Python爬取用户观影数据并分析用户与电影之间的隐藏信息!

https://movie.douban.com/

https://movie.douban.com/subject/24733428/reviews?start=0

i=0
url = "https://movie.douban.com/subject/24733428/reviews?start=" + str(i * 20)
r = requests.get(url, headers=headers)
r.encoding = 'utf8'
s = (r.content)
selector = etree.HTML(s)
 
 
for item in selector.xpath('//*[@class="review-list  "]/div'):
    userid = (item.xpath('.//*[@class="main-hd"]/a[2]/@href'))[0].replace("https://www.douban.com/people/","").replace("/", "")
    username = (item.xpath('.//*[@class="main-hd"]/a[2]/text()'))[0]
    print(userid)
    print(username)
    print("-----")

#https://movie.douban.com/people/{用户名称}/collect?start=15&sort=time&rating=all&filter=all&mode=grid
https://movie.douban.com/people/mumudancing/collect?start=15&sort=time&rating=all&filter=all&mode=grid

url = "https://movie.douban.com/people/mumudancing/collect?start=15&sort=time&rating=all&filter=all&mode=grid"
r = requests.get(url, headers=headers)
r.encoding = 'utf8'
s = (r.content)
selector = etree.HTML(s)
for item in selector.xpath('//*[@class="grid-view"]/div[@class="item"]'):
    text1 = item.xpath('.//*[@class="title"]/a/em/text()')
    text2 = item.xpath('.//*[@class="title"]/a/text()')
    text1 = (text1[0]).replace(" ", "")
    text2 = (text2[1]).replace(" ", "").replace("\n", "")
    print(text1+text1)
    print("-----")

# 初始化execl表
def initexcel(filename):
    # 创建一个workbook 设置编码
    workbook = xlwt.Workbook(encoding='utf-8')
    # 创建一个worksheet
    worksheet = workbook.add_sheet('sheet1')
    workbook.save(str(filename)+'.xls')
    ##写入表头
    value1 = [["用户", "影评"]]
    book_name_xls = str(filename)+'.xls'
    write_excel_xls_append(book_name_xls, value1)

# 写入execl
def write_excel_xls_append(path, value):
    index = len(value)  # 获取需要写入数据的行数
    workbook = xlrd.open_workbook(path)  # 打开工作簿
    sheets = workbook.sheet_names()  # 获取工作簿中的所有表格
    worksheet = workbook.sheet_by_name(sheets[0])  # 获取工作簿中所有表格中的的第一个表格
    rows_old = worksheet.nrows  # 获取表格中已存在的数据的行数
    new_workbook = copy(workbook)  # 将xlrd对象拷贝转化为xlwt对象
    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格
    for i in range(0, index):
        for j in range(0, len(value[i])):
            new_worksheet.write(i+rows_old, j, value[i][j])  # 追加写入数据，注意是从i+rows_old行开始写入
    new_workbook.save(path)  # 保存工作簿

def read_excel():
    # 打开workbook
    data = xlrd.open_workbook('豆瓣.xls')
    # 获取sheet页
    table = data.sheet_by_name('sheet1')
    # 已有内容的行数和列数
    nrows = table.nrows
    datalist=[]
    for row in range(nrows):
        temp_list = table.row_values(row)
        if temp_list[0] != "用户" and temp_list[1] != "影评":
            data = []
            data.append([str(temp_list[0]), str(temp_list[1])])
            datalist.append(data)
    return datalist

###分析1：电影观看次数排行
def analysis1():
    dict ={}
    ###从excel读取数据
    movie_data = read_excel()
    for i in range(0, len(movie_data)):
        key = str(movie_data[i][0][1])
        try:
            dict[key] = dict[key] +1
        except:
            dict[key]=1
    ###从小到大排序
    dict = sorted(dict.items(), key=lambda kv: (kv[1], kv[0]))
    name=[]
    num=[]
    for i in range(len(dict)-1,len(dict)-16,-1):
        print(dict[i])
        name.append(((dict[i][0]).split("/"))[0])
        num.append(dict[i][1])
    plt.figure(figsize=(16, 9))
    plt.title('电影观看次数排行(高->低)')
    plt.bar(name, num, facecolor='lightskyblue', edgecolor='white')
    plt.savefig('电影观看次数排行.png')

###分析2：用户画像（用户观影相同率最高）
def analysis2():
    dict = {}
    ###从excel读取数据
    movie_data = read_excel()
 
 
    userlist=[]
    for i in range(0, len(movie_data)):
        user = str(movie_data[i][0][0])
        moive = (str(movie_data[i][0][1]).split("/"))[0]
        #print(user)
        #print(moive)
 
 
        try:
            dict[user] = dict[user]+","+str(moive)
        except:
            dict[user] =str(moive)
            userlist.append(user)
 
 
    num_dict={}
    # 待画像用户(取第一个）
    flag_user=userlist[0]
    movies = (dict[flag_user]).split(",")
    for i in range(0,len(userlist)):
        #判断是否是待画像用户
        if flag_user != userlist[i]:
            num_dict[userlist[i]]=0
            #待画像用户的所有电影
            for j in range(0,len(movies)):
                #判断当前用户与待画像用户共同电影个数
                if movies[j] in dict[userlist[i]]:
                    # 相同加1
                    num_dict[userlist[i]] = num_dict[userlist[i]]+1
    ###从小到大排序
    num_dict = sorted(num_dict.items(), key=lambda kv: (kv[1], kv[0]))
    #用户名称
    username = []
    #观看相同电影次数
    num = []
    for i in range(len(num_dict) - 1, len(num_dict) - 9, -1):
        username.append(num_dict[i][0])
        num.append(num_dict[i][1])
 
 
    plt.figure(figsize=(25, 9))
    plt.title('用户画像（用户观影相同率最高）')
    plt.scatter(username, num, color='r')
    plt.plot(username, num)
    plt.savefig('用户画像（用户观影相同率最高）.png')

###分析3：用户之间进行电影推荐（与其他用户同时被观看过）
def analysis3():
    dict = {}
    ###从excel读取数据
    movie_data = read_excel()
 
 
    userlist=[]
    for i in range(0, len(movie_data)):
        user = str(movie_data[i][0][0])
        moive = (str(movie_data[i][0][1]).split("/"))[0]
        #print(user)
        #print(moive)
 
 
        try:
            dict[user] = dict[user]+","+str(moive)
        except:
            dict[user] =str(moive)
            userlist.append(user)
 
 
    num_dict={}
    # 待画像用户(取第2个）
    flag_user=userlist[0]
    print(flag_user)
    movies = (dict[flag_user]).split(",")
    for i in range(0,len(userlist)):
        #判断是否是待画像用户
        if flag_user != userlist[i]:
            num_dict[userlist[i]]=0
            #待画像用户的所有电影
            for j in range(0,len(movies)):
                #判断当前用户与待画像用户共同电影个数
                if movies[j] in dict[userlist[i]]:
                    # 相同加1
                    num_dict[userlist[i]] = num_dict[userlist[i]]+1
    ###从小到大排序
    num_dict = sorted(num_dict.items(), key=lambda kv: (kv[1], kv[0]))
 
 
    # 去重（用户与观影率最高的用户两者之间重复的电影去掉）
    user_movies = dict[flag_user]
    new_movies = dict[num_dict[len(num_dict)-1][0]].split(",")
    for i in range(0,len(new_movies)):
        if new_movies[i] not in user_movies:
            print("给用户（"+str(flag_user)+"）推荐电影："+str(new_movies[i]))

###分析4：电影之间进行电影推荐（与其他电影同时被观看过）
def analysis4():
    dict = {}
    ###从excel读取数据
    movie_data = read_excel()
 
 
    userlist=[]
    for i in range(0, len(movie_data)):
        user = str(movie_data[i][0][0])
        moive = (str(movie_data[i][0][1]).split("/"))[0]
        try:
            dict[user] = dict[user]+","+str(moive)
        except:
            dict[user] =str(moive)
            userlist.append(user)
 
 
    movie_list=[]
    # 待获取推荐的电影
    flag_movie = "送你一朵小红花"
    for i in range(0,len(userlist)):
        if flag_movie in dict[userlist[i]]:
             moives = dict[userlist[i]].split(",")
             for j in range(0,len(moives)):
                 if moives[j] != flag_movie:
                     movie_list.append(moives[j])
 
 
    data_dict = {}
    for key in movie_list:
        data_dict[key] = data_dict.get(key, 0) + 1
 
 
    ###从小到大排序
    data_dict = sorted(data_dict.items(), key=lambda kv: (kv[1], kv[0]))
    for i in range(len(data_dict) - 1, len(data_dict) -16, -1):
            print("根据电影"+str(flag_movie)+"]推荐："+str(data_dict[i][0]))