python——餐饮数据分析· 话不多说，直接上代码。
⼀、import pandas as pdimport numpy as npimport matplotlib.pyplot as pltinfo = pd.read_csv('meal_order_info.csv', encoding='utf-8')info_before = pd.read_csv('info_new.csv', encoding='utf-8')#将两个数据拼起来info_all = pd.concat([info_before, info])print('查看各表的维数：\n', info.shape, info_before.shape, info_all.shape)info.head()split()：就是将⼀个字符串分隔成多个字符串组成的列表enumerate(info['use_start_time'])基本应⽤就是⽤来遍历⼀个集合对象，它在遍历的同时还可以得到当前元素的索引位置#条件选取order_status==1的数据info = info_all[info_all['order_status'].isin(['1'])]# data = info_all[info_all['order_status']==1]info = info.reset_index(drop=True)# 统计每⽇⽤餐⼈数与营业额#将时间后⾯的师傅们去掉for i, k in enumerate(info['use_start_time']):y = k.split()y = pd.to_datetime(y[0])info.loc[i, 'use_start_time'] = y#提取三列数据# info[['use_start_time', 'number_consumers','accounts_payable']]groupbyday = info[['use_start_time', 'number_consumers','accounts_payable']].groupby(by='use_start_time')#以时间分组sale_day = groupbyday.sum()#定义列名称sale_day.columns = ['⼈数', '销量']sale_day散点图：fig1 = plt.figure()#定义⼀个数组作为横坐标x = np.arange(1,244,1)#图⼤⼩设置plt.figure(figsize=(12, 6))y = sale_day['⼈数']plt.plot(x,y,'.r')plt.xlabel('x')plt.ylabel('y')plt.savefig('散点图.png',dpi=300) plt.show()进阶散点图#x，y变量变化nbins = 200H, xedges, yedges = np.histogram2d(x,y,bins=nbins) # H needs to be rotated and flippedH = np.rot90(H)H = np.flipud(H)# 将zeros maskHmasked = np.ma.masked_where(H==0,H)fig2 = plt.figure()#图⼤⼩设置plt.figure(figsize=(12, 6))plt.pcolormesh(xedges,yedges,Hmasked)plt.xlabel('x')plt.ylabel('y')cbar = plt.colorbar()cbar.ax.set_ylabel('Counts')plt.savefig('2D.png',dpi=300)plt.show()折线图：# 每⽇⽤餐⼈数折线图# 解决中⽂显⽰问题plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = Falseplt.figure(figsize=(12, 6))plt.title('每⽇⽤餐⼈数折线图')plt.xlabel('⽇期')plt.ylabel('⽤餐⼈数')plt.plot(sale_day['⼈数'])plt.show()plt.close# 画出每⽇销售额的折线图# 新建画板plt.figure(figsize=(12, 6))plt.title('每⽇销售额的折线图')plt.xlabel('⽇期')plt.ylabel('销售额')plt.plot(sale_day['销量'])plt.show()plt.close折线图进阶——双坐标轴plt.figure(figsize=(12, 6))Y1=sale_day['⼈数']Y2=sale_day['销量']#标题plt.title("title")#create figure#fig, ax =plt.subplots(1)# Plot y1 vs x in blue on the left vertical axis.plt.xlabel("时间")plt.ylabel("y1", color="g")plt.tick_params(axis="y", labelcolor="g")plt.plot(Y1, "g-", linewidth=2)fig.autofmt_xdate(rotation=50)# Plot y2 vs x in red on the right vertical axis.plt.twinx()plt.ylabel("y2", color="r")plt.tick_params(axis="y", labelcolor="r")plt.plot(Y2, "r-", linewidth=2)#To save your graph#plt.savefig('saltandtemp_V1.png' ,dpi=300)plt.show()⼆、info_august = pd.read_csv('meal_order_info.csv', encoding='utf-8') users_august = pd.read_csv('users.csv', encoding='gbk')# 提取订单状态为1的数据info_august_new = info_august[info_august['order_status'].isin(['1'])] info_august_new = info_august_new.reset_index(drop=True)print('提取的订单数据维数：', info_august_new.shape)info_august_new.head()info_august_new.to_csv('info_august_new.csv', index=False, encoding='utf-8')# 匹配⽤户的最后⼀次⽤餐时间for i in range(1, len(info_august_new)):num = users_august[users_august['USER_ID'] ==info_august_new.iloc[i-1, 1]].index.tolist()users_august.iloc[num[0], 14] = info_august_new.iloc[i-1, 9]users_august.iloc[num[0], 14] = info_august_new.iloc[i-1, 9]user = users_augustuser['LAST_VISITS'] = user['LAST_VISITS'].fillna(999)user = user.drop(user[user['LAST_VISITS'] == 999].index.tolist())user = user.iloc[:, [0, 2, 12, 14]]user.head()# 读取数据users = pd.read_csv('user_loss.csv', encoding='gbk')info = pd.read_csv('info_new.csv', encoding='utf-8')print('历史客户信息表的维数：', users.shape)print('历史订单表的维数：', info.shape)# 将时间转为时间格式users['CREATED'] = pd.to_datetime(users['CREATED'])info['use_start_time'] = pd.to_datetime(info['use_start_time'])info['lock_time'] = pd.to_datetime(info['lock_time'])# 匹配⽤户的最后⼀次⽤餐时间for i in range(len(users)):info1 = info.iloc[info[info['name'] == users.iloc[i, 2]].index.tolist(), :]if sum(info['name']==users.iloc[i, 2]) != 0:users.iloc[i, 14] = max(info1['use_start_time'])# 特征选取# 提取有效订单info = info.loc[info['order_status'] == 1, ['emp_id', 'number_consumers', 'expenditure']] info = info.rename(columns={'emp_id': 'USER_ID'}) # 修改列名info.head()info_user.to_csv('info_user.csv', index=False, encoding='utf-8')import pandas as pdfrom sklearn.preprocessing import StandardScalerimport numpy as npuser_value1.columns = ['USER_ID', 'F'] # 修改列名print('F特征的最⼤值：', max(user_value1['F']))print('F特征的最⼩值：', min(user_value1['F']))# 构建M特征user_value2 = info[['emp_id', 'expenditure']].groupby(by='emp_id').sum() user_value2 = pd.DataFrame(user_value2).reset_index()user_value2.columns = ["USER_ID", "M"]user_value = pd.merge(user_value1, user_value2, on='USER_ID') print('M特征的最⼤值：', max(user_value['M']))print('M特征的最⼩值：', min(user_value['M']))# 构建R特征user_value = pd.merge(user_value, user, on='USER_ID') # 合并两个表# 转换时间格式for i, k in enumerate(user_value['LAST_VISITS']):y = k.split()y = pd.to_datetime(y[0])user_value.loc[i, 'LAST_VISITS'] = ylast_time = pd.to_datetime(user_value['LAST_VISITS'])deadline = pd.to_datetime("2016-8-31") # 观测窗⼝结束时间user_value['R'] = deadline - last_timeprint('R特征的最⼤值：', max(user_value['R']))print('R特征的最⼩值：', min(user_value['R']))# 代码 7-5# 特征提取user_value = user_value.iloc[:, [0, 3, 6, 1, 2]]user_value.to_csv("user_value.csv", encoding="utf-8_sig", index=False)USER_ID = user_value['USER_ID']ACCOUNT = user_value['ACCOUNT']user_value = user_value.iloc[:, [2, 3, 4]]user_value.iloc[:, 0] = [i.days for i in user_value.iloc[:, 0]]# 标准差标准化standard = StandardScaler().fit_transform(user_value)np.savez('standard.npz', standard)print(standard)三、聚类standard = np.load('standard.npz')['arr_0']k = 3 # 聚类中⼼数# 构建模型kmeans_model = KMeans(n_clusters=k, n_jobs=3, random_state=123)fit_kmeans = kmeans_model.fit(standard) # 模型训练print('聚类中⼼：\n', kmeans_model.cluster_centers_)print('样本的类别标签：\n', kmeans_bels_)# 统计不同类别样本的数⽬r1 = pd.Series(kmeans_bels_).value_counts()print('最终每个类别的数⽬为：\n', r1)# 代码 7-7# %matplotlib inlineimport matplotlib.pyplot as plt# 中⽂和负号的正常显⽰plt.rcParams['font.sans-serif'] = 'SimHei'plt.rcParams['axes.unicode_minus'] = False# 绘制雷达图N = len(kmeans_model.cluster_centers_[0])# 设置雷达图的⾓度，⽤于平分切开⼀个圆⾯angles = np.linspace(0, 2 * np.pi, N, endpoint=False)# 为了使雷达图⼀圈封闭起来angles = np.concatenate((angles, [angles[0]]))# 绘图fig = plt.figure(figsize=(7, 7))ax = fig.add_subplot(111, polar=True)sam = ['r','g','b']lstype = ['-','--','-.']lab = []for i in range(len(kmeans_model.cluster_centers_)):values = kmeans_model.cluster_centers_[i]feature = ['R','F','M']values = np.concatenate((values, [values[0]]))# 绘制折线图ax.plot(angles, values, sam[i], linestyle=lstype[i], linewidth=2, markersize=10)ax.fill(angles, values, alpha=0.5) # 填充颜⾊#ax.set_thetagrids(angles * 180 / np.pi, feature, fontsize=15) # 添加每个特征的标签 plt.title('客户群特征分布图') # 添加标题ax.grid(True)lab.append('客户群' + str(i+1))plt.legend(lab)#plt.savefig('征分布图.png')plt.show()plt.close。
