上机题：调试运行讲解的示例题，并发挥自己的创意，改写相应程序。

# 代码 7-1

import pandas as pd

import matplotlib.pyplot as plt

# 导入数据

info = pd.read_csv('../data/meal_order_info.csv', encoding='utf-8')

info_before = pd.read_csv('../data/info_new.csv', encoding='utf-8')

# 合并数据

info_all = pd.concat([info_before, info])

print('查看各表的维数：\n', info.shape, info_before.shape, info_all.shape)

# 提取订单状态为1的数据

info = info_all[info_all['order_status'].isin(['1'])]

info = info.reset_index(drop=True)

# 统计每日用餐人数与营业额

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

groupbyday = info[['use_start_time', 'number_consumers',

                   'accounts_payable']].groupby(by='use_start_time')

sale_day = groupbyday.sum()

sale_day.columns = ['人数', '销量']

# 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'] = False

plt.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.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.close

# 代码 7-2

info_august = pd.read_csv('../data/meal_order_info.csv', encoding='utf-8')

users_august = pd.read_csv('../data/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.to_csv('../tmp/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_august

user['LAST_VISITS'] = user['LAST_VISITS'].fillna(999)

user = user.drop(user[user['LAST_VISITS'] == 999].index.tolist())

user = user.iloc[:, [0, 2, 12, 14]]

print(user.head())

user.to_csv('../tmp/users_august.csv', index=False, encoding='utf-8')

# 代码 7-3

# 读取数据

users = pd.read_csv('../data/user_loss.csv', encoding='gbk')

info = pd.read_csv('../data/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

print(info.head())

user = users.iloc[:, [0, 2, 14, 37]]

print(user.head())

# 合并两个表

info_user = pd.merge(user, info, left_on='USER_ID', right_on='USER_ID', how='left')

info_user.to_csv('../tmp/info_user.csv', index=False, encoding='utf-8')

print(info_user.head())