根据客户需求变化的频率对产品进行排序

Question

想象一下给出以下 dataframe。

import pandas as pd 
products = ['Apple', 'Apple', 'Carrot', 'Eggplant', 'Eggplant']
customer_demand_date = ['2023-01-01', '2023-01-07', '2023-01-01', '2023-01-01', '2023-01-07', '2023-01-14']
col_02_2023 = [0, 20, 0, 0, 0, 10]
col_03_2023 = [20, 30, 10, 0, 10, 0]
col_04_2023 = [10, 40, 50, 30, 40, 10]
col_05_2023 = [40, 40, 60, 50, 60, 20]

data = {'Products': products,
        'customer_demand_date': customer_demand_date,
        '02_2023': col_02_2023,
        '03_2023': col_03_2023,
        '04_2023': col_04_2023,
        '05_2023': col_05_2023}

df = pd.DataFrame(data)

print(df) 

Products customer_demand_date  02_2023  03_2023  04_2023  05_2023
0    Apple           2023-01-01        0       20       10       40
1    Apple           2023-01-07       20       30       40       40
2   Carrot           2023-01-01        0       10       50       60
3      Egg           2023-01-01        0        0       30       50
4      Egg           2023-01-07        0       10       40       60
5      Egg           2023-01-14        0        0       10       20

我有产品列、custome_demand_date（每周都有新的客户对未来几个月的产品需求）和数量需求的月份。 如何确定哪个产品在几个月内客户需求变化最频繁，并按照变化频率从高到低的顺序对产品进行排序？ 我曾尝试按产品分组，累积需求数量，但没有一个可以横向（每个客户需求日期）和纵向（每月）分析数据。 所需的 output：

Sorted products      Ranking(or %, or count of changes) 
Egg                  1 (or 70% or 13)   
Apple                2 (or 52% or 8)
Carrot               3 (22% or 3)

排名或更改频率百分比或更改次数。

• 注意：所需的百分比 output 是随机数

如果您有任何聪明的方法来解决这个问题，我将不胜感激？ 谢谢

Answer 1

一种方法是定义一个 function 来计算水平和垂直变化，您可以将这些变化分别应用于每个组。

import pandas as pd
from io import StringIO

def change_freq(x, months):
    # count horizontal changes
    chngs_horizontal = x[months].diff(axis=1).fillna(0).astype(bool).sum().sum()
    # count vertical changes
    chngs_vertical = x[months].diff(axis=0).fillna(0).astype(bool).sum().sum()
    return chngs_horizontal+chngs_vertical

# sample data
data = StringIO("""
Products,customer_demand_date,02_2023,03_2023,04_2023,05_2023
Apple,2023-01-01,0,20,10,40
Apple,2023-01-07,20,30,40,40
Carrot,2023-01-01,0,10,50,60
Egg,2023-01-01,0,0,30,50
Egg,2023-01-07,0,10,40,60
Egg,2023-01-14,0,0,10,20
""")

df = pd.read_csv(data, sep=",")

# count horizontal and vertical changes by product
result = df.groupby('Products').apply(change_freq, ['02_2023','03_2023','04_2023','05_2023'])
result = result.sort_values(ascending=False).to_frame('count_changes')
result['rank'] = result['count_changes'].rank(ascending=False)

这返回

          count_changes  rank
Products
Egg                  13   1.0
Apple                 8   2.0
Carrot                3   3.0

Answer 2

要找到 y 方向的方差，可以使用带有 lambda function 的groupby("Products") ：

var_y=(df.loc[:,~df.columns.isin(['customer_demand_date','HEHE'])].groupby("Products").agg(lambda x: x.pct_change().fillna(0).astype(bool).sum())).reset_index(level=0)

要找到 x 方向的方差 pct_change 解释为 True 或 False 可以使用 astype(bool)：

var_x=pd.concat([df[["Products"]], df.iloc[:,2:].pct_change(axis='columns').replace(np.inf, 1).fillna(0).astype(bool).sum(axis=1).rename('sum_x')], axis=1)

将两者添加/组合在一起最终看起来像这样：

   Products  sum_x  sum_y  sum_xy
0     Apple      5      3       8
1    Carrot      3      0       3
2  Eggplant      7      6      13 

下面是完整的代码：

import pandas as pd
import numpy as np
products = ['Apple', 'Apple', 'Carrot', 'Eggplant', 'Eggplant', 'Eggplant']
customer_demand_date = ['2023-01-01', '2023-01-07', '2023-01-01', '2023-01-01', '2023-01-07','2023-01-14']
col_02_2023 = [0, 20, 0, 0, 0, 0]
col_03_2023 = [20, 30, 10, 0, 10, 0]
col_04_2023 = [10, 40, 50, 30, 40, 10]
col_05_2023 = [40, 40, 60, 50, 60, 20]

data = {'Products': products,
        'customer_demand_date': customer_demand_date,
        '02_2023': col_02_2023,
        '03_2023': col_03_2023,
        '04_2023': col_04_2023,
        '05_2023': col_05_2023}


df = pd.DataFrame(data)
var_y=(df.loc[:,~df.columns.isin(['customer_demand_date','HEHE'])].groupby("Products").agg(lambda x: x.pct_change().fillna(0).astype(bool).sum())).reset_index(level=0)
var_y["sum_y"]=var_y.iloc[:,1:].sum(axis="columns")
var_x=pd.concat([df[["Products"]], df.iloc[:,2:].pct_change(axis='columns').replace(np.inf, 1).fillna(0).astype(bool).sum(axis=1).rename('sum_x')], axis=1)
var_x_sum=var_x.groupby("Products", as_index=False).agg(sum_x=('sum_x','sum'))
var_total=pd.concat([var_x_sum,var_y["sum_y"]],axis=1)
var_total["sum_xy"]=var_total.iloc[:,1:].sum(axis="columns")
print(var_total)