.sort(key=lambda x:x['price'])
        # 遍历booklist
        for book in book_list:
            print(book)
        # 展示价格最低的前10家 柱状图
        # 店铺的名称
        top10_store = [book_list[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.bar(x, y)
        plt.barh(x, y)
        plt.show()
        # 存储成csv文件
        df = pd.DataFrame(book_list)
        df.to_csv('dangdang.csv')
    spider_dangdang('9787115428028')
    
    image.png
    image.png
    价格最低Top10

    4.练习--爬重庆-影讯

    import requests
    from lxml import html
    from matplotlib import pyplot as plt
    plt.rcParams["font.sans-serif"] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    people_list = []
    counts = []
    # 目标站点地址
    url = 'https://movie.douban.com/cinema/later/chongqing/'
    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
    # 提取目标站的信息
    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
        people = li.xpath('./ul/li/span/text()')[0]
        people = int(people.replace('人想看', ''))
        # print(people)
        people_list.append({
            'title': title,
            'date': date,
            'type': type,
            'country': country,
            'people': people
        })
        counts.append(country)
    # 按照想看人数进行排序
    people_list.sort(key=lambda x:x['people'], reverse=True)
    # 遍历people_list
    for num in people_list:
        print(num)
    # 展示想看人数top5
    top5 = [people_list[i] for i in range(5)]
    x = [x['title'] for x in top5]
    print(x)
    y = [x['people'] for x in top5]
    print(y)
    plt.barh(x, y)
    plt.show()
    # 国家占比
    china = 0
    japan = 0
    hongkong = 0
    russia = 0
    for i in range(22):
        if counts[i] == '中国大陆':
            china += 1
        elif counts[i] == '日本':
            japan += 1
        elif counts[i] == '香港':
            hongkong += 1
        else:
            russia += 1
    count1 = ['中国大陆', '日本', '香港', '俄罗斯']
    count2 = [china, japan, hongkong, russia]
    plt.pie(count2, shadow=True, labels=count1, autopct='%1.1f%%')
    plt.legend(loc=2)
    plt.axis('equal')
    plt.show()
    
    image.png
    展示想看人数top5
    电影国家占比

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