文章目录
本文从购买力、购买时间偏好两个维度分析会员的消费特征。
以会员消费总金额、消费次数、商品购买数量代表会员购买力；
同时按季节和天对会员消费行为进行消费时间偏好分析。
同时对会员及非会员的消费次数和消费金额进行对比分析。
导入包及数据
import matplotlibimport warningsimport reimport pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltfrom sklearn.cluster import KMeansfrom sklearn.metrics import silhouette_scorefrom sklearn.preprocessing import StandardScaler,MinMaxScaler%matplotlib inlineplt.rcParams['font.sans-serif'] = 'SimHei'plt.rcParams['axes.unicode_minus'] = Falsematplotlib.rcParams.update({'font.size' : 16})plt.style.use('ggplot')warnings.filterwarnings('ignore')data1=pd.read_excel('./2018-C-Chinese的副本/附件1-会员信息表.xlsx')data2=pd.read_excel('./2018-C-Chinese的副本/附件3-会员消费明细表.xlsx')数据探索与预处理
1.附件一会员信息表探索与预处理
#查看是否缺失数据print('会员信息表一共有{}行记录，{}列'.format(data1.shape[0],data1.shape[1]))print('数据缺失情况为:\n',data1.isnull().sum())print('会员不重复卡号kh的信息有',len(data1['kh'].unique()))#会员卡号去重print("去除重复值前的数据量", data1.shape) data1.drop_duplicates(subset=['kh'],keep='first',inplace=True)print("去除重复值后的数据量", data1.shape) #去除登记时间的缺失值，并去重print("去除重复值前的数据量", data1.shape) data1.dropna(subset='djsj',inplace=True)print("去除登记时间的缺失值，并去重数据量", data1.shape) #性别上缺失的比例较少，所以使用众数填充data1['xb'].fillna(data1['xb'].mode().values[0],inplace=True)#检验是否在登记时间这一字段中存在异常值，若存在异常值，则无法进行基本操作data1_1 = data1['djsj'] + pd.Timedelta(days=1)#查看处理完成的数据缺失值情况data1.isnull().sum()#对于出生日期的处理#由于出生日期缺失值过多，且存在较多的异常值，不能贸然删除#故下面另建一个数据集L来保存“出生日期”和‘性别’信息，方便下面对会员的性别和年龄信息进行统计L = pd.DataFrame(data1.loc[data1['csny'].notnull(),['csny','xb']])L['age'] = L['csny'].astype(str).apply(lambda x:x[0:3]+'0')L.drop('csny',axis=1,inplace=True)L['age'].value_counts()# 出生日期这列中出现较多的异常值，以一个正常人寿命为100年算起，我们假定会员年龄范围在1922-2022年起# 将超过该范围的值当作异常值进行剔除L['age'] = L['age'].astype(int)condaition = 'age >= 1922 and age <=2022'L = L.query(condaition)L.index = range(L.shape[0])L['age'].value_counts()# 用于与销售流水表进行合并的数据只取['会员卡号', '性别', '登记时间']这三列，将出生日期这列意义不大的进行删除（这列信息最有可能出错），并重置索引data1.drop('csny', axis = 1, inplace = True)data1.index = range(data1.shape[0])print('数据清洗之后共有{}行记录，{}列字段，字段分别为{}'.format(data1.shape[0], data1.shape[1], data1.columns.tolist()))2.附件三会员销售流水表
对于不是本地会员的会员当作非会员处理。
#检查是否含有缺失值print('未处理时的数据数目：',data2.shape[0],data2.shape[1])print('缺失值数据数目:',data2.isnull().sum())#检查将售价、数量、金额、积分是否都大于0print('商品售价大于0的数量：{} \t 全部记录有{}'.format(len(data2['sj']>0),len(data2['sj'])))print('商品数量大于0的数量：{} \t 全部记录有{}'.format(len(data2['sl']>0),len(data2['sl'])))print('会员积分大于0的数量：{} \t 全部记录有{}'.format(len(data2['jf']>0),len(data2['jf'])))data2.drop(['syjh', 'gzbm', 'gzmc'], axis = 1, inplace = True)# 重置索引data2.index = range(data2.shape[0])3.将会员信息表喝会员消费信息明细进行合并
#按照两表的卡号信息将两表合并，将附件2中有会员卡号的进行左合并,得到附件1会员和非会员的数据data_merge = pd.merge(data2,data1,on='kh',how='left')data_merge# 再次查看金额>0,积分>0,数量>0index1 = data_merge['je'] > 0index2 = data_merge['jf'] > 0index3 = data_merge['sl'] > 0data_merge1 = data_merge.loc[index1 & index2 & index3,:]data_merge1.index = range(data_merge1.shape[0])data_merge1.shape#创造字段检查其是否为会员data_merge1['vip'] = 1data_merge1data_merge1.loc[data_merge1['xb'].isnull(),'vip'] = 0data_merge14.处理附件二
data_total = pd.read_excel('./2018-C-Chinese的副本/附件2-销售流水表.xlsx')print(data_total.shape)print(data_total.isnull().sum()) #未发现缺失值data_total = data_total.drop_duplicates()print(data_total.shape)#检查将售价、数量、金额、积分是否都大于0print('商品售价大于0的数量：{} \t 全部记录有{}'.format(len(data_total['sj']>0),len(data_total['sj'])))print('商品数量大于0的数量：{} \t 全部记录有{}'.format(len(data_total['sl']>0),len(data_total['sl'])))print('积分大于0的数量：{} \t 全部记录有{}'.format(len(data_total['je']>0),len(data_total['je'])))#检验是否在-登记时间这一字段中存在异常值，若存在异常值，则无法进行基本操作data_total_1 = data_total['dtime'] + pd.Timedelta(days=1)#经检验日期时间没有问题5.附件二和上处理的一三合并的文件进行二次合并
#检查各个数据集长度print(f'附件二{len(data_total)}\t附件一和三合并{len(data_merge1)}')data23 = pd.merge(data_total,data_merge1,on=['dtime','spbm','je'],how='left')data23.loc[data23['vip'].isnull(),'vip'] = 0data23.drop(['kh','sj_y','sl_y','kh','xb','djsj'],axis=1,inplace=True)fig, axs = plt.subplots(1, 2, figsize = (12, 7), dpi = 100)axs[0].pie([len(data23.loc[data23['vip']==1,'dtime'].unique()),len(data23.loc[data23['vip']==0,'dtime'].unique())], labels = ['vip','normal'], wedgeprops = {'width': 0.4}, counterclock = False, autopct = '%.2f%%', pctdistance = 0.8)axs[0].set_title('total dingdan%')axs[1].pie([data23.loc[data23['vip'] == 1, 'je'].sum(), data23.loc[data23['vip'] == 0, 'je'].sum()], labels = ['vip', 'normal'], wedgeprops = {'width': 0.4}, counterclock = False, autopct = '%.2f%%', pctdistance = 0.8)axs[1].set_title('total je%')会员统计分析
分析会员的年龄构成、男女比例等基本信息
#处理男女比例这一列，女0，男1L['xb'] = L['xb'].apply(lambda x:'male' if x==1 else 'female')sex_sort = L['xb'].value_counts()#将年龄划分为不同年龄段#老年（1920-1950）、中年（1960-1990）、青年（1990-2010）L['age group']='middle'L.loc[L['age'] <= 1950,'age group'] = 'old'L.loc[L['age'] >= 1990,'age group'] = 'young'res = L['age group'].value_counts()#使用上述预处理后的数据L，包含两个字段，分别是age和性别，先画出年龄条形图fig, axs = plt.subplots(1,2,figsize=(16,7),dpi=100)#绘制条形图ax = sns.countplot(x='age',data = L,ax = axs[0])for p in ax.patches: height = p.get_height() ax.text(x = p.get_x() + (p.get_width() / 2), y = height + 500, s = '{:.0f}'.format(height), ha = 'center')axs[0].set_title('year of birth')# 绘制饼图axs[1].pie(sex_sort, labels = sex_sort.index, wedgeprops = {'width': 0.4}, counterclock = False, autopct = '%.2f%%', pctdistance = 0.8)axs[1].set_title('male vs female')
# 绘制各个年龄段的饼图plt.figure(figsize = (8, 6), dpi = 100)plt.pie(res.values, labels = ['middle', 'young', 'old'], autopct = '%.2f%%', pctdistance = 0.8, counterclock = False, wedgeprops = {'width': 0.4})plt.title('fenbu')#plt.savefig('./age fenbu.png')分析会员的总订单占比，总消费金额占比等消费情况
fig, axs = plt.subplots(1, 2, figsize = (12, 7), dpi = 100)axs[0].pie([len(data_merge1.loc[data_merge1['vip']==1,'dtime'].unique()),len(data_merge1.loc[data_merge1['vip']==0,'dtime'].unique())], labels = ['vip','normal'], wedgeprops = {'width': 0.4}, counterclock = False, autopct = '%.2f%%', pctdistance = 0.8)axs[0].set_title('total dingdan%')axs[1].pie([data_merge1.loc[data_merge1['vip'] == 1, 'je'].sum(), data_merge1.loc[data_merge1['vip'] == 0, 'je'].sum()], labels = ['vip', 'normal'], wedgeprops = {'width': 0.4}, counterclock = False, autopct = '%.2f%%', pctdistance = 0.8)axs[1].set_title('total je%')
分别以季度和天为单位，分析不同时间段会员的消费时间偏好
不同季度和天为单位的消费时间偏好
# 将会员的消费数据另存为另一个数据集df_vip = df1.dropna()df_vip.drop(['会员'], axis = 1, inplace = True)df_vip.index = range(df_vip.shape[0])df_vip.info()# 将“消费产生的时间”转变成日期格式df_vip['消费产生的时间'] = pd.to_datetime(df_vip['消费产生的时间'])# 新增四列数据，季度、天、年份和月份的字段df_vip['年份'] = df_vip['消费产生的时间'].dt.yeardf_vip['月份'] = df_vip['消费产生的时间'].dt.monthdf_vip['季度'] = df_vip['消费产生的时间'].dt.quarterdf_vip['天'] = df_vip['消费产生的时间'].dt.daydf_vip.head()# 前提假设：2015-2018年之间，消费者偏好在时间上不会发生太大的变化（均值），消费偏好——>以不同时间的订单数来衡量quarters_list, quarters_order = orders(df_vip, '季度', 3)days_list, days_order = orders(df_vip, '天', 36)time_list = [quarters_list, days_list]order_list = [quarters_order, days_order]maxindex_list = [quarters_order.index(max(quarters_order)), days_order.index(max(days_order))]fig, axs = plt.subplots(1, 2, figsize = (18, 7), dpi = 100)colors = np.random.choice(['r', 'g', 'b', 'orange', 'y'], replace = False, size = len(axs))titles = ['季度的均值消费偏好', '天数的均值消费偏好']labels = ['季度', '天数']for i in range(len(axs)): ax = axs[i] ax.plot(time_list[i], order_list[i], linestyle = '-.', c = colors[i], marker = 'o', alpha = 0.85) ax.axvline(x = time_list[i][maxindex_list[i]], linestyle = '--', c = 'k', alpha = 0.8) ax.set_title(titles[i]) ax.set_xlabel(labels[i]) ax.set_ylabel('均值消费订单数') print(f'{titles[i]}最优的时间为: {time_list[i][maxindex_list[i]]}\t 对应的均值消费订单数为: {order_list[i][maxindex_list[i]]}')plt.savefig('./季度和天数的均值消费偏好情况.png')
不同年份之间的的季度或天数的消费订单差异
# 自定义函数来绘制不同年份之间的的季度或天数的消费订单差异def plot_qd(df, label_y, label_m, nrow, ncol): """ df: 为DataFrame的数据集 label_y: 为年份的字段标签 label_m: 为标签的一个列表 n_row: 图的行数 n_col: 图的列数 """ # 必须去掉最后一年的数据，只能对2015-2017之间的数据进行分析 y_list = np.sort(df[label_y].unique().tolist())[:-1] colors = np.random.choice(['r', 'g', 'b', 'orange', 'y', 'k', 'c', 'm'], replace = False, size = len(y_list)) markers = ['o', '^', 'v'] plt.figure(figsize = (8, 6), dpi = 100) fig, axs = plt.subplots(nrow, ncol, figsize = (16, 7), dpi = 100) for k in range(len(label_m)): m_list = np.sort(df[label_m[k]].unique().tolist()) for i in range(len(y_list)): order_m = [] index1 = df[label_y] == y_list[i] for j in range(len(m_list)): index2 = df[label_m[k]] == m_list[j] order_m.append(len(df.loc[index1 & index2, '消费产生的时间'].unique())) axs[k].plot(m_list, order_m, linestyle ='-.', c = colors[i], alpha = 0.8, marker = markers[i], label = y_list[i], markersize = 4) axs[k].set_xlabel(f'{label_m[k]}') axs[k].set_ylabel('消费订单数') axs[k].set_title(f'2015-2018年会员的{label_m[k]}消费订单差异') axs[k].legend() plt.savefig(f'./2015-2018年会员的{"和".join(label_m)}消费订单差异.png')plot_qd(df_vip, '年份', ['季度', '天'], 1, 2)
不同年份之间的月份消费订单差异
# 自定义函数来绘制不同年份之间的月份消费订单差异def plot_ym(df, label_y, label_m): """ df: 为DataFrame的数据集 label_y: 为年份的字段标签 label_m: 为月份的字段标签 """ # 必须去掉最后一年的数据，只能对2015-2017之间的数据进行分析 y_list = np.sort(df[label_y].unique().tolist())[:-1] m_list = np.sort(df[label_m].unique().tolist()) colors = np.random.choice(['r', 'g', 'b', 'orange', 'y'], replace = False, size = len(y_list)) markers = ['o', '^', 'v'] fig, axs = plt.subplots(1, 2, figsize = (18, 8), dpi = 100) for i in range(len(y_list)): order_m = [] money_m = [] index1 = df[label_y] == y_list[i] for j in range(len(m_list)): index2 = df[label_m] == m_list[j] order_m.append(len(df.loc[index1 & index2, '消费产生的时间'].unique())) money_m.append(df.loc[index1 & index2, '消费金额'].sum()) axs[0].plot(m_list, order_m, linestyle ='-.', c = colors[i], alpha = 0.8, marker = markers[i], label = y_list[i]) axs[1].plot(m_list, money_m, linestyle ='-.', c = colors[i], alpha = 0.8, marker = markers[i], label = y_list[i]) axs[0].set_xlabel('月份') axs[0].set_ylabel('消费订单数') axs[0].set_title('2015-2018年会员的消费订单差异') axs[1].set_xlabel('月份') axs[1].set_ylabel('消费金额总数') axs[1].set_title('2015-2018年会员的消费金额差异') axs[0].legend() axs[1].legend() plt.savefig('./2015-2018年会员的消费订单和金额差异.png')# 调用函数plot_ym(df_vip, '年份', '月份')
# 再来分析下时间上的差差异——消费订单数df_vip['时间'] = df_vip['消费产生的时间'].dt.hourx_list, order_nums = orders(df_vip, '时间', 1)maxindex = order_nums.index(max(order_nums))plt.figure(figsize = (8, 6), dpi = 100)plt.plot(x_list, order_nums, linestyle = '-.', marker = 'o', c = 'm', alpha = 0.8)plt.xlabel('小时')plt.ylabel('消费订单')plt.axvline(x = x_list[maxindex], linestyle = '--', c = 'r', alpha = 0.6)plt.title('2015-2018年各段小时的销售订单数')plt.savefig('./2015-2018年各段小时的销售订单数.png')
会员与非会员统计分析
fig, axs = plt.subplots(1, 2, figsize = (12, 7), dpi = 100)axs[0].pie([len(data23.loc[data23['vip']==1,'dtime'].unique()),len(data23.loc[data23['vip']==0,'dtime'].unique())], labels = ['vip','normal'], wedgeprops = {'width': 0.4}, counterclock = False, autopct = '%.2f%%', pctdistance = 0.8)axs[0].set_title('total dingdan%')axs[1].pie([data23.loc[data23['vip'] == 1, 'je'].sum(), data23.loc[data23['vip'] == 0, 'je'].sum()], labels = ['vip', 'normal'], wedgeprops = {'width': 0.4}, counterclock = False, autopct = '%.2f%%', pctdistance = 0.8)axs[1].set_title('total je%')
来源地址：https://blog.csdn.net/m0_52427832/article/details/126745244
