Python分析春节档七部电影的口碑和票房数据，并可视化显示

以你之姓@ 2022-10-31 00:48 228阅读 0赞

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以下文章来源于Python爬虫数据分析挖掘 ，作者李运辰

**Python爬虫、数据分析、网站开发等案例教程视频免费在线观看**

https://space.bilibili.com/523606542

# ![a2b6984089fbb14c9cf0ff607a09682a.png][] #

# 前言 #

今年给大家爬取**『大年初一』**上映的几部热门数据（**评分**、**时长**、**类型**）以及相关网友评论等数据

对评分、时长、类型进行**图表可视化**

采用不同词云图案对**七部电影『评论』词云秀**！！！！

# 数据获取 #

**1.评分数据**

网页分析

![04ed74c43df8f05a16745ef29ac1509a.png][]

查看网页源代码，可以见到目标数据在标签<ul class="lists">,通过xpath解析就可以获取。下面直接上代码！

编程实现

headers = {
                'Host':'movie.douban.com',
                'user-agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3947.100 Safari/537.36',
                'cookie':'bid=uVCOdCZRTrM; douban-fav-remind=1; __utmz=30149280.1603808051.2.2.utmcsr=google|utmccn=(organic)|utmcmd=organic|utmctr=(not%20provided); __gads=ID=7ca757265e2366c5-22ded2176ac40059:T=1603808052:RT=1603808052:S=ALNI_MYZsGZJ8XXb1oU4zxzpMzGdK61LFA; dbcl2="165593539:LvLaPIrgug0"; push_doumail_num=0; push_noty_num=0; __utmv=30149280.16559; ll="118288"; __yadk_uid=DnUc7ftXIqYlQ8RY6pYmLuNPqYp5SFzc; _vwo_uuid_v2=D7ED984782737D7813CC0049180E68C43|1b36a9232bbbe34ac958167d5bdb9a27; ct=y; ck=ZbYm; __utmc=30149280; __utmc=223695111; __utma=30149280.1867171825.1603588354.1613363321.1613372112.11; __utmt=1; __utmb=30149280.2.10.1613372112; ap_v=0,6.0; _pk_ref.100001.4cf6=%5B%22%22%2C%22%22%2C1613372123%2C%22https%3A%2F%2Fwww.douban.com%2Fmisc%2Fsorry%3Foriginal-url%3Dhttps%253A%252F%252Fmovie.douban.com%252Fsubject%252F34841067%252F%253Ffrom%253Dplaying_poster%22%5D; _pk_ses.100001.4cf6=*; __utma=223695111.788421403.1612839506.1613363340.1613372123.9; __utmb=223695111.0.10.1613372123; __utmz=223695111.1613372123.9.4.utmcsr=douban.com|utmccn=(referral)|utmcmd=referral|utmcct=/misc/sorry; _pk_id.100001.4cf6=e2e8bde436a03ad7.1612839506.9.1613372127.1613363387.',
            }
     url="https://movie.douban.com/cinema/nowplaying/zhanjiang/"
    r = requests.get(url,headers=headers)
    r.encoding = 'utf8'
    s = (r.content)
    selector = etree.HTML(s)
    li_list = selector.xpath('//*[@id="nowplaying"]/div[2]/ul/li')
    
    
    dict = {}
    for item in li_list:
        name = item.xpath('.//*[@class="stitle"]/a/@title')[0].replace(" ","").replace("\n","")
        rate = item.xpath('.//*[@class="subject-rate"]/text()')[0].replace(" ", "").replace("\n", "")
        dict[name] = float(rate)
        print("电影="+name)
        print("评分="+rate)
        print("-------")

![c4ac3d30d192d0f47e794f3c4a2a99e0.png][]

**电影名**和**评分数据**已经爬取下来，并且**降序排序**，后面会进行**可视化。**

**2.时长和电影类型**

**网页分析**

![7fd3f7b3480a12c3ed6a501c3bfc2d65.png][]

在页面源码中，**电影时长**的网页标签是roperty="v:runtime"，**电影类型**的网页标签对应是property="v:genre"

编程实现

###时长
    def getmovietime():
        url = "https://movie.douban.com/cinema/nowplaying/zhanjiang/"
        r = requests.get(url, headers=headers)
        r.encoding = 'utf8'
        s = (r.content)
        selector = etree.HTML(s)
        li_list = selector.xpath('//*[@id="nowplaying"]/div[2]/ul/li')
    
    
        for item in li_list:
            title = item.xpath('.//*[@class="stitle"]/a/@title')[0].replace(" ", "").replace("\n", "")
            href = item.xpath('.//*[@class="stitle"]/a/@href')[0].replace(" ", "").replace("\n", "")
    
    
            r = requests.get(href, headers=headers)
            r.encoding = 'utf8'
            s = (r.content)
            selector = etree.HTML(s)
            times = selector.xpath('//*[@property="v:runtime"]/text()')
            type = selector.xpath('//*[@property="v:genre"]/text()')
    
    
            print(title)
            print(times)
            print(type)
    
    
            print("-------")

![450275cc3723e1669dc9654f5a1dceca.png][]

**3.评论数据**

**网页分析**

![1eec3b2a5129dd4bb50e8da5837f9ec5.png][]

查看网页代码，评论数据的**目标标签**是<div class="mod-bd" id="comments">

（不知道如何分析，可以看上一篇文章python爬取44130条用户观影数据，分析挖掘用户与电影之间的隐藏信息！，这篇文章也是分析豆瓣电影，里面有详细介绍）。

下面开始爬取这**七部电影的评论数据！！！！**

编程实现

###评论数据
    def getmoviecomment():
        url = "https://movie.douban.com/cinema/nowplaying/zhanjiang/"
        r = requests.get(url, headers=headers)
        r.encoding = 'utf8'
        s = (r.content)
        selector = etree.HTML(s)
        li_list = selector.xpath('//*[@id="nowplaying"]/div[2]/ul/li')
    
    
        for item in li_list:
            title = item.xpath('.//*[@class="stitle"]/a/@title')[0].replace(" ", "").replace("\n", "")
            href = item.xpath('.//*[@class="stitle"]/a/@href')[0].replace(" ", "").replace("\n", "").replace("/?from=playing_poster", "")
            print("电影=" + title)
            print("链接=" + href)
            ###
            with open(title+".txt","a+",encoding='utf-8') as f:
                for k in range(0,200,20):
                    url = href+"/comments?start="+str(k)+"&limit=20&status=P&sort=new_score"
                    r = requests.get(url, headers=headers)
                    r.encoding = 'utf8'
                    s = (r.content)
                    selector = etree.HTML(s)
                    li_list = selector.xpath('//*[@class="comment-item "]')
                    for items in  li_list:
    
    
                        text = items.xpath('.//*[@class="short"]/text()')[0]
                        f.write(str(text)+"\n")
    
    
            print("-------")
            time.sleep(4)

![dcc56df031ab69cddde59e1c8471bf0c.png][]

![f0573cdf52c2ef5b260c5d82a84c085e.png][]

将这些评论数据分别保存到**文本文件**中，后面将这些评论数据采用**不同的图形进行可视化展示！！！！**

# 数据可视化 #

**1.评分数据可视化**

###画图
    font_size = 10  # 字体大小
    fig_size = (13, 10)  # 图表大小
    
    
    
    
    data = ([datas])
    
    
    # 更新字体大小
    mpl.rcParams['font.size'] = font_size
    # 更新图表大小
    mpl.rcParams['figure.figsize'] = fig_size
    # 设置柱形图宽度
    bar_width = 0.35
    
    
    index = np.arange(len(data[0]))
    # 绘制评分
    rects1 = plt.bar(index, data[0], bar_width, color='#0072BC')
    
    
    # X轴标题
    plt.xticks(index + bar_width, itemNames)
    # Y轴范围
    plt.ylim(ymax=10, ymin=0)
    # 图表标题
    plt.title(u'豆瓣评分')
    # 图例显示在图表下方
    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.03), fancybox=True, ncol=5)
    
    
    # 添加数据标签
    def add_labels(rects):
        for rect in rects:
            height = rect.get_height()
            plt.text(rect.get_x() + rect.get_width() / 2, height, height, ha='center', va='bottom')
            # 柱形图边缘用白色填充，纯粹为了美观
            rect.set_edgecolor('white')
    
    
    add_labels(rects1)
    
    
    # 图表输出到本地
    plt.savefig('豆瓣评分.png')

![7ffee86c07bb7226b518e264457840df.png][]

在热映的这七部电影中，《你好，李焕英》**评分最高**（8.3），《唐人街探案3》**最低**（5.8），这有点出乎意料（**唐人街探案3**热度远比**你好，李焕英**热度要高）。

**2.时长和类型可视化**

时长数据可视化

#####时长可视化
    itemNames.reverse()
    datas.reverse()
    
    
    # 绘图。
    fig, ax = plt.subplots()
    b = ax.barh(range(len(itemNames)), datas, color='#6699CC')
    
    
    # 为横向水平的柱图右侧添加数据标签。
    for rect in b:
        w = rect.get_width()
        ax.text(w, rect.get_y() + rect.get_height() / 2, '%d' %
                int(w), ha='left', va='center')
    
    
    # 设置Y轴纵坐标上的刻度线标签。
    ax.set_yticks(range(len(itemNames)))
    ax.set_yticklabels(itemNames)
    plt.title('电影时长（分钟）', loc='center', fontsize='15',
              fontweight='bold', color='red')
    
    
    #plt.show()
    plt.savefig("电影时长（分钟）")

![0059134a9c889efb21eb4b2726fd7413.png][]

图中的电影时长均在**120分钟左右**

**最长**的电影《**唐人街探案3**》（136分钟），时长**最短**的是《**熊出没·狂野大陆**》（99分钟）

电影类型数据可视化

#####2.类型可视化
    ###从小到大排序
    dict = sorted(dict.items(), key=lambda kv: (kv[1], kv[0]))
    print(dict)
    
    
    itemNames = []
    datas = []
    for i in range(len(dict) - 1, -1, -1):
        itemNames.append(dict[i][0])
        datas.append(dict[i][1])
    
    
    x = range(len(itemNames))
    plt.plot(x, datas, marker='o', mec='r', mfc='w', label=u'电影类型')
    plt.legend()  # 让图例生效
    plt.xticks(x, itemNames, rotation=45)
    plt.margins(0)
    plt.subplots_adjust(bottom=0.15)
    plt.xlabel(u"类型")  # X轴标签
    plt.ylabel("数量")  # Y轴标签
    plt.title("电影类型统计")  # 标题
    plt.savefig("电影类型统计.png")

![c875d35f7e56b839842f83b39389a314.png][]

将这七部电影的**类型进行统计**（有的电影属于多个类型，比如**'动作'**, **'奇幻'**, **'冒险'**）。七部电影中其中有**四部是属于喜剧。**科幻、犯罪、悬疑、冒险均**属于其中一部。**

**3.评论数据词云可视化**

使用**七种不同**的图案进行词云可视化，因此将绘图的**代码封装成函数**！！！

####词云代码
    def jieba_cloud(file_name, icon):
        with open(file_name, 'r', encoding='utf8') as f:
            text = f.read()
            text = text.replace('\n',"").replace("\u3000","").replace("，","").replace("。","")
            word_list = jieba.cut(text)
            result = " ".join(word_list)  # 分词用 隔开
            # 制作中文云词
            icon_name = ""
            if icon == "1":
                icon_name ='fas fa-envira'
            elif icon == "2":
                icon_name = 'fas fa-dragon'
            elif icon == "3":
                icon_name = 'fas fa-dog'
            elif icon == "4":
                icon_name = 'fas fa-cat'
            elif icon == "5":
                icon_name = 'fas fa-dove'
            elif icon == "6":
                icon_name = 'fab fa-qq'
            elif icon == "7":
                icon_name = 'fas fa-cannabis'
            """
            # icon_name='',#国旗
            # icon_name='fas fa-dragon',#翼龙
            icon_name='fas fa-dog',#狗
            # icon_name='fas fa-cat',#猫
            # icon_name='fas fa-dove',#鸽子
            # icon_name='fab fa-qq',#qq
            """
            picp = file_name.split('.')[0] + '.png'
            if icon_name is not None and len(icon_name) > 0:
                gen_stylecloud(text=result, icon_name=icon_name, font_path='simsun.ttc', output_name=picp)  # 必须加中文字体，否则格式错误
            else:
                gen_stylecloud(text=result, font_path='simsun.ttc', output_name=picp)  # 必须加中文字体，否则格式错误
    
    
        return picp

开始对这七部电影评论数据进行绘图

![04b563ed23c1829ab9679059ab5ee9ae.png][]

###评论数据词云
    def commentanalysis():
        lists = ['刺杀小说家','你好，李焕英','人潮汹涌','侍神令','唐人街探案3','新神榜：哪吒重生','熊出没·狂野大陆']
        for i in range(0,len(lists)):
           title =lists[i]+".txt"
           jieba_cloud(title , (i+1))

废话不多说了，下面开始**『词云秀』！！！！！！！**

**1.刺杀小说家**

![f46c6d26c98d786c092ff79281ddce0e.png][]

**2.人潮汹涌**

![281b148e382797012847455b56373310.png][]

**3.熊出没·狂野大陆**

![02c94b679e517b1e7ae3b037b9511c84.png][]

**4.新神榜：哪吒重生**

![296bc3475ef97d9b8498da7e3e205bdc.png][]

**5.唐人街探案3**

![27cdb96736eb0c28018ea3c581f25d42.png][]

**6.你好，李焕英**

![531013d2de6d1cc66b9830d8dac64368.png][]

**7.侍神令**

![71166a79ab3628fc50c78a7db09a9d6a.png][]

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