根据总和为目标值的最接近组合加入两个数据帧
Join two dataframes based on closest combination that sums up to a target value
我正在尝试根据 df2 列 Sales 中最接近的行组合连接到两个数据帧下方，总计为 df1[ 中的目标值=47=] 列 Total Sales、列 Name 和 Date 在连接时两个数据框中的列应该相同（如预期输出所示）。
例如：在 df1 中，行号 0 应仅与 df2 行 0 和 1 匹配，因为列 Name & Date 相同，即姓名：John 和日期：2021-10-01.
df1 :
df1 = pd.DataFrame({"Name":{"0":"John","1":"John","2":"Jack","3":"Nancy","4":"Ahmed"},
"Date":{"0":"2021-10-01","1":"2021-11-01","2":"2021-10-10","3":"2021-10-12","4":"2021-10-30"},
"Total Sales":{"0":15500,"1":5500,"2":17600,"3":20700,"4":12000}})
Name Date Total Sales
0 John 2021-10-01 15500
1 John 2021-11-01 5500
2 Jack 2021-10-10 17600
3 Nancy 2021-10-12 20700
4 Ahmed 2021-10-30 12000
df2 :
df2 = pd.DataFrame({"ID":{"0":"JO1","1":"JO2","2":"JO3","3":"JO4","4":"JA1","5":"JA2","6":"NA1",
"7":"NA2","8":"NA3","9":"NA4","10":"AH1","11":"AH2","12":"AH3","13":"AH3"},
"Name":{"0":"John","1":"John","2":"John","3":"John","4":"Jack","5":"Jack","6":"Nancy","7":"Nancy",
"8":"Nancy","9":"Nancy","10":"Ahmed","11":"Ahmed","12":"Ahmed","13":"Ahmed"},
"Date":{"0":"2021-10-01","1":"2021-10-01","2":"2021-11-01","3":"2021-11-01","4":"2021-10-10","5":"2021-10-10","6":"2021-10-12","7":"2021-10-12",
"8":"2021-10-12","9":"2021-10-12","10":"2021-10-30","11":"2021-10-30","12":"2021-10-30","13":"2021-10-29"},
"Sales":{"0":10000,"1":5000,"2":1000,"3":5500,"4":10000,"5":7000,"6":20000,
"7":100,"8":500,"9":100,"10":5000,"11":7000,"12":10000,"13":12000}})
ID Name Date Sales
0 JO1 John 2021-10-01 10000
1 JO2 John 2021-10-01 5000
2 JO3 John 2021-11-01 1000
3 JO4 John 2021-11-01 5500
4 JA1 Jack 2021-10-10 10000
5 JA2 Jack 2021-10-10 7000
6 NA1 Nancy 2021-10-12 20000
7 NA2 Nancy 2021-10-12 100
8 NA3 Nancy 2021-10-12 500
9 NA4 Nancy 2021-10-12 100
10 AH1 Ahmed 2021-10-30 5000
11 AH2 Ahmed 2021-10-30 7000
12 AH3 Ahmed 2021-10-30 10000
13 AH3 Ahmed 2021-10-29 12000
预期输出：
Name Date Total Sales Comb IDs Comb Total
0 John 2021-10-01 15500 JO1, JO2 15000.0
1 John 2021-11-01 5500 JO4 5500.0
2 Jack 2021-10-10 17600 JA1, JA2 17000.0
3 Nancy 2021-10-12 20700 NA1, NA2, NA3, NA4 20700.0
4 Ahmed 2021-10-30 12000 AH1, AH2 12000.0
我在下面尝试的是一次只处理一行，但我不确定如何在 pandas 数据帧中应用它以获得预期的输出。
下面脚本中的变量 numbers 表示 df2 中的 Sales 列，下面的变量 target 表示 Total Sales 中的列df1.
import itertools
import math
numbers = [1000, 5000, 3000]
target = 6000
best_combination = ((None,))
best_result = math.inf
best_sum = 0
for L in range(0, len(numbers)+1):
for combination in itertools.combinations(numbers, L):
sum = 0
for number in combination:
sum += number
result = target - sum
if abs(result) < abs(best_result):
best_result = result
best_combination = combination
best_sum = sum
print("\nbest sum{} = {}".format(best_combination, best_sum))
[Out] best sum(1000, 5000) = 6000
将您编写的找到最佳总和的代码转化为一个函数（我们称它为 opt，它具有目标参数和数据框（它将是 [=11= 的子集） ].需要return一个最优组合对应的ID列表
编写另一个函数，它接受 3 个参数名称、日期和目标（我们称之为 calc）。此函数将根据名称和日期过滤 df2，并将其与目标一起传递给 opt 函数，并 return 该函数的结果。最后，遍历 df1 的行，并使用行参数调用 calc（或者使用 pandas.DataFrame.apply
我正在尝试根据 df2 列 Sales 中最接近的行组合连接到两个数据帧下方，总计为 df1[ 中的目标值=47=] 列 Total Sales、列 Name 和 Date 在连接时两个数据框中的列应该相同（如预期输出所示）。
例如：在 df1 中，行号 0 应仅与 df2 行 0 和 1 匹配，因为列 Name & Date 相同，即姓名：John 和日期：2021-10-01.
df1 :
df1 = pd.DataFrame({"Name":{"0":"John","1":"John","2":"Jack","3":"Nancy","4":"Ahmed"},
"Date":{"0":"2021-10-01","1":"2021-11-01","2":"2021-10-10","3":"2021-10-12","4":"2021-10-30"},
"Total Sales":{"0":15500,"1":5500,"2":17600,"3":20700,"4":12000}})
Name Date Total Sales
0 John 2021-10-01 15500
1 John 2021-11-01 5500
2 Jack 2021-10-10 17600
3 Nancy 2021-10-12 20700
4 Ahmed 2021-10-30 12000
df2 :
df2 = pd.DataFrame({"ID":{"0":"JO1","1":"JO2","2":"JO3","3":"JO4","4":"JA1","5":"JA2","6":"NA1",
"7":"NA2","8":"NA3","9":"NA4","10":"AH1","11":"AH2","12":"AH3","13":"AH3"},
"Name":{"0":"John","1":"John","2":"John","3":"John","4":"Jack","5":"Jack","6":"Nancy","7":"Nancy",
"8":"Nancy","9":"Nancy","10":"Ahmed","11":"Ahmed","12":"Ahmed","13":"Ahmed"},
"Date":{"0":"2021-10-01","1":"2021-10-01","2":"2021-11-01","3":"2021-11-01","4":"2021-10-10","5":"2021-10-10","6":"2021-10-12","7":"2021-10-12",
"8":"2021-10-12","9":"2021-10-12","10":"2021-10-30","11":"2021-10-30","12":"2021-10-30","13":"2021-10-29"},
"Sales":{"0":10000,"1":5000,"2":1000,"3":5500,"4":10000,"5":7000,"6":20000,
"7":100,"8":500,"9":100,"10":5000,"11":7000,"12":10000,"13":12000}})
ID Name Date Sales
0 JO1 John 2021-10-01 10000
1 JO2 John 2021-10-01 5000
2 JO3 John 2021-11-01 1000
3 JO4 John 2021-11-01 5500
4 JA1 Jack 2021-10-10 10000
5 JA2 Jack 2021-10-10 7000
6 NA1 Nancy 2021-10-12 20000
7 NA2 Nancy 2021-10-12 100
8 NA3 Nancy 2021-10-12 500
9 NA4 Nancy 2021-10-12 100
10 AH1 Ahmed 2021-10-30 5000
11 AH2 Ahmed 2021-10-30 7000
12 AH3 Ahmed 2021-10-30 10000
13 AH3 Ahmed 2021-10-29 12000
预期输出：
Name Date Total Sales Comb IDs Comb Total
0 John 2021-10-01 15500 JO1, JO2 15000.0
1 John 2021-11-01 5500 JO4 5500.0
2 Jack 2021-10-10 17600 JA1, JA2 17000.0
3 Nancy 2021-10-12 20700 NA1, NA2, NA3, NA4 20700.0
4 Ahmed 2021-10-30 12000 AH1, AH2 12000.0
我在下面尝试的是一次只处理一行，但我不确定如何在 pandas 数据帧中应用它以获得预期的输出。
下面脚本中的变量 numbers 表示 df2 中的 Sales 列，下面的变量 target 表示 Total Sales 中的列df1.
import itertools
import math
numbers = [1000, 5000, 3000]
target = 6000
best_combination = ((None,))
best_result = math.inf
best_sum = 0
for L in range(0, len(numbers)+1):
for combination in itertools.combinations(numbers, L):
sum = 0
for number in combination:
sum += number
result = target - sum
if abs(result) < abs(best_result):
best_result = result
best_combination = combination
best_sum = sum
print("\nbest sum{} = {}".format(best_combination, best_sum))
[Out] best sum(1000, 5000) = 6000
将您编写的找到最佳总和的代码转化为一个函数（我们称它为 opt，它具有目标参数和数据框（它将是 [=11= 的子集） ].需要return一个最优组合对应的ID列表
编写另一个函数，它接受 3 个参数名称、日期和目标（我们称之为 calc）。此函数将根据名称和日期过滤 df2，并将其与目标一起传递给 opt 函数，并 return 该函数的结果。最后，遍历 df1 的行，并使用行参数调用 calc（或者使用 pandas.DataFrame.apply