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# UserBehaviorAnalysisforCompetitionWebsite
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"# wechat-xinshixun"
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"# wechat-xinshixun"
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"# wechat-xinshixun"
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# 代码7-6
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import numpy as np
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from sklearn.preprocessing import scale, MaxAbsScaler
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from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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import pandas as pd
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mode_data1 = pd.read_csv('../tmp/mode_data1.csv', index_col=0)
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# 对数据做中心标准化
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scale_data = scale(mode_data1)
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# 使用K-Means聚类算法建模
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result = KMeans(n_clusters=5, random_state=1234).fit(scale_data)
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# 查看聚类结果
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label = result.labels_ # 获取聚类标签
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# 获取聚类中心
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center = pd.DataFrame(result.cluster_centers_,
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columns=['新闻动态', '教学资源', '项目与合作', '竞赛', '优秀作品'])
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# 改变字体大小
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plt.rcParams.update({'font.size': 10})
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# 自定义画雷达图函数
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def plot(model_center=None,label=None):
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plt.rcParams['axes.unicode_minus'] = False #用于正常显示负号
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plt.rcParams['font.sans-serif'] = 'SimHei' # 正常显示中文
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n = len(label) # 特征个数
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angles = np.linspace(0, 2 * np.pi, n, endpoint=False) # 间隔采样
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angles = np.concatenate((angles, [angles[0]]))
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fig = plt.figure(figsize=(5, 5)) # 创建一个空白的画布
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ax = fig.add_subplot(1, 1, 1, polar=True) # 创建子图
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ax.set_yticklabels([]) # 取消y轴
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ax.set_thetagrids(angles[: -1] * 180 / np.pi, label) # 设置网格线标签
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# ax.set_ylim(model_center.min(),5) # 设置Y轴的范围
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ax.grid(True) # 是否显示网格
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sam = ['b-.', 'k-', 'o--', ':', 'p:'] # 定义折线样式列表
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labels = []
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# 绘制雷达图
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for i in range(5):
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values = np.concatenate((model_center[i], [model_center[i][0]]))
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ax.plot(angles, values, sam[i])
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labels.append('用户群' + str(i + 1),)
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# 添加图例
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plt.legend(labels,bbox_to_anchor=(0.85, 0.85), loc=3)
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plot(scale(result.cluster_centers_), center.columns)
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# plt.savefig('12.png', dpi=1080)
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a = sum(tipdm_data['userid'].isnull())
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b = len(tipdm_data)-sum(tipdm_data['userid'].isnull())
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif']='SimHei'#设置中文显示
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plt.figure(figsize=(4,4))#将画布设定为正方形，则绘制的饼图是正圆
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label=['空值','非空值']#定义饼图的标签，标签是列表
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explode=[0,0]#设定各项距离圆心n个半径
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#plt.pie(values[-1,3:6],explode=explode,labels=label,autopct='%1.1f%%')#绘制饼图
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values=[a,b]
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plt.pie(values,explode=explode,labels=label,autopct='%1.1f%%')#绘制饼图
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plt.title('userid空值与非空值占比')#绘制标题
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plt.show()
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plt.savefig('./饼图')
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#############################
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a = sum(tipdm_data['uniqueVisitorId'].isnull())
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b = len(tipdm_data)-sum(tipdm_data['uniqueVisitorId'].isnull())
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plt.figure(figsize=(4,4))#将画布设定为正方形，则绘制的饼图是正圆
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label=['空值','非空值']#定义饼图的标签，标签是列表
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explode=[0,0]#设定各项距离圆心n个半径
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#plt.pie(values[-1,3:6],explode=explode,labels=label,autopct='%1.1f%%')#绘制饼图
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values=[a,b]
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plt.pie(values,explode=explode,labels=label,autopct='%1.1f%%')#绘制饼图
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plt.title('uniqueVisitorId空值与非空值占比')#绘制标题
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plt.show()
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plt.savefig('./饼图2')#保存图片
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#############
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con_data['reallID'].value_counts().values
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a = pd.DataFrame(con_data['reallID'].value_counts())
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a.columns=['count']
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# a.reset_index(inplace=True)
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b = pd.DataFrame(a['count'].value_counts())
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super_35 = list(b['count'])[34:]
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sum_35 = 0
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for i in super_35:
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sum_35 += int(i)
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dianji = list(b['count'])[:34]
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dianji.append(sum_35)
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tick_label = ["1", "5", "10", "15", "20",'25','30','35']
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plt.bar(range(len(dianji)), dianji)
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plt.xticks([0,4,9,14,19,24,29,34], tick_label)
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plt.title('用户点击网页柱形图')
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plt.xlabel('点击网页数（次）')
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plt.ylabel('用户数（人）')
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plt.show()
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plt.savefig('./饼图3')#保存图片
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#######################################
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mode_data['page_path'].value_counts()
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ind_1 = a[a['count'] == 1].index
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ind_1_1 = [i in ind_1 for i in con_data['reallID']]
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c = pd.DataFrame(con_data[ind_1_1]['page_path'].value_counts())
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#########################################################
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data = con_data
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data = data.fillna('1')
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indx = [i[:4] != '2021' for i in data['date_time']]
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data.loc[indx,'date_time'] = data.loc[indx, 'sessionid']
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data['date_time'] = pd.to_datetime(data['date_time'])
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o=[]
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for k in range(3,35):
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li = reallid_count[reallid_count['count']==k]['reallID'].to_list()
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s=0
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for i in li:
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a=data[data['reallID']==i][['date_time','reallID']]['date_time']
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for j in range(0,k-2):
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num = (a.iloc[j+1]-a.iloc[j]).total_seconds()
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s+= num
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s=s/(k*len(li))
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o.append(s)
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y=o
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plt.plot(range(3,35),y)
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plt.title('平均点击间隔')
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plt.xlabel('点击网页次数（次）')
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plt.ylabel('平均点击间隔（秒）')
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plt.xticks(range(3,35))
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plt.savefig('./饼图4')#保存图片
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#############################################################
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data[data['reallID']==109664.0]['date_time'].shape
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