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DA47. 筛选某店铺最有价值用户中消费最多前5名

描述

题目描述：

现有某店铺会员消费情况sales.csv。包含以下字段：

请你统计最有价值的用户中消费金额最多的前5名用户。

输入描述：

数据集可以从当前目录下sales.csv读取。

输出描述：

请你先对每个用户销售情况的每个特征进行评分，分值为1-4分。再将所有评分拼接到一起形成新的列RFMClass。

评分规则如下： 对于recency特征，值越小越好。对于frequency和monetary值越大越好。如对于recency：

对于frequency和monetary则方法刚好相反。

请你输出评分后的数据的前5行并输出最有价值的用户（评分为“444”）中销售总金额最高的前5位(索引从0开始)，以上数据集的输出如下图所示（两次输出之间有一个空行）。 alt

原站题解

上次编辑到这里，代码来自缓存 点击恢复默认模板

Python 3 解法, 执行用时: 798ms, 内存消耗: 524288KB, 提交时间: 2022-07-18 

import pandas as pd
sales = pd.read_csv('sales.csv')
sales["monetary"] = sales["monetary"].astype("float")
sales["R_Quartile"] = pd.qcut(sales["recency"], [0, 0.25, 0.5, 0.75, 1],
                              labels=["4", "3", "2", "1"]).astype("str")
sales["F_Quartile"] = pd.qcut(sales["frequency"], [0, 0.25, 0.5, 0.75, 1], 
                              labels=["1", "2", "3", "4"]).astype("str")
sales["M_Quartile"] = pd.qcut(sales["monetary"], [0, 0.25, 0.5, 0.75, 1], 
                              labels=["1", "2", "3", "4"]).astype("str")
sales['RFMClass']=sales['R_Quartile']+sales['F_Quartile']+sales['M_Quartile']
sales1=sales[['user_id','recency','frequency','monetary','RFMClass']]
data1=sales1.head(5)
print(data1)
data2=sales1[sales1['RFMClass']=='444'].sort_values(by='monetary',ascending=False).head(5).reset_index(drop=True)
print('\n')
print(data2)

Python 3 解法, 执行用时: 803ms, 内存消耗: 524288KB, 提交时间: 2022-08-06 

import pandas as pd
sales = pd.read_csv('sales.csv')

# # 按照结果要求转换类型
# sales[['monetary']] = sales[['monetary']].astype('float32')
# # 求百分位
# des = sales[['recency', 'frequency', 'monetary']].describe().loc['25%':'75%']

# # 计算RFM
# R = sales['recency'].apply(lambda x: 4 if x <= des.iloc[0,0] else (3 if x <= des.iloc[1,0] else (2 if x <= des.iloc[2,0] else 1))).astype('str')
# F = sales['frequency'].apply(lambda x: 1 if x <= des.iloc[0,1] else (2 if x <= des.iloc[1,1] else (3 if x <= des.iloc[2,1] else 4))).astype('str')
# M = sales['monetary'].apply(lambda x: 1 if x <= des.iloc[0,2] else (2 if x <= des.iloc[1,2] else (3 if x <= des.iloc[2,2] else 4))).astype('str')

# # 合并RFM
# sales['RFMClass'] = R+F+M
# # 
# print(sales.head())

# # 筛选444用户
# sales1 = sales[sales['RFMClass'] == '444'].sort_values(by='monetary', ascending=False).reset_index(drop=True)
# #
# print(sales1.head())
sales['monetary']=sales['monetary'].astype('float')
R=pd.qcut(sales['recency'],[0,0.25,0.5,0.75,1],['4','3','2','1']).astype('str')
F=pd.qcut(sales['frequency'],[0,0.25,0.5,0.75,1],['1','2','3','4']).astype('str')
M=pd.qcut(sales['monetary'],[0,0.25,0.5,0.75,1],['1','2','3','4']).astype('str')
sales['RFMClass']=R+F+M
print(sales.head(5))
print('\n')
sales1=sales[sales['RFMClass']=='444'].sort_values('monetary',ascending=False).reset_index(drop=True)
print(sales1.head(5))

Python 3 解法, 执行用时: 806ms, 内存消耗: 524288KB, 提交时间: 2022-07-03 

import pandas as pd
sales = pd.read_csv('sales.csv')
sales_quantile=sales.quantile([0.25,0.5,0.75])   #获取分位数
dict_list=sales_quantile.to_dict('records')   # 转化为内容为字典的列表
def deal(x,name):
    if name == 'recency':
        if x<=dict_list[0][name]:
            return '4'
        elif x>dict_list[0][name] and x<=dict_list[1][name]:
            return '3'
        elif x>dict_list[1][name] and x<=dict_list[2][name]:
            return '2'
        else:
            return '1'
    else:
        if x<=dict_list[0][name]:
            return '1'
        elif x>dict_list[0][name] and x<=dict_list[1][name]:
            return '2'
        elif x>dict_list[1][name] and x<=dict_list[2][name]:
            return '3'
        else:
            return '4'
sales['RFMClass'] = sales['recency'].apply(lambda x: deal(x,'recency'))+sales['frequency'].apply(lambda x: deal(x,'frequency'))+sales['monetary'].apply(lambda x: deal(x,'monetary'))   #字符串的+可以拼接
print(sales.head(5),'\n')
print(sales[sales['RFMClass']=='444'].sort_values(by='monetary',ascending='False').reset_index(drop-True).head(5))

Python 3 解法, 执行用时: 810ms, 内存消耗: 524288KB, 提交时间: 2022-06-28 

import pandas as pd
sales = pd.read_csv('sales.csv')
def  deal(x,name):
    if name == 'recency':
        if x <= dict_list[0][name]:
            return '4'
        elif x > dict_list[0][name] and x <= dict_list[1][name]:
            return '3'
        elif x > dict_list[1][name] and x <= dict_list[2][name]:
            return '2'
        else:
            return '1'
    else:
        if x <= dict_list[0][name]:
            return '1'
        elif x > dict_list[0][name] and x <= dict_list[1][name]:
            return '2'
        elif x > dict_list[1][name] and x <= dict_list[2][name]:
            return '3'
        else:
            return '4'
sales_quantile = sales.quantile([.25,.50,.75])
dict_list = sales_quantile.to_dict('records')
sales['RFMClass'] = sales['recency'].apply(lambda x: deal(x,'recency'))+sales['frequency'].apply(lambda x: deal(x,'frequency'))+sales['monetary'].apply(lambda x: deal(x,'monetary'))
print(sales.head(5),'\n')
print(sales[sales['RFMClass'] == '444'].sort_values(by= 'monetary',ascending = False).reset_index(drop = True).head(5))

Python 3 解法, 执行用时: 811ms, 内存消耗: 524288KB, 提交时间: 2022-07-23 

import pandas as pd
sales = pd.read_csv('sales.csv')
sales["monetary"] = sales["monetary"].astype("float")
sales["R_Quartile"] = pd.qcut(sales["recency"], [0, 0.25, 0.5, 0.75, 1],
                              labels=["4", "3", "2", "1"]).astype("str")
sales["F_Quartile"] = pd.qcut(sales["frequency"], [0, 0.25, 0.5, 0.75, 1], 
                              labels=["1", "2", "3", "4"]).astype("str")
sales["M_Quartile"] = pd.qcut(sales["monetary"], [0, 0.25, 0.5, 0.75, 1], 
                              labels=["1", "2", "3", "4"]).astype("str")
sales['RFMClass']=sales['R_Quartile']+sales['F_Quartile']+sales['M_Quartile']
sales1 = sales[['user_id', 'recency', 'frequency', 'monetary', 'RFMClass']]
print(sales1.head(5))
print('\n')
df = sales1[sales1['RFMClass'] == '444'].sort_values(by='monetary', ascending=False)
print(df.head(5).reset_index(drop=True))

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