如何在Python中计算所有其他产品的加权平均值

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英文:

How to calculate weighted average of all other products in python

问题

我需要创建一个新列,该列是所有子类别(在一个类别内)的加权平均价格(根据类别内的收入进行加权),除了子类别列中的那一个,即对于第一行,我只需要A2和A3的加权平均价格,因为子类别列中的值是A1。可以有人帮忙吗?

英文:

I have a dataframe as below

category    sub category    price     revenue
A             A1              100     1000
A             A2              110     990
A             A3              120     890
B             B1              90      1200
B             B2              100     1100
B             B3              95      1050

I need to create a new column which is the weighted avg price(weighted with revenue within a category) for all subcategories(within a category) except the one in subcategory column,i.e for the 1st row, I need the weighted avg price of A2 & A3 only since A1 is the value in sub-category column. Can someone pls help?

答案1

得分: 1

你可以手动计算加权均值,同时减去自身的值:

tmp = (df.set_index(['category', 'sub category'])
         .eval('prod=price*revenue')
       )

g = tmp.groupby(level=0)

out = (g['prod'].transform('sum')
       .sub(tmp['prod'])
       .div(g['revenue'].transform('sum').sub(tmp['revenue']))
       )

输出:

category  sub category
A         A1              114.734043
          A2              109.417989
          A3              104.974874
B         B1               97.558140
          B2               92.333333
          B3               94.782609
dtype: float64
英文:

You can compute the weighted mean manually, while subtracting the self values:

tmp = (df.set_index(['category', 'sub category'])
         .eval('prod=price*revenue')
       )

g = tmp.groupby(level=0)

out = (g['prod'].transform('sum')
       .sub(tmp['prod'])
       .div(g['revenue'].transform('sum').sub(tmp['revenue']))
       )

Output:

category  sub category
A         A1              114.734043
          A2              109.417989
          A3              104.974874
B         B1               97.558140
          B2               92.333333
          B3               94.782609
dtype: float64

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  • 本文由 发表于 2023年7月13日 13:36:30
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