Python学习的第四天

爬虫

• 在Gogle浏览器上安装Xpath Helper插件
• 实例：爬图书的价格，排序等

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)))
    # print('qwertyui', ul_list)

    # 遍历 ul_list
    for li in ul_list:
        #  图书名称
        title = li.xpath('./a/@title')[0].strip()
        print('title---', 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]
        #print(price)
        price = float(price.replace('¥',''))
        print(price)
        # 图书卖家名称
        store = li.xpath('./p[@class="search_shangjia"]/a/text()')
        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')

• 作业：爬电影网站，得到电影名、想看人数等信息，绘制分析图

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_douban():
    movie_list = []
    # 目标站点地址
    url = 'https://movie.douban.com/cinema/later/chongqing/?qq-pf-to=pcqq.group'
    print(url)
    # 添加头文件，伪装成浏览器，防止被发现
    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
    selector = html.fromstring(html_data)
    ul_list = selector.xpath("//div[@id='showing-soon']/div")
    print('重庆即将上映的电影有',len(ul_list),'部') #打印有多少部电影
    # 遍历 ul_list
    for li in ul_list:
        i = 0
        # i = 0
        # 电影名
        name_movie = li.xpath("./div/h3/a/text()")[0]
        # print('电影名', name_movie)
        # 上映日期
        line_data = li.xpath("./div/ul/li[1]/text()")[i]
        # print('上映日期', line_data)
        # 类型
        movie_type = li.xpath("./div/ul/li[2]/text()")[i]
        # print('电影类型', movie_type)
        # 上映国家
        movie_city = li.xpath("./div/ul/li[3]/text()")[i]
        # print('上映国家', movie_city)
        # 想看人数
        movie_wantnum = li.xpath("./div/ul/li/span/text()")[i]
        # print('想看人数', movie_wantnum)
        movie_wantnum = int(movie_wantnum.replace('人想看', ''))
        movie_list.append({
            '电影名': name_movie,
            '上映日期': line_data,
            '类型': movie_type,
            '上映国家': movie_city,
            '想看人数': movie_wantnum,
        })
        i += 1
    movie_list.sort(key=lambda a: a['想看人数'], reverse=True)
    print(movie_list)
    movie_city = []
    for i in movie_list:
        movie_city.append(i['上映国家'])
    print('上映国家', movie_city)
    ################# 绘制上映国家云词 
    text = ' '.join(movie_city)
    print(text)
    from wordcloud import WordCloud
    import imageio
    mask = imageio.imread('./image/china.jpg')
    WordCloud(
        font_path='msyh.ttc',
        background_color='black',
        width=800,
        height=600,
        collocations=False,  # 相邻两个重复词之间的匹配
        mask=mask
    ).generate(text).to_file('上映国家.png')

    # 绘制 上映国家占比
    from random import randint
    from matplotlib import pyplot as plt
    # 解决乱码
    plt.rcParams["font.sans-serif"] = ['SimHei']
    plt.rcParams['axes.unicode_minus'] = False
    import numpy as np
    counts = {} # 上映国家和人数
    lli = [] # 上映国家
    lii = [] # 每个国家出现的次数
    for i in movie_city:
        counts[i] = counts.get(i, 0)+1
    print(counts)
    items = list(counts.items())  # 将字典转换为列表
    print(len(items))
    items.sort(key=lambda x: x[1], reverse=True)  # 使用函数从大到小排序
    for i in range(len(items)): # 有多少个国家循环多少次，解包 出国家和国家的次数
        role, count = items[i]  # 序列解包
        lli.append(count)
        lii.append(role)
    #     for _ in range(count):
    #         li.append(role)
    # print(li)
    print(lii)
    print(lli)
    explode = [0.1, 0, 0, 0]
    plt.pie(lli, explode=explode, shadow=True, labels=lii, autopct='%1.1f%%')

    # top5_store = [movie_list[i] for i in range(5)]
    name_movie = [] #电影名
    for i in movie_list:
        name_movie.append(i['电影名'])
        if len(name_movie) >= 5:
            break
    print('电影名', name_movie)
    # 想看人数
    movie_wantnum = []
    for i in movie_list:
        movie_wantnum.append(i['想看人数'])
        if len(movie_wantnum) >= 5:
            break
    print('想看人数', movie_wantnum)
    plt.barh(name_movie, movie_wantnum)

    plt.show()


spider_douban()


# # # 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)

上映国家云图.PNG

上映国家占比.PNG

想看人数TOP5电影.PNG